Research ArticleAssessment of Surface Runoff for Tank Watershed inTamil Nadu Using Hydrologic Modeling
Marykutty Abraham 1 and Riya AnnMathew 2
1Sathyabama University Chennai India2Anna University Chennai India
Correspondence should be addressed to Marykutty Abraham marykutty abyahoocoin
Received 1 February 2018 Accepted 16 April 2018 Published 5 June 2018
Academic Editor Yun-tai Chen
Copyright copy 2018 Marykutty Abraham and Riya Ann Mathew This is an open access article distributed under the CreativeCommons Attribution License which permits unrestricted use distribution and reproduction in any medium provided theoriginal work is properly cited
Providing safe and wholesome water in sufficient quantity on a sustainable basis remains elusive for large population especially insemiarid regions and hence water balance estimation is vital to assess water availability in a watershed The water balance study isformulated to assess the runoff that can be harvested for effective utilization The study area is Urapakkam watershed with a chainof 3 tanks having an aerial extent of 4576 km2 with hard rock formation underneath and thus has limited scope for groundwaterrecharge Hence surface water is the main water source in this area Runoff computed for the watershed using USDA-NRCS modelvaried from 9495mm to 232434mm and the corresponding rainfall varied from 5757mm to 36080mm respectively A simpleregression model was developed for the watershed to compute runoff from annual rainfall Average annual runoff estimated for thewatershed was around 37 of the rainfall for the study period from 2000-01 to 2013-14 Statistical analysis and test of significancefor runoff obtained by NRCS model and regression model did not show any significant difference thus proving that regressionmodel is efficient in runoff computation for ungauged basinsThe volume of water accessible for fifty percent dependable flow yearis obtained as 246 MCM and even if 50 of it can be effectively harnessed the water available in the watershed is 123 MCM Thewater demand of the area is estimated as 0148 MCM for domestic purpose and 0171 MCM for irrigation purpose which is muchlower than the available runoff that can be harnessed from the watershedThus there is scope to harvest 123MCMof water which ismore than the demand of the watershedThe study reveals that it is feasible to harvest andmanage water effectively if its availabilityand demand are computed accurately
1 Introduction
The global annual per capita water supply is around 7000m3[1] which shows that the earth has sufficient freshwaterfor the needs of the population According to Food andAgriculture Organisation [2] average per capita water avail-ability ranges from near zero in Kuwait to around 10000m3in Tajikistan In addition to the spatial variability there isseasonal variability in these areas which results in extremeevents Cosgrove and Rijsberman [3] predicted that by theyear 2025 about 60 of the worldrsquos population will face acutewater shortage Groundwater table is highly influenced byurbanization of an area
Water demand is increasing in most of the cities as percapita water use of an urban citizen is almost twice thanthat of a rural citizen and India is rapidly urbanizing As
the Indian cities are facing water problems groundwaterexploitation has become inevitable which leads to the declineof groundwater table To sustain urban cities there is anurgent need to augment groundwater resources The percapita water availability of India is 1545m3 per annum[4]
Monsoonal climate prevalent in India causes half ofthe precipitation to occur within a period of 15 days andmore than 90 of the annual runoff during four monsoonmonths The large regional and seasonal variation in rainfalland runoff in India have prompted human interferencesin hydrological cycle Options that combine augmentationof surface water and groundwater resources can ensuresufficient water availability in a sustainable manner As thestudy area is on the threshold for rapid development andurbanization there will be an impetus for drastic increase
HindawiInternational Journal of GeophysicsVolume 2018 Article ID 2498648 10 pageshttpsdoiorg10115520182498648
2 International Journal of Geophysics
in water demand The villages in the study area are alreadyexperiencing rapid urbanization
Water balance estimates are essential to strengthen thedecision-making pertaining to water management Rainwa-ter harvesting and water conservation measures deserve thehighest priority in water management compared to othermeasures because of the huge investment required and longertime period involved [5] So estimation of water potential isvery important for the effective utilization of the availablewater resources in the context of fast urbanization
The widely accepted empirical method to estimate runofffor an ungauged watershed is Natural Resource ConservationService (NRCS)model formerly known as Soil ConservationService (SCS) model which is based on water balance equa-tion developed by United States Department of Agriculture[6] Rainfall-runoff relation was first proposed by Sherman[7] Surface runoff based on land use soil rainfall duration ofstorm and annual temperature for watersheds was developedby Mockus [8] Mishra and Singh [9] conducted extensiveresearch on Soil Conservation Service-curve number (SCS-CN) method Mishra et al [10] investigated SCS modelfor US fields with size ranging from 03 to 30352 hectareusing rainfall-runoff events and found that the model isappropriate for high rainfall (gt508mm) areas SCS methodwas successfully applied for a forest watershed having per-meable soils [11] Rainfall-runoff relationships using variousempirical and numerical methods have been developed byvarious researchers [12ndash16] Applicability of NRCSmodel wassuccessfully demonstrated by many researchers worldwidefor computing runoff [11 17ndash19] NRCS method is widelyaccepted and is proved useful for surface runoff estimation byseveral authors for various watersheds of Tamil Nadu [20 21]Modifications were made for SCS model for steep slopesby Gupta et al [22] Water balance computation of NoyilBasin in Tamil Nadu was presented by Chatterjee et al [23]Central Ground Water Board [24] conducted water balancestudies for upper Yamuna basin and the recharge estimatedwas compared with water table fluctuation method
Thakuriah and Saikia [25] successfully demonstratedmethodology for runoff estimation by integrating remotesensing and geographic information system (GIS) in Burig-anga watershed of Assam Remote sensing data along withground truth verification is widely utilized for mapping vari-ous resources in an economic and efficient way including soilmapping land use mapping and fertility mapping [26 27]The applicability of SCS model was improved by accountingfor spatial and temporal variability in soil and land use withremote sensing and geographic information system [28ndash34]
The literature study reveals that NRCS model is used forhydrologic simulation of small watersheds worldwide for along period and can also give realistic results even for largerwatersheds Hence NRCS model is used in the present studyfor runoff computation owing to its efficiency precision andsimplicity for ungauged watersheds
2 The Study Area and Data Collection
The study area is Urapakkam watershed which consists ofthree tanks namely Urapakkam tank Karanaipuducheri
tank and Periyar Nagar tank Urapakkam watershed with anarea of 4576 km2 is part of Guduvanchery watershed in theAdyar basin in Kanchipuram district of Tamil Nadu IndiaThe area falls under geographical coordinates 12∘ 521015840 N to 12∘471015840 N and 80∘ 421015840 E to 80∘ 521015840 E Elevation of the districtvaries from 05m to 230m above mean sea level The tem-perature ranges between 371∘C and 205∘C [35] Index mapof study area is presented in Figure 1 Rainfall occurs mainlyduring October to December through Northeast monsoonThe area receives an average rainfall of around 1150mmper annum Rainfall data was collected from India Mete-orological Department (IMD) Chennai for fourteen yearsfrom 2000 to 2014 Agriculture is the main livelihood ofthe watershed community Paddy is the major crop andgroundnut sugarcane cereals millets pulses etc are alsocultivated Water from Palar river tanks and wells is used forirrigation in the watershed [36]
3 Methodology
31 Runoff Computation Watershed boundary for Urapak-kam watershed was delineated using District Soil amp Water-shed Atlas [37] and verified with Survey of India toposheetand satellite imagery Land usemapwas prepared for the tankwatershed in GIS platform using ArcGIS-98 software and thearea under each land use is calculated
Surface runoff was assessed by applying NRCS-Hydro-logic model using daily time step [38] The runoff equationis
119876 = (119875 minus 119868119886)2(119875 minus 119868119886+ 119878) (1)
119878 = 25400CNminus 254 (2)
where
119875 is Rainfall in mmCN is curve number which ranges between 0 and 100119878 is storage index depending on the curve number119868119886is initial abstraction a fraction of 119878119876 is runoff in mm
Using 119868119886= 02S (2) takes the form
119876 = (119875 minus 02119878)2(119875 minus 08119878) (3)
Daily rainfall data land use map hydrological soil groupand infiltration study were used to arrive at runoff curvenumbers for NRCS model The watershed curve number isclosely related to soil storage index According to infiltrationcharacteristics soil is grouped under four hydrologic soilgroups namely A B C and D where A has the highestinfiltration rate and D has the least infiltration rate [39]
Surface conditions of a watershed are taken care of bycurve numbersThe curve numbers corresponding to variousland uses and hydrologic soil groups can be obtained from
International Journal of Geophysics 3
UrapakkamWatershed
Adyar Basin
Figure 1 Index map of study area
Table 1 [40] From this the weighted curve number of thewatershed can be determined which corresponds to the aver-age Antecedent Moisture Condition (AMC II) AMC for dryand wet situations is determined using five-day antecedentrainfall which corresponds to less than 13mm andmore than28mm rainfall respectively and gives rise to AMC I andAMC III The curve number determined for AMC II can beconverted for other conditions namely AMC I and AMC IIIusing the following relations [38]
CNI = 42CNII10 minus 00584CNII(4)
CNIII = 23CNII10 + 013CNII (5)
where
CNI is curve number for AMC ICNII is curve number for AMC IICNIII is curve number for AMC III
A simple regression model was developed to compute annualrunoff from rainfall which was then calibrated and vali-dated with NRCS model Such simpler equations can beused for computation of runoff for ungauged watershedsinstead of arduous and data intensive computation methodsPerformance of developed regression model was evaluatedusing statisticalmethods such as coefficient of determinationstandard error of estimate and mean absolute error Thestatistical test of significance used was t-test
Coefficient of Determination (R2) shows the degree oflinear relation between the two variables R2 value close tounity indicates a high degree of association between the twovariables [41]
Standard Error of Estimate (SEE) is an estimate of theaverage deviation of the regression value from the observeddata
SEE = radicsum (119909119894 minus 119909)2119899 minus 2 (6)
4 International Journal of Geophysics
Table 1 Runoff curve numbers
Description of Land use Hydrologic Soil GroupA B C D
Paved parking lots roofs driveways 98 98 98 98Streets and Roads
Paved with curbs and storm sewers 98 98 98 98Gravel 76 85 89 91Dirt 72 82 87 89
Cultivated (Agricultural Crop) LandlowastWithout conservation treatment (no terraces) 72 81 88 91With conservation treatment (terraces contours) 62 71 78 81
Pasture or Range LandPoor (lt50 ground cover or heavily grazed) 68 79 86 89Good (50ndash75 ground cover not heavily grazed) 39 61 74 80
Meadow (grass no grazing mowed for hay) 30 58 71 78Brush (good gt75 ground cover) 30 48 65 73Woods and Forests
Poor (small treesbrush destroyed by over-grazing or burning) 45 66 77 83Fair (grazing but not burned some brush) 36 60 73 79Good (no grazing brush covers ground) 30 55 70 77
Open Spaces (lawns parks golf courses cemeteries etc)Fair (grass covers 50ndash75 of area) 49 69 79 84Good (grass covers gt75 of area) 39 61 74 80
Commercial and Business Districts (85 impervious) 89 92 94 95Industrial Districts (72 impervious) 81 88 91 93Residential Areas
18 Acre lots about 65 impervious 77 85 90 9214 Acre lots about 38 impervious 61 75 83 8712 Acre lots about 25 impervious 54 70 80 851 Acre lots about 20 impervious 51 68 79 84
Source United States Department of Agriculture Soil Conservation Service 1986 [40] Technical Release 55
Mean Absolute Error (MAE) is the average of all absoluteerrors which can be expressed as
MAE = 1119899119899sum119894=1
1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816 (7)
where
119909 is runoff by NRCS model119909119894is runoff by regression model119899 is number of data sets
Test for significance t-test was used to test whether thereis significant difference between runoff computed by NRCSmodel and those estimated by regression model
The assumptions used in this test were samples which areindependent and random in nature and samples which arenormally distributed
Two-independent sample t-test statistics is defined as
119905 = (sum 1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816) 119899radic((sum 1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816)2 minus ((sum 1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816)2 119899)) (119899 minus 1) (119899) (8)
32 Water Balance Study Water availability and demand fordomestic use and irrigation in the watershed were computedseparately Domestic water demandwas obtained as the prod-uct of per capita water requirement and the population Thedifference between water availability and water demand willgive a comprehensive understanding on the total utilizablewater
Estimation of Irrigation Demand Irrigation water demandwas calculated from the method given by Brouwer andHeibloem 1986 [42] Potential evapotranspiration was com-puted from BlaneyndashCriddle equation [43] and is given by
ET119900= 119901 (046119879mean + 8) (9)
where
119879mean is mean daily temperature (∘C)119901 is mean daily percentage of annual daytime hours
Crop coefficient (119870119888) for paddy for various growth stages was
estimated and crop water requirement was determined from
ETcrop = ET119900 times 119870119888 (10)
International Journal of Geophysics 5
Table 2 Land use details and the corresponding curve numbers
Land use category Area Curve Numberkm2
Forest Forest Plantations 1089 79Crop land 0193 81Water bodies 0600 89Barren land 0167 80SettlementResidential area 2527 87
Other water needs such as water to saturate the soil (SAT)percolation and seepage losses (PSL) and water neededfor standing water layer (WL) were used to compute theirrigation need
Effective rainfall (119875119890) was calculated from the formulae
119875119890= 08119875 minus 25 if 119875 gt 75mmmonth
119875119890= 06119875 minus 10 if 119875 lt 75mmmonth (11)
where
119875 is rainfall in mmmonth
Irrigation Need (IN) is calculated as
IN = ETcrop + SAT + PSL +WL minus 119875119890 (12)
4 Result
The runoff for Urapakkam watershed in Adyar basin wascomputed using USDA-NRCS model A simple regressionmodel was also developed and validated for its suitabilityfor runoff computation of the watershed By knowing theutilizable water available in a watershed water supply forirrigation and other purposes can be planned and alternativecropping patterns can be adopted to avoid losses
41 Runoff Computation
411 NRCS Model Watershed boundary for the chain oftanks was taken from District Soil amp Watershed Atlas andverified with Survey of India toposheet and satellite imageryLand use map for Urapakkam watershed was prepared fromsatellite imagery with field verification in GIS platform usingArcGIS and the area under various land uses was determined(Figure 2)
The watershed is in the hydrologic soil group D as perthe soil details Hydrological modeling considers water yearfrom July to June of ensuing year and uses daily time stepUrapakkam tank has both free catchment and interceptedcatchment The hydrological model estimated the runoff forfourteen water years from 2000-01 to 2013-14 with daily timestep Land use details and corresponding curve numbers aregiven in Table 2
Weighted curve number for the watershed with adrainage area of 4576 km2 is 8485 The curve numbers forAntecedent Moisture Conditions I II and III are 7017 8485and 9280 respectively
Figure 2 Land use map of Urapakkam watershed
Water Year
RainfallRunoff
2001
-200
2
2002
-200
3
2003
-200
4
2004
-200
5
2005
-200
6
2006
-200
7
2007
-200
8
2008
-200
9
2009
-201
0
2010
-201
1
2011
-201
2
2012
-201
3
2013
-201
4
2000
-200
10
10
20
30
40
50
60
70
80
Num
ber o
f Day
s
Figure 3 Number of days with rainfall and runoff by NRCS model
Days with rainfall and runoff for the study period aregiven in Figure 3 Monthly rainfall and the correspondingrunoff as estimated from NRCS model are given in Figure 4Table 3 gives monthly rainfall and runoff for the water year2008ndash2009whenfiftypercent dependable flowwas observed
Annual rainfall for the watershed varied from 5757mm(2002ndash03) to 36080mm (2005ndash06) The runoff as estimatedfromNRCSmodel varied from 9495mm to 232434mm andthe corresponding percentage varied from 1649 to 6442 withan average value of 3709 percent for the fourteen water years
6 International Journal of Geophysics
RainfallRunoff
Jan
07
Jan
10
Jan
14
Jan
12
Jan
03
Jan
11
Jan
04
Jan
01
Jan
05
Jan
08
Jan
06
Jan
09
Jan
13
Jan
02Ju
l02
Jul0
9
Jul0
6
Jul0
3
Jul1
3
Jul1
0
Jul0
4
Jul1
1
Jul0
8
Jul0
7
Jul0
5
Jul0
1
Jul1
2
Jul0
0
Month
0200400600800
100012001400
Rainfa
ll R
unoff
(mm
)
Figure 4 Monthly rainfall and runoff by NRCS model for Ura-pakkam tank watershed
Table 3 Monthly rainfall and runoff for Urapakkam watershedusing NRCS model for the water year 2008-2009
Month Rainfall Runoffmm mm
Jul-08 6100 005Aug-08 10100 2749Sep-08 12900 6311Oct-08 25400 6909Nov-08 53400 35289Dec-08 8400 2633Jan-09 1500 000Feb-09 000 000Mar-09 000 000Apr-09 000 000May-09 2400 000Jun-09 3680 000
2000-01 to 2013-14 Bar chart showing annual rainfall andrunoff is given in Figure 5 Volume of runoff for thewatershedvaried between 0434 MCM and 10637 MCM during thefourteen years of study Annual runoff for a representativewater year (2008-09) is obtained as 246 MCM (Table 4)When the rainfall exceeds the threshold limit the proportionof rainfall contributing to runoffwas found to be higherWidevariations were observed in the quantum of runoff from rain-fall of same magnitude due to varying antecedent moistureconditions in the soil Figure 6 shows the average annualrainfall and runoff estimated for Urapakkam watershed
412 Regression Model A Linear regression model wasdeveloped for the watershed to compute annual runoff fromthe annual rainfall Ten-year data from NRCS model wasused for model calibration and four-year data was usedfor validation The regression relationship obtained betweenannual rainfall (mm) and annual runoff (mm) is given as
Runoff = 0736 times rainfall minus 4678 (13)
RainfallRunoff - NRCS ModelRunoff - Regression model
0500
1000150020002500300035004000
Dep
th in
mm
2001
-200
2
2002
-200
3
2003
-200
4
2004
-200
5
2005
-200
6
2006
-200
7
2007
-200
8
2008
-200
9
2009
-201
0
2010
-201
1
2011
-201
2
2012
-201
3
2013
-201
4
2000
-200
1
Water Year
Figure 5 Annual rainfall and runoff for Urapakkam watershed
Rainfall 152578
Runoff 63338
Figure 6 Average annual rainfall and runoff estimated for Ura-pakkam watershed
R2 value was obtained as 0944 during calibration and 0988during validation periodThe plots of rainfall versus runoff asobtained fromNRCSmodel during calibration and validationperiod are shown in Figure 7
413 Validation of Regression Model Figure 8 shows thecomparison between the annual runoff by NRCS modeland the developed regression model which illustrates thatthe regression model could be used to effectively predictrunoff for the watershed even during the validation phaseThe coefficient of determination (R2) for runoff by NRCSmodel versus runoff by regression model was 0939 at 95confidence level (Figure 9) The statistical tests such as t-test MAE and SEE obtained for the two runoff estimates(NRCS model and regression model) were 055 1060 and1390 respectively The average runoff for the watershed bylinear regression model was obtained as 3717 percent ofthe rainfall which ranges from 1279 to 6063 It showsno considerable deviation from runoff computed by NRCSmodel Thus the statistical analysis proves that tedious anddata intensive computation methods can be eliminated andthe volume of runoff from a watershed can be efficiently andeasily computed using simple regression model
International Journal of Geophysics 7
Table 4 Annual rainfall and runoff estimated for Urapakkam watershed using NRCS model for the period 2000-01 to 2013ndash14
Water YearRainfall Runoff
No of days Depth No of days Depth Percentage of Rainfall Volumemm mm MCM
2000-2001 39 109230 28 32396 2966 14832001-2002 49 121110 35 35539 2934 16262002-2003 28 57570 15 9495 1649 04342003-2004 65 176580 36 53750 3044 24602004-2005 44 164800 35 76859 4664 35172005-2006 59 360800 50 232434 6442 106372006-2007 38 137900 23 70120 5085 32092007-2008 71 189950 44 78043 4109 35712008-2009 44 123880 29 53897 4351 24662009-2010 55 143900 33 58224 4046 26652010-2011 67 148900 34 49909 3352 22842011-2012 52 181000 45 73411 4056 33592012-2013 60 143540 36 48412 3373 22152013-2014 64 76930 22 14248 1852 0652Minimum 28 5757 15 9495 1649 0434Maximum 71 36080 44 232434 6442 10637
y = 07369x - 46782
0
500
1000
1500
2000
2500
Runo
ff (m
m) 2
2= 09446
1000 2000 3000 40000Rainfall (mm)
(a) Calibration
500 1000 1500 20000Rainfall (mm)
0
200
400
600
800
Runo
ff (m
m)
22= 09887
(b) Validation
Figure 7 Relationship between annual rainfall and runoff by NRCS model during calibration and validation period
Run off NRCS modelRunoff Regression model
ValidationCalibration
1 2 3 4 5 6 7 8 9 10 11 12 13 14 150
(Years)minus500
0
500
1000
1500
2000
2500
Runo
ff de
pth
(mm
)
Figure 8 Comparison of annual runoff from NRCS model andregression model
42 Water Balance Study Population density of Kanchipu-ram district is 892km2 [44] Assuming the per capita waterdemand to be 100 litersday for domestic purposes the
y = 09488x + 5422922= 09389
500 1000 1500 2000 25000
Runoff - NRCS Model minus500
0
500
1000
1500
2000
2500
Runo
ff - R
egre
ssio
n M
odel
Figure 9 Scatter plot of annual runoff by NRCS model andregression model for Urapakkam watershed for 2000-01 to 2013-14
total domestic water requirement for the population of thewatershed with 4576 km2 is estimated at 407m3day whichis 0148 MCM per annum
8 International Journal of Geophysics
Table 5 Computation of irrigation demand for paddy for the year 2008-09
Item MonthSeptember October November December January February Season
Percentage of annualdaytime hours 119901 026 027 026 027 027
Average Temperature119879mean (∘C) 2600 2480 2460 258 2780
PotentialevapotranspirationEt119900(mmday)
519 514 502 527 561
Crop coefficient119870119888
110 110 120 130 100
Crop waterrequirement
mmday 571 566 603 684 561mmmonth 17126 17538 18682 19165 17400
SAT mm 20000PSL mmmonth4mmday 12000 12400 12400 11200 12400
WL mm119875119890 mm 4860 49980 16780 000 000 000
Irrigation demandmm 15140 000 13158 31082 30365 29800 119545
Irrigation is practiced in a small area of 10 ha in thewatershed The average irrigation demand for the watershedarea is computed to be around 1195mm By considering anefficiency of 70 the actual irrigation water requirement is1708mmThe total irrigation demand for 10 ha of paddy cropin the watershed is estimated as 0171 MCM Details of thecomputation of irrigation demand for paddy are presented inTable 5
The current total domestic and irrigation water require-ment of the watershed is 0320 MCM After meeting all thewater demands of the watershed there is sufficient wateravailable even if only 50 of the total available rainwater canbe effectively harvested
5 Conclusions
Accurate estimation of runoff is essential for the effectivemanagement ofwater resources especially in semiarid regionslike Tamil Nadu NRCS model is a widely accepted model forthe estimation of runoff for small watersheds It is extensivelyused by engineers and hydrologists but the lack of sufficientdata may give poor results With the understanding ofavailable water in a watershed irrigation scheduling croprotation and cropping pattern can be planned
Total volume of runoff available for Urapakkam water-shed from NRCS model for a representative year is 246MCM A simple regression model was developed to estimatedepth of annual runoff for the watershed which is givenas runoff (mm) = 0736 times rainfall (mm) minus 4678 Statisticaltests confirm that there is no significant difference betweenthe runoff estimates from the two models Hence the studyconfirms that it is advantageous to have calibrated regression
models for easy computation of runoff and for the effectivemanagement of water resources for ungauged basins
Water balance study shows that 123 MCM of watercan be effectively stored in the watershed after losses byimproving the drainage and capacity of the storages Thetotal domestic and irrigation demand is estimated at 0320MCM Hence apart from meeting the present domestic andirrigation demand in the watershed additional demand dueto future development and demand of adjoining areas canalso be met from the water available in the watershed ifproperly harvested
Data Availability
The data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this paper
References
[1] I A Shiklomanov ldquoAppraisal and Assessment of world waterresourcesrdquoWater International vol 25 no 1 pp 11ndash32 2000
[2] FAO ldquoFAOActivities on Promoting Fertigationrdquo in Proceedingsof the Regional Workshop on Guidelines for Efficient FertilizersUse through Modern Irrigation FAO Regional Office for theNear East Cairo Egypt 1998
[3] W Cosgrove and F Rijsberman World Water Vision MakingWater Everybodys Business World Water Council EarthscanPublications 2000
International Journal of Geophysics 9
[4] Ministry of Water Resources 2012 Per Capita Water Availabil-ity Press Information Bureau Government of India 26-April2012 httppibnicinnewsitePrintReleaseaspxrelid=82676
[5] S K Gupta and R D Deshpande ldquoWater for India in 2050First-order assessment of available optionsrdquo Current Sciencevol 86 no 9 pp 1216ndash1224 2004
[6] Soil Conservation Service (SCS) Hydrology National Engi-neering Handbook Supplement A Section 4 Chapter 10 SoilConservation Service USDA Washington DC USA 1972
[7] L K Sherman ldquoThe unit hydrograph methodrdquo in Physics of theEarth O E Meinzer Ed pp 514ndash525 Dover Publications IncNew York NY USA 1949
[8] VMockus ldquoEstimation of total (peak rates of) surface runoff forindividual stormsrdquo in Exhibit A of Appendix B Interim SurveyReport Grand (Neosho) River Watershed USDA 1949
[9] S KMishra andV P Singh ldquoAnother look at SCS-CNmethodrdquoJournal of Hydrologic Engineering vol 4 no 3 pp 257ndash2641999
[10] S K Mishra M K Jain P K Bhunya and V P Singh ldquoFieldapplicability of the SCS-CN-basedMishra-Singh general modeland its variantsrdquoWater Resources Management vol 19 no 1 pp37ndash62 2005
[11] K X Soulis J D Valiantzas N Dercas and P A LondraldquoInvestigation of the direct runoff generationmechanism for theanalysis of the SCS-CN method applicability to a partial areaexperimental watershedrdquo Hydrology and Earth System Sciencesvol 13 no 5 pp 605ndash615 2009
[12] P Koteshwaram and S M A Alvi ldquoSecular trends and period-icities in rainfall at west coast stations in Indiardquo Current Sciencevol 38 no 10 pp 229ndash231 1969
[13] K V Ramamurthy M K Sonam and S S Muley ldquoLongterm variation in the rainfall over upper Narmada catchmentrdquoMausam vol 38 no 3 pp 313ndash318 1987
[14] R Jha and R K Jaiswal ldquoAnalysis of rainfall pattern in acatchmentrdquo IAH Journal of Hydrology (India) Roorkee 1992
[15] F H S Chiew M J Stewardson and T A McMahon ldquoCom-parison of six rainfall-runoff modelling approachesrdquo Journal ofHydrology vol 147 no 1-4 pp 1ndash36 1993
[16] E M Lungu ldquoDetection of changes in rainfall and runoffpatternsrdquo Journal ofHydrology vol 147 no 1-4 pp 153ndash167 1993
[17] J Meher and R Jha ldquoTime series analysis of rainfall data overMahanadi River basinrdquo Emerging Science vol 3 no 6 pp 22ndash32 2011
[18] K X Soulis and J D Valiantzas ldquoSCS-CN parameter determi-nation using rainfall-runoff data in heterogeneous watersheds-the two-CN system approachrdquo Hydrology and Earth SystemSciences vol 16 no 3 pp 1001ndash1015 2012
[19] R Kabiri A Chan and R Bai ldquoComparison of SCS and Green-Ampt Methods in Surface Runoff-Flooding Simulation forKlang Watershed in Malaysiardquo Open Journal of Modern Hydro-logy vol 03 no 03 pp 102ndash114 2013
[20] S Mohan andM Abraham ldquoDerivations of simple site-specificrecharge-precipitation relationships A case study from theCuddalore Basin Indiardquo Environmental Geosciences vol 17 no1 pp 37ndash44 2010
[21] R Viji P R Prasanna and R Ilangovan ldquoGIS based SCS-CN method for estimating runoff in Kundahpalam watershedNilgries district Tamilnadurdquo Earth Sciences Research Journalvol 19 no 1 pp 59ndash64 2015
[22] P K Gupta S Punalekar S Panigrahy A Sonakia and J SParihar ldquoRunoff modeling in an agro-forested watershed using
remote sensing and gisrdquo Journal of Hydrologic Engineering vol17 no 11 pp 1255ndash1267 2012
[23] R Chatterjee ldquoGroundwater resources estimationmdash case stud-ies from Indiardquo Journal of the Geological Society of India vol 77no 2 pp 201ndash204 2011
[24] Central Ground Water Board (CGWB) Water Balance Studiesin Upper Yamuna Basin ndash Terminal Report - Project Findingsand Recommendations Chandigarh Central Ground WaterBoard 2000
[25] G Thakuriah and R Saikia ldquoEstimation of Surface RunoffusingNRCSCurve number procedure in BurigangaWatershedAssam India - A Geospatial Approachrdquo International ResearchJournal of Earth Sciences vol 2 no 5 pp 1ndash7 2014
[26] V K Dadhwal ldquoRemote sensing and GIS for agricultural cropacreage and yield estimationrdquo in Proceedings of the InternationalArchives of Photogrammetry andRemote Sensing XXXII pp 58ndash67 1999
[27] G B Geena and P N Ballukraya ldquoEstimation of runoff for Redhills watershed using SCS method and GISrdquo Indian Journal ofScience and Technology vol 4 no 8 pp 899ndash902 2011
[28] K H Syed D C Goodrich D E Myers and S SorooshianldquoSpatial characteristics of thunderstorm rainfall fields and theirrelation to runoffrdquo Journal of Hydrology vol 271 no 1-4 pp 1ndash21 2003
[29] X Zhan and M-L Huang ldquoArcCN-Runoff An ArcGIS toolfor generating curve number and runoff mapsrdquo EnvironmentalModeling and Software vol 19 no 10 pp 875ndash879 2004
[30] K Geetha S K Mishra T I Eldho A K Rastogi and R PPandey ldquoModifications to SCS-CN method for long-term hy-drologic simulationrdquo Journal of Irrigation and Drainage Engi-neering vol 133 no 5 pp 475ndash486 2007
[31] V Kumar M Babu T V Praveen et al ldquoAnalysis of the Runofffor Watershed Using SCS-CN Method and Geographic Infor-mation Systemsrdquo International Journal of Engineering Scienceand Technology 2010
[32] R Pradhan P P Mohan M K Ghose et al ldquoEstimation ofRainfall Runoff using Remote Sensing and GIS in and aroundSingtam East Sikkimrdquo International Journal of Geomatics andGeosciences vol 1 article 466 2010
[33] A Panahi ldquoEvaluating SCS-CN method in estimating theamount of runoff in Soofi Chay basin using GISrdquo Life ScienceJournal vol 10 no 5 pp 271ndash277 2013
[34] S Satheeshkumar S Venkateswaran and R KannanldquoRainfallndashrunoff estimation using SCSndashCN and GIS approachin the Pappiredipatti watershed of the Vaniyar sub basin SouthIndiardquo Modeling Earth Systems and Environment vol 3 no 12017
[35] IMD Temperature of Kanchipuram District Regional Meteoro-logical Centre Chennai India 2013 Temperature of Kanchipu-ram District Regional Meteorological Centre
[36] ENVIS 2015 Kanchipuram District ENVIS Centre TamilnaduhttpwwwtnenvisnicinfilesKANCHIPURAM20pdf
[37] AIS and LUSWatershed Atlas of India Department of Agricul-ture and Co-operation All India Soil and Landuse Survey IARICampus New Delhi India 1990
[38] V T ChowD RMaidment and LWMaysAppliedHydrologyMcGraw-Hill New York New York USA 1988
[39] Natural Resources Conservation Service (NRCS) ldquoChapter 7-Hydrologic Soil Groupsrdquo in Hydrology National EngineeringHandbook title 630 Washington DC USA 2007
10 International Journal of Geophysics
[40] United States Department of Agriculture Soil ConservationService (USDA-SCS) Technical Release 55 Urban hydrologyfor small watersheds USA 1986
[41] A G Bluman Elemtary Statistics McGraw Hill New York NYUSA 6th edition 2008
[42] C Brouwer and M Heibloem ldquoIrrigation Water Needs- Irriga-tion Water Managementrdquo in Training Manual No 3 Food andAgriculture Organisation Rome Italy 1986
[43] R G Allen L S Pereira D Raes and M Smith ldquoCrop evap-otranspiration Guidelines for computing crop water require-mentsrdquo in Irrigation and Drainage Paper No 56 Food andAgricultural Organization of the United Nations Rome Italy1998
[44] Census of India District Census Hand book ndash KanchipuramDirectorate of Census Operations in Tamil Nadu 2011
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2 International Journal of Geophysics
in water demand The villages in the study area are alreadyexperiencing rapid urbanization
Water balance estimates are essential to strengthen thedecision-making pertaining to water management Rainwa-ter harvesting and water conservation measures deserve thehighest priority in water management compared to othermeasures because of the huge investment required and longertime period involved [5] So estimation of water potential isvery important for the effective utilization of the availablewater resources in the context of fast urbanization
The widely accepted empirical method to estimate runofffor an ungauged watershed is Natural Resource ConservationService (NRCS)model formerly known as Soil ConservationService (SCS) model which is based on water balance equa-tion developed by United States Department of Agriculture[6] Rainfall-runoff relation was first proposed by Sherman[7] Surface runoff based on land use soil rainfall duration ofstorm and annual temperature for watersheds was developedby Mockus [8] Mishra and Singh [9] conducted extensiveresearch on Soil Conservation Service-curve number (SCS-CN) method Mishra et al [10] investigated SCS modelfor US fields with size ranging from 03 to 30352 hectareusing rainfall-runoff events and found that the model isappropriate for high rainfall (gt508mm) areas SCS methodwas successfully applied for a forest watershed having per-meable soils [11] Rainfall-runoff relationships using variousempirical and numerical methods have been developed byvarious researchers [12ndash16] Applicability of NRCSmodel wassuccessfully demonstrated by many researchers worldwidefor computing runoff [11 17ndash19] NRCS method is widelyaccepted and is proved useful for surface runoff estimation byseveral authors for various watersheds of Tamil Nadu [20 21]Modifications were made for SCS model for steep slopesby Gupta et al [22] Water balance computation of NoyilBasin in Tamil Nadu was presented by Chatterjee et al [23]Central Ground Water Board [24] conducted water balancestudies for upper Yamuna basin and the recharge estimatedwas compared with water table fluctuation method
Thakuriah and Saikia [25] successfully demonstratedmethodology for runoff estimation by integrating remotesensing and geographic information system (GIS) in Burig-anga watershed of Assam Remote sensing data along withground truth verification is widely utilized for mapping vari-ous resources in an economic and efficient way including soilmapping land use mapping and fertility mapping [26 27]The applicability of SCS model was improved by accountingfor spatial and temporal variability in soil and land use withremote sensing and geographic information system [28ndash34]
The literature study reveals that NRCS model is used forhydrologic simulation of small watersheds worldwide for along period and can also give realistic results even for largerwatersheds Hence NRCS model is used in the present studyfor runoff computation owing to its efficiency precision andsimplicity for ungauged watersheds
2 The Study Area and Data Collection
The study area is Urapakkam watershed which consists ofthree tanks namely Urapakkam tank Karanaipuducheri
tank and Periyar Nagar tank Urapakkam watershed with anarea of 4576 km2 is part of Guduvanchery watershed in theAdyar basin in Kanchipuram district of Tamil Nadu IndiaThe area falls under geographical coordinates 12∘ 521015840 N to 12∘471015840 N and 80∘ 421015840 E to 80∘ 521015840 E Elevation of the districtvaries from 05m to 230m above mean sea level The tem-perature ranges between 371∘C and 205∘C [35] Index mapof study area is presented in Figure 1 Rainfall occurs mainlyduring October to December through Northeast monsoonThe area receives an average rainfall of around 1150mmper annum Rainfall data was collected from India Mete-orological Department (IMD) Chennai for fourteen yearsfrom 2000 to 2014 Agriculture is the main livelihood ofthe watershed community Paddy is the major crop andgroundnut sugarcane cereals millets pulses etc are alsocultivated Water from Palar river tanks and wells is used forirrigation in the watershed [36]
3 Methodology
31 Runoff Computation Watershed boundary for Urapak-kam watershed was delineated using District Soil amp Water-shed Atlas [37] and verified with Survey of India toposheetand satellite imagery Land usemapwas prepared for the tankwatershed in GIS platform using ArcGIS-98 software and thearea under each land use is calculated
Surface runoff was assessed by applying NRCS-Hydro-logic model using daily time step [38] The runoff equationis
119876 = (119875 minus 119868119886)2(119875 minus 119868119886+ 119878) (1)
119878 = 25400CNminus 254 (2)
where
119875 is Rainfall in mmCN is curve number which ranges between 0 and 100119878 is storage index depending on the curve number119868119886is initial abstraction a fraction of 119878119876 is runoff in mm
Using 119868119886= 02S (2) takes the form
119876 = (119875 minus 02119878)2(119875 minus 08119878) (3)
Daily rainfall data land use map hydrological soil groupand infiltration study were used to arrive at runoff curvenumbers for NRCS model The watershed curve number isclosely related to soil storage index According to infiltrationcharacteristics soil is grouped under four hydrologic soilgroups namely A B C and D where A has the highestinfiltration rate and D has the least infiltration rate [39]
Surface conditions of a watershed are taken care of bycurve numbersThe curve numbers corresponding to variousland uses and hydrologic soil groups can be obtained from
International Journal of Geophysics 3
UrapakkamWatershed
Adyar Basin
Figure 1 Index map of study area
Table 1 [40] From this the weighted curve number of thewatershed can be determined which corresponds to the aver-age Antecedent Moisture Condition (AMC II) AMC for dryand wet situations is determined using five-day antecedentrainfall which corresponds to less than 13mm andmore than28mm rainfall respectively and gives rise to AMC I andAMC III The curve number determined for AMC II can beconverted for other conditions namely AMC I and AMC IIIusing the following relations [38]
CNI = 42CNII10 minus 00584CNII(4)
CNIII = 23CNII10 + 013CNII (5)
where
CNI is curve number for AMC ICNII is curve number for AMC IICNIII is curve number for AMC III
A simple regression model was developed to compute annualrunoff from rainfall which was then calibrated and vali-dated with NRCS model Such simpler equations can beused for computation of runoff for ungauged watershedsinstead of arduous and data intensive computation methodsPerformance of developed regression model was evaluatedusing statisticalmethods such as coefficient of determinationstandard error of estimate and mean absolute error Thestatistical test of significance used was t-test
Coefficient of Determination (R2) shows the degree oflinear relation between the two variables R2 value close tounity indicates a high degree of association between the twovariables [41]
Standard Error of Estimate (SEE) is an estimate of theaverage deviation of the regression value from the observeddata
SEE = radicsum (119909119894 minus 119909)2119899 minus 2 (6)
4 International Journal of Geophysics
Table 1 Runoff curve numbers
Description of Land use Hydrologic Soil GroupA B C D
Paved parking lots roofs driveways 98 98 98 98Streets and Roads
Paved with curbs and storm sewers 98 98 98 98Gravel 76 85 89 91Dirt 72 82 87 89
Cultivated (Agricultural Crop) LandlowastWithout conservation treatment (no terraces) 72 81 88 91With conservation treatment (terraces contours) 62 71 78 81
Pasture or Range LandPoor (lt50 ground cover or heavily grazed) 68 79 86 89Good (50ndash75 ground cover not heavily grazed) 39 61 74 80
Meadow (grass no grazing mowed for hay) 30 58 71 78Brush (good gt75 ground cover) 30 48 65 73Woods and Forests
Poor (small treesbrush destroyed by over-grazing or burning) 45 66 77 83Fair (grazing but not burned some brush) 36 60 73 79Good (no grazing brush covers ground) 30 55 70 77
Open Spaces (lawns parks golf courses cemeteries etc)Fair (grass covers 50ndash75 of area) 49 69 79 84Good (grass covers gt75 of area) 39 61 74 80
Commercial and Business Districts (85 impervious) 89 92 94 95Industrial Districts (72 impervious) 81 88 91 93Residential Areas
18 Acre lots about 65 impervious 77 85 90 9214 Acre lots about 38 impervious 61 75 83 8712 Acre lots about 25 impervious 54 70 80 851 Acre lots about 20 impervious 51 68 79 84
Source United States Department of Agriculture Soil Conservation Service 1986 [40] Technical Release 55
Mean Absolute Error (MAE) is the average of all absoluteerrors which can be expressed as
MAE = 1119899119899sum119894=1
1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816 (7)
where
119909 is runoff by NRCS model119909119894is runoff by regression model119899 is number of data sets
Test for significance t-test was used to test whether thereis significant difference between runoff computed by NRCSmodel and those estimated by regression model
The assumptions used in this test were samples which areindependent and random in nature and samples which arenormally distributed
Two-independent sample t-test statistics is defined as
119905 = (sum 1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816) 119899radic((sum 1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816)2 minus ((sum 1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816)2 119899)) (119899 minus 1) (119899) (8)
32 Water Balance Study Water availability and demand fordomestic use and irrigation in the watershed were computedseparately Domestic water demandwas obtained as the prod-uct of per capita water requirement and the population Thedifference between water availability and water demand willgive a comprehensive understanding on the total utilizablewater
Estimation of Irrigation Demand Irrigation water demandwas calculated from the method given by Brouwer andHeibloem 1986 [42] Potential evapotranspiration was com-puted from BlaneyndashCriddle equation [43] and is given by
ET119900= 119901 (046119879mean + 8) (9)
where
119879mean is mean daily temperature (∘C)119901 is mean daily percentage of annual daytime hours
Crop coefficient (119870119888) for paddy for various growth stages was
estimated and crop water requirement was determined from
ETcrop = ET119900 times 119870119888 (10)
International Journal of Geophysics 5
Table 2 Land use details and the corresponding curve numbers
Land use category Area Curve Numberkm2
Forest Forest Plantations 1089 79Crop land 0193 81Water bodies 0600 89Barren land 0167 80SettlementResidential area 2527 87
Other water needs such as water to saturate the soil (SAT)percolation and seepage losses (PSL) and water neededfor standing water layer (WL) were used to compute theirrigation need
Effective rainfall (119875119890) was calculated from the formulae
119875119890= 08119875 minus 25 if 119875 gt 75mmmonth
119875119890= 06119875 minus 10 if 119875 lt 75mmmonth (11)
where
119875 is rainfall in mmmonth
Irrigation Need (IN) is calculated as
IN = ETcrop + SAT + PSL +WL minus 119875119890 (12)
4 Result
The runoff for Urapakkam watershed in Adyar basin wascomputed using USDA-NRCS model A simple regressionmodel was also developed and validated for its suitabilityfor runoff computation of the watershed By knowing theutilizable water available in a watershed water supply forirrigation and other purposes can be planned and alternativecropping patterns can be adopted to avoid losses
41 Runoff Computation
411 NRCS Model Watershed boundary for the chain oftanks was taken from District Soil amp Watershed Atlas andverified with Survey of India toposheet and satellite imageryLand use map for Urapakkam watershed was prepared fromsatellite imagery with field verification in GIS platform usingArcGIS and the area under various land uses was determined(Figure 2)
The watershed is in the hydrologic soil group D as perthe soil details Hydrological modeling considers water yearfrom July to June of ensuing year and uses daily time stepUrapakkam tank has both free catchment and interceptedcatchment The hydrological model estimated the runoff forfourteen water years from 2000-01 to 2013-14 with daily timestep Land use details and corresponding curve numbers aregiven in Table 2
Weighted curve number for the watershed with adrainage area of 4576 km2 is 8485 The curve numbers forAntecedent Moisture Conditions I II and III are 7017 8485and 9280 respectively
Figure 2 Land use map of Urapakkam watershed
Water Year
RainfallRunoff
2001
-200
2
2002
-200
3
2003
-200
4
2004
-200
5
2005
-200
6
2006
-200
7
2007
-200
8
2008
-200
9
2009
-201
0
2010
-201
1
2011
-201
2
2012
-201
3
2013
-201
4
2000
-200
10
10
20
30
40
50
60
70
80
Num
ber o
f Day
s
Figure 3 Number of days with rainfall and runoff by NRCS model
Days with rainfall and runoff for the study period aregiven in Figure 3 Monthly rainfall and the correspondingrunoff as estimated from NRCS model are given in Figure 4Table 3 gives monthly rainfall and runoff for the water year2008ndash2009whenfiftypercent dependable flowwas observed
Annual rainfall for the watershed varied from 5757mm(2002ndash03) to 36080mm (2005ndash06) The runoff as estimatedfromNRCSmodel varied from 9495mm to 232434mm andthe corresponding percentage varied from 1649 to 6442 withan average value of 3709 percent for the fourteen water years
6 International Journal of Geophysics
RainfallRunoff
Jan
07
Jan
10
Jan
14
Jan
12
Jan
03
Jan
11
Jan
04
Jan
01
Jan
05
Jan
08
Jan
06
Jan
09
Jan
13
Jan
02Ju
l02
Jul0
9
Jul0
6
Jul0
3
Jul1
3
Jul1
0
Jul0
4
Jul1
1
Jul0
8
Jul0
7
Jul0
5
Jul0
1
Jul1
2
Jul0
0
Month
0200400600800
100012001400
Rainfa
ll R
unoff
(mm
)
Figure 4 Monthly rainfall and runoff by NRCS model for Ura-pakkam tank watershed
Table 3 Monthly rainfall and runoff for Urapakkam watershedusing NRCS model for the water year 2008-2009
Month Rainfall Runoffmm mm
Jul-08 6100 005Aug-08 10100 2749Sep-08 12900 6311Oct-08 25400 6909Nov-08 53400 35289Dec-08 8400 2633Jan-09 1500 000Feb-09 000 000Mar-09 000 000Apr-09 000 000May-09 2400 000Jun-09 3680 000
2000-01 to 2013-14 Bar chart showing annual rainfall andrunoff is given in Figure 5 Volume of runoff for thewatershedvaried between 0434 MCM and 10637 MCM during thefourteen years of study Annual runoff for a representativewater year (2008-09) is obtained as 246 MCM (Table 4)When the rainfall exceeds the threshold limit the proportionof rainfall contributing to runoffwas found to be higherWidevariations were observed in the quantum of runoff from rain-fall of same magnitude due to varying antecedent moistureconditions in the soil Figure 6 shows the average annualrainfall and runoff estimated for Urapakkam watershed
412 Regression Model A Linear regression model wasdeveloped for the watershed to compute annual runoff fromthe annual rainfall Ten-year data from NRCS model wasused for model calibration and four-year data was usedfor validation The regression relationship obtained betweenannual rainfall (mm) and annual runoff (mm) is given as
Runoff = 0736 times rainfall minus 4678 (13)
RainfallRunoff - NRCS ModelRunoff - Regression model
0500
1000150020002500300035004000
Dep
th in
mm
2001
-200
2
2002
-200
3
2003
-200
4
2004
-200
5
2005
-200
6
2006
-200
7
2007
-200
8
2008
-200
9
2009
-201
0
2010
-201
1
2011
-201
2
2012
-201
3
2013
-201
4
2000
-200
1
Water Year
Figure 5 Annual rainfall and runoff for Urapakkam watershed
Rainfall 152578
Runoff 63338
Figure 6 Average annual rainfall and runoff estimated for Ura-pakkam watershed
R2 value was obtained as 0944 during calibration and 0988during validation periodThe plots of rainfall versus runoff asobtained fromNRCSmodel during calibration and validationperiod are shown in Figure 7
413 Validation of Regression Model Figure 8 shows thecomparison between the annual runoff by NRCS modeland the developed regression model which illustrates thatthe regression model could be used to effectively predictrunoff for the watershed even during the validation phaseThe coefficient of determination (R2) for runoff by NRCSmodel versus runoff by regression model was 0939 at 95confidence level (Figure 9) The statistical tests such as t-test MAE and SEE obtained for the two runoff estimates(NRCS model and regression model) were 055 1060 and1390 respectively The average runoff for the watershed bylinear regression model was obtained as 3717 percent ofthe rainfall which ranges from 1279 to 6063 It showsno considerable deviation from runoff computed by NRCSmodel Thus the statistical analysis proves that tedious anddata intensive computation methods can be eliminated andthe volume of runoff from a watershed can be efficiently andeasily computed using simple regression model
International Journal of Geophysics 7
Table 4 Annual rainfall and runoff estimated for Urapakkam watershed using NRCS model for the period 2000-01 to 2013ndash14
Water YearRainfall Runoff
No of days Depth No of days Depth Percentage of Rainfall Volumemm mm MCM
2000-2001 39 109230 28 32396 2966 14832001-2002 49 121110 35 35539 2934 16262002-2003 28 57570 15 9495 1649 04342003-2004 65 176580 36 53750 3044 24602004-2005 44 164800 35 76859 4664 35172005-2006 59 360800 50 232434 6442 106372006-2007 38 137900 23 70120 5085 32092007-2008 71 189950 44 78043 4109 35712008-2009 44 123880 29 53897 4351 24662009-2010 55 143900 33 58224 4046 26652010-2011 67 148900 34 49909 3352 22842011-2012 52 181000 45 73411 4056 33592012-2013 60 143540 36 48412 3373 22152013-2014 64 76930 22 14248 1852 0652Minimum 28 5757 15 9495 1649 0434Maximum 71 36080 44 232434 6442 10637
y = 07369x - 46782
0
500
1000
1500
2000
2500
Runo
ff (m
m) 2
2= 09446
1000 2000 3000 40000Rainfall (mm)
(a) Calibration
500 1000 1500 20000Rainfall (mm)
0
200
400
600
800
Runo
ff (m
m)
22= 09887
(b) Validation
Figure 7 Relationship between annual rainfall and runoff by NRCS model during calibration and validation period
Run off NRCS modelRunoff Regression model
ValidationCalibration
1 2 3 4 5 6 7 8 9 10 11 12 13 14 150
(Years)minus500
0
500
1000
1500
2000
2500
Runo
ff de
pth
(mm
)
Figure 8 Comparison of annual runoff from NRCS model andregression model
42 Water Balance Study Population density of Kanchipu-ram district is 892km2 [44] Assuming the per capita waterdemand to be 100 litersday for domestic purposes the
y = 09488x + 5422922= 09389
500 1000 1500 2000 25000
Runoff - NRCS Model minus500
0
500
1000
1500
2000
2500
Runo
ff - R
egre
ssio
n M
odel
Figure 9 Scatter plot of annual runoff by NRCS model andregression model for Urapakkam watershed for 2000-01 to 2013-14
total domestic water requirement for the population of thewatershed with 4576 km2 is estimated at 407m3day whichis 0148 MCM per annum
8 International Journal of Geophysics
Table 5 Computation of irrigation demand for paddy for the year 2008-09
Item MonthSeptember October November December January February Season
Percentage of annualdaytime hours 119901 026 027 026 027 027
Average Temperature119879mean (∘C) 2600 2480 2460 258 2780
PotentialevapotranspirationEt119900(mmday)
519 514 502 527 561
Crop coefficient119870119888
110 110 120 130 100
Crop waterrequirement
mmday 571 566 603 684 561mmmonth 17126 17538 18682 19165 17400
SAT mm 20000PSL mmmonth4mmday 12000 12400 12400 11200 12400
WL mm119875119890 mm 4860 49980 16780 000 000 000
Irrigation demandmm 15140 000 13158 31082 30365 29800 119545
Irrigation is practiced in a small area of 10 ha in thewatershed The average irrigation demand for the watershedarea is computed to be around 1195mm By considering anefficiency of 70 the actual irrigation water requirement is1708mmThe total irrigation demand for 10 ha of paddy cropin the watershed is estimated as 0171 MCM Details of thecomputation of irrigation demand for paddy are presented inTable 5
The current total domestic and irrigation water require-ment of the watershed is 0320 MCM After meeting all thewater demands of the watershed there is sufficient wateravailable even if only 50 of the total available rainwater canbe effectively harvested
5 Conclusions
Accurate estimation of runoff is essential for the effectivemanagement ofwater resources especially in semiarid regionslike Tamil Nadu NRCS model is a widely accepted model forthe estimation of runoff for small watersheds It is extensivelyused by engineers and hydrologists but the lack of sufficientdata may give poor results With the understanding ofavailable water in a watershed irrigation scheduling croprotation and cropping pattern can be planned
Total volume of runoff available for Urapakkam water-shed from NRCS model for a representative year is 246MCM A simple regression model was developed to estimatedepth of annual runoff for the watershed which is givenas runoff (mm) = 0736 times rainfall (mm) minus 4678 Statisticaltests confirm that there is no significant difference betweenthe runoff estimates from the two models Hence the studyconfirms that it is advantageous to have calibrated regression
models for easy computation of runoff and for the effectivemanagement of water resources for ungauged basins
Water balance study shows that 123 MCM of watercan be effectively stored in the watershed after losses byimproving the drainage and capacity of the storages Thetotal domestic and irrigation demand is estimated at 0320MCM Hence apart from meeting the present domestic andirrigation demand in the watershed additional demand dueto future development and demand of adjoining areas canalso be met from the water available in the watershed ifproperly harvested
Data Availability
The data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this paper
References
[1] I A Shiklomanov ldquoAppraisal and Assessment of world waterresourcesrdquoWater International vol 25 no 1 pp 11ndash32 2000
[2] FAO ldquoFAOActivities on Promoting Fertigationrdquo in Proceedingsof the Regional Workshop on Guidelines for Efficient FertilizersUse through Modern Irrigation FAO Regional Office for theNear East Cairo Egypt 1998
[3] W Cosgrove and F Rijsberman World Water Vision MakingWater Everybodys Business World Water Council EarthscanPublications 2000
International Journal of Geophysics 9
[4] Ministry of Water Resources 2012 Per Capita Water Availabil-ity Press Information Bureau Government of India 26-April2012 httppibnicinnewsitePrintReleaseaspxrelid=82676
[5] S K Gupta and R D Deshpande ldquoWater for India in 2050First-order assessment of available optionsrdquo Current Sciencevol 86 no 9 pp 1216ndash1224 2004
[6] Soil Conservation Service (SCS) Hydrology National Engi-neering Handbook Supplement A Section 4 Chapter 10 SoilConservation Service USDA Washington DC USA 1972
[7] L K Sherman ldquoThe unit hydrograph methodrdquo in Physics of theEarth O E Meinzer Ed pp 514ndash525 Dover Publications IncNew York NY USA 1949
[8] VMockus ldquoEstimation of total (peak rates of) surface runoff forindividual stormsrdquo in Exhibit A of Appendix B Interim SurveyReport Grand (Neosho) River Watershed USDA 1949
[9] S KMishra andV P Singh ldquoAnother look at SCS-CNmethodrdquoJournal of Hydrologic Engineering vol 4 no 3 pp 257ndash2641999
[10] S K Mishra M K Jain P K Bhunya and V P Singh ldquoFieldapplicability of the SCS-CN-basedMishra-Singh general modeland its variantsrdquoWater Resources Management vol 19 no 1 pp37ndash62 2005
[11] K X Soulis J D Valiantzas N Dercas and P A LondraldquoInvestigation of the direct runoff generationmechanism for theanalysis of the SCS-CN method applicability to a partial areaexperimental watershedrdquo Hydrology and Earth System Sciencesvol 13 no 5 pp 605ndash615 2009
[12] P Koteshwaram and S M A Alvi ldquoSecular trends and period-icities in rainfall at west coast stations in Indiardquo Current Sciencevol 38 no 10 pp 229ndash231 1969
[13] K V Ramamurthy M K Sonam and S S Muley ldquoLongterm variation in the rainfall over upper Narmada catchmentrdquoMausam vol 38 no 3 pp 313ndash318 1987
[14] R Jha and R K Jaiswal ldquoAnalysis of rainfall pattern in acatchmentrdquo IAH Journal of Hydrology (India) Roorkee 1992
[15] F H S Chiew M J Stewardson and T A McMahon ldquoCom-parison of six rainfall-runoff modelling approachesrdquo Journal ofHydrology vol 147 no 1-4 pp 1ndash36 1993
[16] E M Lungu ldquoDetection of changes in rainfall and runoffpatternsrdquo Journal ofHydrology vol 147 no 1-4 pp 153ndash167 1993
[17] J Meher and R Jha ldquoTime series analysis of rainfall data overMahanadi River basinrdquo Emerging Science vol 3 no 6 pp 22ndash32 2011
[18] K X Soulis and J D Valiantzas ldquoSCS-CN parameter determi-nation using rainfall-runoff data in heterogeneous watersheds-the two-CN system approachrdquo Hydrology and Earth SystemSciences vol 16 no 3 pp 1001ndash1015 2012
[19] R Kabiri A Chan and R Bai ldquoComparison of SCS and Green-Ampt Methods in Surface Runoff-Flooding Simulation forKlang Watershed in Malaysiardquo Open Journal of Modern Hydro-logy vol 03 no 03 pp 102ndash114 2013
[20] S Mohan andM Abraham ldquoDerivations of simple site-specificrecharge-precipitation relationships A case study from theCuddalore Basin Indiardquo Environmental Geosciences vol 17 no1 pp 37ndash44 2010
[21] R Viji P R Prasanna and R Ilangovan ldquoGIS based SCS-CN method for estimating runoff in Kundahpalam watershedNilgries district Tamilnadurdquo Earth Sciences Research Journalvol 19 no 1 pp 59ndash64 2015
[22] P K Gupta S Punalekar S Panigrahy A Sonakia and J SParihar ldquoRunoff modeling in an agro-forested watershed using
remote sensing and gisrdquo Journal of Hydrologic Engineering vol17 no 11 pp 1255ndash1267 2012
[23] R Chatterjee ldquoGroundwater resources estimationmdash case stud-ies from Indiardquo Journal of the Geological Society of India vol 77no 2 pp 201ndash204 2011
[24] Central Ground Water Board (CGWB) Water Balance Studiesin Upper Yamuna Basin ndash Terminal Report - Project Findingsand Recommendations Chandigarh Central Ground WaterBoard 2000
[25] G Thakuriah and R Saikia ldquoEstimation of Surface RunoffusingNRCSCurve number procedure in BurigangaWatershedAssam India - A Geospatial Approachrdquo International ResearchJournal of Earth Sciences vol 2 no 5 pp 1ndash7 2014
[26] V K Dadhwal ldquoRemote sensing and GIS for agricultural cropacreage and yield estimationrdquo in Proceedings of the InternationalArchives of Photogrammetry andRemote Sensing XXXII pp 58ndash67 1999
[27] G B Geena and P N Ballukraya ldquoEstimation of runoff for Redhills watershed using SCS method and GISrdquo Indian Journal ofScience and Technology vol 4 no 8 pp 899ndash902 2011
[28] K H Syed D C Goodrich D E Myers and S SorooshianldquoSpatial characteristics of thunderstorm rainfall fields and theirrelation to runoffrdquo Journal of Hydrology vol 271 no 1-4 pp 1ndash21 2003
[29] X Zhan and M-L Huang ldquoArcCN-Runoff An ArcGIS toolfor generating curve number and runoff mapsrdquo EnvironmentalModeling and Software vol 19 no 10 pp 875ndash879 2004
[30] K Geetha S K Mishra T I Eldho A K Rastogi and R PPandey ldquoModifications to SCS-CN method for long-term hy-drologic simulationrdquo Journal of Irrigation and Drainage Engi-neering vol 133 no 5 pp 475ndash486 2007
[31] V Kumar M Babu T V Praveen et al ldquoAnalysis of the Runofffor Watershed Using SCS-CN Method and Geographic Infor-mation Systemsrdquo International Journal of Engineering Scienceand Technology 2010
[32] R Pradhan P P Mohan M K Ghose et al ldquoEstimation ofRainfall Runoff using Remote Sensing and GIS in and aroundSingtam East Sikkimrdquo International Journal of Geomatics andGeosciences vol 1 article 466 2010
[33] A Panahi ldquoEvaluating SCS-CN method in estimating theamount of runoff in Soofi Chay basin using GISrdquo Life ScienceJournal vol 10 no 5 pp 271ndash277 2013
[34] S Satheeshkumar S Venkateswaran and R KannanldquoRainfallndashrunoff estimation using SCSndashCN and GIS approachin the Pappiredipatti watershed of the Vaniyar sub basin SouthIndiardquo Modeling Earth Systems and Environment vol 3 no 12017
[35] IMD Temperature of Kanchipuram District Regional Meteoro-logical Centre Chennai India 2013 Temperature of Kanchipu-ram District Regional Meteorological Centre
[36] ENVIS 2015 Kanchipuram District ENVIS Centre TamilnaduhttpwwwtnenvisnicinfilesKANCHIPURAM20pdf
[37] AIS and LUSWatershed Atlas of India Department of Agricul-ture and Co-operation All India Soil and Landuse Survey IARICampus New Delhi India 1990
[38] V T ChowD RMaidment and LWMaysAppliedHydrologyMcGraw-Hill New York New York USA 1988
[39] Natural Resources Conservation Service (NRCS) ldquoChapter 7-Hydrologic Soil Groupsrdquo in Hydrology National EngineeringHandbook title 630 Washington DC USA 2007
10 International Journal of Geophysics
[40] United States Department of Agriculture Soil ConservationService (USDA-SCS) Technical Release 55 Urban hydrologyfor small watersheds USA 1986
[41] A G Bluman Elemtary Statistics McGraw Hill New York NYUSA 6th edition 2008
[42] C Brouwer and M Heibloem ldquoIrrigation Water Needs- Irriga-tion Water Managementrdquo in Training Manual No 3 Food andAgriculture Organisation Rome Italy 1986
[43] R G Allen L S Pereira D Raes and M Smith ldquoCrop evap-otranspiration Guidelines for computing crop water require-mentsrdquo in Irrigation and Drainage Paper No 56 Food andAgricultural Organization of the United Nations Rome Italy1998
[44] Census of India District Census Hand book ndash KanchipuramDirectorate of Census Operations in Tamil Nadu 2011
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International Journal of Geophysics 3
UrapakkamWatershed
Adyar Basin
Figure 1 Index map of study area
Table 1 [40] From this the weighted curve number of thewatershed can be determined which corresponds to the aver-age Antecedent Moisture Condition (AMC II) AMC for dryand wet situations is determined using five-day antecedentrainfall which corresponds to less than 13mm andmore than28mm rainfall respectively and gives rise to AMC I andAMC III The curve number determined for AMC II can beconverted for other conditions namely AMC I and AMC IIIusing the following relations [38]
CNI = 42CNII10 minus 00584CNII(4)
CNIII = 23CNII10 + 013CNII (5)
where
CNI is curve number for AMC ICNII is curve number for AMC IICNIII is curve number for AMC III
A simple regression model was developed to compute annualrunoff from rainfall which was then calibrated and vali-dated with NRCS model Such simpler equations can beused for computation of runoff for ungauged watershedsinstead of arduous and data intensive computation methodsPerformance of developed regression model was evaluatedusing statisticalmethods such as coefficient of determinationstandard error of estimate and mean absolute error Thestatistical test of significance used was t-test
Coefficient of Determination (R2) shows the degree oflinear relation between the two variables R2 value close tounity indicates a high degree of association between the twovariables [41]
Standard Error of Estimate (SEE) is an estimate of theaverage deviation of the regression value from the observeddata
SEE = radicsum (119909119894 minus 119909)2119899 minus 2 (6)
4 International Journal of Geophysics
Table 1 Runoff curve numbers
Description of Land use Hydrologic Soil GroupA B C D
Paved parking lots roofs driveways 98 98 98 98Streets and Roads
Paved with curbs and storm sewers 98 98 98 98Gravel 76 85 89 91Dirt 72 82 87 89
Cultivated (Agricultural Crop) LandlowastWithout conservation treatment (no terraces) 72 81 88 91With conservation treatment (terraces contours) 62 71 78 81
Pasture or Range LandPoor (lt50 ground cover or heavily grazed) 68 79 86 89Good (50ndash75 ground cover not heavily grazed) 39 61 74 80
Meadow (grass no grazing mowed for hay) 30 58 71 78Brush (good gt75 ground cover) 30 48 65 73Woods and Forests
Poor (small treesbrush destroyed by over-grazing or burning) 45 66 77 83Fair (grazing but not burned some brush) 36 60 73 79Good (no grazing brush covers ground) 30 55 70 77
Open Spaces (lawns parks golf courses cemeteries etc)Fair (grass covers 50ndash75 of area) 49 69 79 84Good (grass covers gt75 of area) 39 61 74 80
Commercial and Business Districts (85 impervious) 89 92 94 95Industrial Districts (72 impervious) 81 88 91 93Residential Areas
18 Acre lots about 65 impervious 77 85 90 9214 Acre lots about 38 impervious 61 75 83 8712 Acre lots about 25 impervious 54 70 80 851 Acre lots about 20 impervious 51 68 79 84
Source United States Department of Agriculture Soil Conservation Service 1986 [40] Technical Release 55
Mean Absolute Error (MAE) is the average of all absoluteerrors which can be expressed as
MAE = 1119899119899sum119894=1
1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816 (7)
where
119909 is runoff by NRCS model119909119894is runoff by regression model119899 is number of data sets
Test for significance t-test was used to test whether thereis significant difference between runoff computed by NRCSmodel and those estimated by regression model
The assumptions used in this test were samples which areindependent and random in nature and samples which arenormally distributed
Two-independent sample t-test statistics is defined as
119905 = (sum 1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816) 119899radic((sum 1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816)2 minus ((sum 1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816)2 119899)) (119899 minus 1) (119899) (8)
32 Water Balance Study Water availability and demand fordomestic use and irrigation in the watershed were computedseparately Domestic water demandwas obtained as the prod-uct of per capita water requirement and the population Thedifference between water availability and water demand willgive a comprehensive understanding on the total utilizablewater
Estimation of Irrigation Demand Irrigation water demandwas calculated from the method given by Brouwer andHeibloem 1986 [42] Potential evapotranspiration was com-puted from BlaneyndashCriddle equation [43] and is given by
ET119900= 119901 (046119879mean + 8) (9)
where
119879mean is mean daily temperature (∘C)119901 is mean daily percentage of annual daytime hours
Crop coefficient (119870119888) for paddy for various growth stages was
estimated and crop water requirement was determined from
ETcrop = ET119900 times 119870119888 (10)
International Journal of Geophysics 5
Table 2 Land use details and the corresponding curve numbers
Land use category Area Curve Numberkm2
Forest Forest Plantations 1089 79Crop land 0193 81Water bodies 0600 89Barren land 0167 80SettlementResidential area 2527 87
Other water needs such as water to saturate the soil (SAT)percolation and seepage losses (PSL) and water neededfor standing water layer (WL) were used to compute theirrigation need
Effective rainfall (119875119890) was calculated from the formulae
119875119890= 08119875 minus 25 if 119875 gt 75mmmonth
119875119890= 06119875 minus 10 if 119875 lt 75mmmonth (11)
where
119875 is rainfall in mmmonth
Irrigation Need (IN) is calculated as
IN = ETcrop + SAT + PSL +WL minus 119875119890 (12)
4 Result
The runoff for Urapakkam watershed in Adyar basin wascomputed using USDA-NRCS model A simple regressionmodel was also developed and validated for its suitabilityfor runoff computation of the watershed By knowing theutilizable water available in a watershed water supply forirrigation and other purposes can be planned and alternativecropping patterns can be adopted to avoid losses
41 Runoff Computation
411 NRCS Model Watershed boundary for the chain oftanks was taken from District Soil amp Watershed Atlas andverified with Survey of India toposheet and satellite imageryLand use map for Urapakkam watershed was prepared fromsatellite imagery with field verification in GIS platform usingArcGIS and the area under various land uses was determined(Figure 2)
The watershed is in the hydrologic soil group D as perthe soil details Hydrological modeling considers water yearfrom July to June of ensuing year and uses daily time stepUrapakkam tank has both free catchment and interceptedcatchment The hydrological model estimated the runoff forfourteen water years from 2000-01 to 2013-14 with daily timestep Land use details and corresponding curve numbers aregiven in Table 2
Weighted curve number for the watershed with adrainage area of 4576 km2 is 8485 The curve numbers forAntecedent Moisture Conditions I II and III are 7017 8485and 9280 respectively
Figure 2 Land use map of Urapakkam watershed
Water Year
RainfallRunoff
2001
-200
2
2002
-200
3
2003
-200
4
2004
-200
5
2005
-200
6
2006
-200
7
2007
-200
8
2008
-200
9
2009
-201
0
2010
-201
1
2011
-201
2
2012
-201
3
2013
-201
4
2000
-200
10
10
20
30
40
50
60
70
80
Num
ber o
f Day
s
Figure 3 Number of days with rainfall and runoff by NRCS model
Days with rainfall and runoff for the study period aregiven in Figure 3 Monthly rainfall and the correspondingrunoff as estimated from NRCS model are given in Figure 4Table 3 gives monthly rainfall and runoff for the water year2008ndash2009whenfiftypercent dependable flowwas observed
Annual rainfall for the watershed varied from 5757mm(2002ndash03) to 36080mm (2005ndash06) The runoff as estimatedfromNRCSmodel varied from 9495mm to 232434mm andthe corresponding percentage varied from 1649 to 6442 withan average value of 3709 percent for the fourteen water years
6 International Journal of Geophysics
RainfallRunoff
Jan
07
Jan
10
Jan
14
Jan
12
Jan
03
Jan
11
Jan
04
Jan
01
Jan
05
Jan
08
Jan
06
Jan
09
Jan
13
Jan
02Ju
l02
Jul0
9
Jul0
6
Jul0
3
Jul1
3
Jul1
0
Jul0
4
Jul1
1
Jul0
8
Jul0
7
Jul0
5
Jul0
1
Jul1
2
Jul0
0
Month
0200400600800
100012001400
Rainfa
ll R
unoff
(mm
)
Figure 4 Monthly rainfall and runoff by NRCS model for Ura-pakkam tank watershed
Table 3 Monthly rainfall and runoff for Urapakkam watershedusing NRCS model for the water year 2008-2009
Month Rainfall Runoffmm mm
Jul-08 6100 005Aug-08 10100 2749Sep-08 12900 6311Oct-08 25400 6909Nov-08 53400 35289Dec-08 8400 2633Jan-09 1500 000Feb-09 000 000Mar-09 000 000Apr-09 000 000May-09 2400 000Jun-09 3680 000
2000-01 to 2013-14 Bar chart showing annual rainfall andrunoff is given in Figure 5 Volume of runoff for thewatershedvaried between 0434 MCM and 10637 MCM during thefourteen years of study Annual runoff for a representativewater year (2008-09) is obtained as 246 MCM (Table 4)When the rainfall exceeds the threshold limit the proportionof rainfall contributing to runoffwas found to be higherWidevariations were observed in the quantum of runoff from rain-fall of same magnitude due to varying antecedent moistureconditions in the soil Figure 6 shows the average annualrainfall and runoff estimated for Urapakkam watershed
412 Regression Model A Linear regression model wasdeveloped for the watershed to compute annual runoff fromthe annual rainfall Ten-year data from NRCS model wasused for model calibration and four-year data was usedfor validation The regression relationship obtained betweenannual rainfall (mm) and annual runoff (mm) is given as
Runoff = 0736 times rainfall minus 4678 (13)
RainfallRunoff - NRCS ModelRunoff - Regression model
0500
1000150020002500300035004000
Dep
th in
mm
2001
-200
2
2002
-200
3
2003
-200
4
2004
-200
5
2005
-200
6
2006
-200
7
2007
-200
8
2008
-200
9
2009
-201
0
2010
-201
1
2011
-201
2
2012
-201
3
2013
-201
4
2000
-200
1
Water Year
Figure 5 Annual rainfall and runoff for Urapakkam watershed
Rainfall 152578
Runoff 63338
Figure 6 Average annual rainfall and runoff estimated for Ura-pakkam watershed
R2 value was obtained as 0944 during calibration and 0988during validation periodThe plots of rainfall versus runoff asobtained fromNRCSmodel during calibration and validationperiod are shown in Figure 7
413 Validation of Regression Model Figure 8 shows thecomparison between the annual runoff by NRCS modeland the developed regression model which illustrates thatthe regression model could be used to effectively predictrunoff for the watershed even during the validation phaseThe coefficient of determination (R2) for runoff by NRCSmodel versus runoff by regression model was 0939 at 95confidence level (Figure 9) The statistical tests such as t-test MAE and SEE obtained for the two runoff estimates(NRCS model and regression model) were 055 1060 and1390 respectively The average runoff for the watershed bylinear regression model was obtained as 3717 percent ofthe rainfall which ranges from 1279 to 6063 It showsno considerable deviation from runoff computed by NRCSmodel Thus the statistical analysis proves that tedious anddata intensive computation methods can be eliminated andthe volume of runoff from a watershed can be efficiently andeasily computed using simple regression model
International Journal of Geophysics 7
Table 4 Annual rainfall and runoff estimated for Urapakkam watershed using NRCS model for the period 2000-01 to 2013ndash14
Water YearRainfall Runoff
No of days Depth No of days Depth Percentage of Rainfall Volumemm mm MCM
2000-2001 39 109230 28 32396 2966 14832001-2002 49 121110 35 35539 2934 16262002-2003 28 57570 15 9495 1649 04342003-2004 65 176580 36 53750 3044 24602004-2005 44 164800 35 76859 4664 35172005-2006 59 360800 50 232434 6442 106372006-2007 38 137900 23 70120 5085 32092007-2008 71 189950 44 78043 4109 35712008-2009 44 123880 29 53897 4351 24662009-2010 55 143900 33 58224 4046 26652010-2011 67 148900 34 49909 3352 22842011-2012 52 181000 45 73411 4056 33592012-2013 60 143540 36 48412 3373 22152013-2014 64 76930 22 14248 1852 0652Minimum 28 5757 15 9495 1649 0434Maximum 71 36080 44 232434 6442 10637
y = 07369x - 46782
0
500
1000
1500
2000
2500
Runo
ff (m
m) 2
2= 09446
1000 2000 3000 40000Rainfall (mm)
(a) Calibration
500 1000 1500 20000Rainfall (mm)
0
200
400
600
800
Runo
ff (m
m)
22= 09887
(b) Validation
Figure 7 Relationship between annual rainfall and runoff by NRCS model during calibration and validation period
Run off NRCS modelRunoff Regression model
ValidationCalibration
1 2 3 4 5 6 7 8 9 10 11 12 13 14 150
(Years)minus500
0
500
1000
1500
2000
2500
Runo
ff de
pth
(mm
)
Figure 8 Comparison of annual runoff from NRCS model andregression model
42 Water Balance Study Population density of Kanchipu-ram district is 892km2 [44] Assuming the per capita waterdemand to be 100 litersday for domestic purposes the
y = 09488x + 5422922= 09389
500 1000 1500 2000 25000
Runoff - NRCS Model minus500
0
500
1000
1500
2000
2500
Runo
ff - R
egre
ssio
n M
odel
Figure 9 Scatter plot of annual runoff by NRCS model andregression model for Urapakkam watershed for 2000-01 to 2013-14
total domestic water requirement for the population of thewatershed with 4576 km2 is estimated at 407m3day whichis 0148 MCM per annum
8 International Journal of Geophysics
Table 5 Computation of irrigation demand for paddy for the year 2008-09
Item MonthSeptember October November December January February Season
Percentage of annualdaytime hours 119901 026 027 026 027 027
Average Temperature119879mean (∘C) 2600 2480 2460 258 2780
PotentialevapotranspirationEt119900(mmday)
519 514 502 527 561
Crop coefficient119870119888
110 110 120 130 100
Crop waterrequirement
mmday 571 566 603 684 561mmmonth 17126 17538 18682 19165 17400
SAT mm 20000PSL mmmonth4mmday 12000 12400 12400 11200 12400
WL mm119875119890 mm 4860 49980 16780 000 000 000
Irrigation demandmm 15140 000 13158 31082 30365 29800 119545
Irrigation is practiced in a small area of 10 ha in thewatershed The average irrigation demand for the watershedarea is computed to be around 1195mm By considering anefficiency of 70 the actual irrigation water requirement is1708mmThe total irrigation demand for 10 ha of paddy cropin the watershed is estimated as 0171 MCM Details of thecomputation of irrigation demand for paddy are presented inTable 5
The current total domestic and irrigation water require-ment of the watershed is 0320 MCM After meeting all thewater demands of the watershed there is sufficient wateravailable even if only 50 of the total available rainwater canbe effectively harvested
5 Conclusions
Accurate estimation of runoff is essential for the effectivemanagement ofwater resources especially in semiarid regionslike Tamil Nadu NRCS model is a widely accepted model forthe estimation of runoff for small watersheds It is extensivelyused by engineers and hydrologists but the lack of sufficientdata may give poor results With the understanding ofavailable water in a watershed irrigation scheduling croprotation and cropping pattern can be planned
Total volume of runoff available for Urapakkam water-shed from NRCS model for a representative year is 246MCM A simple regression model was developed to estimatedepth of annual runoff for the watershed which is givenas runoff (mm) = 0736 times rainfall (mm) minus 4678 Statisticaltests confirm that there is no significant difference betweenthe runoff estimates from the two models Hence the studyconfirms that it is advantageous to have calibrated regression
models for easy computation of runoff and for the effectivemanagement of water resources for ungauged basins
Water balance study shows that 123 MCM of watercan be effectively stored in the watershed after losses byimproving the drainage and capacity of the storages Thetotal domestic and irrigation demand is estimated at 0320MCM Hence apart from meeting the present domestic andirrigation demand in the watershed additional demand dueto future development and demand of adjoining areas canalso be met from the water available in the watershed ifproperly harvested
Data Availability
The data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this paper
References
[1] I A Shiklomanov ldquoAppraisal and Assessment of world waterresourcesrdquoWater International vol 25 no 1 pp 11ndash32 2000
[2] FAO ldquoFAOActivities on Promoting Fertigationrdquo in Proceedingsof the Regional Workshop on Guidelines for Efficient FertilizersUse through Modern Irrigation FAO Regional Office for theNear East Cairo Egypt 1998
[3] W Cosgrove and F Rijsberman World Water Vision MakingWater Everybodys Business World Water Council EarthscanPublications 2000
International Journal of Geophysics 9
[4] Ministry of Water Resources 2012 Per Capita Water Availabil-ity Press Information Bureau Government of India 26-April2012 httppibnicinnewsitePrintReleaseaspxrelid=82676
[5] S K Gupta and R D Deshpande ldquoWater for India in 2050First-order assessment of available optionsrdquo Current Sciencevol 86 no 9 pp 1216ndash1224 2004
[6] Soil Conservation Service (SCS) Hydrology National Engi-neering Handbook Supplement A Section 4 Chapter 10 SoilConservation Service USDA Washington DC USA 1972
[7] L K Sherman ldquoThe unit hydrograph methodrdquo in Physics of theEarth O E Meinzer Ed pp 514ndash525 Dover Publications IncNew York NY USA 1949
[8] VMockus ldquoEstimation of total (peak rates of) surface runoff forindividual stormsrdquo in Exhibit A of Appendix B Interim SurveyReport Grand (Neosho) River Watershed USDA 1949
[9] S KMishra andV P Singh ldquoAnother look at SCS-CNmethodrdquoJournal of Hydrologic Engineering vol 4 no 3 pp 257ndash2641999
[10] S K Mishra M K Jain P K Bhunya and V P Singh ldquoFieldapplicability of the SCS-CN-basedMishra-Singh general modeland its variantsrdquoWater Resources Management vol 19 no 1 pp37ndash62 2005
[11] K X Soulis J D Valiantzas N Dercas and P A LondraldquoInvestigation of the direct runoff generationmechanism for theanalysis of the SCS-CN method applicability to a partial areaexperimental watershedrdquo Hydrology and Earth System Sciencesvol 13 no 5 pp 605ndash615 2009
[12] P Koteshwaram and S M A Alvi ldquoSecular trends and period-icities in rainfall at west coast stations in Indiardquo Current Sciencevol 38 no 10 pp 229ndash231 1969
[13] K V Ramamurthy M K Sonam and S S Muley ldquoLongterm variation in the rainfall over upper Narmada catchmentrdquoMausam vol 38 no 3 pp 313ndash318 1987
[14] R Jha and R K Jaiswal ldquoAnalysis of rainfall pattern in acatchmentrdquo IAH Journal of Hydrology (India) Roorkee 1992
[15] F H S Chiew M J Stewardson and T A McMahon ldquoCom-parison of six rainfall-runoff modelling approachesrdquo Journal ofHydrology vol 147 no 1-4 pp 1ndash36 1993
[16] E M Lungu ldquoDetection of changes in rainfall and runoffpatternsrdquo Journal ofHydrology vol 147 no 1-4 pp 153ndash167 1993
[17] J Meher and R Jha ldquoTime series analysis of rainfall data overMahanadi River basinrdquo Emerging Science vol 3 no 6 pp 22ndash32 2011
[18] K X Soulis and J D Valiantzas ldquoSCS-CN parameter determi-nation using rainfall-runoff data in heterogeneous watersheds-the two-CN system approachrdquo Hydrology and Earth SystemSciences vol 16 no 3 pp 1001ndash1015 2012
[19] R Kabiri A Chan and R Bai ldquoComparison of SCS and Green-Ampt Methods in Surface Runoff-Flooding Simulation forKlang Watershed in Malaysiardquo Open Journal of Modern Hydro-logy vol 03 no 03 pp 102ndash114 2013
[20] S Mohan andM Abraham ldquoDerivations of simple site-specificrecharge-precipitation relationships A case study from theCuddalore Basin Indiardquo Environmental Geosciences vol 17 no1 pp 37ndash44 2010
[21] R Viji P R Prasanna and R Ilangovan ldquoGIS based SCS-CN method for estimating runoff in Kundahpalam watershedNilgries district Tamilnadurdquo Earth Sciences Research Journalvol 19 no 1 pp 59ndash64 2015
[22] P K Gupta S Punalekar S Panigrahy A Sonakia and J SParihar ldquoRunoff modeling in an agro-forested watershed using
remote sensing and gisrdquo Journal of Hydrologic Engineering vol17 no 11 pp 1255ndash1267 2012
[23] R Chatterjee ldquoGroundwater resources estimationmdash case stud-ies from Indiardquo Journal of the Geological Society of India vol 77no 2 pp 201ndash204 2011
[24] Central Ground Water Board (CGWB) Water Balance Studiesin Upper Yamuna Basin ndash Terminal Report - Project Findingsand Recommendations Chandigarh Central Ground WaterBoard 2000
[25] G Thakuriah and R Saikia ldquoEstimation of Surface RunoffusingNRCSCurve number procedure in BurigangaWatershedAssam India - A Geospatial Approachrdquo International ResearchJournal of Earth Sciences vol 2 no 5 pp 1ndash7 2014
[26] V K Dadhwal ldquoRemote sensing and GIS for agricultural cropacreage and yield estimationrdquo in Proceedings of the InternationalArchives of Photogrammetry andRemote Sensing XXXII pp 58ndash67 1999
[27] G B Geena and P N Ballukraya ldquoEstimation of runoff for Redhills watershed using SCS method and GISrdquo Indian Journal ofScience and Technology vol 4 no 8 pp 899ndash902 2011
[28] K H Syed D C Goodrich D E Myers and S SorooshianldquoSpatial characteristics of thunderstorm rainfall fields and theirrelation to runoffrdquo Journal of Hydrology vol 271 no 1-4 pp 1ndash21 2003
[29] X Zhan and M-L Huang ldquoArcCN-Runoff An ArcGIS toolfor generating curve number and runoff mapsrdquo EnvironmentalModeling and Software vol 19 no 10 pp 875ndash879 2004
[30] K Geetha S K Mishra T I Eldho A K Rastogi and R PPandey ldquoModifications to SCS-CN method for long-term hy-drologic simulationrdquo Journal of Irrigation and Drainage Engi-neering vol 133 no 5 pp 475ndash486 2007
[31] V Kumar M Babu T V Praveen et al ldquoAnalysis of the Runofffor Watershed Using SCS-CN Method and Geographic Infor-mation Systemsrdquo International Journal of Engineering Scienceand Technology 2010
[32] R Pradhan P P Mohan M K Ghose et al ldquoEstimation ofRainfall Runoff using Remote Sensing and GIS in and aroundSingtam East Sikkimrdquo International Journal of Geomatics andGeosciences vol 1 article 466 2010
[33] A Panahi ldquoEvaluating SCS-CN method in estimating theamount of runoff in Soofi Chay basin using GISrdquo Life ScienceJournal vol 10 no 5 pp 271ndash277 2013
[34] S Satheeshkumar S Venkateswaran and R KannanldquoRainfallndashrunoff estimation using SCSndashCN and GIS approachin the Pappiredipatti watershed of the Vaniyar sub basin SouthIndiardquo Modeling Earth Systems and Environment vol 3 no 12017
[35] IMD Temperature of Kanchipuram District Regional Meteoro-logical Centre Chennai India 2013 Temperature of Kanchipu-ram District Regional Meteorological Centre
[36] ENVIS 2015 Kanchipuram District ENVIS Centre TamilnaduhttpwwwtnenvisnicinfilesKANCHIPURAM20pdf
[37] AIS and LUSWatershed Atlas of India Department of Agricul-ture and Co-operation All India Soil and Landuse Survey IARICampus New Delhi India 1990
[38] V T ChowD RMaidment and LWMaysAppliedHydrologyMcGraw-Hill New York New York USA 1988
[39] Natural Resources Conservation Service (NRCS) ldquoChapter 7-Hydrologic Soil Groupsrdquo in Hydrology National EngineeringHandbook title 630 Washington DC USA 2007
10 International Journal of Geophysics
[40] United States Department of Agriculture Soil ConservationService (USDA-SCS) Technical Release 55 Urban hydrologyfor small watersheds USA 1986
[41] A G Bluman Elemtary Statistics McGraw Hill New York NYUSA 6th edition 2008
[42] C Brouwer and M Heibloem ldquoIrrigation Water Needs- Irriga-tion Water Managementrdquo in Training Manual No 3 Food andAgriculture Organisation Rome Italy 1986
[43] R G Allen L S Pereira D Raes and M Smith ldquoCrop evap-otranspiration Guidelines for computing crop water require-mentsrdquo in Irrigation and Drainage Paper No 56 Food andAgricultural Organization of the United Nations Rome Italy1998
[44] Census of India District Census Hand book ndash KanchipuramDirectorate of Census Operations in Tamil Nadu 2011
Hindawiwwwhindawicom Volume 2018
Journal of
ChemistryArchaeaHindawiwwwhindawicom Volume 2018
Marine BiologyJournal of
Hindawiwwwhindawicom Volume 2018
BiodiversityInternational Journal of
Hindawiwwwhindawicom Volume 2018
EcologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2018
Forestry ResearchInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
International Journal of
Geophysics
Environmental and Public Health
Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
International Journal of
Microbiology
Hindawiwwwhindawicom Volume 2018
Public Health Advances in
AgricultureAdvances in
Hindawiwwwhindawicom Volume 2018
Agronomy
Hindawiwwwhindawicom Volume 2018
International Journal of
Hindawiwwwhindawicom Volume 2018
MeteorologyAdvances in
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018Hindawiwwwhindawicom Volume 2018
ChemistryAdvances in
ScienticaHindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Geological ResearchJournal of
Analytical ChemistryInternational Journal of
Hindawiwwwhindawicom Volume 2018
Submit your manuscripts atwwwhindawicom
4 International Journal of Geophysics
Table 1 Runoff curve numbers
Description of Land use Hydrologic Soil GroupA B C D
Paved parking lots roofs driveways 98 98 98 98Streets and Roads
Paved with curbs and storm sewers 98 98 98 98Gravel 76 85 89 91Dirt 72 82 87 89
Cultivated (Agricultural Crop) LandlowastWithout conservation treatment (no terraces) 72 81 88 91With conservation treatment (terraces contours) 62 71 78 81
Pasture or Range LandPoor (lt50 ground cover or heavily grazed) 68 79 86 89Good (50ndash75 ground cover not heavily grazed) 39 61 74 80
Meadow (grass no grazing mowed for hay) 30 58 71 78Brush (good gt75 ground cover) 30 48 65 73Woods and Forests
Poor (small treesbrush destroyed by over-grazing or burning) 45 66 77 83Fair (grazing but not burned some brush) 36 60 73 79Good (no grazing brush covers ground) 30 55 70 77
Open Spaces (lawns parks golf courses cemeteries etc)Fair (grass covers 50ndash75 of area) 49 69 79 84Good (grass covers gt75 of area) 39 61 74 80
Commercial and Business Districts (85 impervious) 89 92 94 95Industrial Districts (72 impervious) 81 88 91 93Residential Areas
18 Acre lots about 65 impervious 77 85 90 9214 Acre lots about 38 impervious 61 75 83 8712 Acre lots about 25 impervious 54 70 80 851 Acre lots about 20 impervious 51 68 79 84
Source United States Department of Agriculture Soil Conservation Service 1986 [40] Technical Release 55
Mean Absolute Error (MAE) is the average of all absoluteerrors which can be expressed as
MAE = 1119899119899sum119894=1
1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816 (7)
where
119909 is runoff by NRCS model119909119894is runoff by regression model119899 is number of data sets
Test for significance t-test was used to test whether thereis significant difference between runoff computed by NRCSmodel and those estimated by regression model
The assumptions used in this test were samples which areindependent and random in nature and samples which arenormally distributed
Two-independent sample t-test statistics is defined as
119905 = (sum 1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816) 119899radic((sum 1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816)2 minus ((sum 1003816100381610038161003816119909119894 minus 1199091003816100381610038161003816)2 119899)) (119899 minus 1) (119899) (8)
32 Water Balance Study Water availability and demand fordomestic use and irrigation in the watershed were computedseparately Domestic water demandwas obtained as the prod-uct of per capita water requirement and the population Thedifference between water availability and water demand willgive a comprehensive understanding on the total utilizablewater
Estimation of Irrigation Demand Irrigation water demandwas calculated from the method given by Brouwer andHeibloem 1986 [42] Potential evapotranspiration was com-puted from BlaneyndashCriddle equation [43] and is given by
ET119900= 119901 (046119879mean + 8) (9)
where
119879mean is mean daily temperature (∘C)119901 is mean daily percentage of annual daytime hours
Crop coefficient (119870119888) for paddy for various growth stages was
estimated and crop water requirement was determined from
ETcrop = ET119900 times 119870119888 (10)
International Journal of Geophysics 5
Table 2 Land use details and the corresponding curve numbers
Land use category Area Curve Numberkm2
Forest Forest Plantations 1089 79Crop land 0193 81Water bodies 0600 89Barren land 0167 80SettlementResidential area 2527 87
Other water needs such as water to saturate the soil (SAT)percolation and seepage losses (PSL) and water neededfor standing water layer (WL) were used to compute theirrigation need
Effective rainfall (119875119890) was calculated from the formulae
119875119890= 08119875 minus 25 if 119875 gt 75mmmonth
119875119890= 06119875 minus 10 if 119875 lt 75mmmonth (11)
where
119875 is rainfall in mmmonth
Irrigation Need (IN) is calculated as
IN = ETcrop + SAT + PSL +WL minus 119875119890 (12)
4 Result
The runoff for Urapakkam watershed in Adyar basin wascomputed using USDA-NRCS model A simple regressionmodel was also developed and validated for its suitabilityfor runoff computation of the watershed By knowing theutilizable water available in a watershed water supply forirrigation and other purposes can be planned and alternativecropping patterns can be adopted to avoid losses
41 Runoff Computation
411 NRCS Model Watershed boundary for the chain oftanks was taken from District Soil amp Watershed Atlas andverified with Survey of India toposheet and satellite imageryLand use map for Urapakkam watershed was prepared fromsatellite imagery with field verification in GIS platform usingArcGIS and the area under various land uses was determined(Figure 2)
The watershed is in the hydrologic soil group D as perthe soil details Hydrological modeling considers water yearfrom July to June of ensuing year and uses daily time stepUrapakkam tank has both free catchment and interceptedcatchment The hydrological model estimated the runoff forfourteen water years from 2000-01 to 2013-14 with daily timestep Land use details and corresponding curve numbers aregiven in Table 2
Weighted curve number for the watershed with adrainage area of 4576 km2 is 8485 The curve numbers forAntecedent Moisture Conditions I II and III are 7017 8485and 9280 respectively
Figure 2 Land use map of Urapakkam watershed
Water Year
RainfallRunoff
2001
-200
2
2002
-200
3
2003
-200
4
2004
-200
5
2005
-200
6
2006
-200
7
2007
-200
8
2008
-200
9
2009
-201
0
2010
-201
1
2011
-201
2
2012
-201
3
2013
-201
4
2000
-200
10
10
20
30
40
50
60
70
80
Num
ber o
f Day
s
Figure 3 Number of days with rainfall and runoff by NRCS model
Days with rainfall and runoff for the study period aregiven in Figure 3 Monthly rainfall and the correspondingrunoff as estimated from NRCS model are given in Figure 4Table 3 gives monthly rainfall and runoff for the water year2008ndash2009whenfiftypercent dependable flowwas observed
Annual rainfall for the watershed varied from 5757mm(2002ndash03) to 36080mm (2005ndash06) The runoff as estimatedfromNRCSmodel varied from 9495mm to 232434mm andthe corresponding percentage varied from 1649 to 6442 withan average value of 3709 percent for the fourteen water years
6 International Journal of Geophysics
RainfallRunoff
Jan
07
Jan
10
Jan
14
Jan
12
Jan
03
Jan
11
Jan
04
Jan
01
Jan
05
Jan
08
Jan
06
Jan
09
Jan
13
Jan
02Ju
l02
Jul0
9
Jul0
6
Jul0
3
Jul1
3
Jul1
0
Jul0
4
Jul1
1
Jul0
8
Jul0
7
Jul0
5
Jul0
1
Jul1
2
Jul0
0
Month
0200400600800
100012001400
Rainfa
ll R
unoff
(mm
)
Figure 4 Monthly rainfall and runoff by NRCS model for Ura-pakkam tank watershed
Table 3 Monthly rainfall and runoff for Urapakkam watershedusing NRCS model for the water year 2008-2009
Month Rainfall Runoffmm mm
Jul-08 6100 005Aug-08 10100 2749Sep-08 12900 6311Oct-08 25400 6909Nov-08 53400 35289Dec-08 8400 2633Jan-09 1500 000Feb-09 000 000Mar-09 000 000Apr-09 000 000May-09 2400 000Jun-09 3680 000
2000-01 to 2013-14 Bar chart showing annual rainfall andrunoff is given in Figure 5 Volume of runoff for thewatershedvaried between 0434 MCM and 10637 MCM during thefourteen years of study Annual runoff for a representativewater year (2008-09) is obtained as 246 MCM (Table 4)When the rainfall exceeds the threshold limit the proportionof rainfall contributing to runoffwas found to be higherWidevariations were observed in the quantum of runoff from rain-fall of same magnitude due to varying antecedent moistureconditions in the soil Figure 6 shows the average annualrainfall and runoff estimated for Urapakkam watershed
412 Regression Model A Linear regression model wasdeveloped for the watershed to compute annual runoff fromthe annual rainfall Ten-year data from NRCS model wasused for model calibration and four-year data was usedfor validation The regression relationship obtained betweenannual rainfall (mm) and annual runoff (mm) is given as
Runoff = 0736 times rainfall minus 4678 (13)
RainfallRunoff - NRCS ModelRunoff - Regression model
0500
1000150020002500300035004000
Dep
th in
mm
2001
-200
2
2002
-200
3
2003
-200
4
2004
-200
5
2005
-200
6
2006
-200
7
2007
-200
8
2008
-200
9
2009
-201
0
2010
-201
1
2011
-201
2
2012
-201
3
2013
-201
4
2000
-200
1
Water Year
Figure 5 Annual rainfall and runoff for Urapakkam watershed
Rainfall 152578
Runoff 63338
Figure 6 Average annual rainfall and runoff estimated for Ura-pakkam watershed
R2 value was obtained as 0944 during calibration and 0988during validation periodThe plots of rainfall versus runoff asobtained fromNRCSmodel during calibration and validationperiod are shown in Figure 7
413 Validation of Regression Model Figure 8 shows thecomparison between the annual runoff by NRCS modeland the developed regression model which illustrates thatthe regression model could be used to effectively predictrunoff for the watershed even during the validation phaseThe coefficient of determination (R2) for runoff by NRCSmodel versus runoff by regression model was 0939 at 95confidence level (Figure 9) The statistical tests such as t-test MAE and SEE obtained for the two runoff estimates(NRCS model and regression model) were 055 1060 and1390 respectively The average runoff for the watershed bylinear regression model was obtained as 3717 percent ofthe rainfall which ranges from 1279 to 6063 It showsno considerable deviation from runoff computed by NRCSmodel Thus the statistical analysis proves that tedious anddata intensive computation methods can be eliminated andthe volume of runoff from a watershed can be efficiently andeasily computed using simple regression model
International Journal of Geophysics 7
Table 4 Annual rainfall and runoff estimated for Urapakkam watershed using NRCS model for the period 2000-01 to 2013ndash14
Water YearRainfall Runoff
No of days Depth No of days Depth Percentage of Rainfall Volumemm mm MCM
2000-2001 39 109230 28 32396 2966 14832001-2002 49 121110 35 35539 2934 16262002-2003 28 57570 15 9495 1649 04342003-2004 65 176580 36 53750 3044 24602004-2005 44 164800 35 76859 4664 35172005-2006 59 360800 50 232434 6442 106372006-2007 38 137900 23 70120 5085 32092007-2008 71 189950 44 78043 4109 35712008-2009 44 123880 29 53897 4351 24662009-2010 55 143900 33 58224 4046 26652010-2011 67 148900 34 49909 3352 22842011-2012 52 181000 45 73411 4056 33592012-2013 60 143540 36 48412 3373 22152013-2014 64 76930 22 14248 1852 0652Minimum 28 5757 15 9495 1649 0434Maximum 71 36080 44 232434 6442 10637
y = 07369x - 46782
0
500
1000
1500
2000
2500
Runo
ff (m
m) 2
2= 09446
1000 2000 3000 40000Rainfall (mm)
(a) Calibration
500 1000 1500 20000Rainfall (mm)
0
200
400
600
800
Runo
ff (m
m)
22= 09887
(b) Validation
Figure 7 Relationship between annual rainfall and runoff by NRCS model during calibration and validation period
Run off NRCS modelRunoff Regression model
ValidationCalibration
1 2 3 4 5 6 7 8 9 10 11 12 13 14 150
(Years)minus500
0
500
1000
1500
2000
2500
Runo
ff de
pth
(mm
)
Figure 8 Comparison of annual runoff from NRCS model andregression model
42 Water Balance Study Population density of Kanchipu-ram district is 892km2 [44] Assuming the per capita waterdemand to be 100 litersday for domestic purposes the
y = 09488x + 5422922= 09389
500 1000 1500 2000 25000
Runoff - NRCS Model minus500
0
500
1000
1500
2000
2500
Runo
ff - R
egre
ssio
n M
odel
Figure 9 Scatter plot of annual runoff by NRCS model andregression model for Urapakkam watershed for 2000-01 to 2013-14
total domestic water requirement for the population of thewatershed with 4576 km2 is estimated at 407m3day whichis 0148 MCM per annum
8 International Journal of Geophysics
Table 5 Computation of irrigation demand for paddy for the year 2008-09
Item MonthSeptember October November December January February Season
Percentage of annualdaytime hours 119901 026 027 026 027 027
Average Temperature119879mean (∘C) 2600 2480 2460 258 2780
PotentialevapotranspirationEt119900(mmday)
519 514 502 527 561
Crop coefficient119870119888
110 110 120 130 100
Crop waterrequirement
mmday 571 566 603 684 561mmmonth 17126 17538 18682 19165 17400
SAT mm 20000PSL mmmonth4mmday 12000 12400 12400 11200 12400
WL mm119875119890 mm 4860 49980 16780 000 000 000
Irrigation demandmm 15140 000 13158 31082 30365 29800 119545
Irrigation is practiced in a small area of 10 ha in thewatershed The average irrigation demand for the watershedarea is computed to be around 1195mm By considering anefficiency of 70 the actual irrigation water requirement is1708mmThe total irrigation demand for 10 ha of paddy cropin the watershed is estimated as 0171 MCM Details of thecomputation of irrigation demand for paddy are presented inTable 5
The current total domestic and irrigation water require-ment of the watershed is 0320 MCM After meeting all thewater demands of the watershed there is sufficient wateravailable even if only 50 of the total available rainwater canbe effectively harvested
5 Conclusions
Accurate estimation of runoff is essential for the effectivemanagement ofwater resources especially in semiarid regionslike Tamil Nadu NRCS model is a widely accepted model forthe estimation of runoff for small watersheds It is extensivelyused by engineers and hydrologists but the lack of sufficientdata may give poor results With the understanding ofavailable water in a watershed irrigation scheduling croprotation and cropping pattern can be planned
Total volume of runoff available for Urapakkam water-shed from NRCS model for a representative year is 246MCM A simple regression model was developed to estimatedepth of annual runoff for the watershed which is givenas runoff (mm) = 0736 times rainfall (mm) minus 4678 Statisticaltests confirm that there is no significant difference betweenthe runoff estimates from the two models Hence the studyconfirms that it is advantageous to have calibrated regression
models for easy computation of runoff and for the effectivemanagement of water resources for ungauged basins
Water balance study shows that 123 MCM of watercan be effectively stored in the watershed after losses byimproving the drainage and capacity of the storages Thetotal domestic and irrigation demand is estimated at 0320MCM Hence apart from meeting the present domestic andirrigation demand in the watershed additional demand dueto future development and demand of adjoining areas canalso be met from the water available in the watershed ifproperly harvested
Data Availability
The data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this paper
References
[1] I A Shiklomanov ldquoAppraisal and Assessment of world waterresourcesrdquoWater International vol 25 no 1 pp 11ndash32 2000
[2] FAO ldquoFAOActivities on Promoting Fertigationrdquo in Proceedingsof the Regional Workshop on Guidelines for Efficient FertilizersUse through Modern Irrigation FAO Regional Office for theNear East Cairo Egypt 1998
[3] W Cosgrove and F Rijsberman World Water Vision MakingWater Everybodys Business World Water Council EarthscanPublications 2000
International Journal of Geophysics 9
[4] Ministry of Water Resources 2012 Per Capita Water Availabil-ity Press Information Bureau Government of India 26-April2012 httppibnicinnewsitePrintReleaseaspxrelid=82676
[5] S K Gupta and R D Deshpande ldquoWater for India in 2050First-order assessment of available optionsrdquo Current Sciencevol 86 no 9 pp 1216ndash1224 2004
[6] Soil Conservation Service (SCS) Hydrology National Engi-neering Handbook Supplement A Section 4 Chapter 10 SoilConservation Service USDA Washington DC USA 1972
[7] L K Sherman ldquoThe unit hydrograph methodrdquo in Physics of theEarth O E Meinzer Ed pp 514ndash525 Dover Publications IncNew York NY USA 1949
[8] VMockus ldquoEstimation of total (peak rates of) surface runoff forindividual stormsrdquo in Exhibit A of Appendix B Interim SurveyReport Grand (Neosho) River Watershed USDA 1949
[9] S KMishra andV P Singh ldquoAnother look at SCS-CNmethodrdquoJournal of Hydrologic Engineering vol 4 no 3 pp 257ndash2641999
[10] S K Mishra M K Jain P K Bhunya and V P Singh ldquoFieldapplicability of the SCS-CN-basedMishra-Singh general modeland its variantsrdquoWater Resources Management vol 19 no 1 pp37ndash62 2005
[11] K X Soulis J D Valiantzas N Dercas and P A LondraldquoInvestigation of the direct runoff generationmechanism for theanalysis of the SCS-CN method applicability to a partial areaexperimental watershedrdquo Hydrology and Earth System Sciencesvol 13 no 5 pp 605ndash615 2009
[12] P Koteshwaram and S M A Alvi ldquoSecular trends and period-icities in rainfall at west coast stations in Indiardquo Current Sciencevol 38 no 10 pp 229ndash231 1969
[13] K V Ramamurthy M K Sonam and S S Muley ldquoLongterm variation in the rainfall over upper Narmada catchmentrdquoMausam vol 38 no 3 pp 313ndash318 1987
[14] R Jha and R K Jaiswal ldquoAnalysis of rainfall pattern in acatchmentrdquo IAH Journal of Hydrology (India) Roorkee 1992
[15] F H S Chiew M J Stewardson and T A McMahon ldquoCom-parison of six rainfall-runoff modelling approachesrdquo Journal ofHydrology vol 147 no 1-4 pp 1ndash36 1993
[16] E M Lungu ldquoDetection of changes in rainfall and runoffpatternsrdquo Journal ofHydrology vol 147 no 1-4 pp 153ndash167 1993
[17] J Meher and R Jha ldquoTime series analysis of rainfall data overMahanadi River basinrdquo Emerging Science vol 3 no 6 pp 22ndash32 2011
[18] K X Soulis and J D Valiantzas ldquoSCS-CN parameter determi-nation using rainfall-runoff data in heterogeneous watersheds-the two-CN system approachrdquo Hydrology and Earth SystemSciences vol 16 no 3 pp 1001ndash1015 2012
[19] R Kabiri A Chan and R Bai ldquoComparison of SCS and Green-Ampt Methods in Surface Runoff-Flooding Simulation forKlang Watershed in Malaysiardquo Open Journal of Modern Hydro-logy vol 03 no 03 pp 102ndash114 2013
[20] S Mohan andM Abraham ldquoDerivations of simple site-specificrecharge-precipitation relationships A case study from theCuddalore Basin Indiardquo Environmental Geosciences vol 17 no1 pp 37ndash44 2010
[21] R Viji P R Prasanna and R Ilangovan ldquoGIS based SCS-CN method for estimating runoff in Kundahpalam watershedNilgries district Tamilnadurdquo Earth Sciences Research Journalvol 19 no 1 pp 59ndash64 2015
[22] P K Gupta S Punalekar S Panigrahy A Sonakia and J SParihar ldquoRunoff modeling in an agro-forested watershed using
remote sensing and gisrdquo Journal of Hydrologic Engineering vol17 no 11 pp 1255ndash1267 2012
[23] R Chatterjee ldquoGroundwater resources estimationmdash case stud-ies from Indiardquo Journal of the Geological Society of India vol 77no 2 pp 201ndash204 2011
[24] Central Ground Water Board (CGWB) Water Balance Studiesin Upper Yamuna Basin ndash Terminal Report - Project Findingsand Recommendations Chandigarh Central Ground WaterBoard 2000
[25] G Thakuriah and R Saikia ldquoEstimation of Surface RunoffusingNRCSCurve number procedure in BurigangaWatershedAssam India - A Geospatial Approachrdquo International ResearchJournal of Earth Sciences vol 2 no 5 pp 1ndash7 2014
[26] V K Dadhwal ldquoRemote sensing and GIS for agricultural cropacreage and yield estimationrdquo in Proceedings of the InternationalArchives of Photogrammetry andRemote Sensing XXXII pp 58ndash67 1999
[27] G B Geena and P N Ballukraya ldquoEstimation of runoff for Redhills watershed using SCS method and GISrdquo Indian Journal ofScience and Technology vol 4 no 8 pp 899ndash902 2011
[28] K H Syed D C Goodrich D E Myers and S SorooshianldquoSpatial characteristics of thunderstorm rainfall fields and theirrelation to runoffrdquo Journal of Hydrology vol 271 no 1-4 pp 1ndash21 2003
[29] X Zhan and M-L Huang ldquoArcCN-Runoff An ArcGIS toolfor generating curve number and runoff mapsrdquo EnvironmentalModeling and Software vol 19 no 10 pp 875ndash879 2004
[30] K Geetha S K Mishra T I Eldho A K Rastogi and R PPandey ldquoModifications to SCS-CN method for long-term hy-drologic simulationrdquo Journal of Irrigation and Drainage Engi-neering vol 133 no 5 pp 475ndash486 2007
[31] V Kumar M Babu T V Praveen et al ldquoAnalysis of the Runofffor Watershed Using SCS-CN Method and Geographic Infor-mation Systemsrdquo International Journal of Engineering Scienceand Technology 2010
[32] R Pradhan P P Mohan M K Ghose et al ldquoEstimation ofRainfall Runoff using Remote Sensing and GIS in and aroundSingtam East Sikkimrdquo International Journal of Geomatics andGeosciences vol 1 article 466 2010
[33] A Panahi ldquoEvaluating SCS-CN method in estimating theamount of runoff in Soofi Chay basin using GISrdquo Life ScienceJournal vol 10 no 5 pp 271ndash277 2013
[34] S Satheeshkumar S Venkateswaran and R KannanldquoRainfallndashrunoff estimation using SCSndashCN and GIS approachin the Pappiredipatti watershed of the Vaniyar sub basin SouthIndiardquo Modeling Earth Systems and Environment vol 3 no 12017
[35] IMD Temperature of Kanchipuram District Regional Meteoro-logical Centre Chennai India 2013 Temperature of Kanchipu-ram District Regional Meteorological Centre
[36] ENVIS 2015 Kanchipuram District ENVIS Centre TamilnaduhttpwwwtnenvisnicinfilesKANCHIPURAM20pdf
[37] AIS and LUSWatershed Atlas of India Department of Agricul-ture and Co-operation All India Soil and Landuse Survey IARICampus New Delhi India 1990
[38] V T ChowD RMaidment and LWMaysAppliedHydrologyMcGraw-Hill New York New York USA 1988
[39] Natural Resources Conservation Service (NRCS) ldquoChapter 7-Hydrologic Soil Groupsrdquo in Hydrology National EngineeringHandbook title 630 Washington DC USA 2007
10 International Journal of Geophysics
[40] United States Department of Agriculture Soil ConservationService (USDA-SCS) Technical Release 55 Urban hydrologyfor small watersheds USA 1986
[41] A G Bluman Elemtary Statistics McGraw Hill New York NYUSA 6th edition 2008
[42] C Brouwer and M Heibloem ldquoIrrigation Water Needs- Irriga-tion Water Managementrdquo in Training Manual No 3 Food andAgriculture Organisation Rome Italy 1986
[43] R G Allen L S Pereira D Raes and M Smith ldquoCrop evap-otranspiration Guidelines for computing crop water require-mentsrdquo in Irrigation and Drainage Paper No 56 Food andAgricultural Organization of the United Nations Rome Italy1998
[44] Census of India District Census Hand book ndash KanchipuramDirectorate of Census Operations in Tamil Nadu 2011
Hindawiwwwhindawicom Volume 2018
Journal of
ChemistryArchaeaHindawiwwwhindawicom Volume 2018
Marine BiologyJournal of
Hindawiwwwhindawicom Volume 2018
BiodiversityInternational Journal of
Hindawiwwwhindawicom Volume 2018
EcologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2018
Forestry ResearchInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
International Journal of
Geophysics
Environmental and Public Health
Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
International Journal of
Microbiology
Hindawiwwwhindawicom Volume 2018
Public Health Advances in
AgricultureAdvances in
Hindawiwwwhindawicom Volume 2018
Agronomy
Hindawiwwwhindawicom Volume 2018
International Journal of
Hindawiwwwhindawicom Volume 2018
MeteorologyAdvances in
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018Hindawiwwwhindawicom Volume 2018
ChemistryAdvances in
ScienticaHindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Geological ResearchJournal of
Analytical ChemistryInternational Journal of
Hindawiwwwhindawicom Volume 2018
Submit your manuscripts atwwwhindawicom
International Journal of Geophysics 5
Table 2 Land use details and the corresponding curve numbers
Land use category Area Curve Numberkm2
Forest Forest Plantations 1089 79Crop land 0193 81Water bodies 0600 89Barren land 0167 80SettlementResidential area 2527 87
Other water needs such as water to saturate the soil (SAT)percolation and seepage losses (PSL) and water neededfor standing water layer (WL) were used to compute theirrigation need
Effective rainfall (119875119890) was calculated from the formulae
119875119890= 08119875 minus 25 if 119875 gt 75mmmonth
119875119890= 06119875 minus 10 if 119875 lt 75mmmonth (11)
where
119875 is rainfall in mmmonth
Irrigation Need (IN) is calculated as
IN = ETcrop + SAT + PSL +WL minus 119875119890 (12)
4 Result
The runoff for Urapakkam watershed in Adyar basin wascomputed using USDA-NRCS model A simple regressionmodel was also developed and validated for its suitabilityfor runoff computation of the watershed By knowing theutilizable water available in a watershed water supply forirrigation and other purposes can be planned and alternativecropping patterns can be adopted to avoid losses
41 Runoff Computation
411 NRCS Model Watershed boundary for the chain oftanks was taken from District Soil amp Watershed Atlas andverified with Survey of India toposheet and satellite imageryLand use map for Urapakkam watershed was prepared fromsatellite imagery with field verification in GIS platform usingArcGIS and the area under various land uses was determined(Figure 2)
The watershed is in the hydrologic soil group D as perthe soil details Hydrological modeling considers water yearfrom July to June of ensuing year and uses daily time stepUrapakkam tank has both free catchment and interceptedcatchment The hydrological model estimated the runoff forfourteen water years from 2000-01 to 2013-14 with daily timestep Land use details and corresponding curve numbers aregiven in Table 2
Weighted curve number for the watershed with adrainage area of 4576 km2 is 8485 The curve numbers forAntecedent Moisture Conditions I II and III are 7017 8485and 9280 respectively
Figure 2 Land use map of Urapakkam watershed
Water Year
RainfallRunoff
2001
-200
2
2002
-200
3
2003
-200
4
2004
-200
5
2005
-200
6
2006
-200
7
2007
-200
8
2008
-200
9
2009
-201
0
2010
-201
1
2011
-201
2
2012
-201
3
2013
-201
4
2000
-200
10
10
20
30
40
50
60
70
80
Num
ber o
f Day
s
Figure 3 Number of days with rainfall and runoff by NRCS model
Days with rainfall and runoff for the study period aregiven in Figure 3 Monthly rainfall and the correspondingrunoff as estimated from NRCS model are given in Figure 4Table 3 gives monthly rainfall and runoff for the water year2008ndash2009whenfiftypercent dependable flowwas observed
Annual rainfall for the watershed varied from 5757mm(2002ndash03) to 36080mm (2005ndash06) The runoff as estimatedfromNRCSmodel varied from 9495mm to 232434mm andthe corresponding percentage varied from 1649 to 6442 withan average value of 3709 percent for the fourteen water years
6 International Journal of Geophysics
RainfallRunoff
Jan
07
Jan
10
Jan
14
Jan
12
Jan
03
Jan
11
Jan
04
Jan
01
Jan
05
Jan
08
Jan
06
Jan
09
Jan
13
Jan
02Ju
l02
Jul0
9
Jul0
6
Jul0
3
Jul1
3
Jul1
0
Jul0
4
Jul1
1
Jul0
8
Jul0
7
Jul0
5
Jul0
1
Jul1
2
Jul0
0
Month
0200400600800
100012001400
Rainfa
ll R
unoff
(mm
)
Figure 4 Monthly rainfall and runoff by NRCS model for Ura-pakkam tank watershed
Table 3 Monthly rainfall and runoff for Urapakkam watershedusing NRCS model for the water year 2008-2009
Month Rainfall Runoffmm mm
Jul-08 6100 005Aug-08 10100 2749Sep-08 12900 6311Oct-08 25400 6909Nov-08 53400 35289Dec-08 8400 2633Jan-09 1500 000Feb-09 000 000Mar-09 000 000Apr-09 000 000May-09 2400 000Jun-09 3680 000
2000-01 to 2013-14 Bar chart showing annual rainfall andrunoff is given in Figure 5 Volume of runoff for thewatershedvaried between 0434 MCM and 10637 MCM during thefourteen years of study Annual runoff for a representativewater year (2008-09) is obtained as 246 MCM (Table 4)When the rainfall exceeds the threshold limit the proportionof rainfall contributing to runoffwas found to be higherWidevariations were observed in the quantum of runoff from rain-fall of same magnitude due to varying antecedent moistureconditions in the soil Figure 6 shows the average annualrainfall and runoff estimated for Urapakkam watershed
412 Regression Model A Linear regression model wasdeveloped for the watershed to compute annual runoff fromthe annual rainfall Ten-year data from NRCS model wasused for model calibration and four-year data was usedfor validation The regression relationship obtained betweenannual rainfall (mm) and annual runoff (mm) is given as
Runoff = 0736 times rainfall minus 4678 (13)
RainfallRunoff - NRCS ModelRunoff - Regression model
0500
1000150020002500300035004000
Dep
th in
mm
2001
-200
2
2002
-200
3
2003
-200
4
2004
-200
5
2005
-200
6
2006
-200
7
2007
-200
8
2008
-200
9
2009
-201
0
2010
-201
1
2011
-201
2
2012
-201
3
2013
-201
4
2000
-200
1
Water Year
Figure 5 Annual rainfall and runoff for Urapakkam watershed
Rainfall 152578
Runoff 63338
Figure 6 Average annual rainfall and runoff estimated for Ura-pakkam watershed
R2 value was obtained as 0944 during calibration and 0988during validation periodThe plots of rainfall versus runoff asobtained fromNRCSmodel during calibration and validationperiod are shown in Figure 7
413 Validation of Regression Model Figure 8 shows thecomparison between the annual runoff by NRCS modeland the developed regression model which illustrates thatthe regression model could be used to effectively predictrunoff for the watershed even during the validation phaseThe coefficient of determination (R2) for runoff by NRCSmodel versus runoff by regression model was 0939 at 95confidence level (Figure 9) The statistical tests such as t-test MAE and SEE obtained for the two runoff estimates(NRCS model and regression model) were 055 1060 and1390 respectively The average runoff for the watershed bylinear regression model was obtained as 3717 percent ofthe rainfall which ranges from 1279 to 6063 It showsno considerable deviation from runoff computed by NRCSmodel Thus the statistical analysis proves that tedious anddata intensive computation methods can be eliminated andthe volume of runoff from a watershed can be efficiently andeasily computed using simple regression model
International Journal of Geophysics 7
Table 4 Annual rainfall and runoff estimated for Urapakkam watershed using NRCS model for the period 2000-01 to 2013ndash14
Water YearRainfall Runoff
No of days Depth No of days Depth Percentage of Rainfall Volumemm mm MCM
2000-2001 39 109230 28 32396 2966 14832001-2002 49 121110 35 35539 2934 16262002-2003 28 57570 15 9495 1649 04342003-2004 65 176580 36 53750 3044 24602004-2005 44 164800 35 76859 4664 35172005-2006 59 360800 50 232434 6442 106372006-2007 38 137900 23 70120 5085 32092007-2008 71 189950 44 78043 4109 35712008-2009 44 123880 29 53897 4351 24662009-2010 55 143900 33 58224 4046 26652010-2011 67 148900 34 49909 3352 22842011-2012 52 181000 45 73411 4056 33592012-2013 60 143540 36 48412 3373 22152013-2014 64 76930 22 14248 1852 0652Minimum 28 5757 15 9495 1649 0434Maximum 71 36080 44 232434 6442 10637
y = 07369x - 46782
0
500
1000
1500
2000
2500
Runo
ff (m
m) 2
2= 09446
1000 2000 3000 40000Rainfall (mm)
(a) Calibration
500 1000 1500 20000Rainfall (mm)
0
200
400
600
800
Runo
ff (m
m)
22= 09887
(b) Validation
Figure 7 Relationship between annual rainfall and runoff by NRCS model during calibration and validation period
Run off NRCS modelRunoff Regression model
ValidationCalibration
1 2 3 4 5 6 7 8 9 10 11 12 13 14 150
(Years)minus500
0
500
1000
1500
2000
2500
Runo
ff de
pth
(mm
)
Figure 8 Comparison of annual runoff from NRCS model andregression model
42 Water Balance Study Population density of Kanchipu-ram district is 892km2 [44] Assuming the per capita waterdemand to be 100 litersday for domestic purposes the
y = 09488x + 5422922= 09389
500 1000 1500 2000 25000
Runoff - NRCS Model minus500
0
500
1000
1500
2000
2500
Runo
ff - R
egre
ssio
n M
odel
Figure 9 Scatter plot of annual runoff by NRCS model andregression model for Urapakkam watershed for 2000-01 to 2013-14
total domestic water requirement for the population of thewatershed with 4576 km2 is estimated at 407m3day whichis 0148 MCM per annum
8 International Journal of Geophysics
Table 5 Computation of irrigation demand for paddy for the year 2008-09
Item MonthSeptember October November December January February Season
Percentage of annualdaytime hours 119901 026 027 026 027 027
Average Temperature119879mean (∘C) 2600 2480 2460 258 2780
PotentialevapotranspirationEt119900(mmday)
519 514 502 527 561
Crop coefficient119870119888
110 110 120 130 100
Crop waterrequirement
mmday 571 566 603 684 561mmmonth 17126 17538 18682 19165 17400
SAT mm 20000PSL mmmonth4mmday 12000 12400 12400 11200 12400
WL mm119875119890 mm 4860 49980 16780 000 000 000
Irrigation demandmm 15140 000 13158 31082 30365 29800 119545
Irrigation is practiced in a small area of 10 ha in thewatershed The average irrigation demand for the watershedarea is computed to be around 1195mm By considering anefficiency of 70 the actual irrigation water requirement is1708mmThe total irrigation demand for 10 ha of paddy cropin the watershed is estimated as 0171 MCM Details of thecomputation of irrigation demand for paddy are presented inTable 5
The current total domestic and irrigation water require-ment of the watershed is 0320 MCM After meeting all thewater demands of the watershed there is sufficient wateravailable even if only 50 of the total available rainwater canbe effectively harvested
5 Conclusions
Accurate estimation of runoff is essential for the effectivemanagement ofwater resources especially in semiarid regionslike Tamil Nadu NRCS model is a widely accepted model forthe estimation of runoff for small watersheds It is extensivelyused by engineers and hydrologists but the lack of sufficientdata may give poor results With the understanding ofavailable water in a watershed irrigation scheduling croprotation and cropping pattern can be planned
Total volume of runoff available for Urapakkam water-shed from NRCS model for a representative year is 246MCM A simple regression model was developed to estimatedepth of annual runoff for the watershed which is givenas runoff (mm) = 0736 times rainfall (mm) minus 4678 Statisticaltests confirm that there is no significant difference betweenthe runoff estimates from the two models Hence the studyconfirms that it is advantageous to have calibrated regression
models for easy computation of runoff and for the effectivemanagement of water resources for ungauged basins
Water balance study shows that 123 MCM of watercan be effectively stored in the watershed after losses byimproving the drainage and capacity of the storages Thetotal domestic and irrigation demand is estimated at 0320MCM Hence apart from meeting the present domestic andirrigation demand in the watershed additional demand dueto future development and demand of adjoining areas canalso be met from the water available in the watershed ifproperly harvested
Data Availability
The data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this paper
References
[1] I A Shiklomanov ldquoAppraisal and Assessment of world waterresourcesrdquoWater International vol 25 no 1 pp 11ndash32 2000
[2] FAO ldquoFAOActivities on Promoting Fertigationrdquo in Proceedingsof the Regional Workshop on Guidelines for Efficient FertilizersUse through Modern Irrigation FAO Regional Office for theNear East Cairo Egypt 1998
[3] W Cosgrove and F Rijsberman World Water Vision MakingWater Everybodys Business World Water Council EarthscanPublications 2000
International Journal of Geophysics 9
[4] Ministry of Water Resources 2012 Per Capita Water Availabil-ity Press Information Bureau Government of India 26-April2012 httppibnicinnewsitePrintReleaseaspxrelid=82676
[5] S K Gupta and R D Deshpande ldquoWater for India in 2050First-order assessment of available optionsrdquo Current Sciencevol 86 no 9 pp 1216ndash1224 2004
[6] Soil Conservation Service (SCS) Hydrology National Engi-neering Handbook Supplement A Section 4 Chapter 10 SoilConservation Service USDA Washington DC USA 1972
[7] L K Sherman ldquoThe unit hydrograph methodrdquo in Physics of theEarth O E Meinzer Ed pp 514ndash525 Dover Publications IncNew York NY USA 1949
[8] VMockus ldquoEstimation of total (peak rates of) surface runoff forindividual stormsrdquo in Exhibit A of Appendix B Interim SurveyReport Grand (Neosho) River Watershed USDA 1949
[9] S KMishra andV P Singh ldquoAnother look at SCS-CNmethodrdquoJournal of Hydrologic Engineering vol 4 no 3 pp 257ndash2641999
[10] S K Mishra M K Jain P K Bhunya and V P Singh ldquoFieldapplicability of the SCS-CN-basedMishra-Singh general modeland its variantsrdquoWater Resources Management vol 19 no 1 pp37ndash62 2005
[11] K X Soulis J D Valiantzas N Dercas and P A LondraldquoInvestigation of the direct runoff generationmechanism for theanalysis of the SCS-CN method applicability to a partial areaexperimental watershedrdquo Hydrology and Earth System Sciencesvol 13 no 5 pp 605ndash615 2009
[12] P Koteshwaram and S M A Alvi ldquoSecular trends and period-icities in rainfall at west coast stations in Indiardquo Current Sciencevol 38 no 10 pp 229ndash231 1969
[13] K V Ramamurthy M K Sonam and S S Muley ldquoLongterm variation in the rainfall over upper Narmada catchmentrdquoMausam vol 38 no 3 pp 313ndash318 1987
[14] R Jha and R K Jaiswal ldquoAnalysis of rainfall pattern in acatchmentrdquo IAH Journal of Hydrology (India) Roorkee 1992
[15] F H S Chiew M J Stewardson and T A McMahon ldquoCom-parison of six rainfall-runoff modelling approachesrdquo Journal ofHydrology vol 147 no 1-4 pp 1ndash36 1993
[16] E M Lungu ldquoDetection of changes in rainfall and runoffpatternsrdquo Journal ofHydrology vol 147 no 1-4 pp 153ndash167 1993
[17] J Meher and R Jha ldquoTime series analysis of rainfall data overMahanadi River basinrdquo Emerging Science vol 3 no 6 pp 22ndash32 2011
[18] K X Soulis and J D Valiantzas ldquoSCS-CN parameter determi-nation using rainfall-runoff data in heterogeneous watersheds-the two-CN system approachrdquo Hydrology and Earth SystemSciences vol 16 no 3 pp 1001ndash1015 2012
[19] R Kabiri A Chan and R Bai ldquoComparison of SCS and Green-Ampt Methods in Surface Runoff-Flooding Simulation forKlang Watershed in Malaysiardquo Open Journal of Modern Hydro-logy vol 03 no 03 pp 102ndash114 2013
[20] S Mohan andM Abraham ldquoDerivations of simple site-specificrecharge-precipitation relationships A case study from theCuddalore Basin Indiardquo Environmental Geosciences vol 17 no1 pp 37ndash44 2010
[21] R Viji P R Prasanna and R Ilangovan ldquoGIS based SCS-CN method for estimating runoff in Kundahpalam watershedNilgries district Tamilnadurdquo Earth Sciences Research Journalvol 19 no 1 pp 59ndash64 2015
[22] P K Gupta S Punalekar S Panigrahy A Sonakia and J SParihar ldquoRunoff modeling in an agro-forested watershed using
remote sensing and gisrdquo Journal of Hydrologic Engineering vol17 no 11 pp 1255ndash1267 2012
[23] R Chatterjee ldquoGroundwater resources estimationmdash case stud-ies from Indiardquo Journal of the Geological Society of India vol 77no 2 pp 201ndash204 2011
[24] Central Ground Water Board (CGWB) Water Balance Studiesin Upper Yamuna Basin ndash Terminal Report - Project Findingsand Recommendations Chandigarh Central Ground WaterBoard 2000
[25] G Thakuriah and R Saikia ldquoEstimation of Surface RunoffusingNRCSCurve number procedure in BurigangaWatershedAssam India - A Geospatial Approachrdquo International ResearchJournal of Earth Sciences vol 2 no 5 pp 1ndash7 2014
[26] V K Dadhwal ldquoRemote sensing and GIS for agricultural cropacreage and yield estimationrdquo in Proceedings of the InternationalArchives of Photogrammetry andRemote Sensing XXXII pp 58ndash67 1999
[27] G B Geena and P N Ballukraya ldquoEstimation of runoff for Redhills watershed using SCS method and GISrdquo Indian Journal ofScience and Technology vol 4 no 8 pp 899ndash902 2011
[28] K H Syed D C Goodrich D E Myers and S SorooshianldquoSpatial characteristics of thunderstorm rainfall fields and theirrelation to runoffrdquo Journal of Hydrology vol 271 no 1-4 pp 1ndash21 2003
[29] X Zhan and M-L Huang ldquoArcCN-Runoff An ArcGIS toolfor generating curve number and runoff mapsrdquo EnvironmentalModeling and Software vol 19 no 10 pp 875ndash879 2004
[30] K Geetha S K Mishra T I Eldho A K Rastogi and R PPandey ldquoModifications to SCS-CN method for long-term hy-drologic simulationrdquo Journal of Irrigation and Drainage Engi-neering vol 133 no 5 pp 475ndash486 2007
[31] V Kumar M Babu T V Praveen et al ldquoAnalysis of the Runofffor Watershed Using SCS-CN Method and Geographic Infor-mation Systemsrdquo International Journal of Engineering Scienceand Technology 2010
[32] R Pradhan P P Mohan M K Ghose et al ldquoEstimation ofRainfall Runoff using Remote Sensing and GIS in and aroundSingtam East Sikkimrdquo International Journal of Geomatics andGeosciences vol 1 article 466 2010
[33] A Panahi ldquoEvaluating SCS-CN method in estimating theamount of runoff in Soofi Chay basin using GISrdquo Life ScienceJournal vol 10 no 5 pp 271ndash277 2013
[34] S Satheeshkumar S Venkateswaran and R KannanldquoRainfallndashrunoff estimation using SCSndashCN and GIS approachin the Pappiredipatti watershed of the Vaniyar sub basin SouthIndiardquo Modeling Earth Systems and Environment vol 3 no 12017
[35] IMD Temperature of Kanchipuram District Regional Meteoro-logical Centre Chennai India 2013 Temperature of Kanchipu-ram District Regional Meteorological Centre
[36] ENVIS 2015 Kanchipuram District ENVIS Centre TamilnaduhttpwwwtnenvisnicinfilesKANCHIPURAM20pdf
[37] AIS and LUSWatershed Atlas of India Department of Agricul-ture and Co-operation All India Soil and Landuse Survey IARICampus New Delhi India 1990
[38] V T ChowD RMaidment and LWMaysAppliedHydrologyMcGraw-Hill New York New York USA 1988
[39] Natural Resources Conservation Service (NRCS) ldquoChapter 7-Hydrologic Soil Groupsrdquo in Hydrology National EngineeringHandbook title 630 Washington DC USA 2007
10 International Journal of Geophysics
[40] United States Department of Agriculture Soil ConservationService (USDA-SCS) Technical Release 55 Urban hydrologyfor small watersheds USA 1986
[41] A G Bluman Elemtary Statistics McGraw Hill New York NYUSA 6th edition 2008
[42] C Brouwer and M Heibloem ldquoIrrigation Water Needs- Irriga-tion Water Managementrdquo in Training Manual No 3 Food andAgriculture Organisation Rome Italy 1986
[43] R G Allen L S Pereira D Raes and M Smith ldquoCrop evap-otranspiration Guidelines for computing crop water require-mentsrdquo in Irrigation and Drainage Paper No 56 Food andAgricultural Organization of the United Nations Rome Italy1998
[44] Census of India District Census Hand book ndash KanchipuramDirectorate of Census Operations in Tamil Nadu 2011
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Submit your manuscripts atwwwhindawicom
6 International Journal of Geophysics
RainfallRunoff
Jan
07
Jan
10
Jan
14
Jan
12
Jan
03
Jan
11
Jan
04
Jan
01
Jan
05
Jan
08
Jan
06
Jan
09
Jan
13
Jan
02Ju
l02
Jul0
9
Jul0
6
Jul0
3
Jul1
3
Jul1
0
Jul0
4
Jul1
1
Jul0
8
Jul0
7
Jul0
5
Jul0
1
Jul1
2
Jul0
0
Month
0200400600800
100012001400
Rainfa
ll R
unoff
(mm
)
Figure 4 Monthly rainfall and runoff by NRCS model for Ura-pakkam tank watershed
Table 3 Monthly rainfall and runoff for Urapakkam watershedusing NRCS model for the water year 2008-2009
Month Rainfall Runoffmm mm
Jul-08 6100 005Aug-08 10100 2749Sep-08 12900 6311Oct-08 25400 6909Nov-08 53400 35289Dec-08 8400 2633Jan-09 1500 000Feb-09 000 000Mar-09 000 000Apr-09 000 000May-09 2400 000Jun-09 3680 000
2000-01 to 2013-14 Bar chart showing annual rainfall andrunoff is given in Figure 5 Volume of runoff for thewatershedvaried between 0434 MCM and 10637 MCM during thefourteen years of study Annual runoff for a representativewater year (2008-09) is obtained as 246 MCM (Table 4)When the rainfall exceeds the threshold limit the proportionof rainfall contributing to runoffwas found to be higherWidevariations were observed in the quantum of runoff from rain-fall of same magnitude due to varying antecedent moistureconditions in the soil Figure 6 shows the average annualrainfall and runoff estimated for Urapakkam watershed
412 Regression Model A Linear regression model wasdeveloped for the watershed to compute annual runoff fromthe annual rainfall Ten-year data from NRCS model wasused for model calibration and four-year data was usedfor validation The regression relationship obtained betweenannual rainfall (mm) and annual runoff (mm) is given as
Runoff = 0736 times rainfall minus 4678 (13)
RainfallRunoff - NRCS ModelRunoff - Regression model
0500
1000150020002500300035004000
Dep
th in
mm
2001
-200
2
2002
-200
3
2003
-200
4
2004
-200
5
2005
-200
6
2006
-200
7
2007
-200
8
2008
-200
9
2009
-201
0
2010
-201
1
2011
-201
2
2012
-201
3
2013
-201
4
2000
-200
1
Water Year
Figure 5 Annual rainfall and runoff for Urapakkam watershed
Rainfall 152578
Runoff 63338
Figure 6 Average annual rainfall and runoff estimated for Ura-pakkam watershed
R2 value was obtained as 0944 during calibration and 0988during validation periodThe plots of rainfall versus runoff asobtained fromNRCSmodel during calibration and validationperiod are shown in Figure 7
413 Validation of Regression Model Figure 8 shows thecomparison between the annual runoff by NRCS modeland the developed regression model which illustrates thatthe regression model could be used to effectively predictrunoff for the watershed even during the validation phaseThe coefficient of determination (R2) for runoff by NRCSmodel versus runoff by regression model was 0939 at 95confidence level (Figure 9) The statistical tests such as t-test MAE and SEE obtained for the two runoff estimates(NRCS model and regression model) were 055 1060 and1390 respectively The average runoff for the watershed bylinear regression model was obtained as 3717 percent ofthe rainfall which ranges from 1279 to 6063 It showsno considerable deviation from runoff computed by NRCSmodel Thus the statistical analysis proves that tedious anddata intensive computation methods can be eliminated andthe volume of runoff from a watershed can be efficiently andeasily computed using simple regression model
International Journal of Geophysics 7
Table 4 Annual rainfall and runoff estimated for Urapakkam watershed using NRCS model for the period 2000-01 to 2013ndash14
Water YearRainfall Runoff
No of days Depth No of days Depth Percentage of Rainfall Volumemm mm MCM
2000-2001 39 109230 28 32396 2966 14832001-2002 49 121110 35 35539 2934 16262002-2003 28 57570 15 9495 1649 04342003-2004 65 176580 36 53750 3044 24602004-2005 44 164800 35 76859 4664 35172005-2006 59 360800 50 232434 6442 106372006-2007 38 137900 23 70120 5085 32092007-2008 71 189950 44 78043 4109 35712008-2009 44 123880 29 53897 4351 24662009-2010 55 143900 33 58224 4046 26652010-2011 67 148900 34 49909 3352 22842011-2012 52 181000 45 73411 4056 33592012-2013 60 143540 36 48412 3373 22152013-2014 64 76930 22 14248 1852 0652Minimum 28 5757 15 9495 1649 0434Maximum 71 36080 44 232434 6442 10637
y = 07369x - 46782
0
500
1000
1500
2000
2500
Runo
ff (m
m) 2
2= 09446
1000 2000 3000 40000Rainfall (mm)
(a) Calibration
500 1000 1500 20000Rainfall (mm)
0
200
400
600
800
Runo
ff (m
m)
22= 09887
(b) Validation
Figure 7 Relationship between annual rainfall and runoff by NRCS model during calibration and validation period
Run off NRCS modelRunoff Regression model
ValidationCalibration
1 2 3 4 5 6 7 8 9 10 11 12 13 14 150
(Years)minus500
0
500
1000
1500
2000
2500
Runo
ff de
pth
(mm
)
Figure 8 Comparison of annual runoff from NRCS model andregression model
42 Water Balance Study Population density of Kanchipu-ram district is 892km2 [44] Assuming the per capita waterdemand to be 100 litersday for domestic purposes the
y = 09488x + 5422922= 09389
500 1000 1500 2000 25000
Runoff - NRCS Model minus500
0
500
1000
1500
2000
2500
Runo
ff - R
egre
ssio
n M
odel
Figure 9 Scatter plot of annual runoff by NRCS model andregression model for Urapakkam watershed for 2000-01 to 2013-14
total domestic water requirement for the population of thewatershed with 4576 km2 is estimated at 407m3day whichis 0148 MCM per annum
8 International Journal of Geophysics
Table 5 Computation of irrigation demand for paddy for the year 2008-09
Item MonthSeptember October November December January February Season
Percentage of annualdaytime hours 119901 026 027 026 027 027
Average Temperature119879mean (∘C) 2600 2480 2460 258 2780
PotentialevapotranspirationEt119900(mmday)
519 514 502 527 561
Crop coefficient119870119888
110 110 120 130 100
Crop waterrequirement
mmday 571 566 603 684 561mmmonth 17126 17538 18682 19165 17400
SAT mm 20000PSL mmmonth4mmday 12000 12400 12400 11200 12400
WL mm119875119890 mm 4860 49980 16780 000 000 000
Irrigation demandmm 15140 000 13158 31082 30365 29800 119545
Irrigation is practiced in a small area of 10 ha in thewatershed The average irrigation demand for the watershedarea is computed to be around 1195mm By considering anefficiency of 70 the actual irrigation water requirement is1708mmThe total irrigation demand for 10 ha of paddy cropin the watershed is estimated as 0171 MCM Details of thecomputation of irrigation demand for paddy are presented inTable 5
The current total domestic and irrigation water require-ment of the watershed is 0320 MCM After meeting all thewater demands of the watershed there is sufficient wateravailable even if only 50 of the total available rainwater canbe effectively harvested
5 Conclusions
Accurate estimation of runoff is essential for the effectivemanagement ofwater resources especially in semiarid regionslike Tamil Nadu NRCS model is a widely accepted model forthe estimation of runoff for small watersheds It is extensivelyused by engineers and hydrologists but the lack of sufficientdata may give poor results With the understanding ofavailable water in a watershed irrigation scheduling croprotation and cropping pattern can be planned
Total volume of runoff available for Urapakkam water-shed from NRCS model for a representative year is 246MCM A simple regression model was developed to estimatedepth of annual runoff for the watershed which is givenas runoff (mm) = 0736 times rainfall (mm) minus 4678 Statisticaltests confirm that there is no significant difference betweenthe runoff estimates from the two models Hence the studyconfirms that it is advantageous to have calibrated regression
models for easy computation of runoff and for the effectivemanagement of water resources for ungauged basins
Water balance study shows that 123 MCM of watercan be effectively stored in the watershed after losses byimproving the drainage and capacity of the storages Thetotal domestic and irrigation demand is estimated at 0320MCM Hence apart from meeting the present domestic andirrigation demand in the watershed additional demand dueto future development and demand of adjoining areas canalso be met from the water available in the watershed ifproperly harvested
Data Availability
The data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this paper
References
[1] I A Shiklomanov ldquoAppraisal and Assessment of world waterresourcesrdquoWater International vol 25 no 1 pp 11ndash32 2000
[2] FAO ldquoFAOActivities on Promoting Fertigationrdquo in Proceedingsof the Regional Workshop on Guidelines for Efficient FertilizersUse through Modern Irrigation FAO Regional Office for theNear East Cairo Egypt 1998
[3] W Cosgrove and F Rijsberman World Water Vision MakingWater Everybodys Business World Water Council EarthscanPublications 2000
International Journal of Geophysics 9
[4] Ministry of Water Resources 2012 Per Capita Water Availabil-ity Press Information Bureau Government of India 26-April2012 httppibnicinnewsitePrintReleaseaspxrelid=82676
[5] S K Gupta and R D Deshpande ldquoWater for India in 2050First-order assessment of available optionsrdquo Current Sciencevol 86 no 9 pp 1216ndash1224 2004
[6] Soil Conservation Service (SCS) Hydrology National Engi-neering Handbook Supplement A Section 4 Chapter 10 SoilConservation Service USDA Washington DC USA 1972
[7] L K Sherman ldquoThe unit hydrograph methodrdquo in Physics of theEarth O E Meinzer Ed pp 514ndash525 Dover Publications IncNew York NY USA 1949
[8] VMockus ldquoEstimation of total (peak rates of) surface runoff forindividual stormsrdquo in Exhibit A of Appendix B Interim SurveyReport Grand (Neosho) River Watershed USDA 1949
[9] S KMishra andV P Singh ldquoAnother look at SCS-CNmethodrdquoJournal of Hydrologic Engineering vol 4 no 3 pp 257ndash2641999
[10] S K Mishra M K Jain P K Bhunya and V P Singh ldquoFieldapplicability of the SCS-CN-basedMishra-Singh general modeland its variantsrdquoWater Resources Management vol 19 no 1 pp37ndash62 2005
[11] K X Soulis J D Valiantzas N Dercas and P A LondraldquoInvestigation of the direct runoff generationmechanism for theanalysis of the SCS-CN method applicability to a partial areaexperimental watershedrdquo Hydrology and Earth System Sciencesvol 13 no 5 pp 605ndash615 2009
[12] P Koteshwaram and S M A Alvi ldquoSecular trends and period-icities in rainfall at west coast stations in Indiardquo Current Sciencevol 38 no 10 pp 229ndash231 1969
[13] K V Ramamurthy M K Sonam and S S Muley ldquoLongterm variation in the rainfall over upper Narmada catchmentrdquoMausam vol 38 no 3 pp 313ndash318 1987
[14] R Jha and R K Jaiswal ldquoAnalysis of rainfall pattern in acatchmentrdquo IAH Journal of Hydrology (India) Roorkee 1992
[15] F H S Chiew M J Stewardson and T A McMahon ldquoCom-parison of six rainfall-runoff modelling approachesrdquo Journal ofHydrology vol 147 no 1-4 pp 1ndash36 1993
[16] E M Lungu ldquoDetection of changes in rainfall and runoffpatternsrdquo Journal ofHydrology vol 147 no 1-4 pp 153ndash167 1993
[17] J Meher and R Jha ldquoTime series analysis of rainfall data overMahanadi River basinrdquo Emerging Science vol 3 no 6 pp 22ndash32 2011
[18] K X Soulis and J D Valiantzas ldquoSCS-CN parameter determi-nation using rainfall-runoff data in heterogeneous watersheds-the two-CN system approachrdquo Hydrology and Earth SystemSciences vol 16 no 3 pp 1001ndash1015 2012
[19] R Kabiri A Chan and R Bai ldquoComparison of SCS and Green-Ampt Methods in Surface Runoff-Flooding Simulation forKlang Watershed in Malaysiardquo Open Journal of Modern Hydro-logy vol 03 no 03 pp 102ndash114 2013
[20] S Mohan andM Abraham ldquoDerivations of simple site-specificrecharge-precipitation relationships A case study from theCuddalore Basin Indiardquo Environmental Geosciences vol 17 no1 pp 37ndash44 2010
[21] R Viji P R Prasanna and R Ilangovan ldquoGIS based SCS-CN method for estimating runoff in Kundahpalam watershedNilgries district Tamilnadurdquo Earth Sciences Research Journalvol 19 no 1 pp 59ndash64 2015
[22] P K Gupta S Punalekar S Panigrahy A Sonakia and J SParihar ldquoRunoff modeling in an agro-forested watershed using
remote sensing and gisrdquo Journal of Hydrologic Engineering vol17 no 11 pp 1255ndash1267 2012
[23] R Chatterjee ldquoGroundwater resources estimationmdash case stud-ies from Indiardquo Journal of the Geological Society of India vol 77no 2 pp 201ndash204 2011
[24] Central Ground Water Board (CGWB) Water Balance Studiesin Upper Yamuna Basin ndash Terminal Report - Project Findingsand Recommendations Chandigarh Central Ground WaterBoard 2000
[25] G Thakuriah and R Saikia ldquoEstimation of Surface RunoffusingNRCSCurve number procedure in BurigangaWatershedAssam India - A Geospatial Approachrdquo International ResearchJournal of Earth Sciences vol 2 no 5 pp 1ndash7 2014
[26] V K Dadhwal ldquoRemote sensing and GIS for agricultural cropacreage and yield estimationrdquo in Proceedings of the InternationalArchives of Photogrammetry andRemote Sensing XXXII pp 58ndash67 1999
[27] G B Geena and P N Ballukraya ldquoEstimation of runoff for Redhills watershed using SCS method and GISrdquo Indian Journal ofScience and Technology vol 4 no 8 pp 899ndash902 2011
[28] K H Syed D C Goodrich D E Myers and S SorooshianldquoSpatial characteristics of thunderstorm rainfall fields and theirrelation to runoffrdquo Journal of Hydrology vol 271 no 1-4 pp 1ndash21 2003
[29] X Zhan and M-L Huang ldquoArcCN-Runoff An ArcGIS toolfor generating curve number and runoff mapsrdquo EnvironmentalModeling and Software vol 19 no 10 pp 875ndash879 2004
[30] K Geetha S K Mishra T I Eldho A K Rastogi and R PPandey ldquoModifications to SCS-CN method for long-term hy-drologic simulationrdquo Journal of Irrigation and Drainage Engi-neering vol 133 no 5 pp 475ndash486 2007
[31] V Kumar M Babu T V Praveen et al ldquoAnalysis of the Runofffor Watershed Using SCS-CN Method and Geographic Infor-mation Systemsrdquo International Journal of Engineering Scienceand Technology 2010
[32] R Pradhan P P Mohan M K Ghose et al ldquoEstimation ofRainfall Runoff using Remote Sensing and GIS in and aroundSingtam East Sikkimrdquo International Journal of Geomatics andGeosciences vol 1 article 466 2010
[33] A Panahi ldquoEvaluating SCS-CN method in estimating theamount of runoff in Soofi Chay basin using GISrdquo Life ScienceJournal vol 10 no 5 pp 271ndash277 2013
[34] S Satheeshkumar S Venkateswaran and R KannanldquoRainfallndashrunoff estimation using SCSndashCN and GIS approachin the Pappiredipatti watershed of the Vaniyar sub basin SouthIndiardquo Modeling Earth Systems and Environment vol 3 no 12017
[35] IMD Temperature of Kanchipuram District Regional Meteoro-logical Centre Chennai India 2013 Temperature of Kanchipu-ram District Regional Meteorological Centre
[36] ENVIS 2015 Kanchipuram District ENVIS Centre TamilnaduhttpwwwtnenvisnicinfilesKANCHIPURAM20pdf
[37] AIS and LUSWatershed Atlas of India Department of Agricul-ture and Co-operation All India Soil and Landuse Survey IARICampus New Delhi India 1990
[38] V T ChowD RMaidment and LWMaysAppliedHydrologyMcGraw-Hill New York New York USA 1988
[39] Natural Resources Conservation Service (NRCS) ldquoChapter 7-Hydrologic Soil Groupsrdquo in Hydrology National EngineeringHandbook title 630 Washington DC USA 2007
10 International Journal of Geophysics
[40] United States Department of Agriculture Soil ConservationService (USDA-SCS) Technical Release 55 Urban hydrologyfor small watersheds USA 1986
[41] A G Bluman Elemtary Statistics McGraw Hill New York NYUSA 6th edition 2008
[42] C Brouwer and M Heibloem ldquoIrrigation Water Needs- Irriga-tion Water Managementrdquo in Training Manual No 3 Food andAgriculture Organisation Rome Italy 1986
[43] R G Allen L S Pereira D Raes and M Smith ldquoCrop evap-otranspiration Guidelines for computing crop water require-mentsrdquo in Irrigation and Drainage Paper No 56 Food andAgricultural Organization of the United Nations Rome Italy1998
[44] Census of India District Census Hand book ndash KanchipuramDirectorate of Census Operations in Tamil Nadu 2011
Hindawiwwwhindawicom Volume 2018
Journal of
ChemistryArchaeaHindawiwwwhindawicom Volume 2018
Marine BiologyJournal of
Hindawiwwwhindawicom Volume 2018
BiodiversityInternational Journal of
Hindawiwwwhindawicom Volume 2018
EcologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2018
Forestry ResearchInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
International Journal of
Geophysics
Environmental and Public Health
Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
International Journal of
Microbiology
Hindawiwwwhindawicom Volume 2018
Public Health Advances in
AgricultureAdvances in
Hindawiwwwhindawicom Volume 2018
Agronomy
Hindawiwwwhindawicom Volume 2018
International Journal of
Hindawiwwwhindawicom Volume 2018
MeteorologyAdvances in
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018Hindawiwwwhindawicom Volume 2018
ChemistryAdvances in
ScienticaHindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Geological ResearchJournal of
Analytical ChemistryInternational Journal of
Hindawiwwwhindawicom Volume 2018
Submit your manuscripts atwwwhindawicom
International Journal of Geophysics 7
Table 4 Annual rainfall and runoff estimated for Urapakkam watershed using NRCS model for the period 2000-01 to 2013ndash14
Water YearRainfall Runoff
No of days Depth No of days Depth Percentage of Rainfall Volumemm mm MCM
2000-2001 39 109230 28 32396 2966 14832001-2002 49 121110 35 35539 2934 16262002-2003 28 57570 15 9495 1649 04342003-2004 65 176580 36 53750 3044 24602004-2005 44 164800 35 76859 4664 35172005-2006 59 360800 50 232434 6442 106372006-2007 38 137900 23 70120 5085 32092007-2008 71 189950 44 78043 4109 35712008-2009 44 123880 29 53897 4351 24662009-2010 55 143900 33 58224 4046 26652010-2011 67 148900 34 49909 3352 22842011-2012 52 181000 45 73411 4056 33592012-2013 60 143540 36 48412 3373 22152013-2014 64 76930 22 14248 1852 0652Minimum 28 5757 15 9495 1649 0434Maximum 71 36080 44 232434 6442 10637
y = 07369x - 46782
0
500
1000
1500
2000
2500
Runo
ff (m
m) 2
2= 09446
1000 2000 3000 40000Rainfall (mm)
(a) Calibration
500 1000 1500 20000Rainfall (mm)
0
200
400
600
800
Runo
ff (m
m)
22= 09887
(b) Validation
Figure 7 Relationship between annual rainfall and runoff by NRCS model during calibration and validation period
Run off NRCS modelRunoff Regression model
ValidationCalibration
1 2 3 4 5 6 7 8 9 10 11 12 13 14 150
(Years)minus500
0
500
1000
1500
2000
2500
Runo
ff de
pth
(mm
)
Figure 8 Comparison of annual runoff from NRCS model andregression model
42 Water Balance Study Population density of Kanchipu-ram district is 892km2 [44] Assuming the per capita waterdemand to be 100 litersday for domestic purposes the
y = 09488x + 5422922= 09389
500 1000 1500 2000 25000
Runoff - NRCS Model minus500
0
500
1000
1500
2000
2500
Runo
ff - R
egre
ssio
n M
odel
Figure 9 Scatter plot of annual runoff by NRCS model andregression model for Urapakkam watershed for 2000-01 to 2013-14
total domestic water requirement for the population of thewatershed with 4576 km2 is estimated at 407m3day whichis 0148 MCM per annum
8 International Journal of Geophysics
Table 5 Computation of irrigation demand for paddy for the year 2008-09
Item MonthSeptember October November December January February Season
Percentage of annualdaytime hours 119901 026 027 026 027 027
Average Temperature119879mean (∘C) 2600 2480 2460 258 2780
PotentialevapotranspirationEt119900(mmday)
519 514 502 527 561
Crop coefficient119870119888
110 110 120 130 100
Crop waterrequirement
mmday 571 566 603 684 561mmmonth 17126 17538 18682 19165 17400
SAT mm 20000PSL mmmonth4mmday 12000 12400 12400 11200 12400
WL mm119875119890 mm 4860 49980 16780 000 000 000
Irrigation demandmm 15140 000 13158 31082 30365 29800 119545
Irrigation is practiced in a small area of 10 ha in thewatershed The average irrigation demand for the watershedarea is computed to be around 1195mm By considering anefficiency of 70 the actual irrigation water requirement is1708mmThe total irrigation demand for 10 ha of paddy cropin the watershed is estimated as 0171 MCM Details of thecomputation of irrigation demand for paddy are presented inTable 5
The current total domestic and irrigation water require-ment of the watershed is 0320 MCM After meeting all thewater demands of the watershed there is sufficient wateravailable even if only 50 of the total available rainwater canbe effectively harvested
5 Conclusions
Accurate estimation of runoff is essential for the effectivemanagement ofwater resources especially in semiarid regionslike Tamil Nadu NRCS model is a widely accepted model forthe estimation of runoff for small watersheds It is extensivelyused by engineers and hydrologists but the lack of sufficientdata may give poor results With the understanding ofavailable water in a watershed irrigation scheduling croprotation and cropping pattern can be planned
Total volume of runoff available for Urapakkam water-shed from NRCS model for a representative year is 246MCM A simple regression model was developed to estimatedepth of annual runoff for the watershed which is givenas runoff (mm) = 0736 times rainfall (mm) minus 4678 Statisticaltests confirm that there is no significant difference betweenthe runoff estimates from the two models Hence the studyconfirms that it is advantageous to have calibrated regression
models for easy computation of runoff and for the effectivemanagement of water resources for ungauged basins
Water balance study shows that 123 MCM of watercan be effectively stored in the watershed after losses byimproving the drainage and capacity of the storages Thetotal domestic and irrigation demand is estimated at 0320MCM Hence apart from meeting the present domestic andirrigation demand in the watershed additional demand dueto future development and demand of adjoining areas canalso be met from the water available in the watershed ifproperly harvested
Data Availability
The data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this paper
References
[1] I A Shiklomanov ldquoAppraisal and Assessment of world waterresourcesrdquoWater International vol 25 no 1 pp 11ndash32 2000
[2] FAO ldquoFAOActivities on Promoting Fertigationrdquo in Proceedingsof the Regional Workshop on Guidelines for Efficient FertilizersUse through Modern Irrigation FAO Regional Office for theNear East Cairo Egypt 1998
[3] W Cosgrove and F Rijsberman World Water Vision MakingWater Everybodys Business World Water Council EarthscanPublications 2000
International Journal of Geophysics 9
[4] Ministry of Water Resources 2012 Per Capita Water Availabil-ity Press Information Bureau Government of India 26-April2012 httppibnicinnewsitePrintReleaseaspxrelid=82676
[5] S K Gupta and R D Deshpande ldquoWater for India in 2050First-order assessment of available optionsrdquo Current Sciencevol 86 no 9 pp 1216ndash1224 2004
[6] Soil Conservation Service (SCS) Hydrology National Engi-neering Handbook Supplement A Section 4 Chapter 10 SoilConservation Service USDA Washington DC USA 1972
[7] L K Sherman ldquoThe unit hydrograph methodrdquo in Physics of theEarth O E Meinzer Ed pp 514ndash525 Dover Publications IncNew York NY USA 1949
[8] VMockus ldquoEstimation of total (peak rates of) surface runoff forindividual stormsrdquo in Exhibit A of Appendix B Interim SurveyReport Grand (Neosho) River Watershed USDA 1949
[9] S KMishra andV P Singh ldquoAnother look at SCS-CNmethodrdquoJournal of Hydrologic Engineering vol 4 no 3 pp 257ndash2641999
[10] S K Mishra M K Jain P K Bhunya and V P Singh ldquoFieldapplicability of the SCS-CN-basedMishra-Singh general modeland its variantsrdquoWater Resources Management vol 19 no 1 pp37ndash62 2005
[11] K X Soulis J D Valiantzas N Dercas and P A LondraldquoInvestigation of the direct runoff generationmechanism for theanalysis of the SCS-CN method applicability to a partial areaexperimental watershedrdquo Hydrology and Earth System Sciencesvol 13 no 5 pp 605ndash615 2009
[12] P Koteshwaram and S M A Alvi ldquoSecular trends and period-icities in rainfall at west coast stations in Indiardquo Current Sciencevol 38 no 10 pp 229ndash231 1969
[13] K V Ramamurthy M K Sonam and S S Muley ldquoLongterm variation in the rainfall over upper Narmada catchmentrdquoMausam vol 38 no 3 pp 313ndash318 1987
[14] R Jha and R K Jaiswal ldquoAnalysis of rainfall pattern in acatchmentrdquo IAH Journal of Hydrology (India) Roorkee 1992
[15] F H S Chiew M J Stewardson and T A McMahon ldquoCom-parison of six rainfall-runoff modelling approachesrdquo Journal ofHydrology vol 147 no 1-4 pp 1ndash36 1993
[16] E M Lungu ldquoDetection of changes in rainfall and runoffpatternsrdquo Journal ofHydrology vol 147 no 1-4 pp 153ndash167 1993
[17] J Meher and R Jha ldquoTime series analysis of rainfall data overMahanadi River basinrdquo Emerging Science vol 3 no 6 pp 22ndash32 2011
[18] K X Soulis and J D Valiantzas ldquoSCS-CN parameter determi-nation using rainfall-runoff data in heterogeneous watersheds-the two-CN system approachrdquo Hydrology and Earth SystemSciences vol 16 no 3 pp 1001ndash1015 2012
[19] R Kabiri A Chan and R Bai ldquoComparison of SCS and Green-Ampt Methods in Surface Runoff-Flooding Simulation forKlang Watershed in Malaysiardquo Open Journal of Modern Hydro-logy vol 03 no 03 pp 102ndash114 2013
[20] S Mohan andM Abraham ldquoDerivations of simple site-specificrecharge-precipitation relationships A case study from theCuddalore Basin Indiardquo Environmental Geosciences vol 17 no1 pp 37ndash44 2010
[21] R Viji P R Prasanna and R Ilangovan ldquoGIS based SCS-CN method for estimating runoff in Kundahpalam watershedNilgries district Tamilnadurdquo Earth Sciences Research Journalvol 19 no 1 pp 59ndash64 2015
[22] P K Gupta S Punalekar S Panigrahy A Sonakia and J SParihar ldquoRunoff modeling in an agro-forested watershed using
remote sensing and gisrdquo Journal of Hydrologic Engineering vol17 no 11 pp 1255ndash1267 2012
[23] R Chatterjee ldquoGroundwater resources estimationmdash case stud-ies from Indiardquo Journal of the Geological Society of India vol 77no 2 pp 201ndash204 2011
[24] Central Ground Water Board (CGWB) Water Balance Studiesin Upper Yamuna Basin ndash Terminal Report - Project Findingsand Recommendations Chandigarh Central Ground WaterBoard 2000
[25] G Thakuriah and R Saikia ldquoEstimation of Surface RunoffusingNRCSCurve number procedure in BurigangaWatershedAssam India - A Geospatial Approachrdquo International ResearchJournal of Earth Sciences vol 2 no 5 pp 1ndash7 2014
[26] V K Dadhwal ldquoRemote sensing and GIS for agricultural cropacreage and yield estimationrdquo in Proceedings of the InternationalArchives of Photogrammetry andRemote Sensing XXXII pp 58ndash67 1999
[27] G B Geena and P N Ballukraya ldquoEstimation of runoff for Redhills watershed using SCS method and GISrdquo Indian Journal ofScience and Technology vol 4 no 8 pp 899ndash902 2011
[28] K H Syed D C Goodrich D E Myers and S SorooshianldquoSpatial characteristics of thunderstorm rainfall fields and theirrelation to runoffrdquo Journal of Hydrology vol 271 no 1-4 pp 1ndash21 2003
[29] X Zhan and M-L Huang ldquoArcCN-Runoff An ArcGIS toolfor generating curve number and runoff mapsrdquo EnvironmentalModeling and Software vol 19 no 10 pp 875ndash879 2004
[30] K Geetha S K Mishra T I Eldho A K Rastogi and R PPandey ldquoModifications to SCS-CN method for long-term hy-drologic simulationrdquo Journal of Irrigation and Drainage Engi-neering vol 133 no 5 pp 475ndash486 2007
[31] V Kumar M Babu T V Praveen et al ldquoAnalysis of the Runofffor Watershed Using SCS-CN Method and Geographic Infor-mation Systemsrdquo International Journal of Engineering Scienceand Technology 2010
[32] R Pradhan P P Mohan M K Ghose et al ldquoEstimation ofRainfall Runoff using Remote Sensing and GIS in and aroundSingtam East Sikkimrdquo International Journal of Geomatics andGeosciences vol 1 article 466 2010
[33] A Panahi ldquoEvaluating SCS-CN method in estimating theamount of runoff in Soofi Chay basin using GISrdquo Life ScienceJournal vol 10 no 5 pp 271ndash277 2013
[34] S Satheeshkumar S Venkateswaran and R KannanldquoRainfallndashrunoff estimation using SCSndashCN and GIS approachin the Pappiredipatti watershed of the Vaniyar sub basin SouthIndiardquo Modeling Earth Systems and Environment vol 3 no 12017
[35] IMD Temperature of Kanchipuram District Regional Meteoro-logical Centre Chennai India 2013 Temperature of Kanchipu-ram District Regional Meteorological Centre
[36] ENVIS 2015 Kanchipuram District ENVIS Centre TamilnaduhttpwwwtnenvisnicinfilesKANCHIPURAM20pdf
[37] AIS and LUSWatershed Atlas of India Department of Agricul-ture and Co-operation All India Soil and Landuse Survey IARICampus New Delhi India 1990
[38] V T ChowD RMaidment and LWMaysAppliedHydrologyMcGraw-Hill New York New York USA 1988
[39] Natural Resources Conservation Service (NRCS) ldquoChapter 7-Hydrologic Soil Groupsrdquo in Hydrology National EngineeringHandbook title 630 Washington DC USA 2007
10 International Journal of Geophysics
[40] United States Department of Agriculture Soil ConservationService (USDA-SCS) Technical Release 55 Urban hydrologyfor small watersheds USA 1986
[41] A G Bluman Elemtary Statistics McGraw Hill New York NYUSA 6th edition 2008
[42] C Brouwer and M Heibloem ldquoIrrigation Water Needs- Irriga-tion Water Managementrdquo in Training Manual No 3 Food andAgriculture Organisation Rome Italy 1986
[43] R G Allen L S Pereira D Raes and M Smith ldquoCrop evap-otranspiration Guidelines for computing crop water require-mentsrdquo in Irrigation and Drainage Paper No 56 Food andAgricultural Organization of the United Nations Rome Italy1998
[44] Census of India District Census Hand book ndash KanchipuramDirectorate of Census Operations in Tamil Nadu 2011
Hindawiwwwhindawicom Volume 2018
Journal of
ChemistryArchaeaHindawiwwwhindawicom Volume 2018
Marine BiologyJournal of
Hindawiwwwhindawicom Volume 2018
BiodiversityInternational Journal of
Hindawiwwwhindawicom Volume 2018
EcologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2018
Forestry ResearchInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
International Journal of
Geophysics
Environmental and Public Health
Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
International Journal of
Microbiology
Hindawiwwwhindawicom Volume 2018
Public Health Advances in
AgricultureAdvances in
Hindawiwwwhindawicom Volume 2018
Agronomy
Hindawiwwwhindawicom Volume 2018
International Journal of
Hindawiwwwhindawicom Volume 2018
MeteorologyAdvances in
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018Hindawiwwwhindawicom Volume 2018
ChemistryAdvances in
ScienticaHindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Geological ResearchJournal of
Analytical ChemistryInternational Journal of
Hindawiwwwhindawicom Volume 2018
Submit your manuscripts atwwwhindawicom
8 International Journal of Geophysics
Table 5 Computation of irrigation demand for paddy for the year 2008-09
Item MonthSeptember October November December January February Season
Percentage of annualdaytime hours 119901 026 027 026 027 027
Average Temperature119879mean (∘C) 2600 2480 2460 258 2780
PotentialevapotranspirationEt119900(mmday)
519 514 502 527 561
Crop coefficient119870119888
110 110 120 130 100
Crop waterrequirement
mmday 571 566 603 684 561mmmonth 17126 17538 18682 19165 17400
SAT mm 20000PSL mmmonth4mmday 12000 12400 12400 11200 12400
WL mm119875119890 mm 4860 49980 16780 000 000 000
Irrigation demandmm 15140 000 13158 31082 30365 29800 119545
Irrigation is practiced in a small area of 10 ha in thewatershed The average irrigation demand for the watershedarea is computed to be around 1195mm By considering anefficiency of 70 the actual irrigation water requirement is1708mmThe total irrigation demand for 10 ha of paddy cropin the watershed is estimated as 0171 MCM Details of thecomputation of irrigation demand for paddy are presented inTable 5
The current total domestic and irrigation water require-ment of the watershed is 0320 MCM After meeting all thewater demands of the watershed there is sufficient wateravailable even if only 50 of the total available rainwater canbe effectively harvested
5 Conclusions
Accurate estimation of runoff is essential for the effectivemanagement ofwater resources especially in semiarid regionslike Tamil Nadu NRCS model is a widely accepted model forthe estimation of runoff for small watersheds It is extensivelyused by engineers and hydrologists but the lack of sufficientdata may give poor results With the understanding ofavailable water in a watershed irrigation scheduling croprotation and cropping pattern can be planned
Total volume of runoff available for Urapakkam water-shed from NRCS model for a representative year is 246MCM A simple regression model was developed to estimatedepth of annual runoff for the watershed which is givenas runoff (mm) = 0736 times rainfall (mm) minus 4678 Statisticaltests confirm that there is no significant difference betweenthe runoff estimates from the two models Hence the studyconfirms that it is advantageous to have calibrated regression
models for easy computation of runoff and for the effectivemanagement of water resources for ungauged basins
Water balance study shows that 123 MCM of watercan be effectively stored in the watershed after losses byimproving the drainage and capacity of the storages Thetotal domestic and irrigation demand is estimated at 0320MCM Hence apart from meeting the present domestic andirrigation demand in the watershed additional demand dueto future development and demand of adjoining areas canalso be met from the water available in the watershed ifproperly harvested
Data Availability
The data used to support the findings of this study areavailable from the corresponding author upon request
Conflicts of Interest
The authors declare that there are no conflicts of interestregarding the publication of this paper
References
[1] I A Shiklomanov ldquoAppraisal and Assessment of world waterresourcesrdquoWater International vol 25 no 1 pp 11ndash32 2000
[2] FAO ldquoFAOActivities on Promoting Fertigationrdquo in Proceedingsof the Regional Workshop on Guidelines for Efficient FertilizersUse through Modern Irrigation FAO Regional Office for theNear East Cairo Egypt 1998
[3] W Cosgrove and F Rijsberman World Water Vision MakingWater Everybodys Business World Water Council EarthscanPublications 2000
International Journal of Geophysics 9
[4] Ministry of Water Resources 2012 Per Capita Water Availabil-ity Press Information Bureau Government of India 26-April2012 httppibnicinnewsitePrintReleaseaspxrelid=82676
[5] S K Gupta and R D Deshpande ldquoWater for India in 2050First-order assessment of available optionsrdquo Current Sciencevol 86 no 9 pp 1216ndash1224 2004
[6] Soil Conservation Service (SCS) Hydrology National Engi-neering Handbook Supplement A Section 4 Chapter 10 SoilConservation Service USDA Washington DC USA 1972
[7] L K Sherman ldquoThe unit hydrograph methodrdquo in Physics of theEarth O E Meinzer Ed pp 514ndash525 Dover Publications IncNew York NY USA 1949
[8] VMockus ldquoEstimation of total (peak rates of) surface runoff forindividual stormsrdquo in Exhibit A of Appendix B Interim SurveyReport Grand (Neosho) River Watershed USDA 1949
[9] S KMishra andV P Singh ldquoAnother look at SCS-CNmethodrdquoJournal of Hydrologic Engineering vol 4 no 3 pp 257ndash2641999
[10] S K Mishra M K Jain P K Bhunya and V P Singh ldquoFieldapplicability of the SCS-CN-basedMishra-Singh general modeland its variantsrdquoWater Resources Management vol 19 no 1 pp37ndash62 2005
[11] K X Soulis J D Valiantzas N Dercas and P A LondraldquoInvestigation of the direct runoff generationmechanism for theanalysis of the SCS-CN method applicability to a partial areaexperimental watershedrdquo Hydrology and Earth System Sciencesvol 13 no 5 pp 605ndash615 2009
[12] P Koteshwaram and S M A Alvi ldquoSecular trends and period-icities in rainfall at west coast stations in Indiardquo Current Sciencevol 38 no 10 pp 229ndash231 1969
[13] K V Ramamurthy M K Sonam and S S Muley ldquoLongterm variation in the rainfall over upper Narmada catchmentrdquoMausam vol 38 no 3 pp 313ndash318 1987
[14] R Jha and R K Jaiswal ldquoAnalysis of rainfall pattern in acatchmentrdquo IAH Journal of Hydrology (India) Roorkee 1992
[15] F H S Chiew M J Stewardson and T A McMahon ldquoCom-parison of six rainfall-runoff modelling approachesrdquo Journal ofHydrology vol 147 no 1-4 pp 1ndash36 1993
[16] E M Lungu ldquoDetection of changes in rainfall and runoffpatternsrdquo Journal ofHydrology vol 147 no 1-4 pp 153ndash167 1993
[17] J Meher and R Jha ldquoTime series analysis of rainfall data overMahanadi River basinrdquo Emerging Science vol 3 no 6 pp 22ndash32 2011
[18] K X Soulis and J D Valiantzas ldquoSCS-CN parameter determi-nation using rainfall-runoff data in heterogeneous watersheds-the two-CN system approachrdquo Hydrology and Earth SystemSciences vol 16 no 3 pp 1001ndash1015 2012
[19] R Kabiri A Chan and R Bai ldquoComparison of SCS and Green-Ampt Methods in Surface Runoff-Flooding Simulation forKlang Watershed in Malaysiardquo Open Journal of Modern Hydro-logy vol 03 no 03 pp 102ndash114 2013
[20] S Mohan andM Abraham ldquoDerivations of simple site-specificrecharge-precipitation relationships A case study from theCuddalore Basin Indiardquo Environmental Geosciences vol 17 no1 pp 37ndash44 2010
[21] R Viji P R Prasanna and R Ilangovan ldquoGIS based SCS-CN method for estimating runoff in Kundahpalam watershedNilgries district Tamilnadurdquo Earth Sciences Research Journalvol 19 no 1 pp 59ndash64 2015
[22] P K Gupta S Punalekar S Panigrahy A Sonakia and J SParihar ldquoRunoff modeling in an agro-forested watershed using
remote sensing and gisrdquo Journal of Hydrologic Engineering vol17 no 11 pp 1255ndash1267 2012
[23] R Chatterjee ldquoGroundwater resources estimationmdash case stud-ies from Indiardquo Journal of the Geological Society of India vol 77no 2 pp 201ndash204 2011
[24] Central Ground Water Board (CGWB) Water Balance Studiesin Upper Yamuna Basin ndash Terminal Report - Project Findingsand Recommendations Chandigarh Central Ground WaterBoard 2000
[25] G Thakuriah and R Saikia ldquoEstimation of Surface RunoffusingNRCSCurve number procedure in BurigangaWatershedAssam India - A Geospatial Approachrdquo International ResearchJournal of Earth Sciences vol 2 no 5 pp 1ndash7 2014
[26] V K Dadhwal ldquoRemote sensing and GIS for agricultural cropacreage and yield estimationrdquo in Proceedings of the InternationalArchives of Photogrammetry andRemote Sensing XXXII pp 58ndash67 1999
[27] G B Geena and P N Ballukraya ldquoEstimation of runoff for Redhills watershed using SCS method and GISrdquo Indian Journal ofScience and Technology vol 4 no 8 pp 899ndash902 2011
[28] K H Syed D C Goodrich D E Myers and S SorooshianldquoSpatial characteristics of thunderstorm rainfall fields and theirrelation to runoffrdquo Journal of Hydrology vol 271 no 1-4 pp 1ndash21 2003
[29] X Zhan and M-L Huang ldquoArcCN-Runoff An ArcGIS toolfor generating curve number and runoff mapsrdquo EnvironmentalModeling and Software vol 19 no 10 pp 875ndash879 2004
[30] K Geetha S K Mishra T I Eldho A K Rastogi and R PPandey ldquoModifications to SCS-CN method for long-term hy-drologic simulationrdquo Journal of Irrigation and Drainage Engi-neering vol 133 no 5 pp 475ndash486 2007
[31] V Kumar M Babu T V Praveen et al ldquoAnalysis of the Runofffor Watershed Using SCS-CN Method and Geographic Infor-mation Systemsrdquo International Journal of Engineering Scienceand Technology 2010
[32] R Pradhan P P Mohan M K Ghose et al ldquoEstimation ofRainfall Runoff using Remote Sensing and GIS in and aroundSingtam East Sikkimrdquo International Journal of Geomatics andGeosciences vol 1 article 466 2010
[33] A Panahi ldquoEvaluating SCS-CN method in estimating theamount of runoff in Soofi Chay basin using GISrdquo Life ScienceJournal vol 10 no 5 pp 271ndash277 2013
[34] S Satheeshkumar S Venkateswaran and R KannanldquoRainfallndashrunoff estimation using SCSndashCN and GIS approachin the Pappiredipatti watershed of the Vaniyar sub basin SouthIndiardquo Modeling Earth Systems and Environment vol 3 no 12017
[35] IMD Temperature of Kanchipuram District Regional Meteoro-logical Centre Chennai India 2013 Temperature of Kanchipu-ram District Regional Meteorological Centre
[36] ENVIS 2015 Kanchipuram District ENVIS Centre TamilnaduhttpwwwtnenvisnicinfilesKANCHIPURAM20pdf
[37] AIS and LUSWatershed Atlas of India Department of Agricul-ture and Co-operation All India Soil and Landuse Survey IARICampus New Delhi India 1990
[38] V T ChowD RMaidment and LWMaysAppliedHydrologyMcGraw-Hill New York New York USA 1988
[39] Natural Resources Conservation Service (NRCS) ldquoChapter 7-Hydrologic Soil Groupsrdquo in Hydrology National EngineeringHandbook title 630 Washington DC USA 2007
10 International Journal of Geophysics
[40] United States Department of Agriculture Soil ConservationService (USDA-SCS) Technical Release 55 Urban hydrologyfor small watersheds USA 1986
[41] A G Bluman Elemtary Statistics McGraw Hill New York NYUSA 6th edition 2008
[42] C Brouwer and M Heibloem ldquoIrrigation Water Needs- Irriga-tion Water Managementrdquo in Training Manual No 3 Food andAgriculture Organisation Rome Italy 1986
[43] R G Allen L S Pereira D Raes and M Smith ldquoCrop evap-otranspiration Guidelines for computing crop water require-mentsrdquo in Irrigation and Drainage Paper No 56 Food andAgricultural Organization of the United Nations Rome Italy1998
[44] Census of India District Census Hand book ndash KanchipuramDirectorate of Census Operations in Tamil Nadu 2011
Hindawiwwwhindawicom Volume 2018
Journal of
ChemistryArchaeaHindawiwwwhindawicom Volume 2018
Marine BiologyJournal of
Hindawiwwwhindawicom Volume 2018
BiodiversityInternational Journal of
Hindawiwwwhindawicom Volume 2018
EcologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2018
Forestry ResearchInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
International Journal of
Geophysics
Environmental and Public Health
Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
International Journal of
Microbiology
Hindawiwwwhindawicom Volume 2018
Public Health Advances in
AgricultureAdvances in
Hindawiwwwhindawicom Volume 2018
Agronomy
Hindawiwwwhindawicom Volume 2018
International Journal of
Hindawiwwwhindawicom Volume 2018
MeteorologyAdvances in
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018Hindawiwwwhindawicom Volume 2018
ChemistryAdvances in
ScienticaHindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Geological ResearchJournal of
Analytical ChemistryInternational Journal of
Hindawiwwwhindawicom Volume 2018
Submit your manuscripts atwwwhindawicom
International Journal of Geophysics 9
[4] Ministry of Water Resources 2012 Per Capita Water Availabil-ity Press Information Bureau Government of India 26-April2012 httppibnicinnewsitePrintReleaseaspxrelid=82676
[5] S K Gupta and R D Deshpande ldquoWater for India in 2050First-order assessment of available optionsrdquo Current Sciencevol 86 no 9 pp 1216ndash1224 2004
[6] Soil Conservation Service (SCS) Hydrology National Engi-neering Handbook Supplement A Section 4 Chapter 10 SoilConservation Service USDA Washington DC USA 1972
[7] L K Sherman ldquoThe unit hydrograph methodrdquo in Physics of theEarth O E Meinzer Ed pp 514ndash525 Dover Publications IncNew York NY USA 1949
[8] VMockus ldquoEstimation of total (peak rates of) surface runoff forindividual stormsrdquo in Exhibit A of Appendix B Interim SurveyReport Grand (Neosho) River Watershed USDA 1949
[9] S KMishra andV P Singh ldquoAnother look at SCS-CNmethodrdquoJournal of Hydrologic Engineering vol 4 no 3 pp 257ndash2641999
[10] S K Mishra M K Jain P K Bhunya and V P Singh ldquoFieldapplicability of the SCS-CN-basedMishra-Singh general modeland its variantsrdquoWater Resources Management vol 19 no 1 pp37ndash62 2005
[11] K X Soulis J D Valiantzas N Dercas and P A LondraldquoInvestigation of the direct runoff generationmechanism for theanalysis of the SCS-CN method applicability to a partial areaexperimental watershedrdquo Hydrology and Earth System Sciencesvol 13 no 5 pp 605ndash615 2009
[12] P Koteshwaram and S M A Alvi ldquoSecular trends and period-icities in rainfall at west coast stations in Indiardquo Current Sciencevol 38 no 10 pp 229ndash231 1969
[13] K V Ramamurthy M K Sonam and S S Muley ldquoLongterm variation in the rainfall over upper Narmada catchmentrdquoMausam vol 38 no 3 pp 313ndash318 1987
[14] R Jha and R K Jaiswal ldquoAnalysis of rainfall pattern in acatchmentrdquo IAH Journal of Hydrology (India) Roorkee 1992
[15] F H S Chiew M J Stewardson and T A McMahon ldquoCom-parison of six rainfall-runoff modelling approachesrdquo Journal ofHydrology vol 147 no 1-4 pp 1ndash36 1993
[16] E M Lungu ldquoDetection of changes in rainfall and runoffpatternsrdquo Journal ofHydrology vol 147 no 1-4 pp 153ndash167 1993
[17] J Meher and R Jha ldquoTime series analysis of rainfall data overMahanadi River basinrdquo Emerging Science vol 3 no 6 pp 22ndash32 2011
[18] K X Soulis and J D Valiantzas ldquoSCS-CN parameter determi-nation using rainfall-runoff data in heterogeneous watersheds-the two-CN system approachrdquo Hydrology and Earth SystemSciences vol 16 no 3 pp 1001ndash1015 2012
[19] R Kabiri A Chan and R Bai ldquoComparison of SCS and Green-Ampt Methods in Surface Runoff-Flooding Simulation forKlang Watershed in Malaysiardquo Open Journal of Modern Hydro-logy vol 03 no 03 pp 102ndash114 2013
[20] S Mohan andM Abraham ldquoDerivations of simple site-specificrecharge-precipitation relationships A case study from theCuddalore Basin Indiardquo Environmental Geosciences vol 17 no1 pp 37ndash44 2010
[21] R Viji P R Prasanna and R Ilangovan ldquoGIS based SCS-CN method for estimating runoff in Kundahpalam watershedNilgries district Tamilnadurdquo Earth Sciences Research Journalvol 19 no 1 pp 59ndash64 2015
[22] P K Gupta S Punalekar S Panigrahy A Sonakia and J SParihar ldquoRunoff modeling in an agro-forested watershed using
remote sensing and gisrdquo Journal of Hydrologic Engineering vol17 no 11 pp 1255ndash1267 2012
[23] R Chatterjee ldquoGroundwater resources estimationmdash case stud-ies from Indiardquo Journal of the Geological Society of India vol 77no 2 pp 201ndash204 2011
[24] Central Ground Water Board (CGWB) Water Balance Studiesin Upper Yamuna Basin ndash Terminal Report - Project Findingsand Recommendations Chandigarh Central Ground WaterBoard 2000
[25] G Thakuriah and R Saikia ldquoEstimation of Surface RunoffusingNRCSCurve number procedure in BurigangaWatershedAssam India - A Geospatial Approachrdquo International ResearchJournal of Earth Sciences vol 2 no 5 pp 1ndash7 2014
[26] V K Dadhwal ldquoRemote sensing and GIS for agricultural cropacreage and yield estimationrdquo in Proceedings of the InternationalArchives of Photogrammetry andRemote Sensing XXXII pp 58ndash67 1999
[27] G B Geena and P N Ballukraya ldquoEstimation of runoff for Redhills watershed using SCS method and GISrdquo Indian Journal ofScience and Technology vol 4 no 8 pp 899ndash902 2011
[28] K H Syed D C Goodrich D E Myers and S SorooshianldquoSpatial characteristics of thunderstorm rainfall fields and theirrelation to runoffrdquo Journal of Hydrology vol 271 no 1-4 pp 1ndash21 2003
[29] X Zhan and M-L Huang ldquoArcCN-Runoff An ArcGIS toolfor generating curve number and runoff mapsrdquo EnvironmentalModeling and Software vol 19 no 10 pp 875ndash879 2004
[30] K Geetha S K Mishra T I Eldho A K Rastogi and R PPandey ldquoModifications to SCS-CN method for long-term hy-drologic simulationrdquo Journal of Irrigation and Drainage Engi-neering vol 133 no 5 pp 475ndash486 2007
[31] V Kumar M Babu T V Praveen et al ldquoAnalysis of the Runofffor Watershed Using SCS-CN Method and Geographic Infor-mation Systemsrdquo International Journal of Engineering Scienceand Technology 2010
[32] R Pradhan P P Mohan M K Ghose et al ldquoEstimation ofRainfall Runoff using Remote Sensing and GIS in and aroundSingtam East Sikkimrdquo International Journal of Geomatics andGeosciences vol 1 article 466 2010
[33] A Panahi ldquoEvaluating SCS-CN method in estimating theamount of runoff in Soofi Chay basin using GISrdquo Life ScienceJournal vol 10 no 5 pp 271ndash277 2013
[34] S Satheeshkumar S Venkateswaran and R KannanldquoRainfallndashrunoff estimation using SCSndashCN and GIS approachin the Pappiredipatti watershed of the Vaniyar sub basin SouthIndiardquo Modeling Earth Systems and Environment vol 3 no 12017
[35] IMD Temperature of Kanchipuram District Regional Meteoro-logical Centre Chennai India 2013 Temperature of Kanchipu-ram District Regional Meteorological Centre
[36] ENVIS 2015 Kanchipuram District ENVIS Centre TamilnaduhttpwwwtnenvisnicinfilesKANCHIPURAM20pdf
[37] AIS and LUSWatershed Atlas of India Department of Agricul-ture and Co-operation All India Soil and Landuse Survey IARICampus New Delhi India 1990
[38] V T ChowD RMaidment and LWMaysAppliedHydrologyMcGraw-Hill New York New York USA 1988
[39] Natural Resources Conservation Service (NRCS) ldquoChapter 7-Hydrologic Soil Groupsrdquo in Hydrology National EngineeringHandbook title 630 Washington DC USA 2007
10 International Journal of Geophysics
[40] United States Department of Agriculture Soil ConservationService (USDA-SCS) Technical Release 55 Urban hydrologyfor small watersheds USA 1986
[41] A G Bluman Elemtary Statistics McGraw Hill New York NYUSA 6th edition 2008
[42] C Brouwer and M Heibloem ldquoIrrigation Water Needs- Irriga-tion Water Managementrdquo in Training Manual No 3 Food andAgriculture Organisation Rome Italy 1986
[43] R G Allen L S Pereira D Raes and M Smith ldquoCrop evap-otranspiration Guidelines for computing crop water require-mentsrdquo in Irrigation and Drainage Paper No 56 Food andAgricultural Organization of the United Nations Rome Italy1998
[44] Census of India District Census Hand book ndash KanchipuramDirectorate of Census Operations in Tamil Nadu 2011
Hindawiwwwhindawicom Volume 2018
Journal of
ChemistryArchaeaHindawiwwwhindawicom Volume 2018
Marine BiologyJournal of
Hindawiwwwhindawicom Volume 2018
BiodiversityInternational Journal of
Hindawiwwwhindawicom Volume 2018
EcologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2018
Forestry ResearchInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
International Journal of
Geophysics
Environmental and Public Health
Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
International Journal of
Microbiology
Hindawiwwwhindawicom Volume 2018
Public Health Advances in
AgricultureAdvances in
Hindawiwwwhindawicom Volume 2018
Agronomy
Hindawiwwwhindawicom Volume 2018
International Journal of
Hindawiwwwhindawicom Volume 2018
MeteorologyAdvances in
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018Hindawiwwwhindawicom Volume 2018
ChemistryAdvances in
ScienticaHindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Geological ResearchJournal of
Analytical ChemistryInternational Journal of
Hindawiwwwhindawicom Volume 2018
Submit your manuscripts atwwwhindawicom
10 International Journal of Geophysics
[40] United States Department of Agriculture Soil ConservationService (USDA-SCS) Technical Release 55 Urban hydrologyfor small watersheds USA 1986
[41] A G Bluman Elemtary Statistics McGraw Hill New York NYUSA 6th edition 2008
[42] C Brouwer and M Heibloem ldquoIrrigation Water Needs- Irriga-tion Water Managementrdquo in Training Manual No 3 Food andAgriculture Organisation Rome Italy 1986
[43] R G Allen L S Pereira D Raes and M Smith ldquoCrop evap-otranspiration Guidelines for computing crop water require-mentsrdquo in Irrigation and Drainage Paper No 56 Food andAgricultural Organization of the United Nations Rome Italy1998
[44] Census of India District Census Hand book ndash KanchipuramDirectorate of Census Operations in Tamil Nadu 2011
Hindawiwwwhindawicom Volume 2018
Journal of
ChemistryArchaeaHindawiwwwhindawicom Volume 2018
Marine BiologyJournal of
Hindawiwwwhindawicom Volume 2018
BiodiversityInternational Journal of
Hindawiwwwhindawicom Volume 2018
EcologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2018
Forestry ResearchInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
International Journal of
Geophysics
Environmental and Public Health
Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
International Journal of
Microbiology
Hindawiwwwhindawicom Volume 2018
Public Health Advances in
AgricultureAdvances in
Hindawiwwwhindawicom Volume 2018
Agronomy
Hindawiwwwhindawicom Volume 2018
International Journal of
Hindawiwwwhindawicom Volume 2018
MeteorologyAdvances in
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018Hindawiwwwhindawicom Volume 2018
ChemistryAdvances in
ScienticaHindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Geological ResearchJournal of
Analytical ChemistryInternational Journal of
Hindawiwwwhindawicom Volume 2018
Submit your manuscripts atwwwhindawicom
Hindawiwwwhindawicom Volume 2018
Journal of
ChemistryArchaeaHindawiwwwhindawicom Volume 2018
Marine BiologyJournal of
Hindawiwwwhindawicom Volume 2018
BiodiversityInternational Journal of
Hindawiwwwhindawicom Volume 2018
EcologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2018
Forestry ResearchInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
International Journal of
Geophysics
Environmental and Public Health
Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
International Journal of
Microbiology
Hindawiwwwhindawicom Volume 2018
Public Health Advances in
AgricultureAdvances in
Hindawiwwwhindawicom Volume 2018
Agronomy
Hindawiwwwhindawicom Volume 2018
International Journal of
Hindawiwwwhindawicom Volume 2018
MeteorologyAdvances in
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018Hindawiwwwhindawicom Volume 2018
ChemistryAdvances in
ScienticaHindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Geological ResearchJournal of
Analytical ChemistryInternational Journal of
Hindawiwwwhindawicom Volume 2018
Submit your manuscripts atwwwhindawicom
Top Related