Hydrotogical Sciences-Jo umat-des Sciences Hydrologiques, 47(5 ) October 2002 693

Estimation of temporal variation of sediment yield using GIS

UMESH C. KOTHYARI Department of Civil Engineering, Indian Institute of Technology, Roorkee 247667, India umeshfce@iitr.emet.in

MANOJ K. JAIN National Institute of Hydrology, Jal Vigyan Bhawan, Roorkee 247667, India mki(S;nih,ernet,in

KITTUR G. RANGA RAJU Department of Civil Engineering, Indian Institute of Technology, Roorkee 247667, India kgrarfee@iitr.emet.in

Abstract A GIS-based method is proposed for computation of temporal variation of sediment yield during isolated storm events. Data from three Indian catchments, namely Karso and Nagwa in Jharkhand and Kharkari in Rajasthan, have been used. The Integrated Land and Water Information System (ILW1S) GIS package was used for (a) catchment discretization into cell areas using grid networks, (b) evaluation of the spatial variation in catchment topographical characteristics and land use, and (c) presentation of the results obtained. The process of sediment delivery from grid cells to the catchment outlet is represented by the topographical characteristics of the cells. Unit sediment graphs for the catchments are derived by translation of the sediment yield from the grid cells and routing through a linear storage reservoir. The proposed method is found to provide satisfactory estimates of the temporal variation of sediment yield during isolated storm events. The total sediment yield of a storm event may also be computed using the proposed method.

Key words catchment; GIS; sediment delivery ratio; soil erosion; sediment yield

Estimation de la variation temporelle de l'exportation sédimentaire grâce à un SIG Résumé Nous proposons une méthode d'estimation de la variation temporelle de l'exportation sédimentaire lors d'événements pluvieux isolés, basée sur un SIG. Les données de trois bassins versants indiens, Karso et Nagwa dans le Jharkhand et Kharkari dans le Rajasthan, ont été traitées. Le SIG ILWIS (Integrated Land and Water Informa­tion System—système intégré d'informations sur la terre et les eaux) a été utilisé pour (a) discrétiser le bassin versant en mailles à partir des réseaux rastérisés, (b) évaluer la variabilité spatiale des caractéristiques de relief et d'occupation du sol du bassin versant, et (c) présenter les résultats obtenus. Le processus de circulation des sédiments des mailles vers l'exutoire est représenté à partir des caractéristiques topographiques des mailles. Les courbes unitaires d'exportation de sédiments des bassins versants sont obtenues par translation de l'exportation sédimentaire des mailles, puis passage à travers un réservoir linéaire. La méthode proposée produit des estimations satisfaisantes de la variation temporelle de l'exportation sédimentaire lors d'événements pluvieux isolés. Cette méthode permet également de calculer l'exportation sédimentaire totale lors d'un événement.

Mots clefs bassin versant; SIG; rapport de fourniture de sédiments; érosion des sols; exportation sédimentaire

INTRODUCTION

Estimation of sediment yield from catchments is important for many reasons. Deposition of sediment transported by a river into a reservoir reduces the reservoir

Open for discussion until I April 2003

694 Umesh C. Kothvari et al

capacity, thereby adversely affecting the water availability for power generation, irrigation, domestic and industrial use. Sediment deposition on river bed and banks causes widening of flood plains during floods. Estimation of the temporal variation of sediment yield is required in river morphological studies, the design of efficient erosion control structures and also for estimation of concentration and load of chemicals adsorbed to sediment particles. Control of upland erosion does not always reduce the sediment yield immediately, because of the increased erosivity of channel flow in the downstream. Therefore, to obtain realistic estimates of catchment sediment yield and its temporal variation, the entire system of catchment drainage needs to be studied.

A geographical information system (GIS) offers a data management facility that is useful in distributed modelling of sedimentological and hydrological processes and is best suited for quantification of the heterogeneity in rainfall, the topographical and drainage features of a catchment (Schultz, 1994; Rodda, et al, 1999). Therefore, a GIS can be utilized for determination of physical parameters affecting soil erosion in different sub-areas of a catchment.

BRIEF REVIEW

Methods available in the literature for the estimation of sediment yield and its temporal variation can be grouped as (a) empirical and (b) process-based. Empirical methods, which include the universal soil loss equation (USLE) (Wischmeier & Smith, 1978), the modified universal soil loss equation (MUSLE) (Williams, 1978) and the revised universal soil loss equation (RUSLE) (Renard et al, 1991b), combine the soil erosion from all processes in the catchment into one equation which makes use of empirical coefficients to represent the rainfall characteristics, soil properties, ground surface conditions, etc. These methods are simple in application and hence frequently used in different parts of the world (Julien & Tanago, 1991). The process-based methods attempt to solve the fundamental equations for transport of water and sediment. Some of the process-based models for soil erosion include ANSWERS (Beasley et al, 1980), WEPP (Nearing et al, 1989), AGNPS (Young et al, 1987) and SHESED (Wicks & Bathurst, 1996). These models are expected to simulate realistically the process of rainfall-runoff/soil erosion. However, due to temporal variations in rainfall inputs and pronounced spatial heterogeneity prevalent in catchment areas, even the process-based models are found to produce unsatisfactory results (Wu et al, 1993). The effect of temporal variations in rainfall on sediment yield can be appropriately simulated by analysing isolated rainstorm events (Kothyari et al, 1996).

Recently, GIS techniques have been interfaced with some standard hydrological models (both distributed and empirical) to capture the spatial variation in computed quantities. The AGNPS is interfaced with the GRASS GIS system (Srinivasan & Engel, 1994). Likewise, Rewarts & Engel (1991) interfaced the ANSWERS model and the GRASS system. Marshrigni & Cruise (1997) interfaced a GIS with the SLURP model. Mitasova et al (1996) demonstrated the use of GIS for modelling erosion and deposition in a complex terrain using an approach based on unit stream power theory and directional derivatives of the surface representing the sediment transport capacity. Kothyari & Jain (1997) and Jain & Kothyari (2000) used a GIS for estimation of

Estimation of temporal variation of sediment yield using GIS 695

sediment yield resulting from isolated storm events. The present study is aimed at extending the procedure of Jain & Kothyari (2000) for determination of the temporal variation of sediment yield in a catchment. The term "sediment yield" used in these studies essentially means the total suspended sediment load carried by the stream to the catchment outlet.

HYDROLOGICAL DATA

Data obtained on topographical characteristics and temporal variation of sediment yield during isolated storm events are from the Indian catchments of Karso and Nagwa in Jharkhand and Kharkari in Rajasthan. These data were available to the authors from previous studies (Kothyari et ai, 1996). The hydroclimatic conditions of the catch­ments selected are described in Table 1. Karso and Nagwa catchments are gauged by the Damodar Valley Corporation and the Kharkari catchment data are collected by the Soil Conservation Department of Rajasthan. The data compiled for these catchments included the variations with time of rainfall, runoff and sediment yield, topographical details, soil types and land-use patterns. Rainfall was measured using recording raingauges. Automatic water-level recorders were used to measure the stream stage, and runoff was derived using the rating curve. Sediment yield was measured using the Coshocton wheel silt sampler (Brakensiek et al., 1979) and a bottle sampler. The dates of storm events studied are given in Table 1.

Table 1 Hydroclimatic data for the selected catchments.

Catchment

Karso Barakar catchment, Jharkhand (India)

Nagwa Damodar catchment, Jharkhand (India)

Kharkari Sahibi catchment, Rajasthan (India)

Area (km-)

27.93

92.46

16.20

Avg. land slope (%) 7.3

1.3

17.2

Avg. annual precipitation (mm) 1243

1076

303

Dates of selected storm events

27 July 1991 28 July 1991 3 August 1991 4 August 1991 17 August 1991 6 July 1989 20 July 1989 28 July 1989 20 July 1991 22 July 1991 28 August 1991

R value* (MJmmha1 h"1)

30.10 182.13 110.82 144.85 116.61 533.06 574.99 419.60

18.39 1014.03

103.51

* R: rainfall erosivity factor.

METHODOLOGY

There is ample evidence that the USLE and/or RUSLE yield a good estimate of the amount of detached soil (surface erosion) from small watersheds (Ferro & Minacapilli, 1995; Ferro, 1997; Baban & Yusof, 2001; Jain & Goel, 2002). In a larger sized catchment, part of the soil eroded in upland areas gets deposited within the catchment before reaching the outlet. Therefore, such catchments are divided into sub-areas to

696 Umesh C. Kothyari et al.

account for spatial heterogeneity. The cell approach of catchment subdivision has been used extensively (Hadley et al, 1985; Wicks & Bathurst, 1996) and is quite adaptable to collection of input data in a regular pattern with the use of remote sensing and GIS; it accounts for the variation in topographical characteristics in detail. Therefore, a grid-based procedure for discretization of the catchment is adopted in the present study.

According to both Atkinson (1995) and Wang & Yin (1998), a grid is considered to lie in the overland region if the size of the area from which it receives the flow contribution is smaller than or equal to the specified threshold area for initiation of a channel. Grids receiving flow contribution from areas of more than the threshold value are considered to form the channel grids. The threshold area in the present study was chosen such that the total stream length generated using both the threshold and observed total stream lengths on a 1:25 000 topographical map (digitized in vector form) should be the same (Jain & Kothyari, 2000). Thus it was observed that a channel initiation threshold area of 5 ha gives a good reproduction. However, the threshold area is an average indicator and various physiographic regions may have different threshold areas. For the present case, an average value is considered to be reasonably repre­sentative. The generated channel network and the digitized channel network from the toposheet for the Kharkari catchment are shown in Fig. 1, as an illustration. It was ensured that, on average, the generated stream network extended to the tip of most of the first-order streams of the mapped network. Figure 1 shows that the generated channel network with the average channel initiation threshold value gave a close match with the observed channel network digitized at a 1:25 000 scale. Similar results were obtained earlier for Nagwa and Karso catchments by Jain & Kothyari (2000).

Observed channel

Generated channel

Fig. 1 Observed and generated channel networks for the Kharkari catchment.

Estimation of temporal variation of sediment yield using GIS 697

The USLE is adopted for estimation of gross erosion rates in the different cells of a catchment. The eroded sediment is routed from each cell to the catchment outlet using the concept of the sediment delivery ratio, described below. The soil erosion within a cell is estimated using the USLE, which is expressed as:

SEi = RKLSCP (1)

where SE. is the gross amount of soil erosion (t ha" ), R is the rainfall erosivity factor

(MJ mm ha"1 h"1), K is the soil erodibility factor (t ha h ha"1 MJ"1 mm"1), LS is the slope steepness and length factor (dimensionless), C is the cover management factor (dimen-sionless), and P is the supporting practice factor (dimensionless).

Values of the R factor of the USLE, as computed by the method of Wischmeier & Smith (1958) and Wischmeier (1959), are applicable for annual values of erosion and do not apply to individual storm events. Jain & Kothyari (2000) used the method proposed in Cooley (1980) and Renard et al. (1991a) for computing R values, because it produced realistic estimates of soil erosion rates for individual storm events, and this is therefore also used in the present study.

There are many relationships available for estimation of the LS factor (e.g. McCool et ai, 1989; Moore & Wilson, 1992). Of these, the one that is best suited for integration with the GIS is the theoretical relationship proposed by Moore & Wilson (1992), based on unit stream power theory, given as:

r A -_22.13_

n

where As is the specific area (=A/b), defined as the upslope contributing area for the overland grid (A) per unit width normal to the flow direction (b), p is the slope gradient in degrees, n = 0.4, and m - 1.3. For channel grid areas, the value of A is considered to be equal to the value of the threshold area corresponding to the channel initiation. Panuska et al. (1991) showed that the use of equation (2) in the estimation of the LS factor allows the introduction of the three-dimensional hydrological and topographical effect of converging and diverging terrain on soil erosion. They analysed equation (2) and concluded that, for computations of representative estimate of the LS factor in a cell, the minimum cell area of 0.01 km2 (hence maximum permissible slope length = 141 m when flow is in a diagonal direction) is required. Thus a cell size smaller than this is used for soil loss estimation using GIS and the USLE (Jain & Kothyari, 2000).

The values for the factors K, C and P are computed for different grids on overland and channel regions according to Wischmeier & Smith (1978) using the classified satellite data for land cover and soil. The Kharkari catchment was covered by the 1RS 1A LISS II satellite (path 29, row 42, sub-scene A2) in March 1989 and October 1989. The specifications of the Landsat TM and the 1RS LISS images are listed in Table 2. The area of interest was first cut from the entire path/row of the 1RS 1A LISS II scene and then it was geo-coded, according to the method suggested by Sabins (1997), at 36-m pixel resolutions using the Earth Resources Data Analysis System (ERDAS) Imagine image processing software (ERDAS, 1998). The geo-coded scenes were then masked by the boundaries of the catchments derived earlier to delineate the areas lying within the catchment. Land-cover and soil maps were then generated using the supervised classification scheme (Sabins, 1997) using LISS II data. For the Kharkari

(2)

698 Umesh C. Kothyari et al.

Table 2 Landsat TM, 1RS-1C LISS-III and IRS-1B LISS-II sensor parameters.

Parameters

Spectral range (|im) Number of bands Spectral bands (urn) TM1 TM2 TM3 TM4 TM5 TM6 TM7 Ground reso TM1-TM5, TM6 Radiometric

lution (m) TM7

resolution

Landsat TM*

0.45-12.50 7

0.45-0.52 0.52-0.60 0.63-0.69 0.76-0.90 1.55-1.75 10.4-12.5 2.08-2.35

30 120 8 bits

LISS-III'

B2 B3 B4 SW1R

B2-B4 SWIR

0.52-1.70 4

0.52-0.59 0.62-0.68 0.77-0.86 1.50-1.70

23.5 70.5 7 bits

L1SS-1I

Bl B2 B3 B4

0.45-0.86 4

0.45-0.52 0.52-0.59 0.62-0.68 0.77-0.86

36.25

7 bits

Sources: * Sabins (1997); ' National Remote Sensing Agency (1995);;Joseph (1992).

Table 3 Land cover statistics for the catchments studied.

Catchment

Nagwa

Karso

Kharkhari

Land cover

Fairly dense forest Open scrub Agriculture (small grain) Wasteland

Fairly dense forest Open scrub Agriculture (mainly paddy)

Open forest Agriculture Wasteland

Area (km2)

5.55 8.32

59.18 19.41

9.77 1.40

16.76

5.19 8.66 2.63

C factor

0.003 0.040 0.290 0.400

0.003 0.040 0.260

0.010 0.260 0.400

a value

0.76 1.55 2.62 3.08

0.76 1.55 1.55

0.76 2.62 1.55

catchment, three land-cover categories, namely agriculture, forest and wasteland, were identified and mapped. Land-cover information was thus available for each grid of the catchments. Based on land-cover categories, the attribute values for the factor C were assigned to individual grids from the tabulated values of Wischmeier & Smith (1978). For the Karso and Nagwa catchments, the values of parameters K, C and P derived by Jain & Kothyari (2000) through the analysis of similar remote sensing data were used. Table 3 summarizes the land-cover statistics and C factor used for all the catchments. The P factor was taken equal to 0.3 for all three catchments and for all land-cover categories, following Kothyari et al (1996).

The satellite data described above were trained using concurrent information about tonal variations associated to the limited ground truth data and the morphological features such as hills, piedmont, alluvial plains and river terraces. Based on morphological features, image tonal variations, and limited ground truth data, different soil types were distinguished, classified and mapped in the study catchments. Thus supervised classification was used for identifying the different soil types in the entire areas of the catchment. The soils were classified into clay loam, very fine sandy loam

Estimation of temporal variation of sediment yield using GIS 699

Table 4 Soil type statistics for the catchments studied.

Catchment

Nagwa

Karso

Kharkhari

Soil type

Clay loam Very fine sandy loam Sandy loam Loamy sand Clay loam Silty clay Sandv loam Silty loam

Area (km")

5.86 17.00 69.60

5.47 8.44

14.02 6.52 9.70

K factor (t ha h ha ' MJ"1 mm"1)

0.042 0.049 0.057 0.032 0.042 0.037 0.031 0.037

and sandy loam in Nagwa catchment, loamy sand, clay loam and silty clay loam in Karso catchment, and sandy loam and silty loam in Kharkari catchment. The soil characteristics, such as fraction of sand, silt, clay and organic matter and other related parameters for mapped soil categories, were taken from SWCD (1991) for Karso and Nagwa catchments and from Singhal (1992) for Kharkari catchment. Thus the information on soil type in individual grids of all three catchments was known. The parameter K for mapped soil categories was then calculated for each of the grids using the procedure given in Wischmeier & Smith (1978). Calculated values for K factor for mapped soil units are given in Table 4.

Use of equation (1) produces the estimate of gross soil erosion in the overland region and channel region of the catchment. The gross amount of soil erosion for each grid area during a storm event was generated by multiplying the term KLSCP with the R factor for the corresponding storm event given in Table 1. The eroded sediment is routed from each grid to the catchment outlet using the concept described below.

Sediment delivery ratio

The ratio of sediment yield to total surface erosion is termed the sediment delivery ratio (DK). Values of DR for an area are found to be affected by catchment physiography, sediment sources, transport system, texture of eroded material, land cover, etc. (Walling, 1983, 1988; Richard, 1993). However, the variables such as catchment area, land slope and land cover have been used mainly as parameters in the empirical equations for DR (Walling, 1983; Hadley et al, 1985).

Based on the work of Ferro & Minacapilli (1995), Ferro (1997) and Ferro et al. (1998), the following relationship was derived by Jain & Kothyari (2000) for the delivery ratio of a grid sized area:

DR = exp 7^ a\

-YS-^r (3)

where /, is the flow length of the z'th grid, 0, is the slope the z'th grid, y is a coefficient considered as constant for a given catchment, m is the number of grids located in a flow path, and a, is a coefficient related to land use. Note that //G,0'" is the definition of travel time used by Ferro & Minacapilli (1995). Values of the coefficient a, for

700 Umesh C. Kothyari et al

different land uses were adopted from Haan et al. (1994) and these are listed in Table 3. High spatial variation in the a; values for the studied cases may be noted.

Temporal variation of sediment yield

The catchment-stream network was discretized as above and the sequence followed by the flow occurring between a cell and the catchment outlet is illustrated in Fig. 2. First, the time of travel is computed for each of the overland and the channel flow cells; then the time of travel of the surface flow from these cells to the catchment outlet is obtained by following the respective drainage paths downstream to these cells (see Fig. 2). The travel times thus obtained from the N cells (where N is the total number of cells in the catchment) to the catchment outlet can be grouped into different classes of magnitude and these can be presented in the form of a histogram.

Outlet Fig. 2 Schematic for discretization of catchment grid cells using grid networks and depiction of flow sequencing between a cell and the catchment outlet.

For rain occurring during the selected unit duration, the sediment yields from N cells can be computed per unit value of the rainfall erosivity factor, R, of the USLE (equation (1)). The values of DR for grid cells are computed using equation (3). The sediment entrained from the catchment is expected to travel with the flow to the catchment outlet at the speed of the overland flow. Therefore, sediment yields of those cells which belong to the same group of travel times are summed together. The graph of the summed-up sediment yield values vs the corresponding travel times indicates the translation curve for temporal variation of sediment yield, referred to herein as the "translated sediment graph" (Fig. 3). Next the translated sediment graph is routed through a conceptual linear reservoir to account for the time delay due to storage effects on overland flow (Singh, 1988). If one considers that the translated sediment graph represents the input, Si, to the conceptual linear reservoir, which has sediment storage, Ss, at time, t (see Fig. 3), then the sediment outflow, So, of such a reservoir is obtained by using the continuity equation for sediment volume, as follows:

Estimation of temporal variation of sediment yield using GIS 701

Unit duration

H

Inflow

Outflow

Time

Temporal Variation of Translated Sediment load

Conceptual reservoir

Time

Unit sediment g-aph

Fig. 3 Routing of the translated sediment yield graph through a linear reservoir.

Sl(t)-S0{t) = dSs(t) at

(4)

The sediment outflow of the conceptual linear reservoir is considered to be related to its storage by:

Ss(t) = Kr-S0(.t) (5)

where Kr is a storage coefficient. Simultaneous solution of equations (4) and (5) produces the temporal variation of So values, which is termed as the unit sediment graph of the catchment. This unit sediment graph represents the temporal variation of sediment yield generated from rainfall uniformly occurring over the unit duration and having magnitude that corresponds to unit value of the R factor of the USLE.

The unit sediment graph thus obtained can be convoluted with values of R factor of the USLE corresponding to all the unit duration rain blocks of the given storm event. Superimposition of such convoluted graphs produces the temporal variation of sediment yield for the catchment during a given storm event. Note that spatial variation in rainfall can also be accounted for by using different R values in different cell areas of the catchment.

Generation of information through GIS

The parameters needed for estimation of surface erosion, sediment delivery ratio and sediment yield are generated and stored in ILWIS (Integrated Land and Water

702 Umesh C. Kothyari et al.

Information System, ITC, 1992), following Jain & Kothyari (2000). The base map depicting drainage pattern, land use and elevation contours of the study areas were prepared using the Survey of India topographical maps with a scale of 1:25 000. These were converted into digital form using a digitizer. The catchments were discretized into grid networks using IL WIS, as shown in Fig. 2, by adopting square grids of 30 x 30 m for Karso and Kharkari and 50 x 50 m for Nagwa. The contour maps were rasterized using interpolations from isolines and converted into a digital elevation model (DEM) for each catchment. Using the DEMs, grids of flow directions were created for all the catchments, with unique flow direction for each cell which represents the direction of steepest descent amongst the eight permitted choices.

RESULTS AND DISCUSSION

The sediment delivery ratio of different cells in the catchments is computed using equation (3), with the coefficient y equal to unity (Jain & Kothyari, 2000). The surface erosion within each cell as computed using the USLE for unit value of erosivity factor R, is multiplied by the corresponding value of DR to obtain the sediment yield at the outlet of the grid cells. The sediment yields at cell outlets thus obtained were plotted to depict the erosion risk areas, i.e. such areas inside the catchment which supply greater sediment load towards the catchment outlet. Figure 4 depicts, for illustration, the erosion risk areas in the Kharkari catchment. These areas will need to be given special priority during the implementation stage of erosion control measures.

Soil erosion

"

potential

r0.24

-0.19

- 0.14

j- 0.09

-0.04

1 - 0.00

Fig. 4 Map depicting source areas for sediment load in the Kharkari catchment.

Estimation of temporal variation of sediment yield using GIS 703

Temporal variation of sediment yield

Translated sediment graphs were derived for each of the study catchments. The unit duration of rainfall for the purpose of computations was taken as 30 min. The results of routing the translated sediment graphs through linear storage reservoirs (equations (4) and (5)) produced the unit sediment graphs. The values of storage coefficient, Kr

(equation (5)), determined by comparison of the observed temporal variation of sedi­ment yield with those computed by the present method for the selected storm events, were 0.37 for Kharkari (area = 16.2 km'), 0.83 for Karso (area = 27.93 km-) and

(a)

(b)

(c)

S.

80 -i

- Observed

- Computed

10

QOflaflâijiaa

15 20 25

1200

1000 -

800 -

•£ 60° " 400

200

0 +

10 15 20 25

Time (hr)

Fig. 5 Comparison between observed and computed temporal variation of sediment yield: (a) Nagwa catchment for the 6 July 1989 event; (b) Karso catchment for the 3 August 1991 event; and (c) Kharkari catchment for the 28 August 1991 event.

704 Umesh C. Kothyari et al.

0.98 for Nagwa (area = 92.46 km"). The Kr values are found to be directly proportional to the size of the catchments. Such a result is expected as Kr values represent the storage effects on overland flow. Next, the temporal variation of sediment yield was computed for all the storm events by using the unit sediment graphs derived from the above Kr values. The computed temporal variations were graphically compared with the corresponding observed values. Such a comparison is illustrated in Fig. 5(a)-(c) for some of the storm events. It could be seen from these figures (and other such figures, not shown here) that the volume of sediment yield over the duration of a storm is well predicted by the present method. The time to peak of the sediment graph is predicted with a maximum error of ±2 h for most of the data. The rising limb is predicted reasonably well; however, greater errors are produced in the prediction of the falling limbs. The prediction is generally satisfactory, as shown by Fig. 5(a) and (c). Less satisfactory prediction (Fig. 5(b), for instance) was obtained for only a few storm events. Nevertheless, comparison between the observed and computed values as shown by these figures is considered satisfactory because of the measurement and cartographic errors that are endemic in such analysis.

Comparison of the computed values of total sediment yield for the storm duration and the corresponding observed values is shown in Table 5. This table also presents the comparison of computed values and observations for the peaks of sediment graph and the time to peak. Relatively satisfactory agreement between these values may be noted. Large errors, as noticed for data from Karso catchment, are attributed to the uncertainties in the observations (Kothyari & Jain, 1997).

Table 5 Computed and observed values of sediment yield for the catchments studied.

Date of event Peak sediment load (N s" ): Time to peak (h): Total sediment yield for the storm period (t):

Observed Computed Observed Computed Observed Computed Nagwa:

6 July 1989 20 July 1989 28 July 1989

Karso:

27 July 1991 28 July 1991 3 August 1991 4 August 1991 17 August 1991

Kharkari:

20 July 1991 22 July 1991 28 August 1991

1283.66 3074.82 1516.57

49.40 272.99 42.90 62.70 166.66

323.07 7317.81 1032.37

1848.61 1995.35 1549.73

114.79 105.47 67.69 102.64 20.44

246.77 11101.55 1114.47

3.5 4.5 4.5

2.5 1.0 8.5 5.0 9.5

2.0 2.0 2.5

5.0 6.5 3.5

4.5 3.0 10.5 6.5 10.0

2.5 3.0 2.5

2172.81 7143.23 3246.84

117.95 283.63 112.35 156.21 287.61

147.47 3734.25 483.49

4747.15 5128.01 3719.04

189.96 145.17 135.44 252.50 24.50

145.57 5223.30 848.07

CONCLUSIONS

A GIS-based method for computation of sediment yield and its temporal variation during isolated storm events is proposed and tested on data from three catchments in India. The process of sediment delivery from grid cells within the catchment to the

Estimation of temporal variation of sediment yield using GIS 705

catchment outlet is found to be well represented by equation (3), in which the delivery ratio was hypothesized as a function of travel time to the nearest channel. Unit sediment graphs for the catchments have been derived by first translating the sediment yields from the grid cells for unit value of rainfall erosivity factor to the catchment outlet and next routing the same through a linear reservoir. The proposed method is found to provide a satisfactory estimation of the temporal variation of sediment yield during isolated storm events. The total sediment yield for the storm duration is also computed by the proposed method. Thus, computed values of the storm sediment yields are found to compare well with their corresponding observed values.

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Brakensiek, D. L„ Osbom, H. B. & Rawls, W. J. (1979) Field manual for research in agricultural hydrology. Agriculture Handbook no. 224/2, US Dept Agric, Washington DC, USA.

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Received 21 May 2001; accepted 16 April 2002