GIS-supported modelling of areal rainfall in a mountainous...

Hydrohgical Sciences-Journal-des Sciences Hydrologiques, 45(2) April 2000 185

GIS-supported modelling of areal rainfall in a mountainous river basin with monsoon climate in southern India

JULIE WILK & LOTTA ANDERSSON Department of Water and Environmental Studies, Linkôping University, S-58183 Linkôping, Sweden e-mail: [email protected]

Abstract Spatial rainfall patterns and seasonal variability were assessed for a mountainous river basin with monsoon climate. Factors were identified that could explain this variability, and a GIS-supported method to determine the areal distribution of precipitation was developed. To find acceptable regression equations, a division had to be made between rainfall stations dominated by the southwest-monsoons and the northeast-monsoons, respectively. Distance to the southwestern border was the main explaining factor for precipitation at southwest-monsoon dominated stations. For northeast-monsoon dominated stations, altitude and slope were the most important factors. The basin was divided into pixels with characteristics typical for northeast- or southwest-monsoon dominated rainfall stations to allow calculation of spatial rainfall. The difference when comparing regression-based estimates with Thiessen-based estimates was small when considering the annual estimates for the whole basin. However, when analysing seasonal rainfall or sub-catchments, the differences between Thiessen-based and regression-based estimates were significant.

Modélisation assistée par SIG de la pluie spatiale dans un bassin montagneux sous climat de mousson du Sud de l'Inde Résumé La structure spatiale et la variabilité saisonnière de la pluviosité ont été déterminées pour un bassin versant montagneux soumis à un climat de mousson. Les facteurs susceptibles d'expliquer cette variabilité ont été identifiés et une méthode assistée par SIG de détermination de la distribution spatiale des précipitations a été développée. Afin d'obtenir des équations de régression acceptables nous avons dû distinguer les stations pluviométriques soumises à la mousson du SO de celles soumises à la mousson du NE. La distance à la frontière SO s'est révélée être le principal facteur explicatif des précipitations pour les stations soumises à la mousson de SO. Pour les stations soumises à la mousson de NE, l'altitude et la pente se sont révélées être les facteurs explicatifs principaux. Le bassin a été divisé en pixels auquels les caractéristiques des stations soumises à la mousson du NE ou du SO ont été affectées pour pouvoir calculer la pluie spatiale. Les différences entre les estimations fondées sur les régressions et celles fondées sur la méthode de Thiessen sont petites lorsque l'on considère le bassin complet à l'échelle annuelle, mais elles peuvent devenir importantes lorsque l'on considère les sous-bassins à l'échelle saisonnière.

INTRODUCTION

This study is part of a project aiming to determine the effect of land-use changes on streamflow generation. A hydrological model was used to study changes over time. For modelling, accurate areal rainfall records are a fundamental prerequisite, but because of the limitations in the accuracy of this variable, it is often the area of greatest

Open for discussion until 1 October 2000

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186 Julie Wilk & Lotta Andersson

uncertainty. For estimates of streamfiow generation, good rainfall representation is often more important than the choice of complexity of the hydrological model (Gan et al., 1997). A poor assessment of spatial distribution of precipitation is generally made in mountainous basins, since areas with higher elevation are often not gauged, and a dense network of rainfall gauges, needed to record the high variability of precipitation, is usually not available.

In mountainous areas, orography can play an important role in rainfall distribution. Moisture bound air, when lifted over a topographical barrier, releases rain so that the amount of precipitation often tends to increase on the windward side of mountains as the air is lifted (e.g. Niemczynowicz, 1989; Hanson, 1982).

There might, however, be factors that override the effects of elevation on precipitation totals. In monsoon areas, the direction of predominating winds may have a pronounced effect on which areas receive very high amounts of rainfall. This will also cause seasonal variations in the spatial distribution of rainfall (Singh & Kumar, 1997). Singh et al. (1995) found a variation in the amounts of rainfall at the same elevation in different ranges of the Himalayas depending on the orientation of the ranges. Models for the estimation of areal precipitation in monsoon areas should therefore be tested for various seasons, and factors other than the elevation should be considered when making assessments of spatial variation in precipitation.

In this GIS-based study, a number of physiographic factors, in addition to elevation, were calculated for each precipitation station. Correlations between these and rainfall totals were used to calculate the distribution of areal precipitation in the river basin during the two monsoon periods.

The aims of the study were to (a) assess spatial rainfall patterns and seasonal variability, (b) analyse which factors can explain this variability during different seasons, and (c) obtain a GIS-supported method to determine the areal distribution of precipitation, for a mountainous river basin with monsoon climate.

THE UPPER BHAVANI RIVER BASIN

The Bhavani and Moyar rivers flow together into the Bhavanisagar reservoir after which it becomes the Bhavani River (Fig. 1). The Bhavani River is a tributary of the Cauvery, one of the largest rivers in India. This study refers to the combined catchment area of the Bhavani and Moyar rivers, which are referred to as the Upper Bhavani river basin. The basin (4100 km2) is a high altitude area at the confluence of the Eastern and Western Ghats, bounded to the north by the state of Karnataka, to the west by Kerala, to the east by the Bhavanisagar reservoir and to the south by the Coimbatore plateau. The elevation of the river basin ranges from 300 m a.m.s.l. on the plains to 2600 m a.m.s.l. on the Nilgris plateau. Accordingly, the annual rainfall varies from 700 mm on the lowlands to nearly 3000 mm in the hills. The topography is undulating in the lower plateau and uplands while rugged in western parts. The vegetation coverage is a mosaic of grassland, deciduous and coniferous mountainous forests, plantation and agricultural crops.

The main part of the basin has a humid equatorial environment, although the lowland plains are sub-humid. The weather is dry from January to May whereas heavy

GIS-supported modelling of ureal rainfall in a mountainous river basin with monsoon climate 187

Fig. 1 Bhavani River basin showing elevations with 200 m equidistance, rivers, lakes and reservoirs; and locations of precipitation stations (numbers refer to Table 1).

rains are common during the southwest (SW) monsoon (June-September), and the northeast (NE) monsoon (October-December). The SW-monsoon dominates in the western part of the river basin while the NE-monsoon prevails in the eastern region, due to the hills running in a north-south direction which act as a barrier to both monsoons. Both the western and eastern areas can therefore be regarded as rain shadow areas during one of the monsoon periods.

DATA COLLECTION AND DATABASE CONSTRUCTION

Precipitation

The precipitation records of 29 stations were collected. Locations are shown in Fig. 1. Records (9-86 years) were available from government authorities as well as from tea estates (see Table 1). For 10 stations, records were obtained from the Global Precipitation Climatology Centre, Offenbach, Germany. Even though two stations had a series of only nine years, they were included since the coefficient of variation was not higher than for stations with longer time series (Table 1). Fifteen of the stations still operating in 1995, were visited for a quality check. All of them used a cylindrical raingauge placed at ground height, except for Kotagiri, where the gauge was placed one metre above ground. Most gauges did not meet the criteria of being free from vertical obstacles (e.g. trees or buildings) within a distance of four times the height of the obstruction, as recommended by Linacre (1992), for example. This could produce an underestimation of the rainfall.


The records were tested for homogeneity using double mass plots. None of the stations was excluded due to inconsistency. Monthly, seasonal, and annual totals were computed from daily records.

The 27 stations within the basin gave an average of one gauge per 152 km . On average, this is within the established guidelines for the minimum density of precipitation networks for tropical mountainous regions of 100-250 km2 (WMO, 1981), though it has to be considered that the stations are not evenly distributed in the basin (Fig. 1).

Two stations (9 and 12) situated westward of the river basin are included in the spatial rainfall analysis (Table 1). These stations are exposed to the SW-monsoon before it enters the river basin. At Dévala (9), the average annual rainfall was about

Table 1 Precipitation records of the 29 stations studied (see Fig. 1 for geographical location of each station).

No. Raingauging station Annual Fraction of rainfall (%): Elevation Years in Availability CV(%) rainfall (m a.m.s.l.) record of precipita-(mm) tion record

NE- SW-monsoon monsoon

1 2 3

4

5 6 7 8 9

10 11 12 13 14 15

16 17 18

19 20 21 22 23 24 25 26 27 28 29

Adderley Bhavanisaga Reservoir Botanical Gardens, Ootacamund

Centre for Soil and Water Conservation, Ootacamund Chamraj Coonoor Corsely Curzon Dévala Dunsandle Glendale Gudalur Havikkal Kairbetta Kethi

Kilkotagiri Kodanad Kotagiri

Kundah Mettupalayam Mynali Naduvattam Nilgri Library, Ootacamund Periyanayakkanpalayam Pillur Pykara Siruvani Thiashola Upper Bhavani

1646 678 1226

1322

1182 1755 1231 1599 3947 1366 1519 2291 1646 1691 1317

1905 1459 1717

1321 857 1160 2583 1333 827 961

2003 2775 1627 2390

50 48 34

29

41 45 48 51 11 29 52 11 46 47 37

50 42 43

41 50 43 13 31 50 61 16 24 42 15

21 26 50

50

42 24 30 27 82 52 22 79 25 28 38

23 32 29

34 21 35 76 44 22 19 70 69 38 78

1301 290

2245

2011

2134 1740 2000 1677 1005 1829 1600 1021 1570 1281 2103

1798 1951 1950

1753 326

1772 1753 2240 429 427

2073 883

2025 2278

14 31 26

86

23 54 33 34 54 27 52 54 72 36 70

19 54 68

54 74 12 54 55 74 9

37 16 47 9

1980-1995 1980-1995 1918-1927 1968-1972 1984-1995 1891-1995

1972-1995 1891-1954 1961-1995 1961-1995 1891-1954 1968-1995 1943-1995 1891-1954 1923-1995 1958-1995 1891-1954 1978-1995 1975-1995 1891-1954 1891-1954 1980-1995 1891-1954 1891-1995 1980-1994 1891-1954 1940-1995 1891-1995 1970-1980 1891-1930 1976-1995 1948-1995 1970-1980

20 35 33

23

23 34 30 25 17 20 26 23 25 19 29

29 17 21

20 31 37 20 27 41 34 22 22 22 25

CV: coefficient of variation.

GIS-supported modelling of area! rainfall in a mountainous river basin with monsoon climate 189

1400 mm higher than for any of the stations within the river basin. These two stations, being outside the basin boundary, were not included in the analyses of effects of topographic and wind exposure factors within the basin.

Physiographic data

Contour lines were digitized in PC Arc/Info from topographical maps (1:50 000) obtained from the Survey of India. Distances from water divides and dominating wind directions were calculated in Arc View. The vector elevation database was converted to the raster environment of Idrisi, using a grid size of 100 m x 100 m, where physiographic information such as slope, aspect and integration of elevations increases along a line of a specified orientation were calculated.

SPATIAL VARIABILITY OF THE RAINFALL

The average annual precipitation was 1631 mm, with a standard deviation of 681 mm. The coefficient of variation (CV) decreased with increasing annual average rainfall (Fig. 2), which is a commonly observed pattern (Landsberg, 1983). At drier sites, precipitation occurs more sporadically, often from thunderstorm development, whereas

50

40

30 -

20 -

10

1000 2000 3000 Average Annual Precipitation (mm)

4000

Fig. 2 Negative correlation between coefficient of variation and annual average precipitation at the included rainfall stations.


at moister sites, topographical lifting mechanisms give a more predictable precipitation from year to year.

A pronounced decrease in annual rainfall may be observed as one moves eastward. The lowest annual average was monitored at Bhavanisaga reservoir (2—Fig. 1). The annual average monitored at Dévala, situated just outside the western catchment boundary, was about six times higher. The four stations with the heaviest annual rainfall were all situated within 6 km of the western boundary. The high rainfall observed at Dévala shows that the uplift of air from India's west coast to the mountain plateau results in up to 50% higher rainfall totals as compared to the stations just inside the western part of the watershed. The stations with the lowest mean annual precipitation were all situated within 16 km of the eastern boundary.

For India as a whole, the SW-monsoon dominates the annual rainfall, contributing to about 78% (Parthasarathy et al, 1994). Tamil Nadu, however, is the only sub-region of India that receives more of its rainfall during the NE-monsoon (Dhar et al, 1982). The SW-monsoon is characterized as humid and unstable with considerable vertical depth, while the NE-monsoon is stable and of lesser vertical depth (Dhar & Rakhecka, 1983). The NE-monsoon winds are of continental origin and thus are generally, dry but as they pass over the Bay of Bengal they pick up moisture and release rain over Tamil Nadu (Dhar et al, 1982).

Precipitation stations used in this study could be divided into two groups, according to whether they are dominated by the NE-monsoon or the SW-monsoon (Table 1). The classification of the rainfall stations is shown in Fig. 1. The majority of stations derived most of their rainfall from the NE-monsoon, as is typical for Tamil Nadu, but the westernmost stations tended to follow the more general pattern for India, with dominating rainfall totals during the SW-monsoon. At the five stations with annual average rainfalls greater than 2000 mm, 70-80% of the rainfall was accounted for by the SW-monsoon, compared to an average of only 32% for the remaining 24 stations.

EFFECT OF TOPOGRAPHIC AND WIND EXPOSURE FACTORS ON THE SPATIAL VARIABILITY OF RAINFALL

Ten variables were used to find correlation with annual and seasonal (SW-monsoon and NE-monsoon) rainfall averages. Since it could not be assumed that the variables were normally distributed, or that relationships were linear, Kendall's tau-b test, which measures the association between rank orders, was used.

The station at Siruvani (27), which got its main fraction of rainfall from the SW-monsoon (Table 1), was shown to be an outlier in many of the correlations. Siruvani had exceptionally high rainfall, in spite of low altitude, most probably due to its close proximity to the southwest water divide (Fig. 1). The area surrounding Siruvani is covered with dense evergreen forest which is characteristic of the high rainfall areas of the Nilgri plateau. When Siruvani is included, some correlations occurred which were against what was physically expected, i.e. more rainfall on the leeward side of the SW-monsoon, and increased precipitation as one moves away from the plateau above 2000 m. These correlations disappeared when Siruvani records were excluded. Only

GIS-supported modelling ofareal rainfall in a mountainous river basin with monsoon climate 191

correlations with a two-tailed risk level below 5%, and multiple regression equations with significance on a risk level below 5%, both with inclusion and exclusion of Siravani were considered as significant for the basin behaviour. In addition to analyses of all stations, separate analyses were made for stations dominated by the NE- and S W-monsoon, respectively.

The tested variables are defined in Table 2. The distance from the 2000 m contour line, D2000, was selected since it represents a plateau in the central part of the basin. The shortest distance from the southwestern boundary, SWB, and from the northwestern boundary, NEB, were included, since no algorithm existed in the Idrisi environment (Idrisi for Windows, 2.0) that enabled calculation of distances in a specified bearing. The variable SWTO combines the distance from the water divide to the southwest with changes in altitude. The integration of the elevations along a northeastern orientation was not included because of the flatness of the northeastern region, making this variable similar to NED (Table 2).

Table 2 Variables estimated for each rainfall station, which are used in the nonparametric correlations and regression analyses of annual and seasonal rainfall averages.

Variable Definition

ALT Gauge altitude (m a.m.s.l.) SLOPE Gradient of the slope (%) calculated for each cell by comparing the heights of each cell to

that of its neighbours ASP Aspect of the slope (degrees), expressed as the direction of main descent, measured as the

positive difference from bearing 225° (the main rainfall bearing wind direction during the SW-monsoon)

D2000 Distance (km) from the 2000 m contour line SWD Distance (km) from the southwestern catchment boundary along a 225° compass course NED Distance (km) from the northeastern catchment boundary along a 45° compass course SWL Distance (km) from a line along a 135° compass course drawn outside the southwestern

catchment boundary SWB The shortest distance (km) from the southwestern catchment boundary NEB The shortest distance (km) from the northeastern catchment boundary SWTO Integration of elevation increases (m a.m.s.l.) along a line drawn from the southwestern

border of the river basin along a 225° compass course

Explanation of rainfall variability

No significant relationships were found between interstation correlation coefficients and distances between the stations, either for annual precipitation (Fig. 3), or seasonal precipitation. This suggests the existence of a complex precipitation pattern.

Several variables aimed to measure the exposure to the dominating wind direction. As expected, when expressing distances from the same wind direction, these variables were highly intercorrelated (see Appendix).

All stations included The best regression model for annual precipitation indicated that rainfall decreases proportionally to the integrated elevation increases in a southwesterly direction {SWTO), and that the highest precipitation occurs nearer the 2000 m plateau (Table 3).

Julie Wilk & Lotta Andersson

1.20

0.80

Tk

a> %

£1 c o J5

b o

0.40

0.00 • ' M t ^ ^ - i t i ̂ ftfoî^H*. 40

Distance between stations (km) 80

Fig. 3 Correlation between average annual precipitation at adjacent stations (R2) vs distance between stations, indicating that correlations between stations can be very low, even for adjacent stations.

During the SW-monsoon, annual rainfall was also best explained by an equation including distance from the 2000 m plateau and the distance from the southwestern border (SWD) (Table 3). From the correlation analyses, a significant correlation to altitude was also indicated (Table 4).

During the NE-monsoon, it was indicated that rainfall increases as the topography steepens as one moves away from the southwestern border, but the correlations were low.

Only stations dominated by the SW-monsoon included When only the SW-monsoon dominated stations were included, both the ranking-based correlations and the regressions showed that annual precipitation increased significantly as the southwestern border was approached. High negative correlations were obtained for all variables describing distances from the southwestern border, whereas the correlation to SWL (distance from a line outside the SW-border at 135°) was lower (Table 4). The decrease in precipitation for the SW-monsoon dominated stations as one moves away from the southwestern border tends to be more quadratic than linear (Fig. 4). Quadratic regression models were therefore also tested, and the final selection was a quadratic model (Table 3).

Since the annual totals are dominated by high SW-monsoon precipitation, correlations found for the SW-monsoon precipitation were similar to those obtained for the annual precipitation (Tables 3 and 4).


100000 200000 300000 400000 500000 600000

SWTO (ams!) R2 = 0.91 SWB (km] 0.92

2000.

1800,

1600,

1400.

1200,

1000,

300,

600

400

\ \ o

o

a

a

SWD (km) R2 = 0.92 SWL (km) R2 = 0.55

Fig. 4 Comparison of exponential relationships between SW-monsoon precipitation at the SW-monsoon dominated stations and variables related to distance from the southwestern border (SWTO, SWB, SWD) or a 135° line (SWL). See Table 2 for definitions of the variables.

Only stations dominated by the NE-monsoon included For these stations, topographical effects were significantly correlated with rainfall amounts, whereas the importance of distance to the basin boundaries was insignificant. The best multiple regression model for annual total rainfall included the distance to the 2000 m plateau and the steepness of the terrain (Table 3).

During the SW-monsoon, the best regression model included altitude, whereas the best ranking-based correlation included the distance from the 2000 m plateau.

During the NE-monsoon, in stations dominated by the NE-monsoon, high rainfall was mainly correlated to the steepness of the terrain (Tables 3 and 4).

ESTIMATIONS OF AREAL PRECIPITATION

One of the aims of this study was to obtain regression equations that could be used to calculate areal precipitation. One result that makes the use of Thiessen polygons less desirable in this basin is the weak intercorrelation between adjacent rainfall stations (Fig. 3). However, the relationships found to exist with physiographic factors must


Table 3 Variables and adjusted Rl for the best models obtained from regression analyses, valid both with inclusion and exclusion of the outlier Siruvani.

Stations included

All

SW-monsoon dominated

NE-monsoon dominated

Time period

Annual

SW-monsoon

NE-monsoon

Annual

SW-monsoon

NE-monsoon

Annual

SW-monsoon

NE-monsoon

Model

A B

A B

A

A A2

B B2

A A2

B B2

A

A

A

Variables*

-D2000, -SWTO -D2000 Thiessen -SWD, -D2000 -SWL Thiessen +SLOPE, +SWB Thiessen

-SWD -SWD (quadratic) -SWB -SWB (quadratic) -SWD -SWD (quadratic) -SWB -SWB (quadratic) Thiessen

-D2000, +SLOPE

+ALT

+SLOPE

Adj.t.R2

all stations

0.50 0.20

0.47 0.37

0.32

0.80 0.88 0.76 0.86 0.86 0.90 0.83 0.90

0.77

0.78

0.53

Adj. R1

excluding Siruvani

0.53 0.32

0.44 0.21

0.41

0.80 0.87 0.76 0.90 0.82 0.86 0.80 0.88

* See Table 2 for definitions of variables; ' Adj.: adjusted. A: the best linear model; B: the best linear model, excluding variables that could not be calculated with Idrisi; A2 and B2 are nonlinear (quadratic) equations. Models selected for calculations of areal precipitation are in bold type.

explain a significant part of the variance between the stations before it is justifiable to substitute Thiessen-based areal estimates with those based on regression equations.

When including all stations except Siruvani in the calculations, the obtained regression models only explained 21-53% of the variations (Table 3).

The correlations between rainfall and physiographic characteristics differed significantly between stations dominated by the SW-monsoon and those dominated by the NE-monsoon (Table 4), and the regression models were significantly improved after making a division into these two groups (Table 3). This indicates that the basin should be divided into two parts, depending on which monsoon dominates. However, the difficulty with this approach is to know where to draw the dividing line. Rainfall for a specified pixel will be considerably different depending on whether a regression equation based on the SW-group or the NE-group is used.

It was decided to divide the pixels within the basin into groups based on whether the physical characteristic for each pixel was most similar to rainfall gauging stations in the SW-monsoon group or the NE-monsoon group.

Four physiographic variables that differed significantly between the two groups were selected. Altitude was significantly higher in the SW-monsoon group, whereas distance from the 2000 m contour line, distance to the SW-basin boundary, and distance from a 135° line, had higher values in the NE-monsoon dominated group. For each of these factors a digital map was constructed in Idrisi.


Table 4 Significant bivariate correlations, calculated with the nonparametric Kendall's tau-b test, including all stations and with exclusion of Siruvani.

Stations included Time period Variable R All stations

R Excluding Siruvani

All

SW-monsoon dominated

NE-monsoon dominated

Annual SW-monsoon

NE-monsoon

Annual

SW-monsoon

NE-monsoon

Annual

SW-monsoon

NE-monsoon

SLOPE ALT JD2000 SWB SWD SWL SLOPE SWB SWD SWB SWTO SWL SWD SWB SWTO SWB SWD D2000 SLOPE ALT D2000 ALT SWTO SLOPE £>2000

0.39** 0.46**

-0.46** -0.43** -0.40** -0.38** 0.33* 0.28*

-0.69** -0.69** -0.69** -0.54** -0.78** -0.78** -0.69** n.s. n.s. -0.48** 0.57** 0.37*

-0.53** 0.47** 0.36** 0.57**

-0.39*

0.45** 0.56**

-0.56** -0.39** -0.37** -0.33* 0.34* 0.33*

-0.67** -0.67** -0.67** -0.54* -0.78** -0.78** -0.67* 0.56 0.56

See Table 2 for definitions of variables. n.s.: not significant (p > 0.05); *p<0.05; **p<0.01.

The variable ALT for a pixel was classified as typical for SW-monsoon dominated regions if:

ALT > ALTsw -1 s.d.

and (1)

ALT > ALTne + 1 s.d.

and typical for NE-monsoon dominated regions if:

ALT < ALTne + ls.d.

and (2)

ALT < ALTsw - 1 s.d.

where ALTsw is the average altitude for SW-monsoon dominated rainfall stations, and

ALTne is the average altitude for NE-monsoon dominated stations.

The same calculations were made for the distance from the 135° line and from the southwestern border. However, for the distance from the 2000 m plateau, medians and


quartiles were used instead, since these distributions were skewed. Pixels falling between these two groups were left unclassified for the actual physical variable.

For each physical factor, a Boolean map was constructed, where all pixels with characteristics typical for SW-monsoon dominated regions were given a value of 1, and other pixels a value of 0. In the next step, all four maps were overlaid, i.e. a pixel could obtain a value from 0 (no characteristics typical) to 4 (all characteristics typical).

A similar map was made for physiographic factors typical for NW-monsoon dominated areas. Finally, for each pixel, the value of the SW-monsoon characteristics map was subtracted from the NE-monsoon characteristics map. Pixels with a positive value on the final map were classified as belonging to the NE-monsoon dominated areas, whereas pixels with negative values were classified as being SW-monsoon dominated. Pixels with the value 0 were not classified (Fig. 5).

Fig. 5 Classification into areas with physical characteristics typical for rainfall stations dominated by the NE-monsoon (grey), and by the SW-monsoon (white). Black areas are unclassified.

Maps of areal precipitation were created in the Idrisi environment, using 100 m x 100 m grids. No algorithm was included in the program that enabled calculations of distances in a specified bearing (SWD, NED, SWTO). To make it possible to calculate areal precipitation with the help of Idrisi, new regressions were made in the case where these variables were included. Usually, using the shortest distance to the border (SWB), instead of the distance in a specific bearing (SWD) gave similar results (Tables 3 and 4). For the parts of the basin that were unclassified, averages of the two calculations were used.

The following steps were followed (Table 3): (a) the best linear equation from the regression analyses was selected; (b) if the model included SWD, NED or SWTO, a model without these variables was

selected;


(c) if the model included a variable based on the distance from the southwestern border, a nonlinear model was also tested;

(d) if this model obtained a higher R2, the nonlinear model was selected; (e) if the final model obtained an adjusted R2 below 0.50, the model was substituted

by calculations based on Thiessen polygons. In Fig. 6, maps with estimated areal annual and SW-monsoon precipitation are pre

sented. Northeast-monsoon precipitation was excluded since a part of the result was based on the Thiessen method.

(a) • • • • • • • • ES H j g i

• • • •1 • I

0-117 118-294 295-141 442-588 589-735 736-882 883-1029 1030-1176 1177-1323 1324-1470 1471-1617 1618-1764 1765-1911 1912-2058 2059-2205 2206-2350

(b) 0-235 236-470 471-704 705-938 939-1173 1174-1407 1408-1642 1643-1877 1878-2111 2112-2346 2347-2580 2581-2815 2816-3050 3051-3284 3285-3520

MpVtt»

Fig. 6 Calculated average areal precipitation (mm) (a) during the SW-monsoon (June-September) and (b) annually.


A Kilometres

(b) Rainfall Stations

• NE-group • SW-group

Fig. 7 Percentage over- and underestimates of areal precipitation in sub-catchments calculated with the Thiessen method, compared to those calculated with the regression equations (it is suggested that the Thiessen method overestimates areal precipitation in dotted areas, and underestimates it in lined areas), (a) SW-monsoon precipitation and (b) annual precipitation.

Areal precipitation calculated from the regression models and from calculations based on Thiessen polygons were compared for 20 sub-catchments (Fig. 7) for annual and SW-monsoon rainfall.

For the entire basin, discrepancies were negligible for annual rainfall averages, with 1% lower estimates with the Thiessen method. In four sub-catchments, annual estimates were more than 20% higher with the Thiessen method, most notably in sub-catchments situated in the northern part of the basin and in the most southern sub-catchment, containing the Siruvani station (Fig. 7(b)). The southernmost tip of the sub-catchment directly around Siruvani station supports wet evergreen forest indicating heavy rainfall, though this trait is highly overestimated spatially with the Thiessen method. The northern area supports dry deciduous forest and savannah thus indicating that it is a lower rainfall area than is represented by the nearest raingauging stations.

Similarly during the SW-monsoon (Fig. 7(b)), in nine of the basins, precipitation calculated with the Thiessen method was more than 20% higher than that calculated with the regression method, again most notably in the northern part of the basin and the southernmost sub-catchment. For the entire basin, estimates with the Thiessen method were 23% higher, as compared to estimates based on the regression method.

DISCUSSION

Altitude was only included in the regression equation for SW-monsoon precipitation at NE-monsoon dominated stations (Table 3), and only showed correlation consistently with rainfall during the SW-monsoon for the "all stations" group, and for annual/SW-monsoon precipitation for NE-monsoon dominated stations (Table 4). This supports the findings of Singh et al. (1995) that the monsoons do not always show a simple


correlation with elevation, but that other factors, such as rain shadows and the number of times that rain clouds rise over barriers override the effects of altitude.

When all stations were included, neither the spatial precipitation patterns during the SW-monsoon nor the NE-monsoon could be satisfactorily explained from physiographic characteristics. Only for annual precipitation did an equation including the integrated elevation increase in a southwesterly direction and the distance from the 2000 m plateau fulfil the criteria specified (R2 > 0.5), albeit with a rather low degree of explanation (Table 3). After the rainfall stations were divided in two groups, according to dominating monsoon, the selected regression equations explained a high degree of the variability (Table 3), and correlations were significantly higher (Table 4).

Rainfall at stations dominated by the NE-monsoon was significantly correlated to altitude and slope (Table 3). This indicates that the moist air of the NW-monsoon is released as it rises in altitude, especially when the terrain is steep.

At stations dominated by the SW-monsoon, topography related variables could not explain any significant part of the spatial variability (Tables 3 and 4). Instead, distance from the southwestern water divide was the main explaining factor, showing decreasing rainfall with distance from the border. The SW-monsoon rises to the Nilgri plateau on its passage through Kerala and once the plateau area is reached, altitude differences are negligible.

Variables expressing the distance from the southwestern water divide {SWB, SWD) were more successful in explaining spatial variability, compared to the variable expressing the distance from a 135° line outside the southwestern border (SWL). This demonstrates that it was not the directional aspect, but the presence of the water divide acting as a physical barrier to rainfall, that was the main influence on the spatial variations in rainfall.

The northeastern border plays a less significant role as a rain barrier, since the topography in the northeastern portion of the basin is relatively flat (Fig. 1), and since the NE-monsoon has already passed a large landmass before arriving in the Upper Bhavani basin.

The calculated seasonal distribution of rainfall differed significantly (Fig. 7) from the Thiessen method. Over the year, however, these discrepancies cancelled each other out and the calculations based on the Thiessen method were equally representative if only rainfall totals for the whole basin had been of interest (and much simpler to obtain).

At the sub-catchment level, however, the rainfall totals were found to differ to a larger degree according to calculation method (Fig. 7). Few, and poorly spatially represented stations, as well as low correlation between precipitation at adjacent stations, should all be indicators for a need to investigate the relationship between rainfall and physical variables in the basin before calculating areal precipitation. Such analyses are greatly simplified by the use of GIS. However, it should be emphasized that GIS-analysis is very time consuming, and it is therefore recommended to start with variables that are simple to obtain, and to only introduce new variables if necessary.

CONCLUSION

For the purpose of hydrological modelling in mountainous basins, accurate areal precipitation cannot be obtained using the Thiessen polygon method because of high


spatial variability in rainfall between adjacent stations. In addition, the network of rainfall gauges is generally very sparse in mountainous areas. To make more representative estimates of areal precipitation possible, there is a need to find correlations to physical variables that affect rainfall distribution. With the help of GIS, measurement of various physical variables and their spatial distributions is greatly simplified, providing the opportunity for operational inclusion of variables other than elevation in estimates of spatial rainfall.

In the Upper Bhavani basin, which is influenced by more than one monsoon regime, it was shown that satisfactory calculations of areal precipitation, based on regression equations, were found only after a division of the basin into areas dominated by the SW-monsoon and the NE-monsoon, respectively. After such a division, it was shown that distance to the southwestern border was the main factor influencing rainfall at stations obtaining most of their rainfall from the SW-monsoon. For stations that obtained most of their rainfall during the NE-monsoon, altitude and slope were the most important affecting factors. The fact that distance to the water divide was significant in a southwesterly direction was probably due to the fact that the river basin is situated close to the west coast of India and that the relatively steep water divide acted as a physical barrier to precipitation.

The basin was divided into pixels with characteristics typical for stations situated in NE-monsoon or SW-monsoon dominated regions. Based on this division, spatial rainfall was calculated for the entire basin as well as for smaller sub-catchments. While the areal annual rainfall for the entire region showed negligible changes when applying this GIS-based method, the changes were greater for rainfall at the sub-catchment level. It was shown that rainfall most probably was overestimated with the Thiessen polygon method in areas that had characteristics typical for NE-monsoon dominated regions, but with the closest rainfall stations in SW-dominated regions. This demonstrates that, for topographically heterogeneous sub-catchments where rainfall stations are few or unevenly distributed, it is important to consider the relationship between rainfall and physiographic variables in estimates of areal rainfall distribution.

Acknowledgements The authors wish to thank the various authorities and individuals in India and Germany who provided raw data for this study. Thanks are also due to Natalie Lindskog, who assisted in the building of the GIS database. The study was financed by SAREC (Swedish Agency for Research Co-operation with developing countries).

REFERENCES

Dhar, O. N. & Rakhecka, P. R. (1983) Foreshadowing northeast monsoon rainfall over Tamil Nadu, India. Mon. Weath. Rev. I l l , 109-112.

Dhar. O. N., Rakhecka, P. R. & Kulkarni, A. K. (1982) Fluctuations in northeast monsoon rainfall of Tamil Nadu. J. Climate 2,339-345.

Gan, T. Y., Dlamini, E. M. & Biftu, G. F. (1997) Effects of model complexity and structure, data quality and objective functions on hydrologie modelling. J. Hydro!. 192, 81-103.

Hanson, C. L. (1982) Distribution and stochastic generation of annual and monthly precipitation on a mountainous watershed in southeast Idaho. Water Res. Bull. 18, 875-883.

Landsberg, H. E. (1983) Variability of precipitation processes in time and space. In: Proc. Symp. Sampling and Analysis of Rain (Philadelphia, Pennsylvania, USA, October 1981), 3-9. ASTM Special Technical Publ. no. 823..

GIS-supported modelling of area! rainfall in a mountainous river basin with monsoon climate 201

Linacre, E. (1992) Climate, Data and Resources: a Reference and Guide. Roulledge, London, UK. Niemczynowicz, J. (1989) Altitude effect on summer precipitation measured in Swedish mountains. In: Proc. Seventh

Northern Research Basins Symposium (25 May-1 June, Llulissat, Greenland). Parthasarathy, B., Munot, A. A. & Kothavale, D. R. (1994) All-India monthly and seasonal rainfall series 1871-1993.

Theor. Appl Climatol. 49, 217-224. Singh, P. & Kumar, N. (1997) Effect of orography on precipitation in the western Himalayan region. J. Hydrol. 199, 183-

206. Singh, P., Ramasastri, K. S. & Kumar, N. (1995) Topographical influence on precipitation distribution in different ranges

of western Himalayas. Nordic Hydrol. 26, 259-284. WMO (World Meteorological Organization) (1981) Guide to Hydrometerological Practices, WMO no. 168 (fourth edn),

two vols. WMO, Geneva, Switzerland.

Received 30 November 1998; accepted 20 September 1999


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