sensitivity of the himalayan hydrology

SENSITIVITY OF THE HIMALAYAN HYDROLOGYTO LAND-USE AND CLIMATIC CHANGES

KESHAV P. SHARMA1, CHARLES J. VOROSMARTY2, 3 andBERRIEN MOORE III2

1Department of Hydrology and Meteorology, P.O. Box 406, Kathmandu, Nepal2Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham,

NH 03824, U.S.A.3Department of Earth Sciences, University of New Hampshire, Durham, NH 03824, U.S.A.

Abstract. Land-use and climatic changes are of major concerns in the Himalayan region becauseof their potential impacts on a predominantly agriculture-based economy and a regional hydrologydominated by the monsoons. Such concerns are not limited to any particular basin but exist through-out the region including the downstream plains. As a representative basin of the Himalayas, the KosiBasin (54,000 km2) located in the mountainous area of the central Himalayan region was selected asa study area. We used water balance and distributed deterministic modeling approaches to analyzethe hydrologic sensitivity of the basin to projected land-use, and potential climate change scenarios.Runoff increase was higher than precipitation increase in all the potential precipitation change scen-arios applying contemporary temperature. The scenario of contemporary precipitation and a rise intemperature of 4◦C caused a decrease in runoff by two to eight percent depending upon the areasconsidered and models used. In the absence of climatic change, the results from a distributed waterbalance model applied in the humid south of the basin indicated a reduction in runoff by 1.3% in thescenario of maximum increase in forest areas below 4,000 m.

1. Introduction

Considerable concern has been expressed in the past regarding the degradationof the Himalayan environment due to land-use changes in the mountains (Bruijn-zeel and Bremmer, 1989; Eckholm, 1975). Likewise, the effects of global climaticchanges in high mountainous areas, such as the Himalayan region, have been draw-ing significant attention from scientists in recent years (Beniston and Fox, 1996).A particular concern is the predicted rise in temperature in the central Himalayas,which is generally greater than the adjacent areas in south Asia under the scen-ario of doubled CO2 applied to the United Kingdom Meteorology Office (UKMO)coupled climate model (Bhaskaran et al., 1995). The ‘Himalayan dilemma’ (Ivesand Messerli, 1989) associated with the impacts of land-use changes has, hence,become more complex owing to the additional potential effects of global climaticchange.

A vast expanse of water existing as ice and snow is an additional aspect ofsensitivity of the Himalayas to climatic change. More than 10% of the Kosi Basinin the central Himalayas is covered with snow throughout the year (Sharma, 1977).

Climatic Change47: 117–139, 2000.© 2000Kluwer Academic Publishers. Printed in the Netherlands.

118 KESHAV P. SHARMA ET AL.

Receding glaciers, which may provide some evidence of warming (Barry, 1981,1992), have been observed in the Himalayas in the recent past. Kadota and Ageta(1992) report the receding of Shorong glacier in the Dudhkosi Basin, a subbasin ofthe Kosi, by about 30 m within a decade ending 1989. Yamada et al. (1992) reportan accelerating rate of retreat of almost all the studied glaciers in the Kosi andother adjacent Himalayan basins from 1980. Relating these glacial retreats to thereported recent global warming, however, is not straightforward as the Himalayanglaciers have been retreating at least for the past 140 years (Mayewski and Jeshcke,1979).

Since the monsoons are analogous to land breeze and sea breeze at a seasonalscale, their activities are highly related to the differential heating of the Indiansubcontinent and Tibetan Plateau, and the Indian Ocean (Webster, 1987). In thescenario of predicted global warming, the thermal gradient is likely to be steeperdue to higher warming of continent than ocean and a lowering of albedo because ofreduction in snow area. In the background of these physical processes, most GCMs[Goddard Institute for Space Studies (GISS), National Center for Atmospheric Re-search (NCAR), UKMO] indicate the intensification of monsoons in the scenariosof global warming bringing more precipitation over the region (Bhaskaran et al.,1995; Kattenberg et al., 1996; Pant and Rupa Kumar, 1997). The monsoon precip-itation is expected to be enhanced further in a scenario of deforestation (Meehl,1994; Shukla and Mintz, 1982) because of drier soil conditions before the onset ofmonsoon.

The Kosi Basin with an area of about 54,000 km2 represents a major region inthe Himalayas. Although this study falls in the category of meso-scale (Gamble andMeentemeyer, 1996), it can be considered as a study of regional nature. Despite theheterogeneity of mountainous environment in the Himalayan region, most of themeso-scale basins share many similarities in terms of physiography and anthropo-genic activities. For instance, not only the drainage basins of two major rivers in theHimalayas, the Narayani (32,000 km2) and Karnali (42,000 km2), are comparableto the Kosi but also the discharges from these rivers (Sharma, 1993). The study has,therefore, a significant scope to extend the results to other meso-scale Himalayanbasins with additional consideration of principal eco-climatic differences.

2. Study Area

The area considered in this study is the Kosi Basin upstream of Chatara in themountainous region of eastern Nepal and southern Tibet (Figure 1). The study areaincludes the entire mountainous region in the Kosi Basin. The area lies within thelatitudes between 26◦51′ and 29◦79′ and the longitudes between 85◦24′ and 88◦57′.The Kosi River at Chatara drains the highest and the steepest mountain system ofthe world. The average elevation of the basin is 3,800 m but varies from 140 mat Chatara to more than 8,000 m in the Great Himalayan Range. Sagarmatha (Mt.

SENSITIVITY OF THE HIMALAYAN HYDROLOGY 119

Figure 1.Study area showing geographical locations, major tributaries and stream gauging locationsin the Kosi Basin.

Everest), the highest peak of the world, lies close to the center of the basin. Morethan 60% of the basin lies within a circle of 100 km radius from Sagarmatha. Thebasin drains the head-water area of six out of the ten highest peaks of the world(Figure 1) including Kanchanjangha (8,598 m), Lhotse (8,516 m), Yalungkang(8,505 m) next to Kanchanjangha, Makalu (8,463 m), and Chooyu (8,201 m).There are about 500 peaks exceeding 6,000 m in the basin (Pandey, 1987). Thesouthern half of the basin provides high relief topography within a short distanceof about 200 km. The basin can be divided into three major physiographic units:the mountainous zone, Himalayan zone, and Tibetan Plateau.

Geologically, the basin is a part of the region uplifted since the Paleocene Epoch(Molnar, 1986). The age of geological units in the high elevation zone is estimatedas pre-Cambrian to Mesozoic and those in the Middle Mountain are pre-Cambrian


to upper Paleozoic. The region is still believed to be geologically active (Wang andShi, 1982). The Bhotekosi and Arun, two major tributaries of the Kosi River, areconsidered as antecedents because the rivers are believed to predate the uplift ofthe Himalayas (Duff, 1992; Wager, 1937). The mountainous zone of the basin isprimarily dominated by schist, phyllite, and quartzite whereas the high Himalayanzone consists of mainly gneiss and granite. The Tibetan Plateau comprises up toten kilometers thick layer of Tethys’ sediment (Hagen, 1980; Sharma, 1990).

3. Modeling

Watershed or regional scale modeling is widely used for assessing the impacts ofland-use change (Henderson-Sellers et al., 1993; Kite, 1993) and climatic change(Nikolaidis et al., Hu, 1993; Panagoulia, 1991; Rind et al., 1992) on hydrology.Such models are useful to simulate the effects of potential land-use and climaticchanges on the hydrology of a basin. We used the following two approaches(lumped and distributed) to evaluate the expected hydrologic impacts over the KosiBasin as a result of conceivable climatic and land-use changes.

3.1. LUMPED APPROACH

Lumped approach models hydrologic processes considering an entire watershed asa single unit with all the variables and parameters averaged spatially. We usedthe methodology proposed by Wigley and Jones (1985) for assessing the CO2

induced global warming effect on runoff characteristics. The method based on awater balance approach can be expressed as:

R = P − E , (1)

whereR,P , andE are runoff, precipitation and evapotranspiration respectively.Similarly, the long-term average runoff can also be defined as:

R = w ∗ P, (2)

wherew is the runoff ratio obtained from dividing long term runoff by long termprecipitation. The value ofw depends on basin characteristics. The following ex-pression can be obtained for change in runoff by solving the Equations (1) and(2).

r = p − (1− w)ew

, (3)

wheree (= Enew/Epresent) andp (= Pnew/Ppresent) are the relative changes in evap-oration and precipitation respectively as a result of climatic change. These changesalso include the impact of doubled CO2, which may bring CO2 fertilization and


evapotranspiration suppression effects. The later effect is relevant to our calculationas it has a direct impact on the water balance computation.

For the evaluation of changes in runoff using Equation (3), we used theprecipitation scenarios applicable for the Kosi Basin. Evaluation ofE needs theassessment of evaporation changes due to the changes in temperature, land-useand CO2. The multiplicative effects of these three variables are used to computee

given as (Wigley and Jones, 1985):

e = e1 ∗ e2 ∗ e3 , (4)

wheree1, e2, ande3 are the factors affecting evapotranspiration due to temperaturechange, land-use change, and CO2 change, respectively.

We used elevation-based temperature and evaporation relationships (Sharma,1997) to compute change in evapotranspiration due to change in temperature (e1).For the computation ofe2 (evapotranspiration change due to change in forestcover), we used the semi-empirical Calder–Newson model (Calder and Newson,1979) in the following format:

E = PET+ f (a ∗ P − b ∗ PET), (5)

whereE is the total water loss due to evapotranspiration including interceptionloss,PET is the potential evapotranspiration of the basin,f is the fraction of wa-tershed with full canopy cover,a is the interception fraction, andb is the fractionof year canopy is wet.

Evapotranspiration from plants is expected to be suppressed in the scenarioof increased CO2 due to progressive reduction in photorespiration and stomatalopenings (Kimball et al., 1993; Kirschbaum, 1996; Mooney et al., 1991; Nonhebel,1996). The following approximate relationship (Wigley and Jones, 1985) computesevapotranspiration factore3 for the atmospheric condition of doubled CO2.

e3 = 1− 0.3 ∗ d , (6)

whered is the fraction of vegetated area of the watershed.

3.2. DISTRIBUTED DETERMINISTIC MODEL

Distributed deterministic model considers the spatial variation of hydrologic vari-ables and parameters by dividing a watershed into suitable units. We used thegrid-based deterministic Water Balance Model (WBM) to study the land-use aswell as climate change impacts on hydrology (Vorosmarty et al., 1989; Vorosmartyand Moore, 1991; Vorosmarty et al., 1996). The model is based on explicit soilmoisture accounting for each grid. It computes runoff as a residual of the waterbalance equation. The model uses monthly input information on precipitation, po-tential evapotranspiration, temperature, soil texture, vegetation cover, and DigitalElevation Model (DEM) to compute runoff and its components. Total runoff for an


individual month is computed as 50% of the detention storage of moisture in soilwhen precipitation and snowmelt do not meet soil moisture deficit. If the moistureinput to the soil exceeds field capacity then the model uses Equation (7) to computerunoff.

ROi = 0.5[Di + pi(Pi + SRi − PETi)], (7)

whereRi, Di, Pi, SRi, andPETi are runoff (mm), detention storage (mm), pre-cipitation (mm), snowmelt recharge (mm), and potential evapotranspiration (mm)respectively for theith cell. The factorpi is defined as:

pi = Pi

Pi + SRi. (8)

The model accounts for snowmelt depending on the threshold air temperature of–1◦C. Vegetative cover plays a role in the model by influencing the total porosityof soil for holding water. The water capacity, defined in the model as field capacityminus wilting point, is calculated using the soil texture and rooting depth inform-ation. The model considers the root depth for all types of vegetation in lithosol as0.1 m. The root depth of forest varies from 0.1 m for lithosol to 2.5 m for sandy soil.Root depth for grassland varies from 0.1 m to 1.3 m, the latter being the root depthof grassland in silt loam. Water holding capacity varies from 14.0% for lithosol to48.5% for clay. Besides DEM, the model consists of the following five major inputvariables and parameters.

(a) Soil Texture.A significant variation of soil is found in the Himalayan regionin terms of texture, mineral content, depth, and other characteristics (Ghildyal,1981; Pandey, 1987; Shah, 1985; Wenhua, 1993). Soil layers in the high mountainareas are relatively thin due to the influence of a rocky landscape and steep slope.The lower elevation areas are dominated by granular sandy soil mixed with gravel.Similarly, the valleys in high elevation areas consist of glacial coarse soil whereasthe low elevation valleys are dominated by sandy loam and silty clay.

No soil texture map was available for the basin at useful resolution for grid-based modeling. Since topography influences the type of soil in the region, we useda simple soil texture classification based on elevation zones for input to WBM.We considered the soil texture 5,000 m above sea level as lithosol, sandy loambetween 3,000 m to 5,000 m, and mixed texture below 3,000 m. Considering theseelevation ranges, a major portion of the basin (about 62%) contains sandy loam(Figure 2). About 23% and 15% of the areas are covered with lithosol and mixedtexture respectively.

(b) Vegetative Cover.We reclassified the available detailed vegetation map(Sharma, 1997) of the Tamor Basin into the major groups of vegetative cover usedin WBM (Figure 2). The reclassified land-use map of the Tamor Basin for the late1970s showed the following major land-cover distribution: conifer forest (0.6%),broad-leaf and mixed forest (37.4%), shrub-land (0.6%), grassland (3.7%), tundra(27.8%), cultivation (24.5%), and rocks (5.4%). Since changes in vegetation and

SE

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EH

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DR

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Y123Figure 2.WBM input: layers of topography, land-use, and soil texture in the Tamor Basin.


its impacts on hydrology are the major issues considered in this study, we used thefollowing scenarios in the WBM model: present land-use (38% forest), conversionof all the broad leaf forest area into agricultural land (0.6% forest), conversion ofall agricultural land into forest (62% forest), conversion of all types of land-usebelow 4,000 m into agricultural land (0.1% forest), and conversion of all types ofland-use below 4,000 m into forest (73% forest).

(c) Temperature.Temperature layer, used as an input to WBM, is the aver-age temperature recorded at four climatological stations in the Tamor Basin. Wedistributed the average temperature over the basin using the monthly lapse rate val-ues (Sharma, 1997) and DEM30 (DEM with 30 arc-sec resolution; UNEP/GRID,1996). Figure 3 includes the average temperature map thus obtained for the TamorBasin for two selected months. We used the scenarios of present temperature, mod-est rise in temperature (1◦C and 2◦C), and expected maximum rise in temperaturein the scenario of doubled carbon dioxide (4◦C and 5◦C).

(d) Precipitation. Although the density of rain gauges in the basin (450 km2

per station) is the highest of similar basins in Nepal, the estimates of basin pre-cipitation are low as most of the stations are located in low elevation areas orvalleys. We, hence, used the weights for each month and for each station (Sharma,1997). The computation of weights used the information on elevation, relationbetween precipitation and elevation, and the average water balance of the TamorBasin. The weights varied from 1.3 to 2.9 indicating the underestimation of basin-wide precipitation in all cases. The average precipitation obtained for two selectedmonths is included in Figure 3. The precipitation scenarios used in WBM includethe contemporary condition in addition to 5%, 20%, and 50% rise in precipitation.

4. Water Balance of the Kosi Basin

Obtaining a reliable water balance of the Kosi Basin and its subbasins was aformidable task owing to the extreme physiographic variations within the basinand the lack of hydrological and meteorological information about high elevationareas. A long-term and regular precipitation network did not exist for areas 4,500 mabove sea level that represent more than half of the Kosi Basin. The highest stationwith regular and up-to-date data is the station located in Chialsa at 2,770 m (DHM,various years). Nine other stations operated from few months to few years providedsome information on precipitation up to 4,300 m. The meteorological station atTingri in Tibet, located at 4,300 m, was the only station with long-term data inthe northern part of the basin (LIGG/WECS/NEA, 1988). It was the sole availablestation representing the whole area of the Tibetan Plateau in the Kosi Basin thatcovers almost half of the area considered in this study.

Figure 3 (facing page).WBM input: layers of temperature, precipitation, and potential evapotran-spiration for a selected dry month and a wet month in the Tamor Basin.


.


TABLE I

Long-term water balance of the Kosi Basin and its northern dry area and southernhumid area

Area Precipitation Runoff Evapotranspiration

(mm) (mm) (mm)

P R ET = P − RKosi Basin 1288 919 369

North Himalayan dry 536 358 178

area of the Kosi Basin

South Himalayan humid area 1931 1424 507

of the Kosi Basin

The computation of water balance using widely-used interpolation techniques(such as kriging, inverse-distance interpolation, and spheremap interpolation)showed the average annual runoff values higher than the average annual precip-itation for the Kosi Basin and most of its tributaries. We believe that the poorprecipitation-gauging network in the high precipitation zones was the major reasonbehind significant underestimation of basin precipitation. The average elevationof precipitation gauge stations, including all the stations up to 4,300 m, is about1,740 m. On the other hand, the average elevation of the basin is 3,840 m. Theaverage elevation of the basin on the southern side of the Himalayas, coveredwith a better meteorological network, is about 2,500 m. Only about 15% of thestations represent the area between 2,000 m to 3,000 m, which is a relatively highprecipitation zone. To improve the assessment of the spatial distribution of precip-itation, we used statistical relationships for altitudinal variation of precipitation inconjunction with a careful selection of representative stations. We further assumedthat the average precipitation values of the high elevation stations are applicable tothe entire range of southern Himalayas in the basin above 2800 m. In the TibetanPlateau of the basin, some scattered information (LIGG/WECS/NEA, 1988) wasused to extrapolate the data recorded at Tingri.

Table I summarizes the major components of annual water balance based onlong-term observed precipitation and actual discharge. The table also illustratesthe annual water balance of the dry area and wet area in the Kosi Basin. Thetable reflects the distinct influence of a monsoonal climate and the Himalayantopography. Runoff far exceeds evaporation loss in the region because of seasonalprecipitation. The table also shows that the southern half of the basin is four timeswetter than the trans-Himalayan northern half of the basin. Evaporation loss in thesouth is about three times more than the loss in the north.


5. Hydrologic Response

Despite unanimity regarding the issue of monsoon intensification because ofenhanced global warming, the scale of expected increase in precipitation andevapotranspiration varies depending on models used, type of monsoon activities(strong vs. weak), and uncertainties in the level of predictions. Some alternate scen-arios of precipitation, evapotranspiration, and land-use changes follow, togetherwith resulting impacts.

5.1. FORMULATION OF SCENARIOS

(a) Temperature Change Scenarios.The predicted rise in temperature in areasclose to the Kosi Basin in south Asia varies from 1◦C to 4◦C (Bhaskaran et al.,1995; Houghton, 1991). Since uncertainties in these predicted temperatures mayrange from –30% to +50% (Houghton, 1991), temperature change scenarios fordoubled carbon dioxide may range from almost insignificant to 6◦C. We used fivelikely scenarios including 1◦C, 2◦C, 3◦C, 4◦C, and 5◦C for the assessment of theimpacts on precipitation, evapotranspiration and runoff over the basin.

(b) Precipitation Change Scenarios.As described earlier, most global warmingstudies show an increase in precipitation over South Asia. The expected increasesin precipitation over the Indian subcontinent vary from 3% to 20% (Bhaskaran etal., 1995; Houghton, 1991). In view of these estimates, we considered a scenario ofno precipitation change and the four scenarios of increase in annual precipitationincluding: 5%, 10%, 20%, and 50%.

(c) Evapotranspiration Change Scenarios.Considering its practicability andphysical-basis, we selected the methods based on Penman equation and Class Apan evaporation for water balance computation of the Kosi Basin. Consideringmonthly values given by Lambert and Chitrakar (1989), the equation of annualpotential evapotranspiration for Nepal can be expressed as:

PET= 1333− 0.220∗H , (9)

wherePET is the annual potential evapotranspiration (mm) andH is the elevation(m). Equation (9) based on the Penman equation uses climatological data sets of 24locations in Nepal within the elevation range of 72 m to 3,700 m. The coefficientof determination (R2) for monthly values varies from 0.61 to 0.89.

An equation developed for Nepal relating Class A pan evaporation withelevation can be expressed as (Sharma, 1997):

E = 1544− 0.219∗H, n = 16, R2 = 0.46, p > F is < 0.003, (10)

whereE is the annual Class A pan evaporation (mm).Equations (9) and (10) are the elevation-based regression equations derived

from climatological data for the Penman equation and Class A pan evaporation datafor the pan evaporation. All data available for the mountainous areas in Nepal were


considered to derive these statistical equations. Both methods yielded a similarannual potential evapotranspiration when a coefficient of 0.86 was applied to theClass A pan evaporation values.

Equations (9) and (10) are based on pan evaporation and climatological datarecorded in lower elevation and relatively more humid areas in the southern Hi-malayas. Available information is not sufficient to validate them in high elevationareas and semi-arid lands in the northern Himalayas. For instance, these equationsindicate condensation instead of evaporation (sublimation) in areas above 6,000 m.Annual potential evapotranspiration obtained from Equation (9) for Tingri in Tibet(4,300 m) is about 400 mm. On the other hand, the recorded evaporation at Tingrishows the average annual evaporation of about 2,400 mm that is 800 mm more thanthe value we might expect even with the lowest pan coefficient of 0.35 reported inliterature (Shuttleworth, 1993). Hence, for the modeling of high elevation evapo-transpiration in WBM, we assumed the applicability of elevation based equationsup to the elevation of 3,000 m and assumed a constant potential evapotranspirationrate above this elevation, a pattern similar to the areas in the Rocky mountains(Barry, 1981).

An elevation-based temperature equation, developed for the Kosi Basin (Sharma,1997), is given as:

T = 26.7− 0.0059∗H, n = 20, R2 = 0.96, p > F is 0.001, (11)

whereT is the average annual temperature (◦C).Temperature change scenarios can be transformed to evapotranspiration change

scenarios using elevation-based temperature and evaporation equations (Equation(9) or (10) and Equation (11)). For example, a rise of 2◦C in annual temperature isequivalent to a shift of 339 m (Equation (11)) in elevation on the annual evapotran-spiration equations. This change may increase annual potential evapotranspirationor annual pan evaporation by about 6% (Equations (9) and (10)). The increaseis comparable to the global coupled ocean-atmosphere climate model-based es-timates of Meehl and Washington (1993) and the UKMO model-based estimatesof Bhaskaran et al. (1995), which are about 5% to 9% for a similar change intemperature.

(d) Land-Use Change Scenarios.The assessment of existing land-use datashowed that about 25% of the Kosi Basin was covered with forest, the rest be-ing agricultural land, grazing land, and rocky areas. The forest cover was about50% in the more humid southern half of the basin. Although a perception of rapiddeforestation exists in the region, no distinct trend could be established using theavailable information (Sharma, 1997). To select major possible scenarios of land-use changes over the basin, we included four hypothetical scenarios of forest cover,given as: 100%, 50%, 25%, and complete deforestation.

Two major divisions were considered to analyze the influence of land-use usingdistributed hydrologic modeling: high elevation zone (above 4,000 m) and lowelevation zone (below 4,000 m). Primarily due to poor accessibility, there are


negligible agricultural and other human activities in the high elevation zone. Thiszone can be considered as an area free from land-use change. Being the zone ofagriculture, grazing, and timber harvesting, significant anthropogenic influence onvegetative patterns can be expected in the lower elevation zone.

5.2. WATER BALANCE UNDER CHANGED SCENARIOS

We used the methods described in Section 3.1 and Equations (1) to (6) along withthe scenarios described in the last section to assess runoff changes due to potentialchanges in climate and projected land-use. Changes in climate and land-use affectprecipitation and evapotranspiration terms in Equation (3). The runoff ratio (w) forthe Kosi Basin is about 0.72. In addition, we also analyzed the water balance forrunoff ratios of 0.67 and 0.82. The value of 0.67 is the runoff ratio for the Arun(Station No. 600) lying mostly in the Tibetan Plateau. The runoff ratio of 0.82 isan average for the rivers originating in the south facing drainage of the Himalayas.Table II illustrates the results of expected runoff changes under different climateand land-use change scenarios in the Kosi Basin. The illustration is divided intothree parts: the relatively dry area of the Kosi Basin in the Tibetan Plateau, theKosi, and the southern part of the Kosi.

Table II shows that the response of the basin was not dramatic to the imposedchanges in temperature and land-use. Decreasing runoff (relative changes in runoffsmaller than one in Table II) could be expected only in the scenario of temper-ature rise exceeding 4◦C with insignificant change in precipitation pattern andforest cover not exceeding 50%. A major reason for such trifling response was theconsideration of evapotranspiration suppression in the scenario of doubled CO2.Enhanced evapotranspiration is suppressed in the model owing to the impact ofdoubled CO2 in plant physiology. Because of this physiological effect, a slightincrease in temperature in a scenario of doubled CO2 resulted in an increased runoffdespite the increased temperature and forest induced evapotranspiration loss.

Table II shows a significant response of the basin to the scenarios of changingprecipitation. A 5% increase in precipitation resulted in an increase of 7% to 10%runoff in the scenario of 50% forest cover. In the scenario of maximum defor-estation and 5◦C rise in temperature, 50% increase in precipitation caused 67%increase in runoff. Drier areas were hydrologically more responsive than humidareas in the basin. The response of dry areas in the Kosi exceeded 1% to 3% ofthe response of wet areas under the scenarios of contemporary precipitation. Thedifference was more than 15% under the scenario of 50% change in precipitation.

6. Distributed Deterministic Modeling

The assessment of water balance in the Kosi Basin indicated several deficienciesin the supporting database. Although estimates were made for the annual aver-age values of precipitation for some locations in the data deficient high Tibetan


TABLE II

Expected relative change in runoff (r) in the Kosi Basin in different scenarios of temperature,precipitation and land-use changes. The relative changes in runoff are computed for the wet part(w = 0.82), dry part (w = 0.67), and average condition (w = 0.72) of the basin

w = 0.67 Forest= 100% w = 0.72 Forest= 100% w = 0.82 Forest= 100%p p p

T 1.00 1.05 1.10 1.20 1.50 T 1.00 1.05 1.10 1.20 1.50 T 1.00 1.05 1.10 1.20 1.50

1 1.06 1.14 1.21 1.36 1.81 1 1.05 1.12 1.19 1.33 1.74 1 1.03 1.09 1.15 1.27 1.642 1.05 1.12 1.20 1.35 1.80 2 1.04 1.11 1.18 1.32 1.73 2 1.02 1.08 1.15 1.27 1.633 1.04 1.11 1.19 1.34 1.79 3 1.03 1.10 1.17 1.31 1.73 3 1.02 1.08 1.14 1.27 1.634 1.02 1.10 1.17 1.32 1.77 4 1.02 1.09 1.16 1.30 1.71 4 1.01 1.07 1.13 1.26 1.625 1.02 1.09 1.16 1.31 1.76 5 1.01 1.08 1.15 1.29 1.71 5 1.01 1.07 1.13 1.25 1.62


T 1.00 1.05 1.10 1.20 1.50 T 1.00 1.05 1.10 1.20 1.50 T 1.00 1.05 1.10 1.20 1.50

1 1.04 1.10 1.18 1.33 1.78 1 1.02 1.09 1.16 1.30 1.72 1 1.01 1.07 1.13 1.25 1.622 1.02 1.09 1.17 1.32 1.77 2 1.02 1.08 1.15 1.29 1.71 2 1.01 1.07 1.13 1.25 1.623 1.00 1.08 1.15 1.30 1.75 3 1.00 1.07 1.14 1.28 1.70 3 1.00 1.06 1.12 1.25 1.614 0.99 1.06 1.14 1.29 1.74 4 0.99 1.06 1.13 1.27 1.69 4 1.00 1.06 1.12 1.24 1.605 0.98 1.05 1.12 1.27 1.72 5 0.98 1.05 1.12 1.26 1.68 5 0.99 1.05 1.11 1.23 1.60


T 1.00 1.05 1.10 1.20 1.50 T 1.00 1.05 1.10 1.20 1.50 T 1.00 1.05 1.10 1.20 1.50

1 1.02 1.10 1.17 1.32 1.77 1 1.02 1.09 1.16 1.30 1.71 1 1.01 1.07 1.13 1.26 1.622 1.01 1.08 1.16 1.31 1.76 2 1.01 1.08 1.15 1.29 1.70 2 1.00 1.06 1.13 1.25 1.613 1.01 1.07 1.14 1.29 1.74 3 1.00 1.07 1.14 1.27 1.69 3 1.00 1.06 1.12 1.24 1.614 0.98 1.06 1.13 1.28 1.73 4 0.98 1.05 1.12 1.26 1.68 4 0.99 1.05 1.11 1.24 1.605 0.97 1.04 1.12 1.27 1.72 5 0.98 1.05 1.12 1.25 1.67 5 0.99 1.05 1.11 1.23 1.60


T 1.00 1.05 1.10 1.20 1.50 T 1.00 1.05 1.10 1.20 1.50 T 1.00 1.05 1.10 1.20 1.50

1 1.02 1.09 1.17 1.32 1.77 1 1.02 1.08 1.15 1.29 1.71 1 1.01 1.07 1.13 1.25 1.622 1.01 1.08 1.15 1.30 1.75 2 1.00 1.07 1.14 1.28 1.70 2 1.00 1.06 1.12 1.25 1.613 1.00 1.07 1.14 1.29 1.74 3 1.00 1.07 1.14 1.27 1.69 3 1.00 1.06 1.12 1.24 1.614 0.98 1.06 1.13 1.28 1.73 4 0.98 1.05 1.12 1.26 1.68 4 0.99 1.05 1.11 1.24 1.605 0.97 1.04 1.12 1.26 1.71 5 0.97 1.04 1.11 1.25 1.67 5 0.98 1.05 1.11 1.23 1.59

p = relativechange in precipitation,w = runoff coefficient, andT = potentialincrease in temperature (◦C). Section 3.1

definesr , p, andw.

Plateau, no such estimates were practicable for modeling at higher temporal andspatial resolution. We, therefore, selected the Tamor Basin, a subbasin of the Kosifor applying WBM at a monthly time step. The basin, draining the third highestmountain peak in the world, lies entirely in the southern side of the Himalayas.The basin with relatively long hydrological records (1948–1994) lies entirely inNepal. Section 3.2 describes WBM used to model the water balance of the Tamor.


TABLE III

Actual runoff and water balance components of Tamor River basin computed using WBM

Month Actual Average Adjusted Average Computed Computed

runoff temperature precipitation PET actualET soil moisture

(mm) (◦C) (mm) (mm) (mm) (mm)

Jan 31 4 25 32 22 88

Feb 23 5 34 52 32 75

Mar 24 6 73 88 56 67

Apr 34 9 118 110 80 85

May 73 13 200 115 100 120

Jun 184 17 385 93 92 140

Jul 365 18 571 80 80 141

Aug 431 18 510 81 81 140

Sep 309 15 355 63 62 140

Oct 144 11 104 65 59 128

Nov 63 7 23 39 33 108

Dec 41 6 14 28 21 95

Ann 1722 11 2412 846 718 1327

6.1. MODEL RESULTS

Table III presents the average values of water balance components based on theresults of WBM. Figure 4 illustrates the spatial distribution of key water balanceelements for two selected months. Figure 5 compares the actual average basinrunoff with the average basin runoff obtained from WBM.

Figure 5 indicates that the average basin runoff derived from WBM comparedwell with the actual discharge of the river during wet period of the year. WBMunderestimated the discharge during the low-flow season. The average annual basinrunoff computed from WBM (1,671 mm) was only about three percent less thanthe actual average annual basin runoff (1,722 mm). Despite deficiencies in dataand the use of a priori model parameters, the model results can be considered fairlysatisfactory.

Figure 6 illustrates the annual hydrologic response of the Tamor Basin underdifferent hypothetical scenarios of temperature, precipitation, and land-use. Land-use and precipitation pattern remaining the same, the model showed that the annualrunoff might decrease by 9% under the scenario of rise in temperature by 5◦C. Re-garding land-use change, transformation of all agricultural land below 4,000 m intoforest reduced the runoff by 1.3% of the original value in the model at the existingtemperature and precipitation conditions. A similar land-use change resulted in arunoff decrease by about 2.1% in the scenario of 5◦C rise in temperature.


Computed increase in the runoff because of maximum deforestation for ag-ricultural land was about two percent. Among the applied temperature scenariosunder contemporary precipitation pattern, the model output showed the maximumincrease in runoff by 2.7% at 5◦C. Among the selected scenarios of temperature,precipitation, and land-use, the maximum increase in annual runoff (71%) wasobtained with the following combination: 50% increase in precipitation, conversionof all forest into agricultural land, and no change in temperature pattern. In thescenario of maximum deforestation and 5◦C rise in temperature, 50% increase inprecipitation produced 60% increase in runoff.

7. Discussion

The planning of hydrological and meteorological networks in the region is primar-ily guided by obtaining climatological information for economically important andaccessible areas. The networks do not cover the major hydrologically-importanthigh elevation areas in the Himalayas. Available information is, hence, deficient forscientific assessment of hydrologic processes that occur at a basin scale. Despitea detailed consideration of altitudinal variation of precipitation, the estimation ofwater balances at subbasin levels of the Kosi Basin shows significant disparities.Even for the meteorologically homogeneous areas south of the Himalayas, thecomputed actual evapotranspiration varied from 99 mm to 656 mm for differentsubbasins of the Kosi Basin.

A review of disparities in the water balance computations showed that the ex-isting precipitation-gauging network underestimates the basinwide precipitation inmost cases. The existing precipitation-gauging network is not adequate to representtopographical influence on precipitation. The strengthening of existing hydrolo-gical and meteorological networks in high elevation areas is essential for the properquantification of the variables in a refined water balance. In the backdrop of inad-equate information, our assessment of water balance for the Kosi Basin should beconsidered as an approximate estimate.

In the context of a sparse hydrometeorological network, the water balance com-putation is probably the best conceptual approach to evaluate hydrologic responsesto climatic and land-use changes over a basin. Due to several uncertainties in theprediction of climatic and land-use scenarios, we used the water balance approachwith several possible scenarios including some extreme cases. Since the lumpedapproach used relative changes instead of absolute changes, the predicted relativeresponses can be considered fairly reliable when the entire basin is considered asan aggregate unit.

Figure 4 (facing page).WBM output: layers of actual evapotranspiration, soil moisture, and runofffor a selected dry month and a wet month in the Tamor Basin.


.


Figure 5.Actual average runoff and the runoff computed from WBM for the Tamor Basin.

A lumped water balance is a simple approach that can be applied with relativelylittle information. The lumped water balance is probably the only approach thatcan be applied for the studies of a data deficient area, such as the Kosi Basin, asthe application of distributed deterministic models requires numerous assumptionsto substitute for unavailable data. We applied the lumped approach for the KosiBasin as a whole and distributed deterministic approach to the Tamor, a subbasinof the Kosi Basin. The use of a plant efficiency factor to evapotranspiration ratedue to the increased level of CO2 is a favorable feature in the applied lumped waterbalance approach. The applied distributed WBM does not include the effect of CO2

on plant physiology.

8. Conclusions and Recommendations

Results obtained for different scenarios of possible climatic and land-use changesusing a conceptual distributed hydrologic model for the Tamor watershed weresimilar to the results obtained for the Kosi Basin using the water balance approach.The major difference between these two approaches was the degree of responsedepending on the model structure and basin characteristics. Lumped water balancemodel produced a 67% increase in runoff in the Kosi in the scenario of maximumdeforestation, 5◦C rise in temperature, and 50% increase in precipitation. The same


Figure 6. Expected annual runoff change in the Tamor Basin in different scenarios of temperatureand precipitation changes: (a) existing land-use, (b) change of all lands below 4000 m into forestcover, and (c) change of all lands below 4000 m into agricultural land.


climatic scenarios applied to the grid-based model caused a 60% increase in runoffin the Tamor.

Among the scenarios of potential climatic and projected land-use changes con-sidered in this study, hydrologic response of the Kosi Basin was higher than thechange in precipitation. The response was more significant in the drier areas of thebasin. The scenario of temperature rise by 4◦C without any change in precipitationcaused a 2% to 8% decrease in runoff depending upon the areas considered andthe models used. Lower decrease in runoff was obtained from the lumped waterbalance approach primarily due to the inclusion of evapotranspiration suppressionmodel in the scenario of doubled CO2.

The results from distributed deterministic model indicated a reduction in runoffby 1.3% in the Tamor in the scenario of a maximum increase in forest areas below4,000 m. Similarly, in the scenario of contemporary climatic conditions, the modeloutputs showed the runoff increase by two percent in the case of possible maximumdeforestation for agricultural land. Among the different scenarios of land-use andtemperature considered in modeling, the maximum increase in runoff (2.7%) wasfound in the scenario of 5◦C rise in temperature and maximum deforestation withno-change in precipitation pattern.

The assessment of hydrologic response of the Himalayan basin to expectedclimatic changes has more inquisitive importance than an operational value as theresults are based on scenarios with high degree of uncertainties, inadequate infor-mation and models with inadequate validation. A close monitoring of hydrologicand meteorological variables in the Himalayan region with an emphasis on regulardata collection in the high elevation zones is the major prerequisite for evaluatingand modeling hydrologic changes over the region. The implementation of sucha recommendation may need an evaluation of non-traditional methods such asremote sensing considering the remoteness of most areas in the Himalayan basins.

Most of the Himalayan basins are untouched by the modern industrial revolutionand attendant water resources development. Most of the high elevation areas in thebasins are intact with no significant direct anthropogenic influence. These char-acteristics make them ideal for developing bench-mark basins to monitor globalclimatic and other environmental changes.

Acknowledgements

A major portion of this paper is taken from a doctoral thesis submitted by thefirst author to the Graduate School, University of New Hampshire. The authors aregrateful to Dr. S. P. Adhikary, Dr. D. A. Gilmour, and an anonymous reviewer fortheir critical review and useful comments. The work was supported by the NASAMission to Planet Earth grant number NAGW 2669.


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(Received 9 March 1998; in revised form 13 December 1999)

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