Hydrological Sciences–Journal–des Sciences Hydrologiques, 54(3) June 2009
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Simulation of surface runoff and sediment yield using the water erosion prediction project (WEPP) model: a study in Kaneli watershed, Himalaya, India RAAJ. RAMSANKARAN1, U. C. KOTHYARI1 & J. S. RAWAT2 1 Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247 667, Uttarakhand, India
[email protected],
[email protected] 2 Department of Geography, Kumaon University, Almora 263601, Uttarakhand, India
Abstract The Water Erosion Prediction Project (WEPP) model was calibrated and evaluated for estimation of runoff and sediment yield in the data-scarce conditions of the Indian Himalaya. The inputs derived from remote sensing and geographic information system technologies were combined in the WEPP modelling system to simulate surface runoff and sediment yield from the hilly Kaneli watershed. The model parameters were calibrated using measured data on runoff volumes and sediment yield. The calibrated model was validated by producing the monthly runoff and sediment yield simulations and comparing them with data that were not used in calibration. The model was also used to make surface runoff and sediment yield simulations for each of the individual watershed elements, comprising 18 hillslopes and seven channels, and the detailed monthly results for each are presented. Although, no field data on hillslope runoff and sediment yield are currently available for the validation of distributed results produced by the model, the present investigation has demonstrated clearly the applicability of the WEPP model in predicting hydrological variables in a data-scarce situation. Key words GIS; hydrological modelling; India; ungauged watershed; WEPP
Simulation de ruissellement de surface et d’érosion à l’aide du modèle WEPP: cas du bassin versant de Kaneli, Himalaya, Inde Résumé Le modèle WEPP a été calé et évalué pour estimer le ruissellement et l’érosion dans le contexte de données rares de l’Himalaya Indien. Les entrées obtenues par télédétection et par géomatique ont été combinées dans le cadre de modélisation WEPP afin de simuler le ruissellement et l’érosion du bassin versant montagneux de Kaneli. Les paramètres du modèle ont été calés à l’aide de mesures de volumes ruisselés et érodés. Le modèle calé a été validé à travers les simulations mensuelles du ruissellement et de l’érosion, puis leur comparaison avec des données qui n’avaient pas servi au calage. Le modèle a également été utilisé pour simuler le ruissellement et l’érosion pour chacun des éléments individuels du bassin versant. Bien qu’aucune donnée de terrain relative au ruissellement et à l’érosion de versant ne soit actuellement disponible pour la validation des résultats distribués de la modélisation, cette recherche montre clairement l’applicabilité du modèle WEPP pour la prévision de variables hydrologiques en contexte de données rares. Mots clefs SIG; modélisation hydrologique; Inde; bassin versant non jaugé; WEPP
INTRODUCTION Serious environmental and economic problems generated by soil erosion in upland watersheds are a global phenomenon. The soil erosion by rainfall and runoff is a major threat to the long-term productivity of hillside agriculture. Soil erosion is associated with adverse environmental impacts (Clark et al., 1985) and crop productivity loss (Lal, 1995; Pimentel et al., 1995) that makes its understanding important in assessing food security (Daily et al., 1998) and environmental safety (Matson et al., 1997). Therefore, soil erosion is considered as one of the most significant processes related to surface hydrology. Considering specific climate, slope and soil conditions, erosion rates vary mainly according to land-use changes. The quantification of soil loss is a major challenge in natural resources and environmental planning. Runoff estimation is important for realistic assessment of soil erosion and also in planning irrigation activities, drinking water supply management strategies, etc. (Pielke, 1999; Sparovek et al., 2002). Also, the design of facilities and structures based on hydraulic engineering depends on accurate runoff estimation (Yanmaz & Coskun, 1995; McCuen & Okunola, 2002).
Open for discussion until 1 December 2009
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The middle and lower mountains of the Hindu-Kush Himalaya constitute the most densely populated mountain area in the world, and the population here is still increasing. In these areas, the availability of water is already a limiting factor for agricultural production during certain times of the year. Fodder is becoming scarce due to degrading forests and grasslands, fertile topsoil is lost due to surface and gully erosion, and agricultural land has expanded to areas having marginal soil cover. Local residents of the watersheds are worst hit by these processes. Deforestation in headwater areas of the watershed is stated to be the prime cause of the frequent flooding in lowlands of the Indo-Gangetic plains, as well as of reservoir sedimentation. To deal with this situation, researchers need a quantitative understanding of hydrological and physical processes which cause as well as accelerate soil erosion. They also need to know how these processes interact with land-use and management practices, so that soil erosion can be controlled and minimized (Bowen et al., 1998). The surface runoff is mainly responsible for sediment detachment, its transport and deposition (Hergarten et al., 2000). Therefore, runoff plays a major role when analysed in the context of the soil erosion process, and it also has a significant importance by itself. Process-based mathematical models are becoming popular in predicting runoff, soil erosion and sediment yield for different climates with varying land-use and management practices. One such model, the USDA Water Erosion Prediction Project model (version 2004.7, referred to in this study as WEPP), is used to represent the mechanisms involved with the aim of preventing further degradation of important natural resources. The WEPP model mathematically describes the process of soil particle detachment, transport and deposition due to hydrological and mechanical forces acting on hillslopes and channels in a watershed. It is considered to possess state-of-the-art knowledge of erosion science. The WEPP model calculates several surface hydrological parameters (daily runoff, plant transpiration, soil evaporation, deep percolation, water stress for plant growth, and lateral subsurface flow) for predicting soil erosion based on equations and procedures described by Stone et al. (1995). Recent developments in remote sensing (RS) and digital information management systems have enabled many scientists to model the WEPP process in a geographic information system (GIS) environment. Savabi et al. (1995), Cochrane (1999), Flanagan et al. (2000), Ranieri et al. (2002) and Renschler (2003) have made significant contributions to link the WEPP model with GIS. Moreover, it is evident from the studies reported from Spain (Soto & Diaz-Fierros, 1998), the UK (Brazier et al., 2000), Australia (Yu et al., 2000; Yu & Rosewell, 2001) and Brazil (Bacchi et al., 2003) that very limited information on application of the WEPP model using RS and GIS is available for Indian watersheds. Furthermore, no literature is available that demonstrates the application of the WEPP model in data-scarce watersheds. The topographical conditions, soil conditions, rainfall pattern and cultivation practices in the Indian Himalaya are different from those in other parts of the world. Therefore, it is necessary to evaluate the physically-based models such as WEPP for an Indian Himalayan watershed which does not have enough data for parameterising the land-use management conditions. Hence, with the specific objective of calibration and evaluation of the WEPP model for estimation of runoff and sediment yield under Indian data-scarce conditions, the present study was carried out using RS and GIS techniques in the middle Himalayan Kaneli watershed, located in Uttarakhand State, India. It should be noted that use of GIS was not accomplished using any of the freely available automated geospatial interfaces for the WEPP model. The reason is that some of the required base topographical parameters, such as hillslope size and length of channel element, are readily available for each of the hillslopes and channels. Therefore, to avoid repetition, we did not adopt the automated programs and the present hydrological modelling is made in an uncoupled manner that required us to manually exchange the GIS-based data with the model. Details on the inputs used, procedures adopted and the results obtained are discussed in the following sections.
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MATERIALS AND METHODS Model description The WEPP model is a physically-based mathematical model that is intended to determine and/or assess the essential mechanisms controlling soil erosion by water, as well as anthropogenic factors (Laflen et al., 1991; Stone et al., 1992). The processes represented by the WEPP can be broadly characterized as: runoff processes, soil erosion processes, plant growth and residue processes, water-use processes, hydraulic processes and soil processes (Laflen et al., 1991; Flanagan et al., 2001). In general, the WEPP does not consider soil erosion, its transport and deposition processes in permanent channels and perennial streams. However, the watershed version of the model is applicable to field areas that include ephemeral gullies which can be farmed, and it also links these surface erosion processes to the channel network. A detailed description of the processes considered in the WEPP modelling system is available in Flanagan & Nearing (1995); therefore, only a brief review of the model capabilities is presented here. The WEPP model consists of nine components that are related to: climate generation, winter process, irrigation, hydrology, soils, plant growth, residue decomposition, overland flow, and soil erosion and its deposition. The surface runoff and peak discharge is determined based on the kinematic wave representation. The soil erosion model computes soil loss along a slope and sediment yield at the end of the hillslope. Inter-rill and rill erosion processes are considered, while a steady-state sediment continuity equation forms the basis for the computations related to the soil erosion. Input data The climate data on precipitation, temperature, solar radiation and wind information are the key inputs to the WEPP model. In addition, knowledge of plant growth and residue components is required to make an accurate assessment of the plant and residue characteristics above and below the soil. These include: canopy cover and height, above and below ground biomass of living and dead plant material, leaf area index (LAI) and basal area, and are estimated on a daily basis (Laflen et al., 1991; Flanagan et al., 2001). As such, information regarding management practices and their timing is essential input to the model; the plant characteristics need to be described adequately as they will have a large impact on the soil erosion and hydrological processes on the site. The hydraulic processes component computes the hydraulic shearing forces exerted on the soil surface by the surface runoff. This requires information regarding surface runoff volumes, hydraulic roughness, and approximations of runoff duration and peak rate. The final component of the model, the soil processes module, deals with the temporal changes in soil properties important in soil erosion—considering the effect of management practices, weathering, consolidation, and rainfall on soil and surface variables—including random roughness, bulk density, saturated hydraulic conductivity, and the erodibility factors of the rill and inter-rill areas (Laflen et al., 1991; Flanagan et al., 2001). Model structure The WEPP model uses physically-based equations to describe hydrological and sediment generation and transport processes at the hillslope and in-stream scales. The model operates on a continuous daily time step. The watershed model links the hillslope profiles to the channel network. Runoff modelling On hillslopes, the soil water status is updated on a daily basis and is required to obtain infiltration and surface runoff volumes, which is the driving force in the detachment by flowing water in rills and channels (Laflen et al., 1991). Rainfall excess is simulated using the Green-Ampt Mein-Larson (GAML) infiltration equation. The runoff routing is based on either kinematic wave overland flow routing in the single-event mode and an approximate solution, or simplified regression equations in the continuous simulation mode (Stone Copyright © 2009 IAHS Press
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et al., 1992). Two options exist for calculating the peak runoff rate at the hillslope, channel or watershed outlet. Erosion/transport modelling The erosion processes represented in the WEPP model are limited to sheet and rill erosion and erosion occurring in channels where detachment is due to hydraulic shear. Through the erosional components of the model, the three stages of erosion (detachment, transport and deposition) are quantified using the rill–inter-rill concept of describing sediment detachment (Laflen et al., 1991; Lane et al., 1995, Flanagan et al., 2001). There are three soil erodibility parameters in the model: inter-rill erodibility (Ki), rill erodibility (Kr) and critical hydraulic shear (τc). Soil resistance to detachment by raindrop impact determines Ki. Soil resistance to detachment by concentrated rill flow determines Kr. When shear stress of flow exceeds τc, rill detachment begins. For cropland, these erodibility parameters represent those for a freshly tilled soil (fallow) and are derived from empirical equations depending mostly on clay content (Flanagan & Livingston, 1995). Equations were based on results from erodibility experiments on different soils in the USA. All three parameters are automatically adjusted internally in the model on a daily basis, depending on plant characteristics and soil surface conditions. Sediment detachment and deposition in ephemeral gullies or permanent channels is simulated using a steady-state solution of the sediment continuity equation. MODELLING APPROACH Site description and data collection The Kaneli research watershed (Fig. 1), located in Kumaon Himalaya, represents the middle Himalayan region, as it lies between latitudes 29°36′54.5″–29°37′40.15″N and longitudes 79°35′38.7″–79°36′11.6″E. The watershed has a catchment area of 0.67 km2. The maximum and minimum relief seen in the catchment area is 1540 and 1220 m, respectively. The average slope of the watershed is about 22° with convex, uniform and concave slope units covering 55, 35 and 10%
Fig. 1 Study area map with contours and land-use information. Copyright © 2009 IAHS Press
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of the watershed area, respectively. Here the land-use pattern shows that a major portion is lying barren, while agricultural activities and plantations constitute a small portion of the watershed. Average annual rainfall in the catchment is about 2840 mm with an average annual temperature of 20°C, while annual maximum and minimum temperatures are approx. 35°C and 3°C, respectively. Climate parameters required by the WEPP watershed model, including daily precipitation, maximum and minimum air temperature, solar radiation, wind velocity and direction and dew point temperature, were observed at the Automatic Weather Station (AWS) located in Jyoli, approx. 2 km from the research watershed. Precipitation was recorded during every rainfall event using an automatic event logger with a tipping-bucket mechanism. Other climatic parameters were measured on a daily basis. The discharge flowing through the stream at the outlet was continuously measured by recording the staff gauge readings of a pre-calibrated V-notch. Sediment sampling was also done manually twice a day at the outlet of the watershed. The concentration of sediment for each collected sample was determined by filtering, drying and weighing the collected samples. The volume of runoff (m3) times the sediment concentration (mg L-1) corrected for units gave the sediment production from a rainfall event. The sediment production values of all rainfall events of a year were summed up to get the annual value calculated in tonnes. The annual sediment production divided by the watershed area gave the annual sediment yield expressed in tonnes per hectare. Input files and model parameters Input files used for WEPP modelling have been broadly classified into climatic, topographic, soil and management files. Based on the observations, a climate input file was generated for the calibration year 2003 and for the simulation year 2004 using a standalone program called the Break-Point Climate Data Generator (BPCDG), which generates breakpoint climate data using observed standard weather data sets for the WEPP model. In this study, ArcView Spatial AnalystTM was used to facilitate the set-up and description of watershed components such as hillslopes and channels. Channel locations in the watershed had been automatically extracted from the digital elevation model (DEM) by using Spatial AnalystTM. Watershed sub-basins and hillslope boundaries were developed using a combination of hydrological modelling, performed by ArcView Spatial AnalystTM and ground-truthing. Hillslope boundaries were determined from these sub-basins. Hillslopes were then defined by digitizing the hillslope boundaries on the watershed using on-screen digitizing tools available in ArcViewTM. Through this process the Kaneli research watershed was divided into 18 hillslopes and seven channels as permitted by the WEPP watershed model code. Developing representative slope value parameters for each hillslope was particularly challenging. It was defined by drawing a line representing the location of the profile, overlaid on the DEM, using features in Spatial AnalystTM, to obtain an actual elevation profile. The length of the digitized profile line and the area of the hillslope were then used to calculate a width of the hillslope. Soil properties, namely, soil texture, effective hydraulic conductivity, soil layer, depth, composition (percentage of sand, rock, clay and organic matter), were collected through extensive field surveys. During field surveys we found mainly one type of soil existing in the Kaneli watershed (Table 1). Albedo, cation exchange capacity (CEC), inter-rill erodibility and critical shear were estimated using the empirical equations (depending mostly on clay content) as described in the WEPP user summary (Flanagan & Livingston, 1995). Details on the soil properties, including adopted WEPP parameters for erodibility are listed in Table 1. The land-use map prepared through remote sensing techniques was used in simulation of this research watershed. The cloud-free digital data of the study area captured by the IRS-1C LISS-III sensor during March 2003 was used for extracting land-use information. Initially, the satellite data for the given year were registered with the base map for elimination of geometrical errors. Subsequently the geo-referenced image was classified for generating land-use/cover for the study area by the maximum likelihood classifier algorithm in the ERDAS IMAGINE-8.6 digital image Copyright © 2009 IAHS Press
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Table 1 Range of parameters used for sensitivity analysis. Parameters Range of test values Sensitivity ratio * Critical shear (Pascal) 1–6 –0.41 Effective hydraulic conductivity (mm/h) 2–10 –0.78 Interrill soil erodibility (kg s-1 m-4) 100 000–2 000 000 0.21 Rill erodibility (s/m) 0.0001–0.1 0.85 * A negative value indicates the decrease in sediment yield with increase in input parameter values. In the case of effective hydraulic conductivity, the sensitivity ratio obtained is valid for both runoff and sediment yield predictions.
processing software. The major land-use classes identified were barren land and agriculture lands. The land-use map (Fig. 1) indicates that 70% of the watershed area is covered by barren land and the remaining is agricultural. It is important to note that, based on trial and error, the land management parameters of this watershed were identified through the method of best-fitting visual comparison of the observed and computed hydrographs. By this process, the typical management practices identified and used for simulation of the validation data set were: fallow land and conventionally tilled corn, soybeans, and wheat. These management practices supplied as inputs to the model also corresponded well with the actual management practice followed in the watershed. Calibration and simulation methodology By using the input files, the climate data for the year 2004 were supplied to the model in the continuous simulation for estimating runoff and soil erosion. Julian day 1 was taken as 1 January 2004 and different event days were numbered accordingly. Data for the year 2003 were used similarly for model calibration. In order to calibrate the model, sensitivity analyses were performed as described by Nearing et al. (1990), by changing the value of given parameter within the acceptable range and comparing the corresponding computed and observed values of runoff and sediment yield output. The model was found to be most sensitive to the soil hydraulic parameters. Similar observation was also reported by most of the previous studies utilizing the WEPP model. We performed sensitivity analyses of the soil hydraulic parameters by using the runoff and soil erosion data. The sensitivity ratio, S, of a parameter was determined as (McCuen & Snyder, 1983): S=
[(O2 − O1 ) / O12 ] [( I 2 − I1 ) / I12 ]
(1)
where I1 and I2 are the smallest and largest values of the input used, I12 is the average of I1 and I2 ; O1, O2 and O12 are the corresponding values for the output. The parameter S is a function of the chosen input range for nonlinear response. For calibration of model parameters in this study, events of the year 2003 were considered. The values of different soil parameters were adjusted to bring the model-simulated runoff and sediment yield values within the range of the observed values as well as closer to the mean of the observed values. The parameter that produced the maximum sensitivity was adjusted first, followed by the other parameters. Once the model was calibrated, it was run with the calibrated parameters, and runoff and sediment yield values were simulated for the events of simulation year 2004. The modelled values of runoff and sediment yield were evaluated by visual inspection of the graphs that plotted the range of observed and modelled values for all months in the year 2004. To evaluate the model performance, the coefficient of determination (r2) was determined from regression analysis between model-simulated and measured runoff and sediment yield. The NashSutcliffe coefficient of efficiency (COE) was calculated as (Nash & Sutcliffe, 1970):
COE = 1 −
∑ (P − O ) 2 ∑ (O − Om )2
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where P and O are the corresponding modelled and observed values, respectively. The COE
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compares modelled values to the 1:1 line rather than the regression line of the points between measured and modelled values, the extent of which is indicative of a bias in the model. RESULTS AND DISCUSSION Computations revealed that the model parameters, namely, critical hydraulic shear stress, effective hydraulic conductivity, inter-rill erodibility and rill erodibility, were the most sensitive ones. Among these the rill erodibility (Table 1) has the greatest value of the sensitivity ratio. The tested range of these parameters and the average values of their sensitivity ratios for fallow land and conventionally tilled corn, soybeans and wheat management conditions are presented in Table 1. The sensitive parameters were then calibrated and adjusted for the model, as described earlier in the section on methodology. Those calibrated values of the model parameters for fallow land and conventionally tilled corn, soybeans and wheat management conditions are also presented in Table 2. Next the model was run with the calibrated value of the parameters to produce the outputs for comparison with the observed values. Table 2 Adopted WEPP soil parameters for Kaneli micro-watershed. Parameter Albedo Initial saturation level Effective hydraulic conductivity (mm/h) Interrill soil erodibility (kg s-1 m-4) Rill erodibility (s/m) Critical shear (Pascal) Depth Sand Clay Layer (mm) (%) (%) 1 10 50.0 18.5 2 1000 24.4 19 * CEC: cation exchange capacity (in million equivalents per 100 g).
Value 0.52 75% 8.05 100 000 0.0001 6.0 Organic matter (%) 0.457 0.470
CEC * (meq/100 gm) 13.3 10.0
Rock (%) 10.4 34.5
Comparison between the observations and the model simulations One of the notable discrepancies between WEPP model simulations and measured data was in the number of rainfall events that resulted in runoff and sediment yield. Whereas in field measurements most of the 63 rainfall events produced some runoff and sediment yield, albeit often in only small amounts (Fig. 2), at the same time the WEPP model simulated runoff and sediment yield for only 16 events. Closer analysis revealed that the model failed to pick up the less intense or smaller rainfall events that produced measured runoff values of less than 1 mm or sediment yield values of smaller than 0.02 t/ha. The failure of the model to simulate the smaller events was judged not significant as the measured data clearly showed that substantial sediment yield only occurred when rainfall exceeded 15 mm (Fig. 2(b)) or runoff exceeded 3 mm (Fig. 3). Though the modelsimulated values were both higher and lower than the observed values at different times during the validation period (2004), for the same period the modelled annual runoff (71.78 mm) was found to be close to the observed value (65.28 mm). Likewise, total simulated sediment yield (1.59 t/ha) of all the events was found to be close to the total observed sediment yield (1.20 t/ha) for the mentioned period. The r2 between monthly modelled and measured runoff and sediment yield were 0.75 and 0.95, respectively. The calculated COE of the model for monthly runoff and sediment yield simulation were 0.70 and 0.76, indicating a general satisfactory simulation by the WEPP model.
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Runoff and sediment yield from continuous simulations Watershed scale In general, the WEPP model simulated slightly more annual surface runoff than the measured volume under fallow land and conventionally tilled corn, soybeans and wheat management conditions. However, it is evident from Fig. 4(a) and (b) that at the outlet of the watershed, the comparison between the amounts of measured and WEPP-simulated monthly runoff and monthly sediment yield are satisfactory. Nevertheless, a satisfactory comparison could not be achieved between the temporal variation in the hydrographs of observed and computed runoff. It may be noted that in hilly regions like the Kaneli watershed, the temporal variation of the rainfall shows significant spatial variation. Lack of representation of the temporal distribution of the rainfall by the raingauge records is considered to be the likely main reason for this discrepancy. Detailed analysis of WEPP monthly results shows that, between January and May 2004, with 16 storms amounting to 102.75 mm of rainfall, one event produced 0.00015 mm of runoff passing through the watershed outlet. Between June and September 2004 (41 storms; 538.6 mm of rainfall), 15 events produced 71.78 mm of runoff and 105.34 t of sediment, while between October and December 2004 (six storms; 34.25 mm of rainfall), no events produced any runoff passing through the watershed outlet. Considering the input climate data and the observed values, comprehensive analysis of the model results confirmed that the model over-predicted runoff for the high-intensity storms that occurred following a few days of dry weather (unsaturated condition of the antecedent soil moisture). This is considered to be one of the reasons for over-prediction of runoff, as the model does not wholly take into account the temporal variations in the saturation level of the soil. In contrast, the model under-predicts or in some cases fails to simulate runoff for the small-intensity storm events which occurred during the rainy months of July–October when the soil mostly remained saturated or partially saturated due to the antecedent rains. Copyright © 2009 IAHS Press
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Likewise, a closer analysis of the erosion outcomes indicated that the values of the simulated sediment yield matched the measured values for the entire validation period reasonably well. The over-prediction and under-prediction of sediment yield as discerned for some of the rainfall events is attributable to the existing conventional conservation practices that are not adequately accounted for in the model. Similarly, relatively simplistic sediment routing equations in the model, and assumption of static hillslope and channel dimensions during the simulation may also be responsible for the deviations. However, the results of the analysis at the watershed scale confirm that the WEPP watershed model could simulate the runoff and sediment yield from the Kaneli watershed with reasonably good accuracy. Hillslope scale The detailed summary of monthly runoff volume and sediment yield for each of the 18 hillslopes and seven channel elements in the Kaneli watershed are listed in Table 3. These results may help local soil conservationists in identifying the hillslopes and channel reaches vulnerable to soil erosion. Similarly, the hydrological units that are more appropriate for runoff harvesting can also be identified easily based on the distributed results produced by the model as listed in Table 3. Figure 5 is presented to illustrate the strength of the WEPP model in producing the outputs in distributed form. It may be noted in Fig. 5 that the peak of runoff and sediment yield has occurred in various portions of the watershed almost simultaneously. This is understandable, as the watershed is small. The values of runoff and sediment outflow occurring in the upper headwater region of the watershed (i.e. above location A in Fig. 5) are small; about 20% of the corresponding values at the watershed outlet. However, the amount of runoff and sediment yield increases along the downstream locations of the watershed and the same is quantified by Fig. 5. Table 3 Summary of the monthly predicted runoff and sediment yield for each modelling element of Kaneli watershed for the simulation year. Sediment yield Element Surface runoff volume (m3) (t) Jun. Jul. Aug. Sep. Annual Jun. Jul. Aug. HS 1 303.6 436.4 579.1 293.8 1612.9 0.035 0.664 0.445 HS 2 364.6 524.5 697.6 353.9 1940.7 0.042 0.735 0.475 HS 3 572.3 894.4 1138.8 603.2 3208.7 0.062 1.697 1.178 HS 4 598 940.3 1194.2 634.3 3366.8 0.065 2.027 1.436 HS 5 446.2 642.1 855.2 433.6 2377.2 0.052 0.916 0.599 HS 6 404.6 581.5 770.8 390.4 2147.3 0.047 0.685 0.393 HS 7 72.1 136.6 141.3 49 399 0.006 0.03 0.013 HS 8 613.6 1306 1538.7 869.7 4328 0.094 5.228 3.739 HS 9 418.2 821 976.9 569.3 2785.4 0.039 2.023 1.267 HS 10 1167.5 1876.5 2354.8 1137.5 6536.3 0.295 5.94 4.683 HS 11 1562.4 2582.7 3206.6 1528.2 8879.9 0.878 11.619 9.482 HS 12 39.2 65.3 80.1 37.8 222.4 0.004 0.014 0.008 HS 13 0 429.7 560.6 610.5 1600.8 0 0.271 0.067 HS 14 47 214.2 296.2 283.7 841.1 0.002 0.031 0.022 HS 15 15.6 23.4 32.2 26.3 97.6 0.057 0.078 0.102 HS 16 52.7 240.1 332.3 319 944.2 0.003 0.034 0.025 HS 17 54.7 249.2 345.2 334.3 983.4 0.003 0.036 0.025 HS 18 7.4 17 23.5 21.2 69.1 0.001 0.006 0.003 C1 1095.5 1594.5 2085 1037 5814.2 2.7 1.89 3.5 C2 190.5 888.9 1144.3 1068.7 3292.6 0.27 0.42 0.68 C3 251.6 744.3 966 797 2759.2 0.27 0.26 0.54 C4 1572.6 3980.8 4851.1 3402.6 13807.3 1.67 4.13 6.01 C5 2911.9 4837.4 5977.7 2854.3 16581.6 3.4 7.27 10.32 C6 5729.2 10910.4 13394.8 7588.7 37623.2 9.99 15.69 24.28 C7 7539.4 13708.8 16958.6 9348.9 47555.9 17.89 21.2 35.11 outlet HS: hillslope element; C: channel element. Note: Flow occurred only during the monsoon months of June–September for the simulation year.
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Sep. 0.331 0.367 0.842 0.967 0.459 0.329 0.008 2.559 1.078 2.516 4.627 0.006 0.387 0.05 0.092 0.057 0.06 0.006 2.91 1.18 0.69 6.91 7.65 21.76
Annual 1.475 1.619 3.779 4.495 2.025 1.455 0.057 11.62 4.407 13.434 26.606 0.032 0.725 0.104 0.328 0.118 0.123 0.015 11 2.55 1.77 18.72 28.65 71.72
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Since no field data are presently available on hillslope runoff and sediment yield, the validation was not possible at the hillslope scale. The disparities between measured and simulated values reported herein may also come from other sources. For example, measurement procedures and errors in the data sets could exist. Secondly, biases in some of the sub-modules in the WEPP model, especially those related to soil loss can also be a source of error. Thirdly, the absence of actual field data on soil erodibility parameters and the excessive land-use management related parameters may also have effect on the model accuracy. Improving the various transfer functions used to generate input parameters based on local conditions, error screening and model calibration mechanisms could potentially further improve the performance of the WEPP model when applied to the cases of data scarcity. Overall, the results from the model application are considered to be satisfactory, given the cartographic, measurement and estimation errors that are endemic in the type of data used. CONCLUSIONS The present investigation has revealed that the WEPP model is an effective tool for studying the hydrological and soil erosion processes in a middle Himalayan watershed having scarce data. The results presented herein involved collating by means of GIS the measured data needed to parameterize and evaluate the WEPP model. The model parameters were calibrated using part of the available data on monthly runoff and sediment yield at the watershed outlet, and the model results were validated using an independent data set that was not used in calibration. The modelled values at the outlet of the watershed showed reasonably good agreement with observed values of runoff and sediment yield. The model also produced temporal variations of runoff and sediment yield on the hillslope areas. However, no field data are presently available on hillslope runoff and sediment yield, and thus the validation of the distributed model output was not possible. The distributed nature of the model output is helpful in deciding the locations within the watershed that are more useful for water harvesting. Also, the distributed nature of the model output can be helpful in identifying the zones within the watershed that are more prone to soil erosion and, hence, need to be given priority while implementing soil conservation measures. Though in the present study the WEPP model was validated with measured values at the outlet of the watershed, the simulated runoff and sediment yield data at the hillslope level are expected to provide information about the status of a watershed. Thus, a physically-based model such as WEPP can be used not only to identify the critical areas but also to explore the capabilities of the model to identify best management practices. Acknowledgements The authors are thankful to the Natural Resource Data Management Systems (NRDMS) Division of the Department of Science and Technology (DST), New Delhi, India for giving generous funds to carry out the work reported herein. The authors would also like to express thanks to the anonymous reviewers for contributing insightful comments and useful suggestions, which greatly improved the quality of the present work. REFERENCES Bacchi, O. O. S., Reichardt, K. & Sparovek, G. (2003) Sediment spatial distribution evaluated by three methods and its relation to some soil properties. Soil Tillage Res. 69, 117–125. Bowen, W., Baigorria, G., Barrera, V., Cordova, J., Muck, P. & Pastor, R. (1998) A process based model (WEPP) for simulation of soil erosion in the Andes. Critical Infrastructure Protection (CIP) Program Report 403–408. Brazier, R. E., Beven, K. J., Freer, J. & Rowan, J. S. (2000) Equifinality and uncertainty in physically based soil erosion models: application of the glue methodology to WEPP—the water erosion prediction project for sites in the UK and USA. Earth Surf. Process. Landforms 25, 825–845. Clark, E. H., Haverkamp, J. A. & Chapman, W. (1985) Eroding soils—the off-farm impacts. The Conservation Foundation Report, 252–253. Washington DC, USA. Cochrane, T. A. (1999) Methodologies for watershed modeling with GIS and DEMs for parameterization of the WEPP model. PhD Dissertation, Graduate School, Purdue University, West Lafayette, Indiana, USA.
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Received 25 August 2007; accepted 29 December 2008
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