Sensitivity analysis and calibration of the Modified Universal Soil Loss Equation (MUSLE) for the upper Malewa Catchment, Kenya

Simulation models are widely used for studying physical processes such as surface runoff, sediment transport and sediment yield in catchments. Most models need case-specific empirical data for parameterization before being applied especially in regions other than the ones they have been developed. Sensitivity analysis is usually performed to determine the most influential factors of a model so that they can be prioritized for optimization. In this way uncertainties in model outputs can be reduced considerably. This study evaluates the commonly used modified universal soil loss equation (MUSLE) model used for sediment yield simulation for the case of the upper Malewa catchment in Kenya. The conceptual factors of the model are assessed relative to the hydrological factors in the model. Also, the sensitivity of the model to the choice of the objective function in calibration is tested. The Sobol' sensitivity analysis method was used for evaluating the degree of sensitivity of the conceptual and hydrological factors for sediment yield simulations using the MUSLE model. Nash-Sutcliffe Efficiency (NSE) and the modified Nash-Sutcliffe Efficiency (NSEm) are used to test the sensitivity of the model to the choice of the objective function and robustness of model performance with sediment data measured from upper Malewa catchment, Kenya. The results indicate that the conceptual factors are the most sensitive factors of the MUSLE model contributing about 66% of the variability in the output sediment yield. Increased variability of sediment yield output was also observed. This was attributed to interactions of input factors. For the upper Malewa catchment calibration of the MUSLE model indicates that the use of NSEm as an objective function provides stable results, which indicates that the model can satisfactorily be applied for sediment yield simulations.     

Spatial scale dependency issues in the application of the Modified Universal Soil Loss Equation (MUSLE)

ABSTRACT

The MUSLE is used within hydrological models to estimate sediment yields from catchments of various sizes, but the spatial scale dependency issues associated with estimating the MUSLE parameters have not been adequately addressed. In the absence of detailed observed data on both hydrological response and sediment yield, some analytical approaches and hypothetical examples are presented to identify the key issues. The results suggest that methods used to estimate both the erosivity and topographic factors are scale dependent, particularly if a lumped or semi-distributed modelling approach is used. The conclusion is that spatial scale dependencies will add to the uncertainties inherent in the use of the MUSLE if not carefully understood and appropriately addressed. One suggested approach is to apply the erosivity equation to a fixed (small) representative area and then scale up to the total catchment, an approach that recognizes the variability of averaged parameters across different spatial scales.     

Identification of soil erosion and fluvial sediment problems

A computer model has been used to estimate soil loss and sediment yield from irregular field-size units of small watersheds. Input to the model includes spring data (i.e. relating to February through May) for the independent variables of the Universal Soil Loss Equation, and for factors such as surface roughness, an index of overland runoff, and proximity to the stream. Output from the model includes maps of seasonal estimates of potential soil losses, field sediment delivery ratios, and expected sediment yields. On the basis of selected erosion and sediment yield tolerances, the output information has been analysed to identify watershed areas which (1) exhibit both erosion and sediment yield problems; (2) exhibit only erosion problems; (3) exhibit only sediment yield problems; and (4) exhibit neither erosion nor sediment yield problems. The percentage of the watershed area in each category and the percentage of the watershed soil loss and sediment loads contributed by each category are also identified. Application of the procedure for planning remedial control programs for five watersheds is discussed.     

Revised runoff curve number for runoff prediction in the Loess Plateau of China

The soil conservation service (now Natural Resources Conservation Service) Curve Number (SCS-CN), one of the most commonly used methods for surface runoff prediction. The runoff calculated by this method was very sensitive to CN values. In this study, CN values were calculated by both arithmetic mean (CN_C) and least square fit method (CN_F) using observed rainfall-runoff data from 43 sites in the Loess Plateau region, which are considerably different from the CN₂ values obtained from the USDA-SCS handbook table (CN_T). The results showed that using CN_C instead of CN_T for each watershed produce little improvement, while replacing CN_T with CN_F improves the performance of the original SCS-CN method, but still performs poorly in most study sites. This is mainly due to the SCS-CN method using a constant CN value and discounting of the temporal variation in rainfall-runoff process. Therefore, three factors—soil moisture, rainfall depth and intensity—affecting the surface runoff variability are considered to reflect the variation of CN in each watershed, and a new CN value was developed. The reliability of the proposed method was tested with data from 38 watersheds, and then applied to the remaining five typical watersheds using the optimized parameters. The results indicated that the proposed method, which boosted the model efficiencies to 81.83% and 74.23% during calibration and validation cases, respectively, performed better than the original SCS-CN and the Shi and Wang (2020b) method, a modified SCS-CN method based on tabulated CN value. Thus, the proposed method incorporating the influence of the temporal variability of soil moisture, rainfall depth, and intensity factors suggests an accurate runoff prediction for general applications under different hydrological and climatic conditions on the Loess Plateau region.     

Performance of Multivariate Adaptive Regression Splines (MARS) in predicting runoff in mid-Himalayan micro-watersheds with limited data / Performances de régressions par splines multiples et adaptives (MARS) pour la prévision d'écoulement au sein de micro-bassins versants Himalayens d'altitudes intermédiaires avec peu de données

Abstract

Steep topography and land-use transformations in Himalayan watersheds have a major impact on hydrological characteristics and flow regimes, and greatly affect the perenniality and sustainability of water resources in the region. To identify the appropriate conservation measures in a watershed properly, and, in particular, to augment flow during lean periods, accurate estimation of streamflow is essential. Due to the complexity of rainfall—runoff relationships in hilly watersheds and non-availability of reliable data, process-based models have limited applicability. In this study, data-driven models, based upon the Multiple Adaptive Regression Splines (MARS) technique, were employed to predict streamflow (surface runoff, baseflow and total runoff) in three mid-Himalayan micro-watersheds. In addition, the effect of length of historical records on the performance of MARS models was critically evaluated. Though acceptable MARS models could be developed with a 2-year data set, their performance improved considerably with a 3-year data set. Various indicators of model performance, such as correlation coefficient, average deviation, average absolute deviation and modelling efficiency, showed significant improvement for simulation of surface runoff, baseflow and total flow. To further analyse the versatility and general applicability of the MARS approach, 2-year data sets were used to develop the model and test it on a third-year data set to assess its performance. The models simulated the surface runoff, baseflow and total flow reasonably well and can be reliably applied in ungauged small watersheds under identical agro-climatic settings.     

Comparison of stormflow responses of surface‐mined and forested watersheds in the Appalachian Mountains,USA

The results of a hydrological analysis that was conducted as part of a larger, multifaceted, collaborative effort to quantify ecosystem functions in watersheds subjected to land‐use and land‐cover change are presented. The primary goal of the study was to determine whether a small watershed in the Appalachian region (USA) that was recently subjected to surface mining and reclamation practices produces stormflow responses to rain events that are different from those produced by a nearby reference watershed covered by young, second‐growth forest. Water balances indicated that runoff yields did not vary significantly between the two watersheds on an annual basis. Statistically significant differences (p?0·05) in runoff responses were observed on an event basis, however, with the mined/reclaimed watershed producing, on average (a) higher storm runoff coefficients (2·5×), (b) greater total storm runoff (3×), and (c) higher peak hourly runoff rates (2×) when compared with the reference watershed. Results of a unit hydrograph analysis also showed, unexpectedly, that the modelled unit responses of the two watersheds to effective rainfall pulses were similar, despite the noted differences in land cover. Differences in stormflow responses were thus largely explained by dramatic reductions in cumulative rates of rainfall abstraction (measured using infiltrometers) attributable to soil compaction during land reclamation. Additional field hydrological measurements on other mined watersheds will be needed to generalize our results, as well as to understand and predict the cumulative hydrological impacts of widespread surface mining in larger watersheds and river basins. Copyright © 2006 John Wiley & Sons, Ltd.     

Has urbanization changed ecological streamflow characteristics in Maine (USA)?

Abstract

This paper examines the potential effects of urbanization on streamflow in Maine, USA, from 1950 to 2000. The study contrasts nine watersheds in southern Maine, which has seen steady urban growth over the study period, with nine rural watersheds from northern Maine. Historical population data and current land cover data are used to develop an urbanization score for each watershed. Trends in watershed urbanization over the study period are compared to trends in ecologically relevant streamflow characteristics. The results indicate that trends in northern, rural watersheds are much more consistent than the trends in the southern watersheds. Additionally, trends in the southern watersheds are inconsistent with the hydrological characteristics observed in urban watersheds elsewhere, likely due to the comparatively low level of current urban development in Maine's urban watersheds. Our study suggests that urban areas in Maine have not yet reached an urbanization threshold where streamflow impacts become consistently detectable.

Editor Z.W. Kundzewicz

Citation Martin, E.H., Kelleher, C., and Wagener, T., 2012. Has urbanization changed ecological streamflow characteristics in Maine (USA)? Hydrological Sciences Journal, 57 (7), 1337–1354.     

A Modified Neural Network for Improving River Flow Prediction/Un Réseau de Neurones Modifié pour Améliorer la Prévision de L'Écoulement Fluvial

Abstract

Artificial neural network (ANN) models provide huge potential for simulating nonlinear behaviour of hydrological systems. However, the potential of ANN is yet to be fully exploited due to the problems associated with improving the model generalization performance. Generalization refers to the ability of a neural network to correctly process input data that have not been used for calibrating the neural network model. In the hydrological context, better generalization performance implies higher precision of forecasting. The primary objectives of this study are to explore new measures for improving the generalization performance of an ANN-based rainfall–runoff model, and to evaluate the applicability of the new measures. A modified neural network model (entitled goal programming (GP) neural network) for modelling the rainfall–runoff process has been developed, in which three enhancements are made as compared to the widely-used backpropagation (BP) network. The three enhancements are (a) explicit integration of hydrological prior knowledge into the neural network learning; (b) incorporation of a modified training objective function; and (c) reduction of network sensitivity to input errors. Seven watersheds across a range of climatic conditions and watershed areas in China were selected for examining the alternative networks. The results demonstrate that the GP consistently outperformed the BP both in the calibration and verification periods and three proposed measures yielded improvement of performance.     

Quantifying the impacts of climate change and land use on hydrological processes: A comparison between mountain and lowland agricultural watersheds

Mountain and lowland watersheds are two distinct geographical units with considerably different hydrological processes. Understanding their hydrological processes in the context of future climate change and land use scenarios is important for water resource management. This study investigated hydrological processes and their driving factors and eco-hydrological impacts for these two geographical units in the Xitiaoxi watershed, East China, and quantified their differences through hydrological modelling. Hydrological processes in 24 mountain watersheds and 143 lowland watersheds were simulated based on a raster-based Xin'anjiang model and a Nitrogen Dynamic Polder (NDP) model, respectively. These two models were calibrated and validated with an acceptable performance (Nash-Sutcliffe efficiency coefficients of 0.81 and 0.50, respectively) for simulating discharge for mountain watersheds and water level for lowland watersheds. Then, an Indicators of Hydrological Alteration (IHA) model was used to help quantify the alterations to the hydrological process and their resulting eco-hydrological impacts. Based on the validated models, scenario analysis was conducted to evaluate the impacts of climate and land use changes on the hydrological processes. The simulation results revealed that (a) climate change would cause a larger increase in annual runoff than that under land use scenario in the mountain watersheds, with variations of 19.9 and 10.5% for the 2050s, respectively. (b) Land use change was more responsible for the streamflow increment than climate change in the lowland watersheds, causing an annual runoff to increase by 27.4 and 16.2% for the 2050s, respectively. (c) Land use can enhance the response of streamflow to the climatic variation. (d) The above-mentioned hydrological variations were notable in flood and dry season in the mountain watersheds, and they were significant in rice season in the lowland watersheds. (e) Their resulting degradation of ecological diversity was more susceptible to future climate change in the two watersheds. This study demonstrated that mountain and lowland watersheds showed distinct differences in hydrological processes and their responses to climate and land use changes.     

Appropriate vertical discretization of Richards' equation for two‐dimensional watershed‐scale modelling

A number of watershed‐scale hydrological models include Richards' equation (RE) solutions, but the literature is sparse on information as to the appropriate application of RE at the watershed scale. In most published applications of RE in distributed watershed‐scale hydrological modelling, coarse vertical resolutions are used to decrease the computational burden. Compared to point‐ or field‐scale studies, application at the watershed scale is complicated by diverse runoff production mechanisms, groundwater effects on runoff production, runon phenomena and heterogeneous watershed characteristics. An essential element of the numerical solution of RE is that the solution converges as the spatial resolution increases. Spatial convergence studies can be used to identify the proper resolution that accurately describes the solution with maximum computational efficiency, when using physically realistic parameter values. In this study, spatial convergence studies are conducted using the two‐dimensional, distributed‐parameter, gridded surface subsurface hydrological analysis (GSSHA) model, which solves RE to simulate vadose zone fluxes. Tests to determine if the required discretization is strongly a function of dominant runoff production mechanism are conducted using data from two very different watersheds, the Hortonian Goodwin Creek Experimental Watershed and the non‐Hortonian Muddy Brook watershed. Total infiltration, stream flow and evapotranspiration for the entire simulation period are used to compute comparison statistics. The influences of upper and lower boundary conditions on the solution accuracy are also explored. Results indicate that to simulate hydrological fluxes accurately at both watersheds small vertical cell sizes, of the order of 1 cm, are required near the soil surface, but not throughout the soil column. The appropriate choice of approximations for calculating the near soil‐surface unsaturated hydraulic conductivity can yield modest increases in the required cell size. Results for both watersheds are quite similar, even though the soils and runoff production mechanisms differ greatly between the two catchments. Copyright © 2003 John Wiley & Sons, Ltd.     

Impact of spatial variations of land surface parameters on regional evaporation: a case study with remote sensing data

Most precipitation in watersheds is consumed by evaporation, thus techniques to appraise regional evaporation are important to assess the availability of water resources. Many algorithms to estimate evaporation from remotely sensed spectral data have been developed in the recent past. In addition to differences in the physical parameterization of surface fluxes, these algorithms have different solutions for describing spatial variations of the parameters in the soil–vegetation–atmosphere–transfer (SVAT) continuum. In this study, the necessity to spatially distinguish SVAT parameters for computing surface heat fluxes is analysed for the Naivasha watershed in the Kenyan Rift Valley. Landsat Thematic Mapper (TM) spectral data have been used to first delineate the watershed into 15 hydrological units using surface temperature, normalized difference vegetation index and surface albedo as attributes. Thereafter, semi‐empirical relationships between these TM‐based parameters and other SVAT parameters have been applied to compute the spatial variation of SVAT parameters and the associated evaporation from the different hydrological units. The impact of using watershed‐constant or watershed‐distributed SVAT parameters on the fluxes is analysed. The determination of watershed averaged evaporation with area‐aggregated SVAT parameters is feasible without significant loss of accuracy. Distributed evaporation in heterogeneous watersheds, however, can be investigated only with remote sensing flux algorithms that can account for spatially variable air temperature, surface roughness, surface albedo and the stability correction of the temperature profile due to buoyancy. Erroneous results can be expected if area‐aggregated SVAT parameters are used to calculate local evaporation. As most of the recently developed remote sensing flux algorithms are based on areal constant SVAT parameters, direct applications in watersheds are still limited. Copyright © 2001 John Wiley & Sons, Ltd.     

Lumped surface and sub‐surface runoff for erosion modeling within a small hilly watershed in northern Vietnam

Developing models to predict on‐site soil erosion and off‐site sediment transport at the agricultural watershed scale represent an on‐going challenge in research today. This study attempts to simulate the daily discharge and sediment loss using a distributed model that combines surface and sub‐surface runoffs in a small hilly watershed (< 1 km²). The semi‐quantitative model, Predict and Localize Erosion and Runoff (PLER), integrates the Manning–Strickler equation to simulate runoff and the Griffith University Erosion System Template equation to simulate soil detachment, sediment storage and soil loss based on a map resolution of 30 m × 30 m and over a daily time interval. By using a basic input data set and only two calibration coefficients based, respectively, on water velocity and soil detachment, the PLER model is easily applicable to different agricultural scenarios. The results indicate appropriate model performance and a high correlation between measured and predicted data with both Nash–Sutcliffe efficiency (Ef) and correlation coefficient (r²) having values > 0.9. With the simple input data needs, PLER model is a useful tool for daily runoff and soil erosion modeling in small hilly watersheds in humid tropical areas. Copyright © 2013 John Wiley & Sons, Ltd.     

Application of the geographical information systems approach to watershed mass balance studies

This study was undertaken to test the utility of a geographical information systems (GIS) approach to problems of watershed mass balance. This approach proved most useful in exploring the effects that watershed scale, lithology and land use have on chemical weathering rates, and in assessing whether mass balance calculations could be applied to large multilithological watersheds. Water quality data from 52 stations were retrieved from STORET and a complete GIS database consisting of the watershed divide, lithology and land use was compiled for each station. Water quality data were also obtained from 7 experimental watersheds to develop a methodology to estimate annual fluxes from incomplete data sets. The methodology consists of preparing a composite of daily flux data, calculating a best fit sinusoid and integrating the equation to obtain an annual flux. Comparison with annual fluxes calculated from high resolution data sets suggests that this method predicts fluxes within about 10% of the true annual flux. Annual magnesium fluxes (moles km⁻² yr⁻¹) were calculated for all stations and adjusted for fluxes from atmospheric deposition. Magnesium flux was found to be a strong function of the amount of carbonate in the watershed, and silica fluxes were found to increase with the fraction of sandstone present in the watershed. All fluxes were strongly influenced by mining practices, with magnesium fluxes from affected watersheds being 6–10 times higher than fluxes from comparable pristine watersheds. Mining practices enhance chemical weathering by increasing the surface area of unweathered rock to which water has access and by increasing acidity and rate of mineral weathering. Fluxes were also found to increase with watershed size. This scale dependence is most likely caused by the sensitivity of weathering fluxes to even minor quantities of carbonates, which are likely to be found in all lithologies at larger scales. Mass balances were carried out in watersheds where gauged sub-watersheds made up more than 95% of the area. The calculations show large magnesium flux and water balance discrepancies. These errors may be a result of significant groundwater inputs to streams between gauges. The results suggest that improvements in how we measure discharge and estimate fluxes may be required before we can apply mass balance techniques to larger scales. © 1997 John Wiley & Sons, Ltd.     

Hydrological transit times in nested urban and agricultural watersheds in the Greater Toronto Area,Canada

Watershed mean transit times (MTTs) are used to characterize the hydrology of watersheds. Watershed MTTs could have important implications for water quality, as relatively long MTTs imply lengthier water retention, thereby allowing more time for pollutant transformation and more moderate release of pollutants into streams. Although estimates of MTTs are common for undisturbed watersheds, only a few studies to date have applied MTT models to urbanized watersheds. In the present study, we use δ¹⁸O to compare estimates of MTTs for paired suburban‐industrial and agricultural watersheds in Toronto, Canada. Although differences in precipitation δ¹⁸O between the two watersheds were negligible, there were significant differences in stream δ¹⁸O, suggesting differences in water transport pathways. Less damping between input precipitation δ¹⁸O and output stream δ¹⁸O in the suburban‐industrial watershed indicated a larger streamflow contribution from quick‐flow transport pathways. We applied three transit time models to quantify MTTs. Considering overall model fit, root mean square error, and uncertainty in model parameters, the exponential model performed the best of the three models. Optimized MTTs using this distribution across the suburban‐industrial subwatersheds ranged from 2.1 to 2.9 months, whereas those in the agricultural subwatersheds ranged from 2.7 to 7.5 months. The relatively small difference between urban and agricultural MTTs coincides with observations elsewhere. Model efficiencies could potentially be improved, and MTTs estimated more reliably, with a higher sampling frequency that captures a greater volume of overall discharge. Overall, this work provides a distinct first glimpse into the separation of MTTs between paired watersheds with such a large contrast in their land use.     

Hydrological modelling using proxies for gauged precipitation and temperature

Precipitation and temperature time series suffer from many problems, such as short time, inadequate spatial coverage, missing data, and biases from various causes, which are particularly critical in remote areas such as Northern Canada. The development of alternative datasets for using as proxies for inadequate/missing weather data represents a key research area. In this paper, the performance of 6 alternative datasets is evaluated for hydrological modelling over 12 watersheds located across Canada and the contiguous United States. The datasets can be classified into 3 distinct categories: (a) interpolated gridded data, (b) reanalysis data, and (c) climate model outputs. Hydrological simulations were carried out using a lumped conceptual hydrological model calibrated using standard weather data and compared against results using a calibration specific to each alternative dataset. Prior to the hydrological simulations, the alternative datasets were all evaluated with respect to their ability to reproduce gridded daily precipitation and temperature characteristics over North America. The results show that both the reanalysis data and climate model data adequately represent the spatial pattern of daily precipitation and temperature over North America. The North American Regional Reanalysis (NARR) dataset consistently shows the best performance. With respect to hydrological modelling, the observed discharges are accurately represented by both the gridded and NARR datasets, and more so for the NARR data. The National Centers for Environmental Prediction dataset consistently performs worst as it is unable to even capture the seasonal pattern of observed streamflow for 3 out of the 12 watersheds. These results indicate that the NARR dataset could be used as a proxy for gauged precipitation and temperature for hydrological modelling over watersheds where observational datasets are deficient. The results also illustrate the ability of climate model data to be used for performing hydrological modelling when driven by reanalysis data at their boundaries, and especially so for high‐resolution models.     

SWAT-based runoff and sediment simulation in a small watershed,the loessial hilly-gullied region of China:capabilities and challenges

Model calibration and validation are necessary before applying it for scenario assessment and watershed management.This study presented the methodology of evaluating Soil and Water Assessment Tool(SWAT) and tested the feasibility of SWAT on runoff and sediment load simulation in the Zhifanggou watershed located in hilly-gullied region of China.Daily runoff and sediment event data from 1998-2008 were used in this study;data from 1998-2003 were used for calibration and 2004-2008 for validation.The evaluation statistics for the daily runoff simulation showed that the model results were acceptable,but the model underestimated the runoff for high-flow events.For sediment load simulation,the SWAT performed well in capturing the trend of sediment load,while the model tended to underestimate sediment load during both the calibration and validation periods. The disparity between observed and simulated data most likely resulted from limitations of the existing SCS-CN and MUSLE methods in the model.This study indicated that the modification of SWAT components is needed to take rainfall intensity and its duration into account to enhance the model performance on peak flow and sediment load simulation during heavy rainfall season.     

The effect of truck traffic and road water content on sediment delivery from unpaved forest roads

A study investigated the effect of truck‐traffic intensity and road water‐content on the quality of runoff water from unsealed forest roads. Three sections of a gravel‐surfaced forest road were instrumented and exposed to low and high levels of truck traffic during wet winter conditions and dry summer conditions between July 2001 and December 2002. Rainfall, runoff, road moisture, and traffic were measured continuously, and suspended and bedload sediments were integrated measurements over 2‐week site‐service intervals. The median suspended sediment concentration from the three road segments under low truck‐traffic conditions (less than nine return truck passes prior to a storm) was 269 mg l^?1, increasing 2·7‐fold to a median of 725 mg l^?1 under high truck‐traffic conditions (greater than or equal to nine return truck passes prior to a storm). These concentrations, and increases due to traffic, are substantially less than most previously reported values. When these data are expressed as modified universal soil loss equation (MUSLE) erodibility values K, accounting for differences in rainfall energy, site characteristics and runoff, high traffic resulted in a road surface that was four times more erodible than the same road under low traffic conditions. Using multiple regression, traffic explained 36% of the variation in MUSLE erodibility, whereas road water content was not significant in the model. There was little difference in the erodibility of the road when trafficked in low water‐content compared with high water‐content conditions (MUSLE K values of 0·0084 versus 0·0080 respectively). This study shows that, for a good quality well‐maintained gravel forest road, the level of truck traffic affects the sediment concentration of water discharging from the road, whereas the water content of the road at the time of that traffic does not (note that traffic is not allowed during runoff events in Victoria). These conclusions are conditional upon the road being adequately maintained so that trafficking does not compromise the lateral drainage of the road profile. Copyright © 2005 John Wiley & Sons, Ltd.     

Multi‐site evaluation of hydrology component of SWAT in the coastal plain of southwest Georgia

Simulation of watershed scale hydrologic and water quality processes is important for watershed assessments. Proper characterization of the accuracy of these simulations, particularly in cases with limited observed data, is critical. The Soil & Water Assessment Tool (SWAT) is frequently used for watershed scale simulation. The accuracy of the model was assessed by extrapolating calibration results from a well studied Coastal Plain watershed in Southwest Georgia, USA, to watersheds within the same geographic region without further calibration. SWAT was calibrated and validated on a 16.7‐km² subwatershed within the Little River Experimental Watershed by varying six model parameters. The optimized parameter set was then applied to a watershed of similar land use and soils, a smaller watershed with different land use and soils and three larger watersheds within the same drainage system without further calibration. Simulation results with percent bias (PB) ±15% ≤ PB < ±25% and Nash–Sutcliffe efficiency (NSE) 0.50 < NSE ≤ 0.65 were considered to be satisfactory, whereas those with PB < ±10% and 0.75 < NSE ≤ 1.00 were considered very good. With these criteria, simulation results for the five non‐calibration watersheds were satisfactory to very good. Differences across watersheds were attributed to differences in soils, land use, and surficial aquifer characteristics. These results indicate that SWAT can be a useful tool for predicting streamflow for ungauged watersheds with similar physical characteristics to the calibration watershed studied here and provide an indication of the accuracy of hydrologic simulations for ungauged watersheds. Copyright © 2012 John Wiley & Sons, Ltd.     

Applicability of the Modified Universal Soil Loss Equation for prediction of sediment yield in Khanmirza watershed,Iran

Abstract

The present study aims to estimate the sediment yield due to storm rainfall and runoff at the outlet of the Khanmirza watershed (395 km²) located in western Iran. The estimation was made for six storm events using the Modified Universal Soil Loss Equation (MUSLE). All the inputs required for the application of the model were determined through runoff and sediment concentration monitoring at the time of storm events, and field surveys in the study area. The applicability of the model to the study area was then evaluated by comparison of its estimates with those calculated using the measured sediment data. The results of the study demonstrated the efficiency of the MUSLE in estimating storm-associated sediment yield except one storm event in the study area with a high level of agreement and non-significant differences between mean estimated and measured values in the study storm events.