The influence of parameter distribution uncertainty on hydrological and sediment modeling: a case study of SWAT model applied to the Daning watershed of the Three Gorges Reservoir Region, China

Parameter uncertainty involved in hydrological and sediment modeling often refers to the parameter dispersion and the sensitivity of the parameter. However, a limitation of the previous studies lies in that the assignment of range and specification of probability distribution for each parameter is usually difficult and subjective. Therefore, there is great uncertainty in the process of parameter calibration and model prediction. In this study, the impact of probability parameter distribution on hydrological and sediment modeling was evaluated using a semi-distributed model—the Soil and Water Assessment Tool (SWAT) and Monte Carlo method (MC)—in the Daning River watershed of the Three Gorges Reservoir Region (TGRA), China. The classic types of parameter distribution such as uniform, normal and logarithmic normal distribution were involved in this study. Based on results, parameter probability distribution showed a diverse degree of influence on the hydrological and sediment prediction, such as the sampling size, the width of 95% confidence interval (CI), the ranking of the parameter related to uncertainty, as well as the sensitivity of the parameter on model output. It can be further inferred that model parameters presented greater uncertainty in certain regions of the primitive parameter range and parameter samples densely obtained from these regions would lead to a wider 95 CI, resulting in a more doubtful prediction. This study suggested the value of the optimized value obtained by the parameter calibration process could may also be of vital importance in selecting the probability distribution function (PDF). Such cases, where parameter value corresponds to the watershed characteristic, can be used to provide a more credible distribution for both hydrological and sediment modeling.     

Hydrological analysis of the Upper Tiber River Basin,Central Italy: a watershed modelling approach

The quantification of the various components of hydrological processes in a watershed remains a challenging topic as the hydrological system is altered by internal and external drivers. Watershed models have become essential tools to understand the behaviour of a catchment under dynamic processes. In this study, a physically based watershed model called Soil Water Assessment Tool was used to understand the hydrologic behaviour of the Upper Tiber River Basin, Central Italy. The model was successfully calibrated and validated using observed weather and flow data for the period of 1963–1970 and 1971–1978, respectively. Eighteen parameters were evaluated, and the model showed high relative sensitivity to groundwater flow parameters than the surface flow parameters. An analysis of annual hydrological water balance was performed for the entire upper Tiber watershed and selected subbasins. The overall behaviour of the watershed was represented by three categories of parameters governing surface flow, subsurface flow and whole basin response. The base flow contribution has shown that 60% of the streamflow is from shallow aquifer in the subbasins. The model evaluation statistics that evaluate the agreement between the simulated and the observed streamflow at the outlet of a watershed and other three different subbasins has shown a coefficient of determination (R²) from 0.68 to 0.81 and a Nash–Sutcliffe efficiency (E_NS) between 0.51 and 0.8 for the validation period. The components of the hydrologic cycle showed variation for dry and wet periods within the watershed for the same parameter sets. On the basis of the calibrated parameters, the model can be used for the prediction of the impact of climate and land use changes and water resources planning and management. Copyright © 2012 John Wiley & Sons, Ltd.     

Effects of climatic variability on the hydrologic response of a freshwater watershed

A generalized watershed model was used to evaluate the effects of global climate changes on the hydrologic responses of freshwater ecosystems. The Enhanced Trickle Down (ETD) model was applied to W-3 watershed located near Danville, Vermont. Eight years of field data was used to perform model calibration and verification and the results were presented in Nikolaidis et al., (1993). Results from the Goddard Institute for Space Studies (GISS) and the Geophysical Fluid Dynamics Laboratory (GFDL) general circulation models which simulated the doubling of present day atmospheric CO₂ scenarios were used to perform the hydrologic simulations for the W-3 watershed. The results indicate that the W-3 watershed will experience increases in annual evapotranspiration and decreases in annual outflow and soil moisture. Stochastic models that simulate collective statistical properties of meteorological time series were developed to generate data to drive the ETD model in a Monte-Carlo fashion for quantification of the uncertainty in the model predictions due to input time series. This coupled deterministic and stochastic model was used to generate probable scenarios of future hydrology of the W-3 watershed. The predicted evapotranspiration and soil moisture under doubling present day atmospheric CO₂ scenarios exceed the present day uncertainty due to input time series by a factor greater than 2. The results indicate that the hydrologic response of the W-3 watershed will be significantly different than its present day response. The Enhanced Trickle Down model can be used to evaluate land surface feedbacks and assessing water quantity management in the event of climate change.     

The integrated effects of climate and hydrologic uncertainty on future flood risk assessments

This work examines future flood risk within the context of integrated climate and hydrologic modelling uncertainty. The research questions investigated are (1) whether hydrologic uncertainties are a significant source of uncertainty relative to other sources such as climate variability and change and (2) whether a statistical characterization of uncertainty from a lumped, conceptual hydrologic model is sufficient to account for hydrologic uncertainties in the modelling process. To investigate these questions, an ensemble of climate simulations are propagated through hydrologic models and then through a reservoir simulation model to delimit the range of flood protection under a wide array of climate conditions. Uncertainty in mean climate changes and internal climate variability are framed using a risk‐based methodology and are explored using a stochastic weather generator. To account for hydrologic uncertainty, two hydrologic models are considered, a conceptual, lumped parameter model and a distributed, physically based model. In the conceptual model, parameter and residual error uncertainties are quantified and propagated through the analysis using a Bayesian modelling framework. The approach is demonstrated in a case study for the Coralville Dam on the Iowa River, where recent, intense flooding has raised questions about potential impacts of climate change on flood protection adequacy. Results indicate that the uncertainty surrounding future flood risk from hydrologic modelling and internal climate variability can be of the same order of magnitude as climate change. Furthermore, statistical uncertainty in the conceptual hydrological model can capture the primary structural differences that emerge in flood damage estimates between the two hydrologic models. Copyright © 2014 John Wiley & Sons, Ltd.     

A computationally efficient method for uncertainty analysis of SWAT model simulations

The physically based distributed hydrological models are ideal for hydrological simulations; however most of such models do not use the basic equations pertaining to mass, energy and momentum conservation, to represent the physics of the process. This is plausibly due to the lack of complete understanding of the hydrological process. The soil and water assessment tool (SWAT) is one such widely accepted semi-distributed, conceptual hydrological model used for water resources planning. However, the over-parameterization, difficulty in its calibration process and the uncertainty associated with predictions make its applications skeptical. This study considers assessing the predictive uncertainty associated with distributed hydrological models. The existing methods for uncertainty estimation demand high computational time and therefore make them challenging to apply on complex hydrological models. The proposed approach employs the concepts of generalized likelihood uncertainty estimation (GLUE) in an iterative procedure by starting with an assumed prior probability distribution of parameters, and by using mutual information (MI) index for sampling the behavioral parameter set. The distributions are conditioned on the observed information through successive cycles of simulations. During each cycle of simulation, MI is used in conjunction with Markov Chain Monte Carlo procedure to sample the parameter sets so as to increase the number of behavioral sets, which in turn helps reduce the number of cycles/simulations for the analysis. The method is demonstrated through a case study of SWAT model in Illinois River basin in the USA. A comparison of the proposed method with GLUE indicates that the computational requirement of uncertainty analysis is considerably reduced in the proposed approach. It is also noted that the model prediction band, derived using the proposed method, is more effective compared to that derived using the other methods considered in this study.     

A geomorphology-based semi-distributed watershed model

A semi-distributed watershed model was developed that conceptualizes the catchment as a cascade of nonlinear storage elements whose geometric dimensions are derived from the Horton–Strahler ordering of the stream network. Each storage element represents quick storm runoff over land or in a channel segment. The physically based groundwater submodel is parameterized through the application of the Brutsaert–Nieber recession flow analysis and it provides continuous baseflow separation. The model requires the calibration of seven parameters from a one year rainfall–runoff record. It was tested on the Mahantango Creek watershed in the Susquehanna River basin, Pennsylvania.     

Use of a watershed hydrologic model to estimate interbasin groundwater flow in a Costa Rican rainforest

The watershed hydrologic model TOPMODEL was used to estimate interbasin groundwater flow (IGF) into a small lowland rainforest watershed in Costa Rica. IGF is a common hydrological process but often difficult to quantify. Four‐year simulations (2006–2009) using three different model approaches gave estimates of IGF that were very similar to each other (10.1, 10.2, and 9.8 m/year) and to an earlier estimate (10.0 m/year) based on 1998–2002 data from a budget study that did not use a hydrologic simulation model, providing confidence in the new estimates and suggesting each of the three model approaches is viable. Results show no significant temporal variation in IGF during 2006–2009 (or between this period and the earlier study from 1998–2002). Simulations of the 16 consecutive 3‐month periods in 2006–2009 gave 16 values of IGF rate with a mean (10.1 m/year, standard deviation = 0.6 m/year) very similar to the estimates above from the 4‐year simulations. This suggests the modified version of TOPMODEL can be used to model stream discharge and estimate IGF for sub‐annual time periods during which change in water storage is not necessarily equal to zero. Thus, simple watershed models may be used to estimate IGF based on even relatively short calibration periods, making such models useful tools in the study of this widespread hydrological process that affects water and chemical fluxes and budgets but is often difficult and costly to quantify. Copyright © 2013 John Wiley & Sons, Ltd.     

The NHDPlus dataset,watershed subdivision and SWAT model performance

Abstract

The Soil and Water Assessment Tool (SWAT) has been developed to evaluate the effectiveness of agricultural management practices on watershed water quality. Many studies have indicated that watershed subdivision can affect the accuracy of model predictions. Most of them used the minimum drainage area (MDA) to delineate sub-watersheds, and varied the value of MDA depending on the size of the watershed being modelled. Instead of MDA, we use the National Hydrography Dataset Plus (NHDPlus)—an integration of the best features of the National Hydrography Dataset (NHD), Watershed Boundary Dataset (WBD), National Elevation Dataset (NED), and the National Land Cover Dataset (NLCD)—to delineate the watershed. The Kaskaskia River watershed in Illinois, USA, was selected to investigate the individual effects of sub-watershed and hydrologic response unit (HRU) delineations on predicted streamflow, total suspended sediment (TSS) and total nitrogen (TN) losses at two USGS gauges. In addition, an MDA of 3000 ha, and four levels of stream (the 2nd, 3rd, 4th and 5th order) were evaluated. Three levels of HRU threshold (5%, 10% and 15%) were used for each stream order model. The results show that stream order had little effect on predicted streamflow, but a great impact on TSS and TN losses, and the impact of HRU delineation became greater when a higher stream order was used to delineate the watershed. For higher stream order, fewer streams were recognized in SWAT simulations, which resulted in less sediment routing and channel processes, which, in turn, led to less deposition in the channels; thus high sediment losses were obtained at the watershed outlet. However, fewer channel processes led to less in-stream N processes; thus lower TN losses. Overall, the SWAT simulations performed the best when the 2nd stream order was used for delineations comparing with USGS observed data, followed by the 3rd stream order. Therefore, to fully depict the watershed characteristics to perform SWAT simulations, a stream order higher than 3rd order is not recommended for watershed delineation.

Editor D. Koutsoyiannis; Associate editor C. Perrin     

Uncertainty issues of a conceptual water balance model for a semi‐arid watershed in north‐west of China

Hydrological models are useful tools for better understanding the hydrological processes and performing the hydrological prediction. However, the reliability of the prediction depends largely on its uncertainty range. This study mainly focuses on estimating model parameter uncertainty and quantifying the simulation uncertainties caused by sole model parameters and the co‐effects of model parameters and model structure in a lumped conceptual water balance model called WASMOD (Water And Snow balance MODeling system). The validity of statistical hypotheses on residuals made in the model formation is tested as well, as it is the base of parameter estimation and simulation uncertainty evaluation. The bootstrap method is employed to examine the parameter uncertainty in the selected model. The Yingluoxia watershed at the upper reaches of the Heihe River basin in north‐west of China is selected as the study area. Results show that all parameters in the model can be regarded as normally distributed based on their marginal distributions and the Kolmogorov–Smirnov test, although they appear slightly skewed for two parameters. Their uncertainty ranges are different from each other. The model residuals are tested to be independent, homoscedastic and normally distributed. Based on such valid hypotheses of model residuals, simulation uncertainties caused by co‐effects of model parameters and model structure can be evaluated effectively. It is found that the 95% and 99% confidence intervals (CIs) of simulated discharge cover 42.7% and 52.4% of the observations when only parameter uncertainty is considered, indicating that parameter uncertainty has a great effect on simulation uncertainty but still cannot be used to explain all the simulation uncertainty in this study. The 95% and 99% CIs become wider, and the percentages of observations falling inside such CIs become larger when co‐effects of parameters and model structure are considered, indicating that simultaneous consideration of both parameters and model structure uncertainties accounts sufficient contribution for model simulation uncertainty. Copyright © 2012 John Wiley & Sons, Ltd.     

Modelling discharge from a coastal watershed in southeast Sweden using an integrated framework

The field hydrology model DRAINMOD integrated with Arc Hydro in geographical information system (GIS) framework (Arc Hydro–DRAINMOD) was used to simulate the hydrological response of a coastal watershed in southeast Sweden. Arc Hydro–DRAINMOD uses a distributed approach to route water from each field edge to the watershed outlet. In the framework the Arc Hydro data model was used to describe the stream network in the watershed and to connect the individual simulated DRAINMOD‐field outflow time series from each plot using Arc Hydro schema‐links features, which were summed at Arc Hydro schema‐nodes features along the stream network to generate the stream network flow. Hydrology data collected during six periods between 2003 and 2008 were used to test Arc Hydro–DRAINMOD and its performance was evaluated by considering uncertainties in model inputs using generalized likelihood uncertainty estimation (GLUE). The GLUE estimates obtained (uncertainty bands 5% and 95%) agreed satisfactorily with measured monthly discharges. The percentage of time in which the observed discharges were bracketed by the uncertainty bands was 88% in calibration periods and 75% in validation periods. Although monthly time step simulations showed good agreement with observed discharges during the two main discharge events in spring, the contradictory daily time step results indicate that the watershed response simulations on a daily basis need to be improved. The uncertainty analysis showed that in periods of higher discharge, such as spring periods, the uncertainty in prediction was higher. It is important to note that these uncertainty estimations using the GLUE procedure include the uncertainties in measured discharge values, model inputs, boundary conditions and model structures. It was estimated that stream baseflow represented 42% of the total watershed discharge, but further research is needed to confirm this. These results show that the new Arc Hydro–DRAINMOD framework is applicable for predicting discharge from artificially drained watersheds in southeast Sweden. Copyright © 2010 John Wiley & Sons, Ltd.     

Comparison of parameter uncertainty analysis techniques for a TOPMODEL application

Parameter uncertainty in hydrologic modeling is crucial to the flood simulation and forecasting. The Bayesian approach allows one to estimate parameters according to prior expert knowledge as well as observational data about model parameter values. This study assesses the performance of two popular uncertainty analysis (UA) techniques, i.e., generalized likelihood uncertainty estimation (GLUE) and Bayesian method implemented with the Markov chain Monte Carlo sampling algorithm, in evaluating model parameter uncertainty in flood simulations. These two methods were applied to the semi-distributed Topographic hydrologic model (TOPMODEL) that includes five parameters. A case study was carried out for a small humid catchment in the southeastern China. The performance assessment of the GLUE and Bayesian methods were conducted with advanced tools suited for probabilistic simulations of continuous variables such as streamflow. Graphical tools and scalar metrics were used to test several attributes of the simulation quality of selected flood events: deterministic accuracy and the accuracy of 95 % prediction probability uncertainty band (95PPU). Sensitivity analysis was conducted to identify sensitive parameters that largely affect the model output results. Subsequently, the GLUE and Bayesian methods were used to analyze the uncertainty of sensitive parameters and further to produce their posterior distributions. Based on their posterior parameter samples, TOPMODEL’s simulations and the corresponding UA results were conducted. Results show that the form of exponential decline in conductivity and the overland flow routing velocity were sensitive parameters in TOPMODEL in our case. Small changes in these two parameters would lead to large differences in flood simulation results. Results also suggest that, for both UA techniques, most of streamflow observations were bracketed by 95PPU with the containing ratio value larger than 80 %. In comparison, GLUE gave narrower prediction uncertainty bands than the Bayesian method. It was found that the mode estimates of parameter posterior distributions are suitable to result in better performance of deterministic outputs than the 50 % percentiles for both the GLUE and Bayesian analyses. In addition, the simulation results calibrated with Rosenbrock optimization algorithm show a better agreement with the observations than the UA’s 50 % percentiles but slightly worse than the hydrographs from the mode estimates. The results clearly emphasize the importance of using model uncertainty diagnostic approaches in flood simulations.     

A comprehensive assessment framework for attributing trends in streamflow and groundwater storage to climatic and anthropogenic changes: A case study in the typical semi-arid catchments of southern India

The clearest signs of hydrologic change can be observed from the trends in streamflow and groundwater levels in a catchment. During 1980–2007, significant declines in streamflow (−3.03 mm/year) and groundwater levels (−0.22 m/year) were observed in Himayat Sagar (HS) catchment, India. We examined the degree to which hydrologic changes observed in the HS catchment can be attributed to various internal and external drivers of change (climatic and anthropogenic changes). This study used an investigative approach to attribute hydrologic changes. First, it involves to develop a model and test its ability to predict hydrologic trends in a catchment that has undergone significant changes. Second, it examines the relative importance of different causes of change on the hydrologic response. The analysis was carried out using Modified Soil and Water Assessment Tool (SWAT), a semi-distributed rainfall-runoff model coupled with a lumped groundwater model for each sub- catchment. The model results indicated that the decline in potential evapotranspiration (PET) appears to be partially offset by a significant response to changes in rainfall. Measures that enhance recharge, such as watershed hydrological structures, have had limited success in terms of reducing impacts on the catchment-scale water balance. Groundwater storage has declined at a rate of 5 mm/y due to impact of land use changes and this was replaced by a net addition of 2 mm/y by hydrological structures. The impact of land use change on streamflow is an order of magnitude larger than the impact of hydrological structures and about is 2.5 times higher in terms of groundwater impact. Model results indicate that both exogenous and endogenous changes can have large impacts on catchment hydrology and should be considered together. The proposed comprehensive framework and approach demonstrated here is valuable in attributing trends in streamflow and groundwater levels to catchment climatic and anthropogenic changes.     

Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins

Approaches to modeling the continuous hydrologic response of ungauged basins use observable physical characteristics of watersheds to either directly infer values for the parameters of hydrologic models, or to establish regression relationships between watershed structure and model parameters. Both these approaches still have widely discussed limitations, including impacts of model structural uncertainty. In this paper we introduce an alternative, model independent, approach to streamflow prediction in ungauged basins based on empirical evidence of relationships between watershed structure, climate and watershed response behavior. Instead of directly estimating values for model parameters, different hydrologic response behaviors of the watershed, quantified through model independent streamflow indices, are estimated and subsequently regionalized in an uncertainty framework. This results in expected ranges of streamflow indices in ungauged watersheds. A pilot study using 30 UK watersheds shows how this regionalized information can be used to constrain ensemble predictions of any model at ungauged sites. Dominant controlling characteristics were found to be climate (wetness index), watershed topography (slope), and hydrogeology. Main streamflow indices were high pulse count, runoff ratio, and the slope of the flow duration curve. This new approach provided sharp and reliable predictions of continuous streamflow at the ungauged sites tested.     

Performance of a conceptual and physically based model in simulating the response of a semi‐urbanized watershed in San Antonio,Texas

The need for accurate hydrologic analysis and rainfall–runoff modelling tools has been rapidly increasing because of the growing complexity of operational hydrologic and hydraulic problems associated with population growth, rapid urbanization and expansion of agricultural activities. Given the recent advances in remote sensing of physiographic features and the availability of near real‐time precipitation products, rainfall–runoff models are expected to predict runoff more accurately. In this study, we compare the performance and implementation requirements of two rainfall–runoff models for a semi‐urbanized watershed. One is a semi‐distributed conceptual model, the Hydrologic Engineering Center‐Hydrologic Modelling System (HEC‐HMS). The other is a physically based, distributed‐parameter hydrologic model, the Gridded Surface Subsurface Hydrologic Analysis (GSSHA). Four flood events that took place on the Leon Creek watershed, a sub‐watershed of the San Antonio River basin in Texas, were used in this study. The two models were driven by the Multisensor Precipitation Estimator radar products. One event (in 2007) was used for HEC‐HMS and GSSHA calibrations. Two events (in 2004 and 2007) were used for further calibration of HEC‐HMS. Three events (in 2002, 2004 and 2010) were used for model validation. In general, the physically based, distributed‐parameter model performed better than the conceptual model and required less calibration. The two models were prepared with the same minimum required input data, and the effort required to build the two models did not differ substantially. Copyright © 2012 John Wiley & Sons, Ltd.     

Effects of reduced land cover detail on hydrological model response

Problems related to scale continue to be at the forefront of research in hydrology. Past research into issues of scale has focused mainly on digital elevation model grid size, the appropriate number and size of sub‐areas for subdividing a watershed, parameter transferability between watersheds and appropriate scales for linking hydrological and general circulation models. Much less attention has been given to the effects of scale on the representation of land cover and hydrological model response. Recent studies with respect to changes in land cover and hydrologic response have tended to focus on the issue of land cover maturity and the conversion of land through agricultural and forestry practices. The focus of this study is to examine the impact of the level of detail at which land cover is represented in modelling the hydrological response of Wolf Creek Basin in northwest Canada. A grid‐based land cover map with a spatial resolution of 30 m is coarsened or smoothed using several common grid‐based methods of aggregating categorical data, including: pixel thinning, modal smoothing and modal aggregation. A majority rule method based on polygons is also applied to the 30 m base cover. The SLURP hydrologic model is calibrated for the base cover and used as a reference for comparing simulations for the coarsened or ‘generalized’ land cover maps. Results of the simulations are compared to examine the sensitivity of hydrologic response to generalized land cover information. Comparisons of the SLURP model runs for Wolf Creek suggest that reducing the level of detail of land cover information generally has a limited effect on hydrologic response at the outlet. However, results for averages of water balance components across the basin suggest that the local variability of hydrologic response is affected in general. Copyright © 2007 John Wiley & Sons, Ltd.     

Choosing an arbitrary calibration period for hydrologic models: How much does it influence water balance simulations?

The selection of calibration and validation time periods in hydrologic modelling is often done arbitrarily. Nonstationarity can lead to an optimal parameter set for one period which may not accurately simulate another. However, there is still much to be learned about the responses of hydrologic models to nonstationary conditions. We investigated how the selection of calibration and validation periods can influence water balance simulations. We calibrated Soil and Water Assessment Tool hydrologic models with observed streamflow for three United States watersheds (St. Joseph River of Indiana/Michigan, Escambia River of Florida/Alabama, and Cottonwood Creek of California), using time period splits for calibration/validation. We found that the choice of calibration period (with different patterns of observed streamflow, precipitation, and air temperature) influenced the parameter sets, leading to dissimilar simulations of water balance components. In the Cottonwood Creek watershed, simulations of 50-year mean January streamflow varied by 32%, because of lower winter precipitation and air temperature in earlier calibration periods on calibrated parameters, which impaired the ability for models calibrated to earlier periods to simulate later periods. Peaks of actual evapotranspiration for this watershed also shifted from April to May due to different parameter values depending on the calibration period's winter air temperatures. In the St. Joseph and Escambia River watersheds, adjustments of the runoff curve number parameter could vary by 10.7% and 20.8%, respectively, while 50-year mean monthly surface runoff simulations could vary by 23%–37% and 169%–209%, depending on the observed streamflow and precipitation of the chosen calibration period. It is imperative that calibration and validation time periods are chosen selectively instead of arbitrarily, for instance using change point detection methods, and that the calibration periods are appropriate for the goals of the study, considering possible broad effects of nonstationary time series on water balance simulations. It is also crucial that the hydrologic modelling community improves existing calibration and validation practices to better include nonstationary processes.     

Watershed modelling of hydrology and water quality in the Sacramento River watershed,California

Agricultural pollutant runoff is a major source of water contamination in California's Sacramento River watershed where 8500 km² of agricultural land influences water quality. The Soil and Water Assessment Tool (SWAT) hydrology, sediment, nitrate and pesticide transport components were assessed for the Sacramento River watershed. To represent flood conveyance in the area, the model was improved by implementing a flood routing algorithm. Sensitivity/uncertainty analyses and multi‐objective calibration were incorporated into the model application for predicting streamflow, sediment, nitrate and pesticides (chlorpyrifos and diazinon) at multiple watershed sites from 1992 to 2008. Most of the observed data were within the 95% uncertainty interval, indicating that the SWAT simulations were capturing the uncertainties that existed, such as model simplification, observed data errors and lack of agricultural management data. The monthly Nash–Sutcliffe coefficients at the watershed outlet ranged from 0.48 to 0.82, indicating that the model was able to successfully predict streamflow and agricultural pollutant transport after calibration. Predicted sediment loads were highly correlated to streamflow, whereas nitrate, chlorpyrifos and diazinon were moderately correlated to streamflow. This indicates that timing of agricultural management operations plays a role in agricultural pollutant runoff. Best management practices, such as pesticide use limits during wet seasons, could improve water quality in the Sacramento River watershed. The calibrated model establishes a modelling framework for further studies of hydrology, water quality and ecosystem protection in the study area. Copyright © 2012 John Wiley & Sons, Ltd.     

Effects of spatial aggregation of soil spatial information on watershed hydrological modelling

Many researchers have examined the impact of detailed soil spatial information on hydrological modelling due to the fact that such information serves as important input to hydrological modelling, yet is difficult and expensive to obtain. Most research has focused on the effects at single scales; however, the effects in the context of spatial aggregation across different scales are largely missing. This paper examines such effects by comparing the simulated runoffs across scales from watershed models based on two different levels of soil spatial information: the 10‐m‐resolution soil data derived from the Soil‐Land Inference Model (SoLIM) and the 1:24000 scale Soil Survey Geographic (SSURGO) database in the United States. The study was conducted at three different spatial scales: two at different watershed size levels (referred to as full watershed and sub‐basin, respectively) and one at the model minimum simulation unit level. A fully distributed hydrologic model (WetSpa) and a semi‐distributed model (SWAT) were used to assess the effects. The results show that at the minimum simulation unit level the differences in simulated runoff are large, but the differences gradually decrease as the spatial scale of the simulation units increases. For sub‐basins larger than 10 km² in the study area, stream flows simulated by spatially detailed SoLIM soil data do not significantly vary from those by SSURGO. The effects of spatial scale are shown to correlate with aggregation effect of the watershed routing process. The unique findings of this paper provide an important and unified perspective on the different views reported in the literature concerning how spatial detail of soil data affects watershed modelling. Different views result from different scales at which those studies were conducted. In addition, the findings offer a potentially useful basis for selecting details of soil spatial information appropriate for watershed modelling at a given scale. Copyright © 2011 John Wiley & Sons, Ltd.     

Modeling and assessing hydrologic processes for historical and potential land-cover change in the Duoyingping watershed,southwest China

Land-cover change significantly influences hydrologic processes at the watershed level. The mountainous Duoyingping watershed in upstream Yangtze River, China, has undergone dramatic land-cover change in the past three decades. It is likely to maintain this trend in the future, inevitably altering hydrologic processes in the region to a certain degree. Therefore, the effects of land-cover change on runoff, evapotranspiration (ET), and soil moisture in the watershed were assessed using a large-scale Variable Infiltration Capacity (VIC) hydrologic model.To minimize the effect of climate change on simulation results, we used detrended climate data over the period 1980–2005 in forcing the VIC model. The dynamics in the spatial distribution of land-cover types in the Duoyingping watershed from 1980 to 2000 were first examined, revealing that reforestation and deforestation were the major change patterns. On the basis of various land-use policies, potential land-cover scenarios for 2030 were established using an integrated land-use change model (CLUE-S). The scenarios were developed using 2000 land-use data as bases. Finally, the calibrated VIC model was applied in the scenarios to assess land-cover effects on hydrology. Hydrologic simulations showed that the effects of historical land-cover change on hydrology were discernible in the sub-watersheds of Nanba, Yingjing, and Yuxi. The annual ET was projected to decrease by 0.8–22.3% because of deforestation, and increase by 2.3–27.4% because of shrubland–forest conversion. Different potential land-cover scenarios play various roles in the effect on hydrology because of various land-use policies. In the scenario concerning forest protection policy, annual ET increased by more than 15%, while annual runoff decreased by 6%. However, a negligible effect on hydrology was found under the scenario involving cropland expansion. On the basis of the results, it is concluded that ET is more sensitive to land-cover change than are other hydrologic components. Hydrologic alteration caused by reforestation and deforestation during the dry season was more significant than that during wet season. Generally, the proposed approach in the study can be a useful means of assessing hydrologic responses to land-cover change.