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1.
Efficiency of hydrological models mostly depends on the quality of the calibration performed prior to use. In this paper, an automatic calibration framework for the distributed hydrological model HYDROTEL is proposed. The calibration procedure was performed for three watersheds characterized with different hydroclimatological conditions: the Sassandra located in Ivory Coast, Africa, and the Montmorency and Beaurivage watersheds located in Quebec (Canada). Results of one‐a‐time (OAT) sensitivity analysis showed that the order of the most sensitive parameters differs for each watershed. Thus, the sensitivity depends on the hydroclimatic and physiographic characteristics of the watersheds. Co‐linearity indices showed that all model parameters were identifiable, that is, none of the studied parameters could be explained by a combination of the other parameters. Following these findings, an automatic calibration was run. Results indicated there was good agreement between simulated and measured streamflows at the outlet of each watershed; Nash–Sutcliffe efficiency (NSE) ranging between 0.77 and 0.92 and R2 ranging from 0.87 to 0.97. When comparing NSE and R2 values obtained using a process‐oriented, multiple‐objective, manual calibration strategy, a slight increase in model efficiency was reached with the automatic calibration procedure (4.15% for NSE and 2.95% for R2) improving predictions of peak flows for the Montmorency and Beaurivage watersheds (temperate climate conditions) and flows beyond the rainfall season in the Sassandra watershed. The proposed automatic calibration procedure introduced in this paper may be applied to other distributed hydrological model. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

2.
Hydrologic models often require correct estimates of surface macro‐depressional storage to accurately simulate rainfall–runoff processes. Traditionally, depression storage is determined through model calibration or lumped with soil storage components or on an ad hoc basis. This paper investigates a holistic approach for estimating surface depressional storage capacity (DSC) in watersheds using digital elevation models (DEMs). The methodology includes implementing a lumped DSC model to extract geometric properties of storage elements from DEMs of varying grid resolutions and employing a consistency zone criterion to quantify the representative DSC of an isolated watershed. DSC obtained using the consistency zone approach is compared to DSC estimated by “brute force” (BF) optimization method. The BF procedure estimates optimal DSC by calibrating DRAINMOD, a quasi‐process based hydrologic model, with observed streamflow under different climatic conditions. Both methods are applied to determine the DSC for relatively low‐gradient coastal plain watersheds on forested landscape with slopes less than 3%. Results show robustness of the consistency zone approach for estimating depression storage. To test the adequacy of the calculated DSC values obtained, both methods are applied in DRAINMOD to predict the daily watershed flow rates. Comparison between observed and simulated streamflow reveals a marginal difference in performance between BF optimization and consistency zone estimated DSCs during wet periods, but the latter performed relatively better in dry periods. DSC is found to be dependent on seasonal antecedent moisture conditions on surface topography. The new methodology is beneficial in situations where data on depressional storage is unavailable for calibrating models requiring this input parameter. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

3.
The efficiency of the methods of spatial proximity and geostatistics, as well as physico-geographic similarity, is studied as applied to the evaluation of the key model parameters of ungauged watersheds to be used in river runoff calculation by SWAP model. The target geographic objects were 323 experimental watersheds of MOPEX project. The quality of model parameter estimates and reproduction of river runoff hydrographs was analyzed in the case of the use of different similarity methods, and the order of decisions to be made was developed for the problem of river runoff calculation from an ungauged watershed for the entire area under study.  相似文献   

4.
Forecasts of runoff volumes are required in order to maximize the utility of water-supply sources. In remote areas where hydrologic and land-use data are sparse, forecast models are needed; such models should be conceptually rational so they can be transferred to remote watersheds where data are sparse. A series of models were calibrated for a large watershed in India. A spatially-distributed seasonally-varying model having a structure similar to the rational method was found to provide precise, unbiased estimates of 10-day streamflow volumes. The model was tested on a watershed that was not used for calibration, with the results indicating a high correlation between the observed and measured streamflow. Thus, the model should provide good estimates of streamflow volumes on other ungaged watersheds.  相似文献   

5.
This study develops improved Soil Moisture Proxies (SMP) based suspended sediment yield (SMPSY) models corresponding to three antecedent moisture conditions (AMCs) (i.e., AMC-I-AMC-III) by coupling the improved initial abstraction (Ia-λ) model, the SMA procedure and the SMP concept for modelling the rainfall generated suspended sediment yield. The SMPSY models specifically incorporate a watershed storage index (S) model to accentuate the transformation from storm to storm and to avoid the sudden jumps in sediment yield computation. The workability of the SMPSY models is tested using a large dataset of rainfall and sediment yield (98 storm events) from twelve small watersheds and a comparison has been made with the existing MSY model. The goodness-of-fit (GOF) statistics is evaluated in terms of the Nash Sutcliffe efficiency (NSE), and error indices, i.e., root mean square error (RMSE), normalized root mean square error (nRMSE), standard error (SE), mean absolute error (MAE), and RMSE-observations standard deviation ratio (RSR). The NSE values vary from 74.31% to 96.57% and from 75.21% to 91.78%, respectively for the SPMSY and MSY model. The NSE statistics indicate that the SMPSY model has lower uncertainty in simulating sediment yield as compared to the MSY model. The error indices are lower for the SMPSY model than the MSY model for most of the watersheds. These results show that the SMPSY model has less uncertainty and performs better than the MSY model. A sensitivity analysis of the SMPSY model shows that the parameter β is most sensitive followed by parameter S, α and A. Overall, the results show that the characterization of soil moisture variability in terms of SMPs and incorporation of improved delivery ratio and runoff coefficient relationship improves the simulation of the erosion and sediment yield generation process.  相似文献   

6.
Climate change is expected to effect storm runoff and erosion processes in Mediterranean watersheds at multiple spatial scales. Models are typically applied to estimate these impacts; however, the scarcity of spatially distributed data for parameterization, calibration and validation often prevents application of these models, particularly for larger catchments. This report, the first part of a two‐part article, presents an application and evaluation of the MEFIDIS model for two Mediterranean meso‐scale watersheds (115 and 290 km2) in a data‐scarce environment. A multi‐scale assessment method was used that combines quantitative validation and qualitative evaluation, consisting of three steps: (1) calibration at the small (field) scale using results from rainfall simulation experiments; (2) calibration and validation for catchment‐scale results while changing catchment‐scale parameters only (channel roughness and a parameter controlling the distribution of saturated areas); and (3) qualitative evaluation of within‐watershed erosion processes using empirical estimates of sediment delivery ratio and gully location. The results indicate that calibrating MEFIDIS at the field scale can provide reasonable results for catchment runoff and sediment export and for within‐watershed erosion processes. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

7.
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.  相似文献   

8.
How can spatially explicit nonlinear regression modelling be used for obtaining nonpoint source loading estimates in watersheds with limited information? What is the value of additional monitoring and where should future data‐collection efforts focus on? In this study, we address two frequently asked questions in watershed modelling by implementing Bayesian inference techniques to parameterize SPAtially Referenced Regressions On Watershed attributes (SPARROW), a model that empirically estimates the relation between in‐stream measurements of nutrient fluxes and the sources/sinks of nutrients within the watershed. Our case study is the Hamilton Harbour watershed, a mixed agricultural and urban residential area located at the western end of Lake Ontario, Canada. The proposed Bayesian approach explicitly accounts for the uncertainty associated with the existing knowledge from the system and the different types of spatial correlation typically underlying the parameter estimation of watershed models. Informative prior parameter distributions were formulated to overcome the problem of inadequate data quantity and quality, whereas the potential bias introduced from the pertinent assumptions is subsequently examined by quantifying the relative change of the posterior parameter patterns. Our modelling exercise offers the first estimates of export coefficients and delivery rates from the different subcatchments and thus generates testable hypotheses regarding the nutrient export ‘hot spots’ in the studied watershed. Despite substantial uncertainties characterizing our calibration dataset, ranging from 17% to nearly 400%, we arrived at an uncertainty level for the whole‐basin nutrient export estimates of only 36%. Finally, we conduct modelling experiments that evaluate the potential improvement of the model parameter estimates and the decrease of the predictive uncertainty if the uncertainty associated with the current nutrient loading estimates is reduced. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

9.
For the appropriate management of water resources in a watershed, it is essential to calculate the time distribution of runoff for the given rainfall event. In this paper, a kinematic‐wave‐based distributed watershed model using finite element method (FEM), geographical information systems (GIS) and remote‐sensing‐based approach is presented for the runoff simulation of small watersheds. The kinematic wave equations are solved using FEM for overland and channel flow to generate runoff at the outlet of the watershed concerned. The interception loss is calculated by an empirical model based on leaf area index (LAI). The Green‐Ampt Mein Larson (GAML) model is used for the estimation of infiltration. Remotely sensed data has been used to extract land use (LU)/land cover (LC). GIS have been used to prepare finite element grid and input files such as Manning's roughness and slope. The developed overland flow model has been checked with an analytical solution for a hypothetical watershed. The model has been applied to a gauged watershed and an ungauged watershed. From the results, it is seen that the model is able to simulate the hydrographs reasonably well. A sensitivity analysis of the model is carried out with the calibrated infiltration parameters, overland flow Manning's roughness, channel flow Manning's roughness, time step and grid size. The present model is useful in predicting the hydrograph in small, ungauged watersheds. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

10.
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.  相似文献   

11.
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.  相似文献   

12.
The primary objective of the Watershed Model Studies Project, reported herein, was to ascertain the effect of selected watershed characteristics on hydrograph parameters under a rainfall simulator. Since most of the runoff contributing to the peak flow was found to emanate from the lower half of the drainage, a measure of watershed eccentricity utilizing easily measured properties in that area is derived and evaluated as a reliable predictor of peak magnitude. In the process of isolating watershed shape, slope, size, drainage pattern, and soil depth were isolated and, along with rainfall intensity, direction of storm movement, and antecedent moisture conditions, evaluated for the models. Studies were made into the similarities between the models and real world watersheds. Three of the several conclusions are 1) the models exhibit hydrologic responses similar to those of a wide range of real watersheds; 2) watershed shape, of itself, does not have a tremendous effect on peak magnitude, and 3) watershed eccentricity is an effective, easily measured, meaningful, and useful expression of watershed shape insofar as that characteristic affects maximum peak flows and certain time parameters of the hydrograph.  相似文献   

13.
Precipitation is a key control on watershed hydrologic modelling output, with errors in rainfall propagating through subsequent stages of water quantity and quality analysis. Most watershed models incorporate precipitation data from rain gauges; higher‐resolution data sources are available, but they are associated with greater computational requirements and expertise. Here, we investigate whether the Multisensor Precipitation Estimator (MPE or Stage IV Next‐Generation Radar) data improve the accuracy of streamflow simulations using the Soil and Water Assessment Tool (SWAT), compared with rain gauge data. Simulated flows from 2002 to 2010 at five timesteps were compared with observed flows for four nested subwatersheds of the Neuse River basin in North Carolina (21‐, 203‐, 2979‐, and 10 100‐km2 watershed area), using a multi‐objective function, informal likelihood‐weighted calibration approach. Across watersheds and timesteps, total gauge precipitation was greater than radar precipitation, but radar data showed a conditional bias of higher rainfall estimates during large events (>25–50 mm/day). Model parameterization differed between calibrations with the two datasets, despite the fact that all watershed characteristics were the same across simulation scenarios. This underscores the importance of linking calibration parameters to realistic processes. SWAT simulations with both datasets underestimated median and low flows, whereas radar‐based simulations were more accurate than gauge‐based simulations for high flows. At coarser timesteps, differences were less pronounced. Our results suggest that modelling efforts in watersheds with poor rain gauge coverage can be improved with MPE radar data, especially at short timesteps. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.  相似文献   

14.
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15.
Abstract

Using the Monte Carlo (MC) method, this paper derives arithmetic and geometric means and associated variances of the net capillary drive parameter, G, that appears in the Parlange infiltration model, as a function of soil texture and antecedent soil moisture content. Approximate expressions for the arithmetic and geometric statistics of G are also obtained, which compare favourably with MC generated ones. This paper also applies the MC method to evaluate parameter sensitivity and predictive uncertainty of the distributed runoff and erosion model KINEROS2 in a small experimental watershed. The MC simulations of flow and sediment related variables show that those parameters which impart the greatest uncertainty to KINEROS2 model outputs are not necessarily the most sensitive ones. Soil hydraulic conductivity and wetting front net capillary drive, followed by initial effective relative saturation, dominated uncertainties of flow and sediment discharge model outputs at the watershed outlet. Model predictive uncertainty measured by the coefficient of variation decreased with rainfall intensity, thus implying improved model reliability for larger rainfall events. The antecedent relative saturation was the most sensitive parameter in all but the peak arrival times, followed by the overland plane roughness coefficient. Among the sediment related parameters, the median particle size and hydraulic erosion parameters dominated sediment model output uncertainty and sensitivity. Effect of rain splash erosion coefficient was negligible. Comparison of medians from MC simulations and simulations by direct substitution of average parameters with observed flow rates and sediment discharges indicates that KINEROS2 can be applied to ungauged watersheds and still produce runoff and sediment yield predictions within order of magnitude of accuracy.  相似文献   

16.
Many water quality models use some form of the curve number (CN) equation developed by the Soil Conservation Service (SCS; U.S. Depart of Agriculture) to predict storm runoff from watersheds based on an infiltration-excess response to rainfall. However, in humid, well-vegetated areas with shallow soils, such as in the northeastern USA, the predominant runoff generating mechanism is saturation-excess on variable source areas (VSAs). We reconceptualized the SCS–CN equation for VSAs, and incorporated it into the General Watershed Loading Function (GWLF) model. The new version of GWLF, named the Variable Source Loading Function (VSLF) model, simulates the watershed runoff response to rainfall using the standard SCS–CN equation, but spatially distributes the runoff response according to a soil wetness index. We spatially validated VSLF runoff predictions and compared VSLF to GWLF for a subwatershed of the New York City Water Supply System. The spatial distribution of runoff from VSLF is more physically realistic than the estimates from GWLF. This has important consequences for water quality modeling, and for the use of models to evaluate and guide watershed management, because correctly predicting the coincidence of runoff generation and pollutant sources is critical to simulating non-point source (NPS) pollution transported by runoff. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

17.
Two distributed parameter models, a one‐dimensional (1D) model and a two‐dimensional (2D) model, are developed to simulate overland flow in two small semiarid shrubland watersheds in the Jornada basin, southern New Mexico. The models are event‐based and represent each watershed by an array of 1‐m2 cells, in which the cell size is approximately equal to the average area of the shrubs. Each model uses only six parameters, for which values are obtained from field surveys and rainfall simulation experiments. In the 1D model, flow volumes through a fixed network are computed by a simple finite‐difference solution to the 1D kinematic wave equation. In the 2D model, flow directions and volumes are computed by a second‐order predictor–corrector finite‐difference solution to the 2D kinematic wave equation, in which flow routing is implicit and may vary in response to flow conditions. The models are compared in terms of the runoff hydrograph and the spatial distribution of runoff. The simulation results suggest that both the 1D and the 2D models have much to offer as tools for the large‐scale study of overland flow. Because it is based on a fixed flow network, the 1D model is better suited to the study of runoff due to individual rainfall events, whereas the 2D model may, with further development, be used to study both runoff and erosion during multiple rainfall events in which the dynamic nature of the terrain becomes an important consideration. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

18.
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.  相似文献   

19.
Experimental research in the Ethiopian highlands found that saturation excess induced runoff and erosion are common in the sub‐humid conditions. Because most erosion simulation models applied in the highlands are based on infiltration excess, we, as an alternative, developed the Parameter Efficient Distributed (PED) model, which can simulate water and sediment fluxes in landscapes with saturation excess runoff. The PED model has previously only been tested at the outlet of a watershed and not for distributed runoff and sediment concentration within the watershed. In this study, we compare the distributed storm runoff and sediment concentration of the PED model against collected data in the 95‐ha Debre Mawi watershed and three of its nested sub‐watersheds for the 2010 and 2011 rainy seasons. In the PED model framework, the hydrology of the watershed is divided between infiltrating and runoff zones, with erosion only taking place from two surface runoff zones. Daily storm runoff and sediment concentration values, ranging from 0.5 to over 30 mm and from 0.1 to 35 g l?1, respectively, were well simulated. The Nash Sutcliffe efficiency values for the daily storm runoff for outlet and sub‐watersheds ranged from 0.66 to 0.82, and the Nash–Sutcliffe efficiency for daily sediment concentrations were greater than 0.78. Furthermore, the model uses realistic fractional areas for surface and subsurface flow contributions, for example between saturated areas (15%), degraded areas (30%) and permeable areas (55%) at the main outlet, while close similarity was found for the remaining hydrology and erosion parameter values. One exception occurred for the distinctly greater transport limited parameter at the actively gullying lower part of the watershed. The results suggest that the model based on saturation excess provides a good representation of the observed spatially distributed runoff and sediment concentrations within a watershed by modelling the bottom lands (as opposed to the uplands) as the dominant contributor of the runoff and sediment load. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

20.
Predicting the timing of overland flow in burned watersheds can help to estimate debris-flow timing and the location of debris-flow initiation. Numerical models can produce flow predictions, but they are limited by our knowledge of appropriate model parameters. Moreover, opportunities to test and calibrate model parameters in post-wildfire settings are limited by available data (measurements of debris-flow timing are rare). In this study, we use a unique data set of rainfall and flow-timing data to test the extent to which model parameters can be generalized from an individual watershed to other watersheds (0.01 km 2 to >1km 2) within a burned area. Simulations suggest that a single, low, saturated hydraulic conductivity value can be used in post-wildfire landscapes with reasonable results. By contrast, we found that watershed-scale effective Manning roughness parameter values decrease as a power-law function of basin drainage area. Thus a Manning roughness parameter calibrated for a single basin within a burned area may not provide adequate results in a different watershed. However, when flow velocity is modeled independently for hillslopes and channels, and different roughness parameters are used for those morphometric units, there is no drainage-area dependence on the roughness parameters. Moreover, we found that it was possible to use field-measured grain size data to parameterize the roughness for both hillslopes and channels. Thus our results show that, employing this generalizable approach, it is possible to use field measurements to fully parameterize a model that produces peak flow timing to within a few minutes in storms lasting several hours. Further, we demonstrate how model simulations can be leveraged to identify areas within a watershed that are most susceptible to debris flows. This modeling approach could be used for decision making in hazardous burned areas and would be especially useful in ungaged basins. © 2019 John Wiley & Sons, Ltd.  相似文献   

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