Physically based, distributed hydrologic models are increasingly used in assessments of water resources, best management practices, and climate and land use changes. Model performance evaluation in ungauged basins is an important research topic. In this study, we propose a framework for developing Soil and Water Assessment Tool (SWAT) input data, including hydrography, terrain, land use, soil, tile, weather, and management practices, for the Upper Mississippi River basin (UMRB). We also present a performance evaluation of SWAT hydrologic budget and crop yield simulations in the UMRB without calibration. The uncalibrated SWAT model ably predicts annual streamflow at 11 USGS gauges and crop yield at a four-digit hydrologic unit code (HUC) scale. For monthly streamflow simulation, the performance of SWAT is marginally poor compared with that of annual flow, which may be due to incomplete information about reservoirs and dams within the UMRB. Further validation shows that SWAT can predict base flow contribution ratio reasonably well. Compared with three calibrated SWAT models developed in previous studies of the entire UMRB, the uncalibrated SWAT model presented here can provide similar results. Overall, the SWAT model can provide satisfactory predictions on hydrologic budget and crop yield in the UMRB without calibration. The results emphasize the importance and prospects of using accurate spatial input data for the physically based SWAT model. This study also examines biofuel-biomass production by simulating all agricultural lands with switchgrass, producing satisfactory results in estimating biomass availability for biofuel production.

SWAT Ungauged: Hydrological Budget and Crop Yield Predictions in the Upper Mississippi River Basin

https://pubag.nal.usda.gov/download/33737/PDF

Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging

. Hydrologic and water quality (H/WQ) models are widely used to support site-specific environmental assessment, design, planning, and decision making. Calibration and validation (C/V) are fundamental processes used to demonstrate that an H/WQ model can produce suitable results in a particular application. However, the lack of comprehensive guidelines has led to the use of ad hoc, inconsistent, and incomplete C/V processes, which have made it difficult to interpret the myriad of published modeling studies, reduced the utility of many modeling applications, and slowed the advancement of H/WQ modeling. The objective of this article is to provide a generalized structure and process to assist modelers in developing a C/V strategy for H/WQ modeling applications. These best practice recommendations were developed based on an expansive review of the modeling literature, including a special collection of articles on H/WQ model calibration, validation, and use, as well as extensive discussion and debate among the authors. The model C/V recommendations include careful consideration, execution, and documentation of the following elements: (1) goals of model use, (2) data and parameters used in C/V, and (3) model C/V processes. Considerations in element 3 include the warm-up period, C/V strategy complexity, C/V process staging, spatiotemporal allocation of C/V comparison data, manual vs. automatic C/V, and additional diagnostics. Notable examples from the literature are provided for each strategy element. The comprehensive C/V strategy described herein will allow for better interpretation of future modeling studies, improved utility of modeling applications, and more systematic advancement of H/WQ models.

A Recommended Calibration and Validation Strategy for Hydrologic and Water Quality Models

The growing popularity of applying complex, semi-physically based distributed hydrologic models to solve water resource problems poses important issues that must be addressed related to the use of spatial data to calibrate and validate such models. In this study, a single-objective optimization method (GA) and a multi-objective optimization algorithm (SPEA2) were applied to optimize the parameters of the Soil and Water Assessment Tool (SWAT) using observed streamflow data at three monitoring sites within the Reynolds Creek Experimental Watershed, Idaho. Results indicated that different optimization schemes can lead to substantially different objective function values, parameter solutions, and corresponding simulated hydrographs. Thus, the selection of an optimization scheme can potentially impact modeled streamflow. Parameters estimated by optimizing the objective function at three monitoring sites consistently produced better goodness-of-fit than those obtained by optimization at a single monitoring site. This stresses the importance of collecting detailed, spatially distributed data to conduct simultaneous multi-site calibrations. When applied with multi-site data, the single-objective (GA) method better identified parameter solutions in the calibration period, but the multi-objective (SPEA2) method performed better in the validation period. Overall, the application of different optimization schemes in the Reynolds Creek Experimental Watershed demonstrated that the single-objective (GA) and the multi-objective (SPEA2) optimization methods can provide promising results for multi-site calibration and validation of the SWAT model. These results are expected to help the users of SWAT and other distributed hydrologic models understand the sensitivity of distributed hydrologic simulation to different calibration methods and to demonstrate the advantages and disadvantages of single-objective and multi-objective parameter estimation methods.

/pdf/multi-site-calibration-of-the-swat-model-for-hydrologic-3aevje5jmc.pdf

Multi-Site Calibration of the SWAT Model for Hydrologic Modeling

Increased atmospheric CO2 concentration and climate change may significantly impact the hydrological and meteorological processes of a watershed system. Quantifying and understanding hydrological responses to elevated ambient CO2 and climate change is, therefore, critical for formulating adaptive strategies for an appropriate management of water resources. In this study, the Soil and Water Assessment Tool (SWAT) model was applied to assess the effects of increased CO2 concentration and climate change in the Upper Mississippi River Basin (UMRB). The standard SWAT model was modified to represent more mechanistic vegetation type specific responses of stomatal conductance reduction and leaf area increase to elevated CO2 based on physiological studies. For estimating the historical impacts of increased CO2 in the recent past decades, the incremental (i.e., dynamic) rises of CO2 concentration at a monthly time-scale were also introduced into the model. Our study results indicated that about 1–4% of the streamflow in the UMRB during 1986 through 2008 could be attributed to the elevated CO2 concentration. In addition to evaluating a range of future climate sensitivity scenarios, the climate projections by four General Circulation Models (GCMs) under different greenhouse gas emission scenarios were used to predict the hydrological effects in the late twenty-first century (2071–2100). Our simulations demonstrated that the water yield would increase in spring and substantially decrease in summer, while soil moisture would rise in spring and decline in summer. Such an uneven distribution of water with higher variability compared to the baseline level (1961–1990) may cause an increased risk of both flooding and drought events in the basin.

Hydrological effects of the increased CO2 and climate change in the Upper Mississippi River Basin using a modified SWAT

With the popularity of complex hydrologic models, the time taken to run these models is increasing substantially. Comparing and evaluating the efficacy of different optimization algorithms for calibrating computationally intensive hydrologic models is becoming a nontrivial issue. In this study, five global optimization algorithms (genetic algorithms, shuffled complex evolution, particle swarm optimization, differential evolution, and artificial immune system) were tested for automatic parameter calibration of a complex hydrologic model, Soil and Water Assessment Tool (SWAT), in four watersheds. The results show that genetic algorithms (GA) outperform the other four algorithms given model evaluation numbers larger than 2000, while particle swarm optimization (PSO) can obtain better parameter solutions than other algorithms given fewer number of model runs (less than 2000). Given limited computational time, the PSO algorithm is preferred, while GA should be chosen given plenty of computational resources. When applying GA and PSO for parameter optimization of SWAT, small population size should be chosen. Copyright © 2008 John Wiley & Sons, Ltd.

https://swat.tamu.edu/media/90107/hp-paper.pdf

Mike Van Liew

Papers

Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model