Karim C. Abbaspour

SWAT: Model Use, Calibration, and Validation

SWAT (Soil and Water Assessment Tool) is a comprehensive, semi-distributed river basin model that requires a large number of input parameters, which complicates model parameterization and calibration. Several calibration techniques have been developed for SWAT, including manual calibration procedures and automated procedures using the shuffled complex evolution method and other common methods. In addition, SWAT-CUP was recently developed and provides a decision-making framework that incorporates a semi-automated approach (SUFI2) using both manual and automated calibration and incorporating sensitivity and uncertainty analysis. In SWAT-CUP, users can manually adjust parameters and ranges iteratively between autocalibration runs. Parameter sensitivity analysis helps focus the calibration and uncertainty analysis and is used to provide statistics for goodness-of-fit. The user interaction or manual component of the SWAT-CUP calibration forces the user to obtain a better understanding of the overall hydrologic processes (e.g., baseflow ratios, ET, sediment sources and sinks, crop yields, and nutrient balances) and of parameter sensitivity. It is important for future calibration developments to spatially account for hydrologic processes; improve model run time efficiency; include the impact of uncertainty in the conceptual model, model parameters, and measured variables used in calibration; and assist users in checking for model errors. When calibrating a physically based model like SWAT, it is important to remember that all model input parameters must be kept within a realistic uncertainty range and that no automatic procedure can substitute for actual physical knowledge of the watershed.

Estimating Uncertain Flow and Transport Parameters Using a Sequential Uncertainty Fitting Procedure

Inversely obtained hydrologic parameters are always uncertain (nonunique) because of errors associated with the measurements and the invoked conceptual model, among other factors. Quantification of this uncertainty in multidimensional parameter space is often difficult because of complexities in the structure of the objective function. In this study we describe parameter uncertainties using uniform distributions and fit these distributions iteratively within larger absolute intervals such that two criteria are met: (i) bracketing most of the measured data (>90%) within the 95% prediction uncertainty (95PPU) and (ii) obtaining a small ratio (<1) of the average difference between the upper and lower 95PPU and the standard deviation of the measured data. We define a model as calibrated if, upon reaching these two criteria, a significant R 2 exists between the observed and simulated results. A program, SUFI-2, was developed and tested for the calibration of two bottom ash landfills. SUFI-2 performs a combined optimization and uncertainty analysis using a global search procedure and can deal with a large number of parameters through Latin hypercube sampling. We explain the above concepts using an example in which two municipal solid waste incinerator bottom ash monofills were successfully calibrated and tested for flow, and one monofill also for transport. Because of high levels of heavy metals in the leachate, monitoring and modeling of such landfills is critical from environmental points of view.

Application of a SWAT model for estimating runoff and sediment in two mountainous basins in central Iran

Abstract The Soil and Water Assessment Tool (SWAT) was used to model runoff and sediment in the Beheshtabad (3860 km2) and Vanak (3198 km2) watersheds in the northern Karun catchment in central Iran. Model calibration and uncertainty analysis were performed with sequential uncertainty fitting (SUFI-2), which is one of the programs interfaced with SWAT, in the package SWAT-CUP (SWAT Calibration Uncertainty Programs). Two measures were used to assess the goodness of calibration and uncertainty analysis: (a) the percentage of data bracketed by the 95% prediction uncertainty (95PPU) (P factor), and (b) the ratio of average thickness of the 95PPU band to the standard deviation of the corresponding measured variable (D factor). Ideally, the P factor should tend towards 1 with a D factor close to zero. These measures together indicate the strength of the calibration-uncertainty analysis. Runoff and sediment data from four hydrometric stations in each basin were used for calibration and validation. The P factor for Beheshtabad stations ranged from 0.31 to 0.86, while those for Vanak stations were between 0.71 and 0.80. The D factor for Beheshtabad ranged from 0.3 to 1.1, and for Vanak it was 0.77–1.16. These measures indicate a fair model calibration and accounting of uncertainties. The predicted runoff values were quite similar to those for discharge.

A parallelization framework for calibration of hydrological models

Large-scale hydrologic models are being used more and more in watershed management and decision making. Sometimes rapid modeling and analysis is needed to deal with emergency environmental disasters. However, time is often a major impediment in the calibration and application of these models. To overcome this, most projects are run with fewer simulations, resulting in less-than-optimum solutions. In recent years, running time-consuming projects on gridded networks or clouds in Linux systems has become more and more prevalent. But this technology, aside from being tedious to use, has not yet become fully available for common usage in research, teaching, and small to medium-size applications. In this paper we explain a methodology where a parallel processing scheme is constructed to work in the Windows platform. We have parallelized the calibration of the SWAT (Soil and Water Assessment Tool) hydrological model, where one could submit many simultaneous jobs taking advantage of the capabilities of modern PC and laptops. This offers a powerful alternative to the use of grid or cloud computing. Parallel processing is implemented in SWAT-CUP (SWAT Calibration and Uncertainty Procedures) using the optimization program SUFI2 (Sequential Uncertainty FItting ver. 2). We tested the program with large, medium, and small-size hydrologic models on several computer systems, including PCs, laptops, and servers with up to 24 CPUs. The performance was judged by calculating speedup, efficiency, and CPU usage. In each case, the parallelized version performed much faster than the non-parallelized version, resulting in substantial time saving in model calibration.

A Global and Spatially Explicit Assessment of Climate Change Impacts on Crop Production and Consumptive Water Use

Food security and water scarcity have become two major concerns for future humans sustainable development, particularly in the context of climate change. Here we present a comprehensive assessment of climate change impacts on the production and water use of major cereal crops on a global scale with a spatial resolution of 30 arc-minutes for the 2030s (short term) and the 2090s (long term), respectively. Our findings show that impact uncertainties are higher on larger spatial scales (e.g., global and continental) but lower on smaller spatial scales (e.g., national and grid cell). Such patterns allow decision makers and investors to take adaptive measures without being puzzled by a highly uncertain future at the global level. Short-term gains in crop production from climate change are projected for many regions, particularly in African countries, but the gains will mostly vanish and turn to losses in the long run. Irrigation dependence in crop production is projected to increase in general. However, several water poor regions will rely less heavily on irrigation, conducive to alleviating regional water scarcity. The heterogeneity of spatial patterns and the non-linearity of temporal changes of the impacts call for site-specific adaptive measures with perspectives of reducing short- and long-term risks of future food and water security.

System dynamics simulation model for assessing socio-economic impacts of different levels of environmental flow allocation in the Weihe River Basin, China

This study develops a complex system dynamics model (SD) reflecting interactions between water resources, Environmental Flow (EF) and socio-economy using SD software package “Vensim PLE”. The proposed model is employed to assess socio-economic impacts of different levels of EF allocation in the Weihe River Basin of China. Four alternative socio-economic growth patterns and four EF allocation schemes are designed to simulate those impacts. The results reveal that developed SD model performance well in reflecting the dynamic behavior of the system in the current study area. In the meanwhile, an optimal growth pattern considering both socio-economic growth and EF requirements are also found by comparing the different scenario simulation results.

Simulating unsaturated flow and transport in a macroporous soil to tile drains subject to an entrance head: model development and preliminary evaluation

Abstract Accurate prediction of water flow and chemical transport in agricultural soil profiles requires the use of a simulation model that considers the most important physical, hydrological and chemical processes. Two important flow-related processes in tile-drained field systems are macropore flow and water discharge from the tile drains. To better account for these two processes, we extended an existing two-dimensional model (SWMS_2D) by adding a macropore flow component as well as a Hooghoudt type boundary condition that considers the presence of an entrance head at the tile drain. The macropore component is necessary to account for water and solutes short-circuiting the soil matrix, while the drainage entrance head is needed to account for the contraction of streamlines around the drains, a feature that causes delayed discharge. The applicability of the new model to a landfill problem was examined. The simulation results, which included water flow and solute transport, compared well with other models.

Factors Affecting Farmers Decisions on Fertilizer Use: A Case Study for the Chaobai Watershed in Northern China

The Chaobai watershed in northern China is the most important source of drinking water for Beijing. The level of fertilizer use, especially overuse, as well as farming practices in the region have a great impact on the water quality downstream and affect an enormous number of people. This study analyzes the factors influencing the farmers’ decisions on fertilizer use and the implications for water quality. The analysis is based on a survey of 349 farm households. It takes into consideration both farm and farmer specific characteristics and farmers’ subjective evaluations of factors shaping their decisions. Regression models are used to examine the determinants of fertilizer use intensity across farm households and to investigate the factors influencing the overuse of nitrogen. The results suggest that many of these subjective factors have great significance in determining famers’ decisions. The results also show that irrigation, gains in crop yield and higher earning goals are positively correlated with fertilizer use intensity, while farm size, manure application, soil fertility and the distance to fertilizer markets are negatively correlated. Investigation of the overuse problem shows that higher education level significantly reduces the probability of over-fertilization. Based on these findings a few policy relevant implications are discussed.

A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows

Abstract A wavelet-neural network (WNN) hybrid modelling approach for monthly river flow estimation and prediction is developed. This approach integrates discrete wavelet multi-resolution decomposition and a back-propagation (BP) feed-forward multilayer perceptron (FFML) artificial neural network (ANN). The Levenberg-Marquardt (LM) algorithm and the Bayesian regularization (BR) algorithm were employed to perform the network modelling. Monthly flow data from three gauges in the Weihe River in China were used for network training and testing for 48-month-ahead prediction. The comparison of results of the WNN hybrid model with those of the single ANN model show that the former is able to significantly increase the prediction accuracy. Editor D. Koutsoyiannis; Associate editor H. Aksoy Citation Wei, S., Yang, H., Song, J.X., Abbaspour, K., and Xu, Z.X., 2013. A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows. Hydrological Sciences Journal, 58 (2), 374–389.

SWAT-based hydrological modelling of tropical land-use scenarios

Abstract The Hulu Langat basin, a strategic watershed in Malaysia, has in recent decades been exposed to extensive changes in land-use and consequently hydrological conditions. In this work, the impact of Land Use and Cover Change (LUCC) on hydrological conditions (water discharge and sediment load) of the basin were investigated using the Soil and Water Assessment Tool (SWAT). Four land-use scenarios were defined for land-use change impact analysis, i.e. past, present (baseline), future and water conservation planning. The land-use maps, dated 1984, 1990, 1997 and 2002, were defined as the past scenarios for LUCC impact analysis. The present scenario was defined based on the 2006 land-use map. The 2020 land-use map was simulated using a cellular automata-Markov model and defined as the future scenario. Water conservation scenarios were produced based on guidelines published by Malaysia’s Department of Town and Country Planning and Department of Environment. Model calibration and uncertainty analysis was performed using the Sequential Uncertainty Fitting (SUFI-2) algorithm. The model robustness for water discharge simulation for the period 1997–2008 was good. However, due to uncertainties, mainly resulting from intense urban development in the basin, its robustness for sediment load simulation was only acceptable for the calibration period 1997–2004. The optimized model was run using different land-use maps over the periods 1997–2008 and 1997–2004 for water discharge and sediment load estimation, respectively. In comparison to the baseline scenario, SWAT simulation using the past and conservative scenarios showed significant reduction in monthly direct runoff and monthly sediment load, while SWAT simulation based on the future scenario showed significant increase in monthly direct runoff, monthly sediment load and groundwater recharge. Editor D. Koutsoyiannis; Associate editor C. Perrin