Multi-variable and multi-site calibration and validation of SWAT in a large mountainous catchment with high spatial variability

Abstract

Abstract:Many methods developed for calibration and validation of physically based distributed hydrological models are time consuming and computationally intensive. Only a small set of input parameters can be optimized, and the optimization often results in unrealistic values. In this study we adopted a multi-variable and multi-site approach to calibration and validation of the Soil Water Assessment Tool (SWAT) model for the Motueka catchment, making use of extensive field measurements. Not only were a number of hydrological processes (model components) in a catchment evaluated, but also a number of subcatchments were used in the calibration. The internal variables used were PET, annual water yield, daily streamflow, baseflow, and soil moisture. The study was conducted using an 11-year historical flow record (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000); 1990-94 was used for calibration and 1995-2000 for validation. SWAT generally predicted well the PET, water yield and daily streamflow. The predicted daily streamflow matched the observed values, with a Nash-Sutcliffe coefficient of 0Ð78 during calibration and 0Ð72 during validation. However, values for subcatchments ranged from 0Ð31 to 0Ð67 during calibration, and 0Ð36 to 0Ð52 during validation. The predicted soil moisture remained wet compared with the measurement. About 50% of the extra soil water storage predicted by the model can be ascribed to overprediction of precipitation; the remaining 50% discrepancy was likely to be a result of poor representation of soil properties. Hydrological compensations in the modelling results are derived from water balances in the various pathways and storage (evaporation, streamflow, surface runoff, soil moisture and groundwater) and the contributions to streamflow from different geographic areas (hill slopes, variable source areas, sub-basins, and subcatchments). The use of an integrated multi-variable and multi-site method improved the model calibration and validation and highlighted the areas and hydrological processes requiring greater calibration effort.

Improvement of SWAT2000 modelling to assess the impact of dams and sluices on streamflow in the Huai River basin of China

“…This process of calibration of SWAT, especially auto-calibrating algorithms, including the sensitivity analysis of parameters, has been the subject of many hydrological studies (Eckhardt and Arnold, 2001;Benaman et al, 2005;White and Chaubey, 2005;Cao et al, 2006;Bekele and Nicklow, 2007;Kannan et al, 2007). For example, Lenhart et al (2002) conducted a sensitivity analysis in SWAT whereby two approaches were considered as equivalent: one was to change the value of a given parameter by a fixed percentage of the initial value (Lenhart et al, 2002;White and Chaubey, 2005), and the other was to vary it by a fixed percentage of the valid parameter range (Lenhart et al, 2002).…”

“…Muleta and Nicklow (2005) adapted a Genetic Algorithm (GA) for single-objective evaluations, and a Strength Pareto Evolutionary Algorithm for multiobjective optimization. The Shuffled complex evolution (SCE-UA) algorithm (Duan et al, 1992(Duan et al, , 1994 has also been applied to calibrate SWAT in several cases (van Griensven and Bauwens, 2003;Van Liew et al, 2005;Cao et al, 2006).…”

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

“…Streamflow prediction is improved by the assimilation of both soil moisture and streamflow individually and by coupled assimilation. Cao et al (2006) proposed a calibration approach based on integration of multiple internal variables with multi-site locations, resulting in a more realistic parameterization of the hydrological process. De Lannoy et al (2007) assimilated distributed values of soil moisture in an agricultural field, assessing the influence of the biased or the bias-corrected state estimates into a biased model.…”

Estimation of Sediment Yield Change in a Loess Plateau Basin, China

“…The SWAT model tends to underestimate sediment loads for high rainfall events while overestimating sediment loads for low rainfall events ( Figure 6). Runoff simulation impacts measurement uncertainties and uncertainties in model parameterization, and errors or oversimplifications inherent in the model structure can be responsible for this mismatch [32,59]. In this basin, sediment simulation uncertainty is mainly introduced by the observed data used for model calibration and validation.…”