Application on One-at-a-Time Sensitivity Analysis of Semi-Distributed Hydrological Model in Tropical Watershed (original) (raw)
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Sensitivity analysis (SA) aims to identify the key parameters that affect model performance and it plays important roles in model parameterization, calibration, optimization, and uncertainty quantification. However, the increasing complexity of hydrological models means that a large number of parameters need to be estimated. To better understand how these complex models work, efficient SA methods should be applied before the application of hydrological modeling. This study provides a comprehensive review of global SA methods in the field of hydrological modeling. The common definitions of SA and the typical categories of SA methods are described. A wide variety of global SA methods have been introduced to provide a more efficient evaluation framework for hydrological modeling. We review, analyze, and categorize research into global SA methods and their applications, with an emphasis on the research accomplished in the hydrological modeling field. The advantages and disadvantages are also discussed and summarized. An application framework and the typical practical steps involved in SA for hydrological modeling are outlined. Further discussions cover several important and often overlooked topics, including the relationship between parameter identification, uncertainty analysis, and optimization in hydrological modeling, how to deal with correlated parameters, and time-varying SA. Finally, some conclusions and guidance recommendations on SA in hydrological modeling are provided, as well as a list of important future research directions that may facilitate more robust analyses when assessing hydrological modeling performance.
Sensitivity Analysis of Runoff
To design and construct mosthydraulic structures, e.g. dams, it is essential to determine watershed runoff. If a watershed lacks any gaging station, thenhydrologic models can be utilized to estimate runoff. The Soil Water Assessment Tool (SWAT) is one of the most widelyusedcomputerwatershed models. In this model, we need to input meteorological data, such as precipitation, temperature, wind speed, solar radiation, and relative humidity;as well as watershed data, including curvenumberandroughness coefficient, to calculate the watershed runoff. Some watershedshave weather stations, but there is a risk that the recordeddataof a station do not represent the whole watershed and the use of such data may cause error. Consequently, the error of estimated runoff error needs to be determined. This study deals with the sensitivity of runoff estimatedusing the SWAT model to the variations in meteorological parameters, such as precipitation, solar radiation, wind, humidity, and temperature. Results indicate that with a 30% decrease in the average monthly precipitation, sunshine, relative humidity, wind and temperature, we witness,respectively,a 64.27% decrease, 114.67% increase, 45.93% decrease, 126.12% increase, and 39.21% increase in the estimated runoff.. Runoff estimation is found to be most sensitive to wind speed and solar radiation,and least sensitive to temperature.