Effects of hydrologic conditions on SWAT model performance and parameter sensitivity for a small, mixed land use catchment in New Zealand

Me, Wang; Abell, Jonathan Michael; Hamilton, David P.

doi:10.5194/hess-19-4127-2015

Cited by 82 publications

(56 citation statements)

References 51 publications

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“…If the performance on the test set is unsatisfactory (NSE < 0.6), the model is classified as “bad” ( b ), and MuMoC, a hybrid ANN‐IBL model (see section ), is implemented. Even though there are no standard criteria for assessing a model's performance, for typical hydrological modeling, Moriasi et al (), Christiansen (), and Me et al () proposed values of the NSE index above 0.5. To be conservative, this study raised the proposed threshold up to 0.6.…”

Section: Methodsmentioning

confidence: 99%

A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA

Amaranto

Muñoz-Arriola

Solomatine

et al. 2019

Water Resources Research

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The aim of this paper is to improve semiseasonal forecast of groundwater availability in response to climate variables, surface water availability, groundwater level variations, and human water management using a two‐step data‐driven modeling approach. First, we implement an ensemble of artificial neural networks (ANNs) for the 300 wells across the High Plains aquifer (USA). The modeling framework includes a method to choose the most relevant input variables and time lags; an assessment of the effect of exogenous variables on the predictive capabilities of models; and the estimation of the forecast skill based on the Nash‐Sutcliffe efficiency (NSE) index, the normalized root mean square error, and the coefficient of determination (R2). Then, for the ANNs with low‐ accuracy, a MultiModel Combination (MuMoC) based on a hybrid of ANN and an instance‐based learning method is applied. MuMoC uses forecasts from neighboring wells to improve the accuracy of ANNs. An exhaustive‐search optimization algorithm is employed to select the best neighboring wells based on the cross correlation and predictive accuracy criteria. The results show high average ANN forecasting skills across the aquifer (average NSE > 0.9). Spatially distributed metrics of performance showed also higher error in areas of strong interaction between hydrometeorological forcings, irrigation intensity, and the aquifer. In those areas, the integration of the spatial information into MuMoC leads to an improvement of the model accuracy (NSE increased by 0.12), with peaks higher than 0.3 when the optimization objectives for selecting the neighbors were maximized.tT

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Section: Methodsmentioning

confidence: 99%

A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA

Amaranto

Muñoz-Arriola

Solomatine

et al. 2019

Water Resources Research

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“…and river routing (water, sediment, and in-stream water quality) to the watershed outlet through the river network. Additional details on SWAT applications can be found in [33][34][35].…”

Section: Swat Model and Glacier Modulementioning

confidence: 99%

Multi-Objective Calibration of a Distributed Hydrological Model in a Highly Glacierized Watershed in Central Asia

Fang

Jian

Chen

2019

Water

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Understanding glacio-hydrological processes is crucial to water resources management, especially under increasing global warming. However, data scarcity makes it challenging to quantify the contribution of glacial melt to streamflow in highly glacierized catchments such as those in the Tienshan Mountains. This study aims to investigate the glacio-hydrological processes in the SaryDjaz-Kumaric River (SDKR) basin in Central Asia by integrating a degree-day glacier melt algorithm into the macro-scale hydrological Soil and Water Assessment Tool (SWAT) model. To deal with data scarcity in the alpine area, a multi-objective sensitivity analysis and a multi-objective calibration procedure were used to take advantage of all aspects of streamflow. Three objective functions, i.e., the Nash–Sutcliffe efficiency coefficient of logarithms (LogNS), the water balance index (WBI), and the mean absolute relative difference (MARD), were considered. Results show that glacier and snow melt-related parameters are generally sensitive to all three objective functions. Compared to the original SWAT model, simulations with a glacier module match fairly well to the observed streamflow, with the Nash–Sutcliffe efficiency coefficient (NS) and R2 approaching 0.82 and an absolute percentage bias less than 1%. Glacier melt contribution to runoff is 30–48% during the simulation period. The approach of combining multi-objective sensitivity analysis and optimization is an efficient way to identify important hydrological processes and recharge characteristics in highly glacierized catchments.

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“…4908 E. I. Polanco et al: Improving SWAT model performance in the upper Blue Nile Basin 2015; Me et al, 2015;Rafiei Emam et al, 2016;Wang et al, 2017). SWAT provides a wide range of parameters to work with, allowing users to analyze several hydrological processes.…”

Section: Introductionmentioning

confidence: 99%

Improving SWAT model performance in the upper Blue Nile Basin using meteorological data integration and subcatchment discretization

Polanco

Fleifle

Ludwig

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. The Blue Nile Basin is confronted by land degradation problems, insufficient agricultural production, and a limited number of developed energy sources. Hydrological models provide useful tools to better understand such complex systems and improve water resources and land management practices. In this study, SWAT was used to model the hydrological processes in the upper Blue Nile Basin. Comparisons between a Climate Forecast System Reanalysis (CFSR) and a conventional ground weather dataset were done under two sub-basin discretization levels (30 and 87 sub-basins) to create an integrated dataset to improve the spatial and temporal limitations of both datasets. A SWAT error index (SEI) was also proposed to compare the reliability of the models under different discretization levels and weather datasets. This index offers an assessment of the model quality based on precipitation and evapotranspiration. SEI demonstrates to be a reliable additional and useful method to measure the level of error of SWAT. The results showed the discrepancies of using different weather datasets with different sub-basin discretization levels. Datasets under 30 sub-basins achieved Nash-Sutcliffe coefficient (NS) values of −0.51, 0.74, and 0.84; p factors of 0.53, 0.66, and 0.70; and r factors of 1.11, 0.83, and 0.67 for the CFSR, ground, and integrated datasets, respectively. Meanwhile, models under 87 sub-basins achieved NS values of −1.54, 0.43, and 0.80; p factors of 0.36, 0.67, and 0.77; r factors of 0.93, 0.68, and 0.54 for the CFSR, ground, and integrated datasets, respectively. Based on the obtained statistical results, the integrated dataset provides a better model of the upper Blue Nile Basin.

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Effects of hydrologic conditions on SWAT model performance and parameter sensitivity for a small, mixed land use catchment in New Zealand

Cited by 82 publications

References 51 publications

A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA

A Spatially Enhanced Data‐Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA

Multi-Objective Calibration of a Distributed Hydrological Model in a Highly Glacierized Watershed in Central Asia

Improving SWAT model performance in the upper Blue Nile Basin using meteorological data integration and subcatchment discretization

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