Comparison of Semi-Distributed, GIS-Based Hydrological Models for the Prediction of Streamflow in a Large Catchment

Suliman, Ali H. Ahmed; Jajarmizadeh, Milad; Harun, Sobri; Darus, Intan Zaurah Mat

doi:10.1007/s11269-015-0984-0

Cited by 34 publications

(9 citation statements)

References 31 publications

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“…Some previous applications in areas with less runoff yielded relatively poor statistics. Shen et al [54] [22,55]. In fact, Chahinian et al [56] compared four different infiltration-runoff models and all tested models had difficulties simulating low runoff events and even events characterized by a mild rainfall hiatus.…”

Section: Flowmentioning

confidence: 99%

Comparison of SWAT and GWLF Model Simulation Performance in Humid South and Semi-Arid North of China

Kang

Chu

et al. 2017

Water

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Watershed models have gradually been adapted to support both decision and policy making for global environmental pollution control. In this study, two watershed models with different complexity, the Soil and Water Assessment Tool (SWAT) and the Generalized Watershed Loading Function (GWLF), were applied in two catchments in data scarce China, namely the Tunxi and the Hanjiaying basins with contrasting climatic conditions (humid and semi-arid, respectively). The performances of both models were assessed via comparison between simulated and measured monthly streamflow, sediment yield, and total nitrogen. Time series plots as well as four statistical measures (the coefficient of determination (R 2 ), the Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), and RMSE (root mean square error)-observations standard deviation ratio (RSR)) were used to estimate the performance of both models. The results show that both models were generally able to simulate monthly streamflow, sediment, and total nitrogen loadings during the simulation period. However, SWAT performed better for detailed representations, while GWLF could produce much better average values of the observed data. Thus, GWLF offers a user-friendly prospective alternative watershed model that requires little input data and that is applicable for areas where the input data required for SWAT are not always available. SWAT is more suitable for projects that require high accuracy and offers an advantage when measured data are scarce.

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Section: Flowmentioning

confidence: 99%

Comparison of SWAT and GWLF Model Simulation Performance in Humid South and Semi-Arid North of China

Kang

Chu

et al. 2017

Water

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“…Introduction achievable because not all input data are easily available (Suliman et al, 2015;Xu & Yang, 2010;. Calibration of parameters in the physically based models may lead to serious problems, such as scale issues, equifinality, non-uniqueness, and uncertainties about the calibrated model structure and the reliability of input data.…”

Section: Hydrological Modellingmentioning

confidence: 99%

Understanding the Impact of Human Interventions on the Hydrology of Nile Basin Headwaters, the Case of Upper Tekeze Catchments

Gebremicael¹

2019

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“…It is characterized by less model parameters and high forecasting accuracy, and it has been widely used in flood forecasting in various river basins in China [4]. However, as hydrogeological conditions of basins are highly generalized [5], the parameters should be calibrated using historical flood data [6], making it difficult to build a forecast model in the ungauged basins or basins using partial missing observation data [7]. ough the world's construction of hydrological stations continues to develop, there are still many small basins without monitoring stations; these small basins are often the focus areas of flood disaster research.…”

Section: Introductionmentioning

confidence: 99%

“…With the advancement of geographic information technology, remote sensing technology, and computer science, even in the absence of hydrological observatories, the geography of the watershed can be obtained from remote sensing images, including digital elevation model (DEM) [5] and land use and soil classification maps [25]. To fully exploit the advantage of such development, the physical-based distributed hydrological models were created [2,26].…”

Section: Introductionmentioning

confidence: 99%

Flood Prediction in Ungauged Basins by Physical-Based TOPKAPI Model

Kong

Liu

2019

Advances in Meteorology

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Scarce historical flood data in ungauged basins make it difficult to establish empirical and conceptual model forecast in these areas. The physical-based distributed model TOPKAPI is introduced for flood prediction in an ungauged basin by parameter transplant. Five main parameters are selected, and the sensitivity is analyzed by the GLUE method. The Xixian basin and Huangchuan basin in the upper Huaihe basin in China are chosen as study areas. The Xixian basin is regarded as a gauged basin for parameter calibration, and the Huangchuan basin is regarded as an ungauged basin by ignoring the historical discharge data. The model is calibrated in gauged Xixian basin, and then parameters are directly transplanted to adjacent “ungauged” Huangchuan basin to simulate flood forecast in an ungauged basin. The sensitivity analysis shows that soil thickness and soil saturated water content are the most sensitive parameters, and the Manning coefficient of main channel with high Strahler also significantly affects forecast results. According to the simulation results, the TOPKAPI model exhibits good performance in building and the prediction of the ungauged basin, in which the qualified rate of volume and peaks reaches 69.23%, and the average NSE criterion is over 0.67, which is acceptable forecast accuracy and has positive implication for the hydrological forecasting research.

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Comparison of Semi-Distributed, GIS-Based Hydrological Models for the Prediction of Streamflow in a Large Catchment

Cited by 34 publications

References 31 publications

Comparison of SWAT and GWLF Model Simulation Performance in Humid South and Semi-Arid North of China

Comparison of SWAT and GWLF Model Simulation Performance in Humid South and Semi-Arid North of China

Understanding the Impact of Human Interventions on the Hydrology of Nile Basin Headwaters, the Case of Upper Tekeze Catchments

Flood Prediction in Ungauged Basins by Physical-Based TOPKAPI Model

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