A new Xin'anjiang and Sacramento combined rainfall-runoff model and its application

Zhang, Maolin; Wang, Jinwen; Huang, Yanxuan; Yu, Lili; Liu, Shuangquan; Ma, Guonan

doi:10.2166/nh.2021.027

Cited by 7 publications

(5 citation statements)

References 29 publications

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“…In this work, the GR4J hydrological model has been used to simulate the daily streamflow in Kléla Basin. This model is based on the cascade of the reservoirs and links rainfall to runoff (Anshuman et al, 2021;Zhang et al, 2021;Chiew et al, 2022). Scientifically, the model was improved by Perrin et al (2003) and applied by many scholars such as (Ruelland et al, 2010;Kunnath-Poovakka and Eldho, 2019;Zeng et al, 2019;Kodja et al, 2020;Zafari et al, 2022) in various climate regions.…”

Section: Hydrological Modelmentioning

confidence: 99%

Assessment and Prediction of Rainfall-Runoff Models Using GR4J in the Klela Basin in Mali

Keita¹,

Toure²,

Mounir³

et al. 2022

MAS

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The study on water resources is very important for a country like Mali Republic. This is because the climate of the Sahel is projected by many climate scenarios that contribute to a premature dry season. So, the Klela basin being one of the affected areas by the phenomenon is selected for this study. Hence, it is interesting to evaluate this vital resource for a better planning in order to facilitate the decision making from the concerned authorities. For this research, the hydrological model, GR4J, is used to evaluate the dynamics of the surface water flow. The main objective of this study is to assess and predict (using scenarios RCP4.5 and RCP8.5) the correlation between rainfall and runoff in the Klela basin. In tandem with on this objective, the water flow and climate data were used as input data into the GR4J model. The model was calibrated and evaluated using the time series data 2000-2007 and 2008-2013, respectively. The performance of the model was evaluated mainly based on the Nash-Sutcliffe efficiency. The overall outputs display that the surface water flow is declining over time and this is more significant in the worst scenario RCP8.5.

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Section: Hydrological Modelmentioning

confidence: 99%

Assessment and Prediction of Rainfall-Runoff Models Using GR4J in the Klela Basin in Mali

Keita¹,

Toure²,

Mounir³

et al. 2022

MAS

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“…Its performance was also enhanced by structurally modifying the model from earlier experiments. The Sacramento model has worked successfully in a variety of climates, including humid, arid and semi-arid conditions [15,34]. This model generates daily runoff by the input of daily rainfall and potential evapotranspiration data.…”

Section: Gr4j Modelmentioning

confidence: 99%

“…The impermeable surfaces produce quick runoff from rainfall without a time delay while the time wait can be in days for interflow, weeks for base flow and several months for primary base flow [35]. According to Maolin, the Sacramento model provides a better result in a dry season and when the river has reduced flow; it overestimates the flow during a wet season [15].…”

Section: Gr4j Modelmentioning

confidence: 99%

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A Comparative Evaluation of Conceptual Rainfall–Runoff Models for a Catchment in Victoria Australia Using eWater Source

Zafari

Sharma

Navaratna

et al. 2022

Water

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Hydrological modelling at a catchment scale was conducted to investigate the impact of climate change and land-use change individually and in combination with the available streamflow in the Painkalac catchment using an eWater Source hydrological model. This study compares the performance of three inbuilt conceptual models within eWater Source, such as the Australian water balance model (AWBM), Sacramento and GR4J for streamflow simulation. The three-model performance was predicted by bivariate statistics (Nash–Sutcliff efficiency) and univariate (mean, standard deviation) to evaluate the efficiency of model runoff predictions. Potential evapotranspiration (PET) data, daily rainfall data and observed streamflow measured from this catchment are the major inputs to these models. These models were calibrated and validated using eight objective functions while further comparisons of these models were made using objective functions of a Nash–Sutcliffe efficiency (NSE) log daily and an NSE log daily bias penalty. The observed streamflow data were split into three sections. Two-thirds of the data were used for calibration while the remaining one-third of the data was used for validation of the model. Based on the results, it was observed that the performance of the GR4J model is more suitable for the Painkalac catchment in respect of prediction and computational efficiency compared to the Sacramento and AWBM models. Further, the impact of climate change, land-use change and combined scenarios (land-use and climate change) were evaluated using the GR4J model. The results of this study suggest that the higher climate change for the year 2065 will result in approximately 45.67% less streamflow in the reservoir. In addition, the land-use change resulted in approximately 42.26% less flow while combined land-use and higher climate change will produce 48.06% less streamflow compared to the observed flow under the existing conditions.

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“…With the rapid development of information technology, some physical-mechanistic hydrology models such as System Hydrological European (SHE), Soil and Water Assessment Tool (SWAT), Variable Infiltration Capacity (VIC), Topography based Hydrological Model (TOPMODEL) and Xinanjiang Model (XAJ) have been developed and widely used in hydrological simulation, forecasting and prediction, etc. [5][6][7][8][9]. However, the hydrological process is a highly complex, random and nonlinear system which involves many uncertain factors [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model

Shao

Jing

et al. 2022

Water

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The research into rainfall-runoff plays a very important role in water resource management. However, runoff simulation is a challenging task due to its complex formation mechanism, time-varying characteristics and nonlinear hydrological dynamic process. In this study, a nonlinear autoregressive model with exogenous input (NARX) is used to simulate the runoff in the Linyi watershed located in the northeastern part of the Huaihe river basin. In order to better evaluate the performance of NARX, a distributed hydrological model, TOPX, is used to simulate the discharge as a reference, and runoff classification by cluster analysis is used to further improve the accuracy of runoff simulation. Based on the four statistics indexes of the Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC), root mean square error (RMSE) and mean relative bias (Bias), the NARX model is capable of simulating the rainfall-runoff dynamic process satisfactorily, although there is a little underestimation of the peak flow. After runoff classification, underestimation has been improved, and discharge simulation driven by NARX based on runoff classification (C-NARX) is well consistent with the observation. It is feasible to take it as a promising method, which also can be seen as a good reference and replacement for the current rainfall-runoff simulation.

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A new Xin'anjiang and Sacramento combined rainfall-runoff model and its application

Cited by 7 publications

References 29 publications

Assessment and Prediction of Rainfall-Runoff Models Using GR4J in the Klela Basin in Mali

Assessment and Prediction of Rainfall-Runoff Models Using GR4J in the Klela Basin in Mali

A Comparative Evaluation of Conceptual Rainfall–Runoff Models for a Catchment in Victoria Australia Using eWater Source

Application of Rainfall-Runoff Simulation Based on the NARX Dynamic Neural Network Model

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