Improving Parameter Transferability of GR4J Model under Changing Environments Considering Nonstationarity

Abstract

Hydrological nonstationarity has brought great challenges to the reliable application of conceptual hydrological models with time-invariant parameters. To cope with this, approaches have been proposed to consider time-varying model parameters, which can evolve in accordance with climate and watershed conditions. However, the temporal transferability of the time-varying parameter was rarely investigated. This paper aims to investigate the predictive ability and robustness of a hydrological model with time-varying parameter under changing environments. The conceptual hydrological model GR4J (Génie Rural à 4 paramètres Journalier) with only four parameters was chosen and the sensitive parameters were treated as functions of several external covariates that represent the variation of climate and watershed conditions. The investigation was carried out in Weihe Basin and Tuojiang Basin of Western China in the period from 1981 to 2010. Several sub-periods with different climate and watershed conditions were set up to test the temporal parameter transferability of the original GR4J model and the GR4J model with time-varying parameters. The results showed that the performance of streamflow simulation was improved when applying the time-varying parameters. Furthermore, in a series of split-sample tests, the GR4J model with time-varying parameters outperformed the original GR4J model by improving the model robustness. Further studies focus on more diversified model structures and watersheds conditions are necessary to verify the superiority of applying time-varying parameters.

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

“…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. The GR4J is a global conceptual model for rainfall-runoff that needs 4 parameters: X 1 , the maximum volume of the production stock (mm); X 2 , the coefficient of groundwater exchange (mm); X 3 , the maximum volume of the routing store (mm); and X 4 , the time highest ordinate of hydrograph unit UH1 (day) (Figure 2).…”

Advances in Hydrologic Forecasts and Water Resources Management

“…Hydrological nonstationary has brought great challenges to the reliable applications of hydrological models with time-invariant parameters. Two papers [7,8] investigate the predictive ability and robustness of a hydrological model under changing environments. In [7], the authors propose a new method based on empirical mode decomposition (EMD) to synthesize and generate data which be interfered with the non-stationary problems.…”

“…The new synthetic and historical flow data were used to simulate the water supply system of the Hushan reservoir in Taiwan, and the compared results show that the synthetic data are like the historical flow distribution. In [8], the authors investigate the predictive ability and robustness of a conceptual hydrological model (GR4J) with time-varying parameter under changing environments. The results show that the performance of streamflow simulation was improved when applying the time-varying parameters.…”

“…Multidisciplinary research and advanced methodologies in hydrological forecasts, especially in extreme floods and droughts, are widely implemented for water planning and management, which ultimately lead to improved optimum water resources management and effective control under a changing environment. Among them, artificial intelligence (AI) techniques are efficient tools for extracting the key information from complex highly dimensional input-output patterns and are widely used to tackle various hydrological problems such as flood forecasts discussed in this Special Issue [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Over the last decades, many studies have demonstrated that artificial intelligence (AI) techniques, such as machine learning (ML) methods, can produce flood forecasts in a few hours [15][16][17][18][19] while extending to seasonal forecasts many months in advance for larger river basins [20][21][22][23][24].…”

Changes in and Modelling of Hydrological Process for a Semi-Arid Catchment in the Context of Human Disturbance

“…The GR4J model, as a lumped conceptual model, has been widely applied in various climate regions of the world because of its distinctive characteristics in principle and structure (Boumenni et al, 2017;Sezen and Partal, 2019). Zeng et al (2019) investigated the predictive ability and robustness of the GR4J model with timevarying parameters under changing environments and improved the performance of streamflow simulations in Wei River Basin. Ghimire et al (2020) used a range of hydrological modelling approaches for flow simulation and forecasting in the Ayeyarwady Basin and revealed that the GR4J model performed best in simulations and yielded the least biases in daily flow forecasting.…”