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1
He, Shaokun, Lei Gu, Jing Tian, Lele Deng, Jiabo Yin, Zhen Liao, Ziyue Zeng, Youjiang Shen, and Yu Hui. "Machine Learning Improvement of Streamflow Simulation by Utilizing Remote Sensing Data and Potential Application in Guiding Reservoir Operation." Sustainability 13, no. 7 (March 25, 2021): 3645. http://dx.doi.org/10.3390/su13073645.
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Hydro-meteorological datasets are key components for understanding physical hydrological processes, but the scarcity of observational data hinders their potential application in poorly gauged regions. Satellite-retrieved and atmospheric reanalysis products exhibit considerable advantages in filling the spatial gaps in in-situ gauging networks and are thus forced to drive the physically lumped hydrological models for long-term streamflow simulation in data-sparse regions. As machine learning (ML)-based techniques can capture the relationship between different elements, they may have potential in further exploring meteorological predictors and hydrological responses. To examine the application prospects of a physically constrained ML algorithm using earth observation data, we used a short-series hydrological observation of the Hanjiang River basin in China as a case study. In this study, the prevalent modèle du Génie Rural à 9 paramètres Journalier (GR4J-9) hydrological model was used to initially simulate streamflow, and then, the simulated series and remote sensing data were used to train the long short-term memory (LSTM) method. The results demonstrated that the advanced GR4J9–LSTM model chain effectively improves the performance of the streamflow simulation by using more remote sensing data related to the hydrological response variables. Additionally, we derived a reservoir operation model by feeding the LSTM-based simulation outputs, which further revealed the potential application of our proposed technique.
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Zeng, Ling, Lihua Xiong, Dedi Liu, Jie Chen, and Jong-Suk Kim. "Improving Parameter Transferability of GR4J Model under Changing Environments Considering Nonstationarity." Water 11, no. 10 (September 28, 2019): 2029. http://dx.doi.org/10.3390/w11102029.
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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.
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Hublart, P., D. Ruelland, I. García De Cortázar Atauri, and A. Ibacache. "Reliability of a conceptual hydrological model in a semi-arid Andean catchment facing water-use changes." Proceedings of the International Association of Hydrological Sciences 371 (June 12, 2015): 203–9. http://dx.doi.org/10.5194/piahs-371-203-2015.
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Abstract. This paper explores the reliability of low-flow simulations by conceptual models in a semi-arid, Andean catchment (30° S) facing climate variability and water-use changes. Depending on water availability, a significant part of surface water resources are diverted to meet irrigation requirements. In return, these water withdrawals are likely to influence the hydrological behavior of the catchment. The value of model-based analyses thus relies on our ability to adequately represent the complex interactions between climate variability, human-induced flow perturbations and crop water use. In this study, a parsimonious hydrological model (GR4J) including a snow routine was combined with a model of irrigation water-use (IWU) to provide a new, 6-parameter model of the catchment behavior (called GR4J/IWU). The original, 4-parameter GR4J model and the 6-parameter GR6J model were also used as benchmarks to evaluate the usefulness of explicitly accounting for water abstractions. Calibration and validation of these three models were performed successively over two different 5-year periods representing contrasted water-use and climate conditions. Overall, the GR4J/IWU model provided better simulations than the GR4J and GR6J models over both periods. Further research is required to quantify the predictive uncertainty associated with model structures, parameters and inputs.
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Hughes, Justin, Nick Potter, Lu Zhang, and Robert Bridgart. "Conceptual Model Modification and the Millennium Drought of Southeastern Australia." Water 13, no. 5 (March 1, 2021): 669. http://dx.doi.org/10.3390/w13050669.
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Long-term droughts observed in southern Australia have changed relationships between annual rainfall and runoff and tested some of the assumptions implicit in rainfall–runoff models used in these areas. Predictive confidence across these periods is when low using the more commonly used rainfall–runoff models. Here we modified the GR4J model to better represent surface water–groundwater connection and its role in runoff generation. The modified model (GR7J) was tested in 137 catchments in south-east Australia. Models were calibrated during “wetter” periods and simulation across drought periods was assessed against observations. GR7J performed better than GR4J in evaluation during drought periods where bias was significantly lower and showed improved fit across the flow duration curve especially at low flows. The largest improvements in predictive performance were for catchments where there were larger changes in the annual rainfall–runoff relationship. The predictive performance of the GR7J model was more sensitive to objective function used than GR4J. The use of an objective function that combined daily and annual error produced a better goodness of fit when measured against 80, 50 and 20 percent excedance flow quantiles and reduced evaluation bias, especially for the GR7J model.
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Massmann, Carolina. "Supporting M5 model trees with sensitivity information derived from conceptual hydrological models." Journal of Hydroinformatics 17, no. 6 (August 6, 2015): 943–58. http://dx.doi.org/10.2166/hydro.2015.111.
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The main objective of this paper is assessing the usefulness of parameter sensitivity information from conceptual hydrological models for data-driven models, an approach which might allow us to take advantage of the strengths of both data-based and process-based models. This study uses the parameter sensitivity of three widely used conceptual hydrological models (GR4J, Hymod and SAC-SMA) and combines them with M5 model trees. The study was carried out for three case studies dealing with different problems to which model trees are applied: one using model trees as error correctors and two case studies in which model trees were used as rainfall–runoff models and which differ in how the sensitivity information is used. The results show that sensitivity time series can improve the predictions of M5 model trees, especially when they do not include the time series of previous discharge as predictor variables. The use of parameter sensitivity information for clustering the time series resulted in model trees that had a structure consistent with the hydrological processes that were taking place in the considered cluster, indicating that the use of sensitivity indices could be a viable way of introducing hydrological knowledge into data-based models.
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Shin, Mun-Ju, and Chung-Soo Kim. "Component Combination Test to Investigate Improvement of the IHACRES and GR4J Rainfall–Runoff Models." Water 13, no. 15 (August 2, 2021): 2126. http://dx.doi.org/10.3390/w13152126.
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Rainfall–runoff models are not perfect, and the suitability of a model structure depends on catchment characteristics and data. It is important to investigate the pros and cons of a rainfall–runoff model to improve both its high- and low-flow simulation. The production and routing components of the GR4J and IHACRES models were combined to create two new models. Specifically, the GR_IH model is the combination of the production store of the GR4J model and the routing store of the IHACRES model (vice versa in the IH_GR model). The performances of the new models were compared to those of the GR4J and IHACRES models to determine components improving the performance of the two original models. The suitability of the parameters was investigated with sensitivity analysis using 40 years’ worth of spatiotemporally different data for five catchments in Australia. These five catchments consist of two wet catchments, one intermediate catchment, and two dry catchments. As a result, the effective rainfall production and routing components of the IHACRES model were most suitable for high-flow simulation of wet catchments, and the routing component improved the low-flow simulation of intermediate and one dry catchments. Both effective rainfall production and routing components of the GR4J model were suitable for low-flow simulation of one dry catchment. The routing component of the GR4J model improved the low- and high-flow simulation of wet and dry catchments, respectively, and the effective rainfall production component improved both the high- and low-flow simulations of the intermediate catchment relative to the IHACRES model. This study provides useful information for the improvement of the two models.
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Sezen, C., and T. Partal. "The utilization of a GR4J model and wavelet-based artificial neural network for rainfall–runoff modelling." Water Supply 19, no. 5 (November 29, 2018): 1295–304. http://dx.doi.org/10.2166/ws.2018.189.
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Abstract Data-driven models and conceptual models have been utilized in an attempt to perform rainfall–runoff modelling. The aim of this study is comparing the performance of an artificial neural network (ANN) model, wavelet-based artificial neural network (WANN) model and GR4J lumped daily conceptual model for rainfall–runoff modelling of two rivers in the USA. It was obtained that the performance of the data-driven models (ANN, WANN) is better than the GR4J model especially when streamflow data the preceding day (Qt-1) and streamflow data the preceding two days (Qt-2) are used as input data in the ANN and WANN models for the simulation of low and high flows, in particular. On the other hand, when only precipitation and potential evapotranspiration data are used as input variables, the GR4J model performs better than the data-driven models.
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Im, Sung Soo, Do Guen Yoo, and Joong Hoon Kim. "Improvement of GR4J Model Applying Soil Moisture Accounting Process and Its Application in Korea Basin." Journal of Korean Society of Hazard Mitigation 12, no. 3 (June 30, 2012): 255–62. http://dx.doi.org/10.9798/kosham.2012.12.3.255.
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Oueldkaddour, Fatima-Zehrae Elhallaoui, Fatima Wariaghli, Hassane Brirhet, and Ahmed Yahyaoui. "Hydrological modeling of rainfall-runoff of the semi-arid Aguibat Ezziar watershed through the GR4J model." Limnological Review 21, no. 3 (September 1, 2021): 119–26. http://dx.doi.org/10.2478/limre-2021-0011.
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Abstract The management of water resources requires as a first step the modeling of rainfall-runoff. It allows simulating the hydrological behavior of the basin for a good evaluation of the potentiality of this in terms of water production. There are different hydrological models used for water resource assessment, but conceptual models are still the most used due to their simple structure and satisfactory performance. In this study, t he performances of the conceptual model of rainfall and runoff (GR4J) modeled under R with the AirGR package, are used to Aguibat Ezziar the subbasin of the Bouregreg basin in Morocco. The enormous amount of data required and the uncertainty of some of the m makes these models of limited usefulness. The GR4J model allows evaluation of the runoff rates and describes the hydrological behavior of the Aguibat Ezziar watershed, which presents the aim behind writing this paper. A period from 2003 to 2017 has been selected. This period has been divided into two parts: one for calibration (2003-2006), and one for validation (2013-2016). After the calibration of the model and following the performance obtained (Nash higher than 0.72) we can say that the GR4J model behaves well in the Aguibat Ezziar catchment area.
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Kodja, Domiho Japhet, Arsène J. Sègla Akognongbé, Ernest Amoussou, Gil Mahé, E. Wilfrid Vissin, Jean-Emmanuel Paturel, and Constant Houndénou. "Calibration of the hydrological model GR4J from potential evapotranspiration estimates by the Penman-Monteith and Oudin methods in the Ouémé watershed (West Africa)." Proceedings of the International Association of Hydrological Sciences 383 (September 16, 2020): 163–69. http://dx.doi.org/10.5194/piahs-383-163-2020.
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Abstract. The Ouémé watersheds at Bétérou and Bonou has been recently facing increased sensitivity to extreme hydroclimatic phenomena that occurred by flooding or drought events. In the same time, the population growth and the related socio-economic activities increased the pressure state on water resources. In this context, hydrological modeling is an important issue and this study aims at analyzing the calibration of the hydrological model GR4J based on PET Penman-Monteith and Oudin methods. Daily rainfall, Penman-Monteith and Oudin evapotranspiration and daily data flow from the Bétérou and Bonou hydrometric stations on the Ouémé Basin have been implemented in the GR4J model over the period 1971 to 2010. Oudin PET values are slightly higher than the Penman-Monteith PET ones. However, the difference between the two PET methods have only few impacts on the optimization and performance criteria of the GR4J model. The Nash values ranges from 0.83 to 0.91 in Bonou, and 0.52 to 0.70 in Bétérou for the calibration in dry period, while in validation, they are 0.59 to 0.78 in Bétérou, and 0.56 to 0.88 in Bonou in wet season. In view of these results, with the two PET methods used which do not result from the same climatic variables, it should be said that the formulation of PET has only few impacts on the results of GR4J for these tropical basins.
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Wang, Jie, Guoqing Wang, Amgad Elmahdi, Zhenxin Bao, Qinli Yang, Zhangkang Shu, and Mingming Song. "Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods." Open Geosciences 13, no. 1 (January 1, 2021): 401–15. http://dx.doi.org/10.1515/geo-2020-0239.
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Abstract Ensemble hydrologic forecasting which takes advantages of multiple hydrologic models has made much contribution to water resource management. In this study, four hydrological models (the Xin’anjiang model (XAJ), Simhyd, GR4J, and artificial neural network (ANN) models) and three ensemble methods (the simple average, black box-based, and binomial-based methods) were applied and compared to simulate the hydrological process during 1979–1983 in three representative catchments (Daixi, Hengtangcun, and Qiaodongcun). The results indicate that for a single model, the XAJ model and the GR4J model performed relatively well with averaged Nash and Sutcliffe efficiency coefficient (NSE) values of 0.78 and 0.83, respectively. For the ensemble models, the results show that the binomial-based ensemble method (dynamic weight) outperformed with water volume error reduced by 0.8% and NSE value increased by 0.218. The best performance on runoff forecasting occurs in the Hengtang catchment by integrating four hydrologic models based on binomial ensemble method, achieving the water volume error of 2.73% and NSE value of 0.923. Finding would provide scientific support to water engineering design and water resources management in the study areas.
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Ayzel, Georgy, Liubov Kurochkina, Dmitriy Abramov, and Sergei Zhuravlev. "Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks." Hydrology 8, no. 1 (January 8, 2021): 6. http://dx.doi.org/10.3390/hydrology8010006.
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Gridded datasets provide spatially and temporally consistent runoff estimates that serve as reliable sources for assessing water resources from regional to global scales. This study presents LSTM-REG, a regional gridded runoff dataset for northwest Russia based on Long Short-Term Memory (LSTM) networks. LSTM-REG covers the period from 1980 to 2016 at a 0.5° spatial and daily temporal resolution. LSTM-REG has been extensively validated and benchmarked against GR4J-REG, a gridded runoff dataset based on a parsimonious regionalization scheme and the GR4J hydrological model. While both datasets provide runoff estimates with reliable prediction efficiency, LSTM-REG outperforms GR4J-REG for most basins in the independent evaluation set. Thus, the results demonstrate a higher generalization capacity of LSTM-REG than GR4J-REG, which can be attributed to the higher efficiency of the proposed LSTM-based regionalization scheme. The developed datasets are freely available in open repositories to foster further regional hydrology research in northwest Russia.
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Ayzel, Georgy, Liubov Kurochkina, Dmitriy Abramov, and Sergei Zhuravlev. "Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks." Hydrology 8, no. 1 (January 8, 2021): 6. http://dx.doi.org/10.3390/hydrology8010006.
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Gridded datasets provide spatially and temporally consistent runoff estimates that serve as reliable sources for assessing water resources from regional to global scales. This study presents LSTM-REG, a regional gridded runoff dataset for northwest Russia based on Long Short-Term Memory (LSTM) networks. LSTM-REG covers the period from 1980 to 2016 at a 0.5° spatial and daily temporal resolution. LSTM-REG has been extensively validated and benchmarked against GR4J-REG, a gridded runoff dataset based on a parsimonious regionalization scheme and the GR4J hydrological model. While both datasets provide runoff estimates with reliable prediction efficiency, LSTM-REG outperforms GR4J-REG for most basins in the independent evaluation set. Thus, the results demonstrate a higher generalization capacity of LSTM-REG than GR4J-REG, which can be attributed to the higher efficiency of the proposed LSTM-based regionalization scheme. The developed datasets are freely available in open repositories to foster further regional hydrology research in northwest Russia.
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王, 何予. "Comparative Study of GR4J and Xinanjiang Models in Humid Area." Journal of Water Resources Research 10, no. 01 (2021): 11–20. http://dx.doi.org/10.12677/jwrr.2021.101002.
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LUPAKOV, S. YU, A. N. BUGAETS, L. V. GONCHUKOV, YU G. MOTOVILOV, O. V. SOKOLOV, and N. D. BUGAETS. "EXPERIENCE IN USING THE GR4J CONCEPTUAL MODEL FOR RUNOFF SIMULATION IN THE USSURI RIVER BASIN." Meteorologiya i Gidrologiya, no. 2 (February 2023): 57–68. http://dx.doi.org/10.52002/0130-2906-2023-2-57-68.
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The open-source low-parametric conceptual hydrological model GR4J was used for runoff simulations of 17 nested catchments of the Upper Ussuri River basin with areas ranging from 133 to 24400 km2, including the Kirovskii outlet. The data of standard hydrometeorological observations of Primorye Administration for Hydrometeorology and Environmental Monitoring were used for the model calibration and verification. Runoff simulations were performed with a daily step. The criteria commonly used in hydrological community, namely, the Nash-Sutcliffe model efficiency coefficient (NSE), coefficient of determination ( R 2), and bias (BIAS) were used to assess the modeling efficiency. According to the specified criteria, modeling results are mostly in “satisfactory” or better categories. The base of the unit hydrograph expectedly increases with the catchment area, but there were no other relationships found between the values of calibrated model parameters, the annual water content, and the main morphometric parameters of the catchments. It was shown for the studied catchments that the stabilization and the maxima of the modeling efficiency scores are reached at the catchment areas of 1200-1700 km2. The influence of representativeness of the weather station network on the modeling efficiency was demonstrated using complementary meteorological observations from experimental catchments in the Upper Ussuri basin for the simulation of the catastrophic flood in 2016 that was caused by typhoon Lionrock.
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Demirel, M. C., M. J. Booij, and A. Y. Hoekstra. "The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models." Hydrology and Earth System Sciences 19, no. 1 (January 16, 2015): 275–91. http://dx.doi.org/10.5194/hess-19-275-2015.
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Abstract. This paper investigates the skill of 90-day low-flow forecasts using two conceptual hydrological models and one data-driven model based on Artificial Neural Networks (ANNs) for the Moselle River. The three models, i.e. HBV, GR4J and ANN-Ensemble (ANN-E), all use forecasted meteorological inputs (precipitation P and potential evapotranspiration PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low-flow forecasts for five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the models. The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the models are compared based on their skill of low-flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90-day-ahead low flows in the very dry year 2003 without precipitation data. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.
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Demirel, M. C., M. J. Booij, and A. Y. Hoekstra. "The skill of seasonal ensemble low flow forecasts for four different hydrological models." Hydrology and Earth System Sciences Discussions 11, no. 5 (May 23, 2014): 5377–420. http://dx.doi.org/10.5194/hessd-11-5377-2014.
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Abstract. This paper investigates the skill of 90 day low flow forecasts using two conceptual hydrological models and two data-driven models based on Artificial Neural Networks (ANNs) for the Moselle River. One data-driven model, ANN-Indicator (ANN-I), requires historical inputs on precipitation (P), potential evapotranspiration (PET), groundwater (G) and observed discharge (Q), whereas the other data-driven model, ANN-Ensemble (ANN-E), and the two conceptual models, HBV and GR4J, use forecasted meteorological inputs (P and PET), whereby we employ ensemble seasonal meteorological forecasts. We compared low flow forecasts without any meteorological forecasts as input (ANN-I) and five different cases of seasonal meteorological forcing: (1) ensemble P and PET forecasts; (2) ensemble P forecasts and observed climate mean PET; (3) observed climate mean P and ensemble PET forecasts; (4) observed climate mean P and PET and (5) zero P and ensemble PET forecasts as input for the other three models (GR4J, HBV and ANN-E). The ensemble P and PET forecasts, each consisting of 40 members, reveal the forecast ranges due to the model inputs. The five cases are compared for a lead time of 90 days based on model output ranges, whereas the four models are compared based on their skill of low flow forecasts for varying lead times up to 90 days. Before forecasting, the hydrological models are calibrated and validated for a period of 30 and 20 years respectively. The smallest difference between calibration and validation performance is found for HBV, whereas the largest difference is found for ANN-E. From the results, it appears that all models are prone to over-predict low flows using ensemble seasonal meteorological forcing. The largest range for 90 day low flow forecasts is found for the GR4J model when using ensemble seasonal meteorological forecasts as input. GR4J, HBV and ANN-E under-predicted 90 day ahead low flows in the very dry year 2003 without precipitation data, whereas ANN-I predicted the magnitude of the low flows better than the other three models. The results of the comparison of forecast skills with varying lead times show that GR4J is less skilful than ANN-E and HBV. Furthermore, the hit rate of ANN-E is higher than the two conceptual models for most lead times. However, ANN-I is not successful in distinguishing between low flow events and non-low flow events. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.
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Tian, Ye, Yue-Ping Xu, Zongliang Yang, Guoqing Wang, and Qian Zhu. "Integration of a Parsimonious Hydrological Model with Recurrent Neural Networks for Improved Streamflow Forecasting." Water 10, no. 11 (November 14, 2018): 1655. http://dx.doi.org/10.3390/w10111655.
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This study applied a GR4J model in the Xiangjiang and Qujiang River basins for rainfall-runoff simulation. Four recurrent neural networks (RNNs)—the Elman recurrent neural network (ERNN), echo state network (ESN), nonlinear autoregressive exogenous inputs neural network (NARX), and long short-term memory (LSTM) network—were applied in predicting discharges. The performances of models were compared and assessed, and the best two RNNs were selected and integrated with the lumped hydrological model GR4J to forecast the discharges; meanwhile, uncertainties of the simulated discharges were estimated. The generalized likelihood uncertainty estimation method was applied to quantify the uncertainties. The results show that the LSTM and NARX better captured the time-series dynamics than the other RNNs. The hybrid models improved the prediction of high, median, and low flows, particularly in reducing the bias of underestimation of high flows in the Xiangjiang River basin. The hybrid models reduced the uncertainty intervals by more than 50% for median and low flows, and increased the cover ratios for observations. The integration of a hydrological model with a recurrent neural network considering long-term dependencies is recommended in discharge forecasting.
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Zafari, Najibullah, Ashok Sharma, Dimuth Navaratna, Varuni M. Jayasooriya, Craig McTaggart, and Shobha Muthukumaran. "A Comparative Evaluation of Conceptual Rainfall–Runoff Models for a Catchment in Victoria Australia Using eWater Source." Water 14, no. 16 (August 16, 2022): 2523. http://dx.doi.org/10.3390/w14162523.
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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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Lupakov, S. Yu, A. N. Bugaets, L. V. Gonchukov, Yu G. Motovilov, O. V. Sokolov, and N. D. Bugaets. "Using the GR4J Conceptual Model for Runoff Simulation in the Ussuri River Basin." Russian Meteorology and Hydrology 48, no. 2 (February 2023): 128–37. http://dx.doi.org/10.3103/s106837392302005x.
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Shin, Mun-Ju, and Chung-Soo Kim. "Assessment of the suitability of rainfall–runoff models by coupling performance statistics and sensitivity analysis." Hydrology Research 48, no. 5 (September 14, 2016): 1192–213. http://dx.doi.org/10.2166/nh.2016.129.
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Conceptual rainfall–runoff models are widely used to understand the hydrologic responses of catchments of interest. Modellers calculate the model performance statistics for the calibration and validation periods to investigate whether these models serve as satisfactory representations of the natural hydrologic phenomenon. Another useful method to investigate model suitability is sensitivity analysis (SA), which investigates structural uncertainty in the models. However, a comprehensive method is needed, which led us to develop a model suitability index (MSI) by combining the results of model performance statistics and SA. Here, we assessed and compared the suitability of three rainfall–runoff models (GR4J, IHACRES and Sacramento model) for seven Korean catchments using MSI. MSI showed that the GR4J and IHACRES models are suitable, having more than 0.5 MSI, whereas the Sacramento has less than 0.5 MSI, representing unsuitability for most of the Korean catchments. The MSI developed in this study is a quantitative measure that can be used for the comparison of rainfall–runoff models for different catchments. It uses the results of existing model performance statistics and sensitivity indices; hence, users can easily apply this index to their models and catchments to investigate suitability.
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Wei, Xiaojing, Shenglian Guo, and Lihua Xiong. "Improving Efficiency of Hydrological Prediction Based on Meteorological Classification: A Case Study of GR4J Model." Water 13, no. 18 (September 16, 2021): 2546. http://dx.doi.org/10.3390/w13182546.
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Distribution of hydrological parameters is varied under contrasting meteorological conditions. However, how to determine the most suitable parameters on a predefined meteorological condition is challenging. To address this issue, a hydrological prediction method based on meteorological classification is established, which is conducted by using the standardized runoff index (SRI) value to identify three categories, i.e., the dry, normal and wet years. Three different simulation schemes are then adopted for these categories. In each category, two years hydrological data with similar SRI values are divided into a set; then, one-year data are used as the calibration period while the other year is for testing. The Génie Rural à 4 paramètres Journalier (GR4J) rainfall-runoff model, with four parameters x1, x2, x3 and x4, was selected as an experimental model. The generalized likelihood uncertainty estimation (GLUE) method is used to avoid parameter equifinality. Three basins in Australia were used as case studies. As expected, the results show that the distribution of the four parameters of GR4J model is significantly different under varied meteorological conditions. The prediction efficiency in the testing period based on meteorological classification is greater than that of the traditional model under all meteorological conditions. It is indicated that the rainfall-runoff model should be calibrated with a similar SRI year rather than all years. This study provides a new method to improve efficiency of hydrological prediction for the basin.
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Zeng, Ling, Hongwei Bi, Yu Li, Xiulin Liu, Shuai Li, and Jinfeng Chen. "Nonstationary Annual Maximum Flood Frequency Analysis Using a Conceptual Hydrologic Model with Time-Varying Parameters." Water 14, no. 23 (December 5, 2022): 3959. http://dx.doi.org/10.3390/w14233959.
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Recent evidence of the impact of watershed underlying conditions on hydrological processes have made the assumption of stationarity widely questioned. In this study, the temporal variations of frequency distributions of the annual maximum flood were investigated by continuous hydrological simulation considering nonstationarity for Weihe River Basin (WRB) in northwestern China. To this end, two nonstationary versions of the GR4J model were introduced, where the production storage capacity parameter was regarded as a function of time and watershed conditions (e.g., reservoir storage and soil-water conservation land area), respectively. Then the models were used to generate long-term runoff series to derive flood frequency distributions, with synthetic rainfall series generated by a stochastic rainfall model as input. The results show a better performance of the nonstationary GR4J model in runoff simulation than the stationary version, especially for the annual maximum flow series, with the corresponding NSE metric increasing from 0.721 to 0.808. The application of the nonstationary flood frequency analysis indicates the presence of significant nonstationarity in the flood quantiles and magnitudes, where the flood quantiles for an annual exceedance probability of 0.01 range from 4187 m3/s to 8335 m3/s for the past decades. This study can serve as a reference for flood risk management in WRB and possibly for other basins undergoing drastic changes caused by intense human activities.
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Aufar, Yazid, Imas Sukaesih Sitanggang, and Annisa. "Parameter Optimization of Rainfall-runoff Model GR4J using Particle Swarm Optimization on Planting Calendar." International Journal on Advanced Science, Engineering and Information Technology 10, no. 6 (December 27, 2020): 2575. http://dx.doi.org/10.18517/ijaseit.10.6.9110.
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Keita, Souleymane, Adama Toure, Zakari Mahamadou Mounir, Ibrahima Daou, and Oumou Diancoumba. "Assessment and Prediction of Rainfall-Runoff Models Using GR4J in the Klela Basin in Mali." Modern Applied Science 16, no. 4 (October 29, 2022): 52. http://dx.doi.org/10.5539/mas.v16n4p52.
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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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Tian, Ye, Yue-Ping Xu, Martijn J. Booij, and Guoqing Wang. "Uncertainty in Future High Flows in Qiantang River Basin, China." Journal of Hydrometeorology 16, no. 1 (February 1, 2015): 363–80. http://dx.doi.org/10.1175/jhm-d-13-0136.1.
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Abstract Uncertainties in high flows originating from greenhouse gas emissions scenarios, hydrological model structures, and their parameters for the Jinhua River basin, China, were assessed. The baseline (1961–90) and future (2011–40) climates for A1B, A2, and B2 scenarios were downscaled from the general circulation model (GCM) using the Providing Regional Climates for Impacts Studies (PRECIS) regional climate model with a spatial resolution of 50 km × 50 km. Bias-correction methods were applied to the PRECIS-derived temperature and precipitation. The bias-corrected precipitation and temperature were used as inputs for three hydrological models [modèle du Génie Rural à 4 paramètres Journalier (GR4J), Hydrologiska Byråns Vattenbalansavdelning (HBV), and Xinanjiang] to simulate high flows. The parameter uncertainty was considered and quantified in the hydrological model calibration by means of the generalized likelihood uncertainty estimation (GLUE) method for each hydrological model for the three emissions scenarios. It was found that, compared with the high flows in the baseline period, the high flows in the future tended to decrease under scenarios A1B, A2, and B2. The largest uncertainty was observed in HBV, and GR4J had the smallest uncertainty. It was found that the major source of uncertainty in this study was from parameters, followed by the uncertainties from the hydrological model structure, and the emissions scenarios have the smallest uncertainty contribution to high flows in this study.
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Waspodo, Roh Santoso Budi, Siti Komariah, and Vita Ayu Kusuma Dewi. "Optimasi Sumberdaya Air dengan Program Linear (Linear Programming) di DAS Cicatih, Kabupaten Sukabumi, Jawa Barat." Jurnal Keteknikan Pertanian 7, no. 3 (April 1, 2020): 179–84. http://dx.doi.org/10.19028/jtep.07.3.179-184.
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The used of water in Indonesia for various using disposed exceed water supply. One of the efforts to optimize limited water resources is optimimation watershed management with linear programming. Identification of surface water potential in Cicatih watershed, especially in sub-watershed of upper Cicatih, Cibojong river, estimate using GR4J model. This research using discharge data from PLTA Ubrug. Springwater identified based on data from DISTAMBEN. The observation discharge average in Cibojong river was 246 l/s/day and based on GR4J model was 2752 l/s/day. Avaiable discharge was 56241 l/s/day. Grow of the population estimated by exponent method, industry and irrigation area with regression linear. Software Lingo 8.0 was used to help optimize of wáter resources in Cicatih watershed. Based on the result, in 2025, 12 industries and 15784 ha area get the wáter allocation from surface water. About 1083817 people and 75402 ha area get from springwáter. The cost to access surface wáter is higher than in other sectors. It causes the industry to gets an allocation from surface wáter.
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Velázquez-Zapata, Juan Alberto. "Comparing Meteorological Data Sets in the Evaluation of Climate Change Impact on Hydrological Indicators: A Case Study on a Mexican Basin." Water 11, no. 10 (October 11, 2019): 2110. http://dx.doi.org/10.3390/w11102110.
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This study evaluates the choice of the meteorological data set in the simulation of the streamflow of a Mexican basin, in the bias correction of climate simulations, and in the climate change impact on hydrological indicators. The selected meteorological data sets come from stations, two interpolated data sets and one reanalysis data set. The climate simulations were taken from the five-member ensemble from the second generation Canadian Earth System Model (CanESM2) under two representative concentration pathways (RCPs), for a reference period (1981–2000) and two future periods (2041–2060 and 2081–2100). The selected lumped hydrological model is GR4J, which is a daily lumped four-parameter rainfall-runoff model. Firstly, the results show that GR4J can be calibrated and validated with the meteorological data sets to simulate daily streamflow; however, the hydrological model leads to different hydrological responses for the basin. Secondly, the bias correction procedure obtains a similar relative climate change signal for the variables, but the magnitude of the signal strongly varies with the source of meteorological data. Finally, the climate change impact on hydrological indicators also varies depending on the meteorological data source, thus, for the overall mean flow, this uncertainty is greater than the uncertainty related to the natural variability. On the other hand, mixed results were found for high flows. All in all, the selection of meteorological data source should be taken into account in the evaluation of climate change impact on water resources.
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Bouadila, Abdelmounim, Ismail Bouizrou, Mourad Aqnouy, Khalid En-nagre, Yassine El Yousfi, Azzeddine Khafouri, Ismail Hilal, et al. "Streamflow Simulation in Semiarid Data-Scarce Regions: A Comparative Study of Distributed and Lumped Models at Aguenza Watershed (Morocco)." Water 15, no. 8 (April 20, 2023): 1602. http://dx.doi.org/10.3390/w15081602.
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In semi-arid regions such as the southwestern zone of Morocco, better management of water resources is crucial due to the frequent flooding phenomena. In this context, the use of hydrological models is becoming increasingly important, specifically in the Aguenza watershed. A multitude of hydrological models are available to make very efficient modeling, and from this perspective, a comparative approach was adopted using two models with different characteristics. Streamflow simulations were carried out continuously at daily time steps using GR4J and ATHYS (2002–2011). The latter was used also to simulate rainfall-runoff events (1984–2014). Simulation results using the distributed model are very efficient compared to those obtained by the lumped model “GR4J”, which shows the disadvantages of neglecting the hydrological processes during a hydrological study. However, a remarkable improvement was observed in the general appearance of the resulting hydrographs and the performance parameters after using the distributed model ((Calibration: NSE, RSR, and PBIAS increased successively by 8%, 6%, and 45.2%); (Validation: NSE, RSR, and PBIAS increased successively by 6%, 4%, and 8.9%)). In terms of flood event simulations, a good concordance between observed and simulated discharge was observed (NSEmedian = 0.7), indicating its great reliability for simulating rainfall-runoff events in semi-arid and data-scarce regions. This research highlights the importance of using hydrological models, specifically the distributed model ATHYS, for the better management of water resources in semi-arid regions with frequent flooding events.
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Ayzel, Georgy, Liubov Kurochkina, Eduard Kazakov, and Sergei Zhuravlev. "Streamflow prediction in ungauged basins: benchmarking the efficiency of deep learning." E3S Web of Conferences 163 (2020): 01001. http://dx.doi.org/10.1051/e3sconf/202016301001.
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Streamflow prediction is a vital public service that helps to establish flash-flood early warning systems or assess the impact of projected climate change on water management. However, the availability of streamflow observations limits the utilization of the state-of-the-art streamflow prediction techniques to the basins where hydrometric gauging stations exist. Since the most river basins in the world are ungauged, the development of the specialized techniques for the reliable streamflow prediction in ungauged basins (PUB) is of crucial importance. In recent years, the emerging field of deep learning provides a myriad of new models that can breathe new life into the stagnating PUB methods. In the presented study, we benchmark the streamflow prediction efficiency of Long Short-Term Memory (LSTM) networks against the standard technique of GR4J hydrological model parameters regionalization (HMREG) at 200 basins in Northwest Russia. Results show that the LSTM-based regional hydrological model significantly outperforms the HMREG scheme in terms of median Nash-Sutcliffe efficiency (NSE), which is 0.73 and 0.61 for LSTM and HMREG, respectively. Moreover, LSTM demonstrates the comparable median NSE with that for basin-scale calibration of GR4J (0.75). Therefore, this study underlines the high utilization potential of deep learning for the PUB by demonstrating the new state-of-the-art performance in this field.
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Koubodana, Houteta Djan'na, Kossi Atchonouglo, Julien G. Adounkpe, Ernest Amoussou, Domiho Japhet Kodja, Dambré Koungbanane, Koba Yaovi Afoudji, Yao Lombo, and Kossi E. Kpemoua. "Surface runoff prediction and comparison using IHACRES and GR4J lumped models in the Mono catchment, West Africa." Proceedings of the International Association of Hydrological Sciences 384 (November 16, 2021): 63–68. http://dx.doi.org/10.5194/piahs-384-63-2021.
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Abstract. This study aims to assess simulated surface runoff before and after dam construction in the Mono catchment (West Africa) using two lumped models: GR4J (Rural Engineering with 4 Daily Parameters) and IHACRES (Identification of unit Hydrographs and Component flows from Rainfall, Evapotranspiration and Stream data) over two different periods (1964–1986 and 1988–2010). Daily rainfall, mean temperature, evapotranspiration and discharge in situ data were collected for the period 1964–2010. After the model's initialization, calibration and validation; performances analysis have been carried out using multi-objectives functions developed in R software (version 3.5.3). The results indicate that statistical metrics such as the coefficient of determination (R2), the Kling–Gupta Efficiency (KGE), the Nash–Sutcliffe coefficient (NSE) and the Percent of Bias (PBIAS) provide satisfactory insights over the first period of simulation (1964–1986) and low performances over the second period of simulation (1988–2010). In particular, IHACRES model underestimates extreme high runoff of Mono catchment between 1964 and 1986. Conversely, GR4J model overestimates extreme high runoff and has been found to be better for runoff prediction of the river only between 1964 and 1986. Moreover, the study deduced that the robustness of runoff simulation between 1964 and 1986 is better than between 1988 and 2010. Therefore, the weakness of simulated runoff between 1988 and 2010 was certainly due to dam management in the catchment. The study suggests that land cover changes impacts, soil proprieties and climate may also affect surface runoff in the catchment.
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Guan, Xiaoxiang, Jianyun Zhang, Qinli Yang, Xiongpeng Tang, Cuishan Liu, Junliang Jin, Yue Liu, Zhenxin Bao, and Guoqing Wang. "Evaluation of Precipitation Products by Using Multiple Hydrological Models over the Upper Yellow River Basin, China." Remote Sensing 12, no. 24 (December 9, 2020): 4023. http://dx.doi.org/10.3390/rs12244023.
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In this study, 6 widely used precipitation products APHRODITE, CPC_UNI_PRCP, CN05.1, PERSIANN-CDR, Princeton Global Forcing (PGF), and TRMM 3B42 V7 (TMPA), were evaluated against gauge observations (CMA data) from 1998 to 2014, and applied to streamflow simulation over the Upper Yellow River basin (UYRB), using 4 hydrological models (DWBM, RCCC-WBM, GR4J, and VIC). The relative membership degree (u), as the comprehensive evaluation index in the hydrological evaluation, was calculated by the optimum fuzzy model. The results showed that the spatial pattern of precipitation from the CMA dataset and the other 6 precipitation products were very consistent with each other. The satellite-derived rainfall products (SDFE), like PSERSIANN-CDR and TMPA, depicted considerably finer and more detailed spatial heterogeneity. The SDFE and reanalysis (RA) products could estimate the monthly precipitation very well at both gauge and basin-average scales. The runoff simulation results indicated that the APHRODITE and TMPA were superior to the other 4 precipitation datasets, obtaining much higher scores, with average u values of 0.88 and 0.77. The precipitation estimation products tended to show better performance in streamflow simulation at the downstream hydrometric stations. In terms of performance of hydrological models, the RCCC–WBM model showed the best potential for monthly streamflow simulation, followed by the DWBM. It indicated that the monthly models were more flexible than daily conceptual or distributed models in hydrological evaluation of SDFE or RA products, and that the difference in precipitation estimates from various precipitation datasets were more influential in the GR4J and VIC models.
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Ba, Huanhuan, Shenglian Guo, Yun Wang, Xingjun Hong, Yixuan Zhong, and Zhangjun Liu. "Improving ANN model performance in runoff forecasting by adding soil moisture input and using data preprocessing techniques." Hydrology Research 49, no. 3 (August 22, 2017): 744–60. http://dx.doi.org/10.2166/nh.2017.048.
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Abstract This study attempts to improve the accuracy of runoff forecasting from two aspects: one is the inclusion of soil moisture time series simulated from the GR4J conceptual rainfall–runoff model as (ANN) input; the other is preprocessing original data series by singular spectrum analysis (SSA). Three watersheds in China were selected as case studies and the ANN1 model only with runoff and rainfall as inputs without data preprocessing was used to be the benchmark. The ANN2 model with soil moisture as an additional input, the SSA-ANN1 and SSA-ANN2 models with the same inputs as ANN1 and ANN2 using data preprocessing were studied. It is revealed that the degree of improvement by SSA is more significant than by the inclusion of soil moisture. Among the four studied models, the SSA-ANN2 model performs the best.
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TRAORE, Vieux Boukhaly. "Calibrating the Rainfall-Runoff Model GR4J and GR2M on the Koulountou River Basin, a Tributary of the Gambia River." American Journal of Environmental Protection 3, no. 1 (2014): 36. http://dx.doi.org/10.11648/j.ajep.20140301.15.
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Zhang, Xujie, Martijn J. Booij, and Yue-Ping Xu. "Improved Simulation of Peak Flows under Climate Change: Postprocessing or Composite Objective Calibration?" Journal of Hydrometeorology 16, no. 5 (October 1, 2015): 2187–208. http://dx.doi.org/10.1175/jhm-d-14-0218.1.
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Abstract Climate change is expected to have large impacts on peak flows. However, there may be bias in the simulation of peak flows by hydrological models. This study aims to improve the simulation of peak flows under climate change in Lanjiang catchment, east China, by comparing two approaches: postprocessing of peak flows and composite objective calibration. Two hydrological models [Soil and Water Assessment Tool (SWAT) and modèle du Génie Rural à 4 paramètres Journalier (GR4J)] are employed to simulate the daily flows, and the peaks-over-threshold method is used to extract peak flows from the simulated daily flows. Three postprocessing methods, namely, the quantile mapping method and two generalized linear models, are set up to correct the biases in the simulated raw peak flows. A composite objective calibration of the GR4J model by taking the peak flows into account in the calibration process is also carried out. The regional climate model Providing Regional Climates for Impacts Studies (PRECIS) with boundary forcing from two GCMs (HadCM3 and ECHAM5) under greenhouse gas emission scenario A1B is applied to produce the climate data for the baseline period and the future period 2011–40. The results show that the postprocessing methods, particularly quantile mapping method, can correct the biases in the raw peak flows effectively. The composite objective calibration also resulted in a good simulation performance of peak flows. The final estimated peak flows in the future period show an obvious increase compared with those in the baseline period, indicating there will probably be more frequent floods in Lanjiang catchment in the future.
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Chabi, Amedée, Esdras Babadjidé Josué Zandagba, Ezekiel Obada, Eliezer Iboukoun Biao, Eric Adéchina Alamou, and Abel Afouda. "Impact of climate change on water availability in the Oueme catchment at the outlet of the Save's bridge (Benin, West Africa)." Proceedings of the International Association of Hydrological Sciences 384 (November 16, 2021): 255–60. http://dx.doi.org/10.5194/piahs-384-255-2021.
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Abstract. One of the major threats to water resources today remains climate change. The objective of this study is to assess the impact of climate change on water availability in Oueme catchment at Savè. Precipitation provided by three regional climate models (RCMs) was analyzed. Bias in these data was first corrected using the Empirical Quantile Mapping (EQM) method be for etheir use as input to hydrological models. To achieve the objective, six hydrological models were used (AWBM, ModHyPMA, HBV, GR4J, SimHyd and Hymod). In projection, the results showed that the AWBM model appears to be the best. The multi-model approach further improves model performance, with the best obtained with combinations of the models AWBM-ModHyPMA-HBV. The AWBM model showed a fairly good capability for simulating flows in the basin with only HIRHAM5 climate model data as input. Therefore, the simulation with the HIRHAM5 data as inputs to the five (05) hydrological models, showed flows that vary at the horizons (2025, 2055 and 2085) under the scenarios (RCP4.5 and RCP8.5). Indeed, this variation is largely due to anthropogenic greenhouse gas (GHG) emissions.
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Cantoni, Elia, Yves Tramblay, Stefania Grimaldi, Peter Salamon, Hamouda Dakhlaoui, Alain Dezetter, and Vera Thiemig. "Hydrological performance of the ERA5 reanalysis for flood modeling in Tunisia with the LISFLOOD and GR4J models." Journal of Hydrology: Regional Studies 42 (August 2022): 101169. http://dx.doi.org/10.1016/j.ejrh.2022.101169.
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Alp, Harun, Mehmet Cüneyd Demirel, and Ömer Levend Aşıkoğlu. "Effect of Model Structure and Calibration Algorithm on Discharge Simulation in the Acısu Basin, Turkey." Climate 10, no. 12 (December 8, 2022): 196. http://dx.doi.org/10.3390/cli10120196.
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In this study, the Acısu Basin—viz., the headwater of the Gediz Basin—in Turkey, was modelled using three types of hydrological models and three different calibration algorithms. A well-known lumped model (GR4J), a commonly used semi-distributed (SWAT+) model, and a skillful distributed (mHM) hydrological model were built and integrated with the Parameter Estimation Tool (PEST). PEST is a model-independent calibration tool including three algorithms—namely, Levenberg Marquardt (L-M), Shuffled Complex Evolution (SCE), and Covariance Matrix Adoption Evolution Strategy (CMA-ES). The calibration period was 1991–2000, and the validation results were obtained for 2002–2005. The effect of the model structure and calibration algorithm selection on the discharge simulation was evaluated via comparison of nine different model-algorithm combinations. Results have shown that mHM and CMA-ES combination performed the best discharge simulation according to NSE values (calibration: 0.67, validation: 0.60). Although statistically the model results were classified as acceptable, the models mostly missed the peak values in the hydrograph. This problem may be related to the interventions made in 2000–2001 and may be overcome by changing the calibration and validation periods, increasing the number of iterations, or using the naturalized gauge data.
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Vilaseca, F., S. Narbondo, C. Chreties, A. Castro, and A. Gorgoglione. "A comparison between lumped and distributed hydrological models for daily rainfall-runoff simulation." IOP Conference Series: Earth and Environmental Science 958, no. 1 (December 1, 2021): 012016. http://dx.doi.org/10.1088/1755-1315/958/1/012016.
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Abstract In Uruguay, the Santa Lucía Chico watershed has been studied in several hydrologic/hydraulic works due to its economic and social importance. However, few studies have been focused on water balance computation in this watershed. In this work, two daily rainfall-runoff models, a distributed (SWAT) and a lumped one (GR4J), were implemented at two subbasins of the Santa Lucía Chico watershed, with the aim of providing a thorough comparison for simulating daily hydrographs and identify possible scenarios in which each approach is more suitable than the other. Results showed that a distributed and complex model like SWAT performs better in watersheds characterized by anthropic interventions such as dams, which can be explicitly represented. On the other hand, for watersheds with no significant reservoirs, the use of a complex model may not be justified due to the higher effort required in modeling design, implementation, and computational cost, which is not reflected in a significant improvement of model performance.
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Navas, Rafael, Jimena Alonso, Angela Gorgoglione, and R. Willem Vervoort. "Identifying Climate and Human Impact Trends in Streamflow: A Case Study in Uruguay." Water 11, no. 7 (July 12, 2019): 1433. http://dx.doi.org/10.3390/w11071433.
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Land use change is an important driver of trends in streamflow. However, the effects are often difficult to disentangle from climate effects. The aim of this paper is to demonstrate that trends in streamflow can be identified by analysing residuals of rainfall-runoff simulations using a Generalized Additive Mixed Model. This assumes that the rainfall-runoff model removes the average climate forcing from streamflow. The case study involves the Santa Lucía river (Uruguay), the GR4J rainfall-runoff model, three nested catchments ranging from 690 to 4900 km 2 and 35 years of observations (1981–2016). Two exogenous variables were considered to influence the streamflow. Using satellite data, growth in forest cover was identified, while the growth in water licenses was obtained from the water authority. Depending on the catchment, effects of land use change differ, with the largest catchment most impacted by afforestation, while the middle size catchment was more influenced by the growth in water licenses.
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Carlos, Alvarado Mendoza, Pérez Campomanes Giovene, and Pérez Campomanes María. "Hydrological Modeling for Daily Step Flood Forecasts with a Semi Distributed Approach Using the GR4J Model - Camaná River Basin – Arequipa." Civil Engineering and Architecture 11, no. 3 (May 2023): 1137–45. http://dx.doi.org/10.13189/cea.2023.110303.
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Santos, Léonard, Guillaume Thirel, and Charles Perrin. "Continuous state-space representation of a bucket-type rainfall-runoff model: a case study with the GR4 model using state-space GR4 (version 1.0)." Geoscientific Model Development 11, no. 4 (April 19, 2018): 1591–605. http://dx.doi.org/10.5194/gmd-11-1591-2018.
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Abstract. In many conceptual rainfall–runoff models, the water balance differential equations are not explicitly formulated. These differential equations are solved sequentially by splitting the equations into terms that can be solved analytically with a technique called “operator splitting”. As a result, only the solutions of the split equations are used to present the different models. This article provides a methodology to make the governing water balance equations of a bucket-type rainfall–runoff model explicit and to solve them continuously. This is done by setting up a comprehensive state-space representation of the model. By representing it in this way, the operator splitting, which makes the structural analysis of the model more complex, could be removed. In this state-space representation, the lag functions (unit hydrographs), which are frequent in rainfall–runoff models and make the resolution of the representation difficult, are first replaced by a so-called “Nash cascade” and then solved with a robust numerical integration technique. To illustrate this methodology, the GR4J model is taken as an example. The substitution of the unit hydrographs with a Nash cascade, even if it modifies the model behaviour when solved using operator splitting, does not modify it when the state-space representation is solved using an implicit integration technique. Indeed, the flow time series simulated by the new representation of the model are very similar to those simulated by the classic model. The use of a robust numerical technique that approximates a continuous-time model also improves the lag parameter consistency across time steps and provides a more time-consistent model with time-independent parameters.
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Flores, Neftali, Rolando Rodríguez, Santiago Yépez, Victor Osores, Pedro Rau, Diego Rivera, and Francisco Balocchi. "Comparison of Three Daily Rainfall-Runoff Hydrological Models Using Four Evapotranspiration Models in Four Small Forested Watersheds with Different Land Cover in South-Central Chile." Water 13, no. 22 (November 11, 2021): 3191. http://dx.doi.org/10.3390/w13223191.
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We used the lumped rainfall–runoff hydrologic models Génie Rural à 4, 5, 6 paramètres Journalier (GR4J, GR5J and GR6J) to evaluate the most robust model for simulating discharge on four forested small catchments (<40 ha) in south-central Chile. Different evapotranspiration methods were evaluated: Oudin, Hargreaves–Samani and Priestley–Taylor. Oudin’s model allows the achievement of the highest efficiencies in the flow simulation. The more sensitive parameters for each model were identified through a Generalized Probability Uncertainty Estimation (GLUE) model. Our results demonstrate that the three hydrological models were capable of efficiently simulating flow in the four study catchments. However, the GR6J model obtained the most satisfactory results in terms of simulated to measured streamflow closeness. In general, the three models tended to underestimate peak flow, as well as underestimate and overestimate flow events in most of the in situ observations, according to the probability of non-exceedance. We also evaluated the models’ performance in a simulation of summer discharge due to the importance of downstream water supply in the months of greatest scarcity. Again, we found that GR6J obtained the most efficient simulations.
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Tian, Ye, Yue-Ping Xu, and Xu-Jie Zhang. "Assessment of Climate Change Impacts on River High Flows through Comparative Use of GR4J, HBV and Xinanjiang Models." Water Resources Management 27, no. 8 (March 15, 2013): 2871–88. http://dx.doi.org/10.1007/s11269-013-0321-4.
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Lavtar, Katarina, Nejc Bezak, and Mojca Šraj. "Rainfall-Runoff Modeling of the Nested Non-Homogeneous Sava River Sub-Catchments in Slovenia." Water 12, no. 1 (December 31, 2019): 128. http://dx.doi.org/10.3390/w12010128.
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Rainfall-runoff modeling is nowadays applied for water resources management, water system design, real-time forecasting, flood design and can be carried out by using different types of hydrological models. In this study, we focused on lumped conceptual hydrological models and their performance in diverse sub-catchments of the Sava River in Slovenia, related to their size and non-homogeneity. We evaluated the difference between modeled and measured discharges of selected discharge gauging stations, using different model performance criteria that are usually applied in hydrology, connecting the results to geospatial analysis of geological and hydrogeological characteristics, land use, runoff potential, proportion of agglomeration and various meteorological variables. Better model performance was obtained for catchments with a higher runoff potential and with less variations in meteorological variables. Regarding the number of used parameters, the results indicated that the tested Genie Rural 6-parameter Journalier (GR6J) model with 6 parameters performed better than the Genie Rural 4-parameter Journalier (GR4J) model with 4 parameters, especially in the case of larger sub-catchments. These results illustrate the comprehensive nature of lumped models. Thus, they yield good performance in case of the catchments with indistinguishable characteristics.
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Piotrowski, Adam P., Marzena Osuch, and Jarosław J. Napiorkowski. "Joint Optimization of Conceptual Rainfall-Runoff Model Parameters and Weights Attributed to Meteorological Stations." Water Resources Management 33, no. 13 (October 2019): 4509–24. http://dx.doi.org/10.1007/s11269-019-02368-8.
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Abstract Conceptual lumped rainfall-runoff models are frequently used for various environmental problems. To put them into practice, both the model calibration method and data series of the area-averaged precipitation and air temperature are needed. In the case when data from more than one measurement station are available, first the catchment-averaged meteorological data series are usually obtained by some method, and then they are used for calibration of a lumped rainfall-runoff model. However, various optimization methods could easily be applied to simultaneously calibrate both the aggregation weights attributed to various meteorological stations to obtain a lumped meteorological data series and the rainfall-runoff model parameters. This increases the problem dimensionality but allows the optimization procedure to choose the data that are most important for the rainfall-runoff process in a particular catchment, without a priori assumptions. We test the idea using two conceptual models, HBV and GR4J, and three mutually different, relatively recently proposed Evolutionary Computation and Swarm Intelligence optimization algorithms, that are applied to three catchments located in Poland and northwestern USA. We consider two cases: with and without the model error correction applied to the rainfall-runoff models. It is shown that for the calibration period, joint optimization of the weights used to aggregate the meteorological data and the parameters of the rainfall-runoff model improves the results. However, the results for the validation period are inconclusive and depend on the model, error correction, optimization algorithm, and catchment.
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de Vos, N. J., and T. H. M. Rientjes. "Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation." Hydrology and Earth System Sciences 9, no. 1/2 (July 5, 2005): 111–26. http://dx.doi.org/10.5194/hess-9-111-2005.
Full textAbstract:
Abstract. The application of Artificial Neural Networks (ANNs) in rainfall-runoff modelling needs to be researched more extensively in order to appreciate and fulfil the potential of this modelling approach. This paper reports on the application of multi-layer feedforward ANNs for rainfall-runoff modelling of the Geer catchment (Belgium) using both daily and hourly data. The daily forecast results indicate that ANNs can be considered good alternatives for traditional rainfall-runoff modelling approaches, but the simulations based on hourly data reveal timing errors as a result of a dominating autoregressive component. This component is introduced in model simulations by using previously observed runoff values as ANN model input, which is a popular method for indirectly representing the hydrological state of a catchment. Two possible solutions to this problem of lagged predictions are presented. Firstly, several alternatives for representation of the hydrological state are tested as ANN inputs: moving averages over time of observed discharges and rainfall, and the output of the simple GR4J model component for soil moisture. A combination of these hydrological state representers produces good results in terms of timing, but the overall goodness of fit is not as good as the simulations with previous runoff data. Secondly, the possibility of using multiple measures of model performance during ANN training is mentioned.
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Buzacott, Alexander J. V., Bruce Tran, Floris F. van Ogtrop, and R. Willem Vervoort. "Conceptual Models and Calibration Performance—Investigating Catchment Bias." Water 11, no. 11 (November 19, 2019): 2424. http://dx.doi.org/10.3390/w11112424.
Full textAbstract:
Many lumped rainfall-runoff models are available but no single model can account for the uniqueness and variability of all catchments. While there has been progress in developing frameworks for optimal model selection, the process currently selects a range of model structures a priori rather than starting from the hydrological data and processes. In addition, studies on differential split sample tests (DSSTs) have focused on objective function definitions and calibration approaches. In this study, seven hydrological signatures and 12 catchment characteristics from 108 catchments around Australia were extracted for two 7-year time periods: (1) wet and (2) dry. The data was modelled using the GR4J, HBV and SIMHYD models using three objective functions to explore the relationship between model performance, catchment features and identified parameters. The hypothesis is that the hydrological signatures and catchment characteristics reflect catchment behaviour, and that certain signatures and characteristics are associated with better calibration performance. The results show that a greater percentage of catchments achieved a better calibration performance in the wet period compared to the dry period and that better calibration performance is associated with catchments that have greater cumulative flow and a steeper flow duration curve. The findings are consistent across the three models and three objective functions, suggesting that there is a bias in the studied models to wetter catchments. This study echoes the need to develop a conceptual model that can accommodate a wide variety of catchments and climates and provides a foundation to optimise and improve model selection in catchments based on their unique characteristics.
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Shin and Kim. "Analysis of the Effect of Uncertainty in Rainfall-Runoff Models on Simulation Results Using a Simple Uncertainty-Screening Method." Water 11, no. 7 (June 30, 2019): 1361. http://dx.doi.org/10.3390/w11071361.
Full textAbstract:
Various uncertainty analysis methods have been used in various studies to analyze the uncertainty of rainfall-runoff models; however, these methods are difficult to apply immediately as they require a long learning time. In this study, we propose a simple uncertainty-screening method that allows modelers to investigate relatively easily the uncertainty of rainfall-runoff models. The 100 best parameter values of three rainfall-runoff models were extracted using the efficient sampler DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm, and the distribution of the parameter values was investigated. Additionally, the ranges of the values of a model performance evaluation statistic and indicators of hydrologic alteration corresponding to the 100 parameter values for the calibration and validation periods was analyzed. The results showed that the Sacramento model, which has the largest number of parameters, had uncertainties in parameters, and the uncertainty of one parameter influenced all other parameters. Furthermore, the uncertainty in the prediction results of the Sacramento model was larger than those of other models. The IHACRES model had uncertainty in one parameter related to the slow flow simulation. On the other hand, the GR4J model had the lowest uncertainty compared to the other two models. The uncertainty-screening method presented in this study can be easily used when the modelers select rainfall-runoff models with lower uncertainty.
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Batelis, Stamatis C., and Ioannis Nalbantis. "A Multi-Model Multi-Scale Approach to Estimate the Impact of the 2007 Large-Scale Forest Fires in Peloponnese, Greece." Water 14, no. 20 (October 21, 2022): 3348. http://dx.doi.org/10.3390/w14203348.
Full textAbstract:
The hydrological impact of large-scale forest fires in a large basin is investigated on both a daily and an hourly basis. A basin of 877 km2 was chosen, with 37% of its area having been burnt in the summer of 2007. Five models are employed, namely SWAT (semi-distributed), GR4J, GR5J, and GR6J (lumped) for the daily time step, and HEC-HMS (semi-distributed) for the hourly time step. As SWAT and HEC-HMS implement the SCS-CN method, the change in the Curve Number (CN) from pre-fire to post-fire conditions is estimated along with the post-fire trend of CN for both time steps. Regarding the daily time step, a 20% post-fire increase in CN proved necessary for the accurate streamflow prediction, whereas ignoring this led to an underestimation of 22% on average. On an hourly time basis, CN was 95 for burnt areas after the fire, with a mildly decreasing trend after the third year and still above 90 until the fifth year. When neglecting this, peak flow is seriously underestimated (35–70%). The post-fire trend lines of CN for the two-time steps showed statistically equal slopes. Finally, GR models accurately predicted runoff while constraining one model parameter, which proved useful for the realistic prediction of other variables.