Seasonal sub-basin-scale runoff predictions: A regional hydrometeorological Ensemble Kalman Filter framework using global datasets

Borne, Maurus; Lorenz, Christof; Portele, Tanja; Martins, Eduardo; Júnior, Francisco das Chagas Vasconcelos; Kunstmann, Harald

doi:10.1016/j.ejrh.2022.101146

Cited by 4 publications

(3 citation statements)

References 48 publications

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“…Visible variations over the entire region were seen to follow patterns of the gauge dataset after bias correction closely. These results are consistent with other findings; for instance, [12] spotted vast improvement in the monthly and seasonal rainfall values after bias correcting seasonal forecasts using BCSD method. [34] , recently also recorded similar results in Califonia where seasonal rainfall was well represented post BSCD.…”

Section: Resultssupporting

confidence: 93%

Bias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices

Atiah

Johnson

Muthoni

et al. 2023

Heliyon

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Section: Resultssupporting

confidence: 93%

Bias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices

Atiah

Johnson

Muthoni

et al. 2023

Heliyon

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“…Runoff prediction also uses decision trees and their integration methods, such as XGBoost and LGBM, which are also widely used to predict runoff discharge [18]. Maurus Borne emphasized the significance of accurate forecasting in addressing water resource management issues in semi-arid regions where reliable scheduling decisions heavily rely on forecasted data from water resources ministries [19]. Timely mid-and long-term forecasting incorporating rainfall factors and underlying surface characteristics into theoretical models has become increasingly crucial for flood control and drought resistance in river basins.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Factor Combination Model for Medium to Long-Term Runoff Prediction Based on Improved BP Neural Network

Yan,

Gao,

Wen

et al. 2023

Water

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Taking a certain coastal area of Jiangsu province as the research background, this study scientifically predicts the runoff on the medium and long-term time scale according to the changes of various climate factors such as atmospheric circulation, sea surface temperature, and solar activity in the first half of the year. A lag correlation is established between various related climate factors and the monthly runoff process in the research area for the previous 1–6 months. Selecting advantageous factors and constructing a significant factor set. Using the improved BP (Back-Propagation) artificial neural network model and combining it with the sensitivity analysis method, a specific number of 8-factor combinations are selected from the set of significant factors for medium and long-term runoff prediction. After that, the prediction results are compared with the forecasting effects of two multi-factor combination runoff simulation schemes formed by stepwise regression and Spearman rank correlation methods. The study concluded that the multi-factor combination simulation effect formed through sensitivity analysis was the best. The 20% standard forecast qualification rate of the three schemes is not significantly different. The Mean Absolute Relative Error of the multi-factor combination training and validation periods simulated through sensitivity analysis is the smallest among the three schemes, which are 36.61% and 38.01%, respectively. The Nash Efficiency Coefficient in the validation period is 0.45, which is far better than other schemes and has better generalization ability. The Standard Deviation of Relative Error in the training and validation periods is much smaller than other schemes, and the dispersion of relative errors is the smallest.

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“…Another problem is that the TC merging method was preferred at the global scale rather than at the sub-basin level, while the runoff can strongly vary even at a small scale across adjacent sub-basins due to the high spatial heterogeneity of the landscape. In the context of the Republic of Korea where diverse vegetation conditions, complex terrains, and strong seasonal rainfall differences between rainy (wet) and non-rainy (dry) seasons under the summer monsoon's (Changma) effects [31] may lead to high uncertainties in runoff behavior, focusing on improving seasonal runoff estimates at the sub-basin level with more detailed spatial information derived from global products is necessary [32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Generation of High-Resolution Gridded Runoff Product for the Republic of Korea Sub-Basins from Seasonal Merging of Global Reanalysis Datasets

Sunwoo,

Nguyen,

Jun

2023

Water

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Gridded runoff product at the sub-basin level is pivotal for effective hydrologic modeling and applications. Although reanalyses can overcome the lack of traditional stream gauge networks to provide reliable geospatial runoff data, the inherent uncertainties associated with single products are still a problem. This study aims to improve the single products’ limitations over the heterogeneous Republic of Korea region by merging three common global reanalysis datasets to generate a high-quality and long-term gridded runoff product at a high resolution. The merging method relies on triple collocation (TC) analysis, which requires no reference runoff dataset, with a modification that was applied separately to wet and dry seasons (seasonal merging). A comparison between the merged runoff and its parent products at 0.10° grid, on a daily basis, and using the entire 10-year period (2011–2020) against an independent ground-based sub-basin runoff product generally indicated a superior performance of the merged product even at the national scale of Republic of Korea. Moreover, a slight improvement obtained with the seasonal merging compared to the traditional all-time merging highlighted the potential of this modification to address several drawbacks in the TC assumption, especially the non-stationary runoff pattern caused by seasonal rainfall effects in the Republic of Korea. Despite the need for further improvement such as bias correction, the results of this study encourage making a reliable benchmark runoff product at a regional scale, which is beneficial for flood/drought monitoring and artificial intelligence-based hydrologic model training.

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Seasonal sub-basin-scale runoff predictions: A regional hydrometeorological Ensemble Kalman Filter framework using global datasets

Cited by 4 publications

References 48 publications

Bias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices

Bias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices

A Multi-Factor Combination Model for Medium to Long-Term Runoff Prediction Based on Improved BP Neural Network

Generation of High-Resolution Gridded Runoff Product for the Republic of Korea Sub-Basins from Seasonal Merging of Global Reanalysis Datasets

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