Identification of Combined Hydrological Models and Numerical Weather Predictions for Enhanced Flood Forecasting in a Semiurban Watershed

Awol, Frezer Seid; Coulibaly, Paulin; Tsanis, Ioannis K.

doi:10.1061/(asce)he.1943-5584.0002018

Cited by 14 publications

(5 citation statements)

References 83 publications

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“…Based on the comparison of different averaging schemes, the multi-input model averaging scheme (ALL), which includes the largest number of members (24 members), shows the best performance (largest median KGE, NSE and AVE). This is in line with the previous studies, which concluded that a large number of members leads to an improvement in hydrological simulating abilities [8,37,70]. However, how the number of members used in the averaging influences the performance of the averaging methods deserves further investigation.…”

Section: Impacts Of Averaging Size On Performances Of Multimodel Aver...supporting

confidence: 91%

Finding the Optimal Multimodel Averaging Method for Global Hydrological Simulations

Chen

et al. 2021

Remote Sensing

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Global gridded precipitations have been extensively considered as the input of hydrological models for runoff simulations around the world. However, the limitations of hydrologic models and the inaccuracies of the precipitation datasets could result in large uncertainty in hydrological forecasts and water resource estimations. Therefore, it is of great importance to investigate the hydrological value of a weighted combination of hydrological models driven by different precipitation datasets. In addition, due to the diversities of combination members and climate conditions, hydrological simulation for watersheds under different climate conditions may show various sensitivities to the weighted combinations. This study undertakes a comprehensive analysis of various multimodel averaging methods and schemes (i.e., the combination of the members in averaging) to identify the most skillful and reliable multimodel averaging application. To achieve this, four hydrological models driven by six precipitation datasets were used as averaging members. The behaviors of 9 averaging methods and 11 averaging schemes in hydrological simulations were tested over 2277 watersheds distributed in different climate regions in the world. The results show the following: (1) The multi-input averaging schemes (i.e., members consist of one model driven by multiple precipitation datasets) generally perform better than the multimodel averaging schemes (i.e., members consist of multiple models driven by the same precipitation dataset) for each averaging method; (2) The use of multiple members can improve the averaging performances. Six averaging members are found to be necessary and advisable, since using more than six members only imrpoves the estimation results slightly, as compared with using all 24 members; (3) The advantage of using averaging methods for hydrological modeling is region dependent. The averaging methods, in general, produced the best results in the warm temperate region, followed by the snow and equatorial regions, while a large difference among various averaging methods is found in arid and arctic regions. This is mainly due to the different averaging methods being affected to a different extent by the poorly performed members in the arid and arctic regions; (4) the multimodel superensemble method (MMSE) is recommended for its robust and outstanding performance among various climatic regions.

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Section: Impacts Of Averaging Size On Performances Of Multimodel Aver...supporting

confidence: 91%

Finding the Optimal Multimodel Averaging Method for Global Hydrological Simulations

Chen

et al. 2021

Remote Sensing

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“…BMA assigns weights to each model through posteriori probability, which was deduced by priori probability. It can not only provide high-precision multi-model comprehensive prediction, but can quantitatively evaluate the uncertainty of model [ 40 ]. The choice of different prior probabilities has a great impact on BMA results.…”

Section: Introductionmentioning

confidence: 99%

Improved runoff forecasting based on time-varying model averaging method and deep learning

et al. 2022

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In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constructed. The framework determines the main variables suitable for runoff prediction through correlation analysis, and uses TV-DMA and deep learning algorithm to construct an integrated prediction model for runoff. The results demonstrate that the current monthly runoff, the runoff of the previous month, the current monthly temperature, the temperature of the previous month and the current monthly rainfall were the variables suitable for runoff prediction. The results of runoff prediction show that the TV-DMA model has the highest prediction accuracy (with 0.97 Nash-efficiency coefficient (NSE)) and low uncertainty. The interval band of uncertainty was 33.3%-65.5% lower than single model. And the prediction performance of the single model and TV-DMA model in flood season is obviously lower than that in non-flood season. In addition, this study indicate that the current monthly runoff, rainfall and temperature are the important factor affecting the runoff prediction, which should be paid special attention in the runoff prediction.

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“…In merging quantile forecasts and predictive distributions of streamflow, BLP yielded slightly better results than BMA and NGR in terms of continuous rank probability score (CRPS), albeit being more time-consuming in parameter estimation, potentially resulting in suboptimal solutions that could limit its effectiveness [55]. Awol et al [131] highlighted the superiority of forecast merging methods with nonnegative weights, such as CLS and BMA, over those without constraints, like InvR and BGA. The GRC method excelled in multi-model averaging for hydrological applications in cold regions [6,83].…”

Section: Comparison Of Merging Techniquesmentioning

confidence: 99%

Review of Recent Developments in Hydrologic Forecast Merging Techniques

Sheikh,

Coulibaly

2024

Water

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Accurate forecasting in hydrologic modeling is crucial for sustainable water resource management across various sectors, where predicting extreme flow phases holds particular significance due to their severe impact on the territory. Due to the inherent uncertainties in hydrologic forecasting, relying solely on a single rainfall–runoff model may not provide reliable predictions. To address this challenge, over the years, researchers have developed and applied hydrologic forecast merging (HFM) techniques that combine multiple models or ensembles to enhance forecast accuracy, reduce uncertainty, and increase confidence in the forecast. This review summarizes the progress in HFM techniques since the early 1990s and covers developments and applications in flow simulation, uncertainty analysis, monthly and seasonal streamflow predictions, ensemble forecasts, flood forecasting, and climate change analysis. The findings indicate that while HFM techniques outperform individual models regarding forecasting efficiency, their performance across applications is not uniform. Among the different methods, Bayesian model averaging (BMA) is the most popular due to its ability to reduce uncertainty and provide accurate and reliable forecasts in deterministic and probabilistic simulations. With their application simplicity, regression techniques are also robust and efficient as they perform competitively well across different model-merging applications. While specific techniques, such as model-dependent weighted averaging and neural network methods, effectively reduce forecast uncertainty, there is still room for improving forecast accuracy across different lead times. Future research can focus on advanced HFM techniques for estimating optimal weights in time-varying domains and overcoming limitations, like simulating low flows in seasonally dry catchments.

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Identification of Combined Hydrological Models and Numerical Weather Predictions for Enhanced Flood Forecasting in a Semiurban Watershed

Cited by 14 publications

References 83 publications

Finding the Optimal Multimodel Averaging Method for Global Hydrological Simulations

Finding the Optimal Multimodel Averaging Method for Global Hydrological Simulations

Improved runoff forecasting based on time-varying model averaging method and deep learning

Review of Recent Developments in Hydrologic Forecast Merging Techniques

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