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Innovations in Hydrology: Streamflow and Flood Prediction
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Innovations in Hydrology: Streamflow and Flood Prediction

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 1152

Special Issue Editor

Dr. Zhaocai Wang

E-Mail Website
Guest Editor
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Interests: streamflow prediction; cascade reservoir scheduling; water–energy–food–carbon coupling; water quality prediction; water resource management; water–wind–solar–storage multi-energy complementarity; hydrological modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the latest advancements and innovative approaches in streamflow and flood prediction within the field of hydrology. The integration of physical hydrological models with machine learning techniques, as well as deep learning ensemble predictions, has opened new avenues for more accurate and reliable forecasting. This Special Issue aims to bring together cutting-edge research that addresses the challenges and opportunities in this domain, facilitating the exchange of ideas and knowledge among the scientific community.

Main themes include, but are not limited to, the following:

(1) Integration of Physical and Machine Learning Models: This includes studies on how machine learning can enhance the parameterization, calibration, and uncertainty quantification of physical models, as well as how physical understanding can improve the interpretability and generalization of machine learning models for streamflow and flood prediction.

(2) Deep Learning Ensemble Predictions: This involves the development of ensemble strategies that combine multiple deep learning models, the integration of deep learning with other forecasting methods, and the evaluation of ensemble performance in terms of accuracy, reliability, and computational efficiency.

(3) Case Studies and Applications: Real-world case studies demonstrating the successful implementation of these innovative approaches in different hydro-climatic regions are highly valuable.

(4) Uncertainty Analysis and Risk Assessment: This includes the development of probabilistic forecasting frameworks and the use of uncertainty analysis to support robust water management strategies.

In conclusion, this Special Issue on streamflow and flood prediction will serve as a vital platform. By highlighting the integration of physical models and machine learning, as well as deep learning ensembles, we strive to advance this field. These techniques will improve prediction accuracy, facilitating better flood prevention and water resource management, thus enhancing the resilience and sustainability of communities and ecosystems amid hydrological uncertainties.

Dr. Zhaocai Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • streamflow prediction
  • flood prediction
  • physical hydrological models
  • machine learning
  • deep learning
  • uncertainty quantification
  • flood risk assessment
  • water resource management

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Published Papers (2 papers)

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Research

20 pages, 11079 KiB  
Article
A Bayesian Ensemble Learning-Based Scheme for Real-Time Error Correction of Flood Forecasting
by Liyao Peng, Jiemin Fu, Yanbin Yuan, Xiang Wang, Yangyong Zhao and Jian Tong
Water 2025, 17(14), 2048; https://doi.org/10.3390/w17142048 - 8 Jul 2025
Viewed by 245
Abstract
To address the critical demand for high-precision forecasts in flood management, real-time error correction techniques are increasingly implemented to improve the accuracy and operational reliability of the hydrological prediction framework. However, developing a robust error correction scheme remains a significant challenge due to [...] Read more.
To address the critical demand for high-precision forecasts in flood management, real-time error correction techniques are increasingly implemented to improve the accuracy and operational reliability of the hydrological prediction framework. However, developing a robust error correction scheme remains a significant challenge due to the compounded errors inherent in hydrological modeling frameworks. In this study, a Bayesian ensemble learning-based correction (BELC) scheme is proposed which integrates hydrological modeling with multiple machine learning methods to enhance real-time error correction for flood forecasting. The Xin’anjiang (XAJ) model is selected as the hydrological model for this study, given its proven effectiveness in flood forecasting across humid and semi-humid regions, combining structural simplicity with demonstrated predictive accuracy. The BELC scheme straightforwardly post-processes the output of the XAJ model under the Bayesian ensemble learning framework. Four machine learning methods are implemented as base learners: long short-term memory (LSTM) networks, a light gradient-boosting machine (LGBM), temporal convolutional networks (TCN), and random forest (RF). Optimal weights for all base learners are determined by the K-means clustering technique and Bayesian optimization in the BELC scheme. Four baseline schemes constructed by base learners and three ensemble learning-based schemes are also built for comparison purposes. The performance of the BELC scheme is systematically evaluated in the Hengshan Reservoir watershed (Fenghua City, China). Results indicate the following: (1) The BELC scheme achieves better performance in both accuracy and robustness compared to the four baseline schemes and three ensemble learning-based schemes. The average performance metrics for 1–3 h lead times are 0.95 (NSE), 0.92 (KGE), 24.25 m3/s (RMSE), and 8.71% (RPE), with a PTE consistently below 1 h in advance. (2) The K-means clustering technique proves particularly effective with the ensemble learning framework for high flow ranges, where the correction performance exhibits an increment of 62%, 100%, and 100% for 1 h, 2 h, and 3 h lead hours, respectively. Overall, the BELC scheme demonstrates the potential of a Bayesian ensemble learning framework in improving real-time error correction of flood forecasting systems. Full article
(This article belongs to the Special Issue Innovations in Hydrology: Streamflow and Flood Prediction)
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18 pages, 9973 KiB  
Article
Monthly Streamflow Forecasting for the Irtysh River Based on a Deep Learning Model Combined with Runoff Decomposition
by Kaiqiang Yong, Mingliang Li, Peng Xiao, Bing Gao and Chengxin Zheng
Water 2025, 17(9), 1375; https://doi.org/10.3390/w17091375 - 2 May 2025
Cited by 1 | Viewed by 677
Abstract
The mid- and long-term hydrological forecast is important for water resource management and disaster prevention. Moreover, mid- and long-term hydrological forecasts in the region with poorly observed field meteorological data are a great challenge for traditional hydrological models due to the complexity of [...] Read more.
The mid- and long-term hydrological forecast is important for water resource management and disaster prevention. Moreover, mid- and long-term hydrological forecasts in the region with poorly observed field meteorological data are a great challenge for traditional hydrological models due to the complexity of hydrological processes. To address this challenge, a machine learning model, particularly the deep learning model (DL), provides a new tool for improving the accuracy of runoff prediction. In this study, we took the Irtysh River, one of the longest rivers in Central Asia and a well-known trans-boundary river basin with poor field meteorological observations, as an example to develop a deep learning model based on LSTM and combined with runoff decomposition by Maximal Overlap Discrete Wavelet Transform (MODWT) to process target variables for predicting monthly streamflow. We also proposed an XGBoost-SHAP (Extreme Gradient Boost-SHapley Additive Explanations) method for the identification of predictors from large-scale indices for the streamflow forecast. The results suggest that MODWT shows the robustness of runoff decomposition between the training and test period. The deep learning model combined with MODWT shows better performance than the benchmark deep learning model without MODWT illustrated by an increased NSE. The XGBoost-SHAP method well identified the nonlinear relationship between the predictors and streamflow, and the predictors determined by XGBoost-SHAP can be physically explained. Compared with the traditional mutual information method, the XGBoost-SHAP method improves the accuracy of the deep learning model for streamflow forecast. The results of this study illustrate the ability of a deep learning model for mid- and long-term streamflow forecast, and the methods we developed in this study provide an effective approach to improve the streamflow prediction in the scarcely observed catchments. Full article
(This article belongs to the Special Issue Innovations in Hydrology: Streamflow and Flood Prediction)
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