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Advances in Flood and Drought Disaster Forecasting and Early Warnings...
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Advances in Flood and Drought Disaster Forecasting and Early Warnings through Integrating Hydrological and Hydrodynamic Models

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 6453

Special Issue Editors

Dr. Ronghua Liu

E-Mail Website
Guest Editor
China Institute of Water Resources and Hydropower Research, Beijing, China
Interests: flood forecasting and early warning
Dr. Xiaolei Zhang

E-Mail Website
Guest Editor
China Institute of Water Resources and Hydropower Research, Beijing, China
Interests: flash flood disaster prevention database; flash flood disaster risk analysis
Dr. Han Wang

E-Mail Website
Guest Editor Assistant
China Institute of Water Resources and Hydropower Research, Beijing, China
Interests: hydrological modelling; flash flood analysis; flood risk management; early warning system

Special Issue Information

Dear Colleagues,

Nature hazards including floods, droughts and related disasters such as landslides, debris flows, etc., have been significantly eroding the sustainability of communities, societies and livelihoods in both the short and long term. The intensity and severity of such events are projected to increase due to climate change, consequently leading to more people at risk of these induced disasters. To mitigate the impact and thereby increase community resilience to floods and droughts under climate change, an effective approach to improve the accuracy of forecasts and early warnings is required. This can be achieved via the use of well-developed hydrological and hydrodynamic models. The objective of this Special Issue is to motivate the interdisciplinary exchange of experiences among hydrological–hydrodynamic modelers, communities of disaster forecasting and early warning systems for floods and droughts, and decision-makers based on their present work for coping with climate change. The Special Issue encourages both reviews and new developments as well as applications of models that are used for detecting and monitoring disaster events, supporting forecasting and early warning systems and dealing with the complicated interactions between rainfall, temperature, groundwater, surface water and climatic factors. Case studies involving spatial–temporal variations, numerical and physical-based simulations, risk estimation and management are also welcome. We aim to gather recent contributions related to floods and droughts and secondary disasters with respect to forecasts and early warnings.

Therefore, we invite you to submit your work to this Special Issue, including but not limited to research on:

  • Catastrophic flood and drought events;
  • Recent advances in hydrological and hydrodynamic models.
  • Recent advances in flood and drought forecasting and early warning technology
  • Hydrological and hydrodynamic model application in estimating or forecasting floods and droughts risk;
  • Influences of climate change on floods and droughts;
  • Integrated risk management of floods, droughts and secondary disasters;
  • Spatial and temporal variation and characteristics of recent floods and droughts;
  • Simulation of historical disasters using digital twins technology;
  • Impact analysis of flood/drought secondary disasters;
  • Copying with disasters based on communities.

Dr. Ronghua Liu
Dr. Xiaolei Zhang
Guest Editors

Dr. Han Wang
Guest Editor Assistant

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

  • flood and drought disaster
  • forecast and early warning
  • spatio–temporal variation
  • hydrological–hydrodynamic models
  • integrated risk management
  • climate change
  • digital twins

Published Papers (6 papers)

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Research

14 pages, 4903 KiB  
Article
Ensemble Forecasts of Extreme Flood Events with Weather Forecasts, Land Surface Modeling and Deep Learning
by Yuxiu Liu, Xing Yuan, Yang Jiao, Peng Ji, Chaoqun Li and Xindai An
Water 2024, 16(7), 990; https://doi.org/10.3390/w16070990 - 29 Mar 2024
Cited by 1 | Viewed by 913
Abstract
Integrating numerical weather forecasts that provide ensemble precipitation forecasts, land surface hydrological modeling that resolves surface and subsurface hydrological processes, and artificial intelligence techniques that correct the forecast bias, known as the “meteo-hydro-AI” approach, has emerged as a popular flood forecast method. However, [...] Read more.
Integrating numerical weather forecasts that provide ensemble precipitation forecasts, land surface hydrological modeling that resolves surface and subsurface hydrological processes, and artificial intelligence techniques that correct the forecast bias, known as the “meteo-hydro-AI” approach, has emerged as a popular flood forecast method. However, its performance during extreme flood events across different interval basins has received less attention. Here, we evaluated the meteo-hydro-AI approach for forecasting extreme flood events from headwater to downstream sub-basins in the Luo River basin during 2010–2017, with forecast lead times up to 7 days. The proposed meteo-hydro approach based on ECMWF weather forecasts and the Conjunctive Surface-Subsurface Process version 2 land surface model with a spatial resolution of 1 km captured the flood hydrographs quite well. Compared with the ensemble streamflow prediction (ESP) approach based on initial conditions, the meteo-hydro approach increased the Nash-Sutcliffe efficiency of streamflow forecasts at the three outlet stations by 0.27–0.82, decreased the root-mean-squared-error by 22–49%, and performed better in reliability and discrimination. The meteo-hydro-AI approach showed marginal improvement, which suggested further evaluations with larger samples of extreme flood events should be carried out. This study demonstrated the potential of the integrated meteo-hydro-AI approach for ensemble forecasting of extreme flood events. Full article
(This article belongs to the Special Issue Advances in Flood and Drought Disaster Forecasting and Early Warnings through Integrating Hydrological and Hydrodynamic Models)
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16 pages, 21151 KiB  
Article
Comprehensive Risk Assessment Framework for Flash Floods in China
by Qing Li, Yu Li, Lingyun Zhao, Zhixiong Zhang, Yu Wang and Meihong Ma
Water 2024, 16(4), 616; https://doi.org/10.3390/w16040616 - 19 Feb 2024
Viewed by 1028
Abstract
Accurately assessing the risk of flash floods is a fundamental prerequisite for defending against flash flood disasters. The existing methods for assessing flash flood risk are constrained by unclear key factors and challenges in elucidating disaster mechanisms, resulting in less-than-ideal early warning effectiveness. [...] Read more.
Accurately assessing the risk of flash floods is a fundamental prerequisite for defending against flash flood disasters. The existing methods for assessing flash flood risk are constrained by unclear key factors and challenges in elucidating disaster mechanisms, resulting in less-than-ideal early warning effectiveness. This article is based on official statistics of flash flood disaster data from 2017 to 2021. It selects eight categories of driving factors influencing flash floods, such as rainfall, underlying surface conditions, and human activities. Subsequently, a geographical detector is utilized to analyze the explanatory power of each driving factor in flash flood disasters, quantifying the contribution of each factor to the initiation of flash flood; the flash flood potential index (FFPI) was introduced to assess the risk of flash flood disasters in China, leading to the construction of a comprehensive assessment framework for flash flood risk. The results indicate that (1) Flash floods are generally triggered by multiple factors, with rainfall being the most influential factor, directly causing flash floods. Soil type is the second most influential factor, and the combined effects of multiple factors intensify the risk of flash floods. (2) The southeastern, southern, and southwestern regions of China are considered high-risk areas for flash floods, with a high danger level, whereas the northwestern, northern, and northeastern plain regions exhibit a lower danger level. The above research results provide reference and guidance for the prevention and control of flash flood disasters. Full article
(This article belongs to the Special Issue Advances in Flood and Drought Disaster Forecasting and Early Warnings through Integrating Hydrological and Hydrodynamic Models)
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14 pages, 4117 KiB  
Article
Assessing Wet and Dry Periods Using Standardized Precipitation Index Fractal (SPIF) and Polygons: A Novel Approach
by Zekâi Şen
Water 2024, 16(4), 592; https://doi.org/10.3390/w16040592 - 17 Feb 2024
Viewed by 718
Abstract
In the open literature, there are numerous studies on the normal and extreme (flood and drought) behavior of wet and dry periods based on the understanding of the standard precipitation index (SPI), which provides a series of categorizations by considering the standard normal [...] Read more.
In the open literature, there are numerous studies on the normal and extreme (flood and drought) behavior of wet and dry periods based on the understanding of the standard precipitation index (SPI), which provides a series of categorizations by considering the standard normal (Gaussian) probability distribution function (PDF). The numerical meaning of each categorization assessment is quite lacking in terms of future predictions of wet and dry period duration based on historical records. This paper presents a new approach for calculating possible formations of future wet and dry period durations based on historical records through an effective fractal geometric forecasting approach. The essence of the proposed methodology is based on the number of dry periods (steps) of non-overlapping monthly duration along consecutive broken line paths in the SPI classification for wet and dry period durations. It has been observed that the plot of periods on double logarithmic paper falls along a straight line against the number of such periods, implying a power function, which is the essence of fractal geometry. Extending the empirically derived straight line provides the number of periods that may occur in the future over a range of SPI levels. This methodology is referred to as SPI fractal (SPIF), and the classic SPI classification is converted into SPIF wet and dry polygons, which provide additional information about the drought period number within a valid polygonal area, compared to the classic SPI results. The wet and dry period features of any hydro-meteorology time series are constrained in SPIF polygons. The application of the methodology was carried out on monthly rainfall records on the European side of the Istanbul Florya meteorological station in Turkey. Full article
(This article belongs to the Special Issue Advances in Flood and Drought Disaster Forecasting and Early Warnings through Integrating Hydrological and Hydrodynamic Models)
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19 pages, 6065 KiB  
Article
Study on Dynamic Early Warning of Flash Floods in Hubei Province
by Yong Tu, Yanwei Zhao, Lingsheng Meng, Wei Tang, Wentao Xu, Jiyang Tian, Guomin Lyu and Nan Qiao
Water 2023, 15(17), 3153; https://doi.org/10.3390/w15173153 - 3 Sep 2023
Viewed by 1060
Abstract
Flash floods are ferocious and destructive, making their forecasting and early warning difficult and easily causing casualties. In order to improve the accuracy of early warning, a dynamic early warning index system was established based on the distributed spatio-temporally mixed model through a [...] Read more.
Flash floods are ferocious and destructive, making their forecasting and early warning difficult and easily causing casualties. In order to improve the accuracy of early warning, a dynamic early warning index system was established based on the distributed spatio-temporally mixed model through a case study of riverside villages in Hubei Province. Fully taking into account previous rainfall and assuming different rainfall conditions, this work developed a dynamic early warning threshold chart by determining critical rainfall thresholds at different soil moisture levels (dry, normal, wet, and saturated) through pilot calculations, to support a quick query of the critical rainfall at any soil moisture level. The research results show that of the 74 counties and districts in Hubei Province, more than 50% witnessed higher mean critical rainfall than empirical thresholds when the soil was saturated, and about 90% did so when the soil was dry. In 881 towns, a total of 456 early warnings were generated based on dynamic thresholds from 2020 to 2022, 15.2% more than those based on empirical thresholds. From the perspective of total rainfall, dynamic early warnings were generated more frequently in wet years, while empirical early warnings were more frequent in dry years, and the frequency of two warnings were roughly the same in normal years. There were more early warnings based on empirical thresholds in May each year, but more based on dynamic thresholds in June and July, and early warnings generated based on the two methods were almost equal in August and September. Spatially, after dynamic early warning thresholds were adopted, Shiyan and Xiangyang, both northwestern cities in Hubei Province, witnessed significant increases in early warnings. In terms of the early warning mechanism, dynamic early warning took into account the impact of soil moisture and analyzed the flood discharge capacity of river channels according to the flood stage of the riverside villages. On this basis, the rainfall early warning thresholds under different conditions were determined. This is a refined early warning method that could improve the accuracy of flash flood warnings in Hubei Province. Full article
(This article belongs to the Special Issue Advances in Flood and Drought Disaster Forecasting and Early Warnings through Integrating Hydrological and Hydrodynamic Models)
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20 pages, 5972 KiB  
Article
A Hybrid Theory-Driven and Data-Driven Modeling Method for Solving the Shallow Water Equations
by Shunyu Yao, Guangyuan Kan, Changjun Liu, Jinbo Tang, Deqiang Cheng, Jian Guo and Hu Jiang
Water 2023, 15(17), 3140; https://doi.org/10.3390/w15173140 - 1 Sep 2023
Cited by 1 | Viewed by 1023
Abstract
In recent years, mountainous areas in China have faced frequent geological hazards, including landslides, debris flows, and collapses. Effective simulation of these events requires a solver for shallow water equations (SWEs). Traditional numerical methods, such as finite difference and finite volume, face challenges [...] Read more.
In recent years, mountainous areas in China have faced frequent geological hazards, including landslides, debris flows, and collapses. Effective simulation of these events requires a solver for shallow water equations (SWEs). Traditional numerical methods, such as finite difference and finite volume, face challenges in discretizing convection flux terms, while theory-based models need to account for various factors such as shock wave capturing and wave propagation direction, demanding a high-level understanding of the underlying physics. Previous deep learning (DL)-based SWE solvers primarily focused on constructing direct input–output mappings, leading to weak generalization properties when terrain data or stress constitutive relations change. To overcome these limitations, this study introduces a novel SWE solver that combines theory and data-driven methodologies. The core idea is to use artificial neural networks to compute convection flux terms, and to reduce modeling complexity. Theory-based modeling is used to tackle complex terrain and friction terms for the purpose of ensuring generalization. Our method surpasses challenges faced by previous DL-based solvers in capturing terrain and stress variations. We validated our solver’s capabilities by comparing simulation results with analytical solutions, real-world disaster cases, and the widely used Massflow software-generated simulations. This comprehensive comparison confirms our solver’s ability to accurately simulate hazard scenarios and showcases strong generalization on varying terrain and land surface friction. Our proposed method effectively addresses DL-based solver limitations while simplifying the complexities of theory-driven numerical methods, offering a promising approach for hazard dynamics simulation. Full article
(This article belongs to the Special Issue Advances in Flood and Drought Disaster Forecasting and Early Warnings through Integrating Hydrological and Hydrodynamic Models)
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22 pages, 6594 KiB  
Article
Massively Parallel Monte Carlo Sampling for Xinanjiang Hydrological Model Parameter Optimization Using CPU-GPU Computer Cluster
by Guangyuan Kan, Chenliang Li, Depeng Zuo, Xiaodi Fu and Ke Liang
Water 2023, 15(15), 2810; https://doi.org/10.3390/w15152810 - 3 Aug 2023
Cited by 1 | Viewed by 1043
Abstract
The Monte Carlo sampling (MCS) method is a simple and practical way for hydrological model parameter optimization. The MCS procedure is used to generate a large number of data points. Therefore, its computational efficiency is a key issue when applied to large-scale problems. [...] Read more.
The Monte Carlo sampling (MCS) method is a simple and practical way for hydrological model parameter optimization. The MCS procedure is used to generate a large number of data points. Therefore, its computational efficiency is a key issue when applied to large-scale problems. The MCS method is an internally concurrent algorithm that can be parallelized. It has the potential to execute on massively parallel hardware systems such as multi-node computer clusters equipped with multiple CPUs and GPUs, which are known as heterogeneous hardware systems. To take advantage of this, we parallelize the algorithm and implement it on a multi-node computer cluster that hosts multiple INTEL multi-core CPUs and NVIDIA many-core GPUs by using C++ programming language combined with the MPI, OpenMP, and CUDA parallel programming libraries. The parallel parameter optimization method is coupled with the Xinanjiang hydrological model to test the acceleration efficiency when tackling real-world applications that have a very high computational burden. Numerical experiments indicate, on the one hand, that the computational efficiency of the massively parallel parameter optimization method is significantly improved compared to single-core CPU code, and the multi-GPU code achieves the fastest speed. On the other hand, the scalability property of the proposed method is also satisfactory. In addition, the correctness of the proposed method is also tested using sensitivity and uncertainty analysis of the model parameters. Study results indicate good acceleration efficiency and reliable correctness of the proposed parallel optimization methods, which demonstrates excellent prospects in practical applications. Full article
(This article belongs to the Special Issue Advances in Flood and Drought Disaster Forecasting and Early Warnings through Integrating Hydrological and Hydrodynamic Models)
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