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Editorial Board Members’ Collection Series: The Flood Estimation and Forecasting...
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Editorial Board Members’ Collection Series: The Flood Estimation and Forecasting Chain: Meteorological–Hydrological–Hydraulic Forecasts and Predictive Uncertainty towards Operational Decisions

A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrological and Hydrodynamic Processes and Modelling".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 15531

Special Issue Editors

Dr. Andrea Petroselli

E-Mail Website
Guest Editor
Department of Economics, Engineering, Society and Business Organization (DEIM), Tuscia University, 01100 Viterbo, Italy
Interests: rainfall-runoff modeling; flood-prone area estimation; surface hydrology; GIS terrain analysis for hydrogeomorphic applications; hydrological process monitoring and modelling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pingping Luo

E-Mail Website
Guest Editor
School of Water and Environment, Chang’an University, Xi’an 710054, China
Interests: urban flood; flood management; hydrological modeling; water quality analysis; statistical analysis; sustainable water resource management; ecohydrology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modeling and monitoring approaches are pivotal in the comprehension of the flood estimation and forecasting chains. Meteorological, hydrological and hydraulic forecasts and estimations, together with the associated predictive uncertainty, are needed in order to support operational decisions, not only for research but also for water institutions and professional communities.

Indeed, the understanding, simulation and mitigation of flooding scenarios represent ongoing challenges for both researchers and floodplain managers for large basins in watersheds characterized by limited contributing areas, where, often, the modeler is forced to apply simplified models. Recent advancements in remote sensing technologies and computer capabilities have provided a new generation of scenarios to solve the problem, from the use of artificial neural networks to the use of synthetic rainfall generation models and continuous rainfall–runoff modeling.

In this Topical Collection, we welcome the submission of original and innovative research papers focusing on modeling and monitoring aspects related to the whole flood estimation and forecasting chain, in order to address water resource management issues and use the available information to reduce the uncertainty in the estimations as much as possible. Additionally, opportunities arising from new sources of remotely sensed information, which can also be linked to informal unstructured data (e.g., social networks), citizen science approaches and low-cost sensors, among others, are welcomed.

We expect that this Topical Collection will reduce the uncertainty in the determination of design variables linked to water cycle processes and features considered in different meteorological, hydrological and hydraulic processes related to the whole flood estimation and forecasting chain.

Dr. Andrea Petroselli
Prof. Dr. Pingping Luo
Guest Editors

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. Hydrology is an international peer-reviewed open access monthly 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 1800 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

  • hydrological processes
  • hydraulic processes
  • flood estimation
  • flood forecasting
  • modeling and monitoring
  • water resources management
  • wet and arid areas

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Published Papers (5 papers)

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Research

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29 pages, 15893 KiB  
Article
Application of Temporal Fusion Transformers to Run-Of-The-River Hydropower Scheduling
by Rafael Francisco, José Pedro Matos, Rui Marinheiro, Nuno Lopes, Maria Manuela Portela and Pedro Barros
Hydrology 2025, 12(4), 81; https://doi.org/10.3390/hydrology12040081 - 3 Apr 2025
Viewed by 330
Abstract
This study explores the application of Temporal Fusion Transformers (TFTs) to improve the predictability of hourly potential hydropower production for a small run–of–the–river hydropower plant in Portugal. Accurate hourly power forecasts are essential for optimizing participation in the spot electricity market, where deviations [...] Read more.
This study explores the application of Temporal Fusion Transformers (TFTs) to improve the predictability of hourly potential hydropower production for a small run–of–the–river hydropower plant in Portugal. Accurate hourly power forecasts are essential for optimizing participation in the spot electricity market, where deviations incur penalties. This research introduces the novel application of the TFT, a deep–learning model tailored for time series forecasting and uncovering complex patterns, to predict hydropower production based on meteorological data, historical production records, and plant capacity. Key challenges such as filtering observed hydropower outputs (to remove strong, and unpredictable human influence) and adapting the historical series to installed capacity increases are discussed. An analysis of meteorological information from several sources, including ground information, reanalysis, and forecasting models, was also undertaken. Regarding the latter, precipitation forecasts from the European Centre for Medium–Range Weather Forecasts (ECMWF) proved to be more accurate than those of the Global Forecast System (GFS). When combined with ECMWF data, the TFT model achieved significantly higher accuracy in potential hydropower production predictions. This work provides a framework for integrating advanced machine learning models into operational hydropower scheduling, aiming to reduce classical modeling efforts while maximizing energy production efficiency, reliability, and market performance. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: The Flood Estimation and Forecasting Chain: Meteorological–Hydrological–Hydraulic Forecasts and Predictive Uncertainty towards Operational Decisions)
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22 pages, 44511 KiB  
Article
Deep Learning Prediction of Streamflow in Portugal
by Rafael Francisco and José Pedro Matos
Hydrology 2024, 11(12), 217; https://doi.org/10.3390/hydrology11120217 - 19 Dec 2024
Cited by 1 | Viewed by 1265
Abstract
The transformative potential of deep learning models is felt in many research fields, including hydrology and water resources. This study investigates the effectiveness of the Temporal Fusion Transformer (TFT), a deep neural network architecture for predicting daily streamflow in Portugal, and benchmarks it [...] Read more.
The transformative potential of deep learning models is felt in many research fields, including hydrology and water resources. This study investigates the effectiveness of the Temporal Fusion Transformer (TFT), a deep neural network architecture for predicting daily streamflow in Portugal, and benchmarks it against the popular Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model. Additionally, it evaluates the performance of TFTs through selected forecasting examples. Information is provided about key input variables, including precipitation, temperature, and geomorphological characteristics. The study involved extensive hyperparameter tuning, with over 600 simulations conducted to fine–tune performances and ensure reliable predictions across diverse hydrological conditions. The results showed that TFTs outperformed the HBV model, successfully predicting streamflow in several catchments of distinct characteristics throughout the country. TFTs not only provide trustworthy predictions with associated probabilities of occurrence but also offer considerable advantages over classical forecasting frameworks, i.e., the ability to model complex temporal dependencies and interactions across different inputs or weight features based on their relevance to the target variable. Multiple practical applications can rely on streamflow predictions made with TFT models, such as flood risk management, water resources allocation, and support climate change adaptation measures. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: The Flood Estimation and Forecasting Chain: Meteorological–Hydrological–Hydraulic Forecasts and Predictive Uncertainty towards Operational Decisions)
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19 pages, 7581 KiB  
Article
A Spatiotemporal Assessment of the Precipitation Variability and Pattern and an Evaluation of the Predictive Reliability of Global Climate Models over Bihar
by Ahmad Rashiq, Vishwajeet Kumar and Om Prakash
Hydrology 2024, 11(4), 50; https://doi.org/10.3390/hydrology11040050 - 8 Apr 2024
Cited by 4 | Viewed by 2582
Abstract
Climate change is significantly altering precipitation patterns, leading to spatiotemporal changes throughout the world. In particular, the increased frequency and intensity of extreme weather events, leading to heavy rainfall, floods, and droughts, have been a cause of concern. A comprehensive understanding of these [...] Read more.
Climate change is significantly altering precipitation patterns, leading to spatiotemporal changes throughout the world. In particular, the increased frequency and intensity of extreme weather events, leading to heavy rainfall, floods, and droughts, have been a cause of concern. A comprehensive understanding of these changes in precipitation patterns on a regional scale is essential to enhance resilience against the adverse effects of climate change. The present study, focused on the state of Bihar in India, uses a long-term (1901–2020) gridded precipitation dataset to analyze the effect of climate change. Change point detection tests divide the time series into two epochs: 1901–1960 and 1961–2020, with 1960 as the change point year. Modified Mann–Kendall (MMK) and Sen’s slope estimator tests are used to identify trends in seasonal and annual time scales, while Centroidal Day (CD) analysis is performed to determine changes in temporal patterns of rainfall. The results show significant variability in seasonal rainfall, with the nature of pre-monsoon and post-monsoon observed to have flipped in second epoch. The daily rainfall intensity during the monsoon season has increased considerably, particularly in north Bihar, while the extreme rainfall has increased by 60.6 mm/day in the second epoch. The surface runoff increased by approximately 13.43% from 2001 to 2020. Further, 13 Global Climate Models (GCMs) evaluate future scenarios based on Shared Socioeconomic Pathways (SSP) 370 and SSP585. The suitability analysis of these GCMs, based on probability density function (PDF), monthly mean absolute error (MAE), root mean square error (RMSE) and percentage bias (P-Bias), suggests that EC-Earth3-Veg-LR, MIROC6, and MPI-ESM1-2-LR are the three best GCMs representative of rainfall in Bihar. A Bayesian model-averaged (BMA) multi-model ensemble reflects the variability expected in the future with the least uncertainty. The present study’s findings clarify the current state of variability, patterns and trends in precipitation, while suggesting the most appropriate GCMs for better decision-making and preparedness. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: The Flood Estimation and Forecasting Chain: Meteorological–Hydrological–Hydraulic Forecasts and Predictive Uncertainty towards Operational Decisions)
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16 pages, 1409 KiB  
Article
Development of Green Disaster Management Toolkit to Achieve Carbon Neutrality Goals in Flood Risk Management
by Tae Sung Cheong and Sangman Jeong
Hydrology 2024, 11(4), 44; https://doi.org/10.3390/hydrology11040044 - 26 Mar 2024
Viewed by 1895
Abstract
Current flood risk management projects have been criticized for their high carbon emissions, raising the need for carbon emission reduction and carbon absorption efforts to mitigate environmental impacts and achieve carbon neutrality goals. The research develops a comprehensive green disaster risk management toolkit [...] Read more.
Current flood risk management projects have been criticized for their high carbon emissions, raising the need for carbon emission reduction and carbon absorption efforts to mitigate environmental impacts and achieve carbon neutrality goals. The research develops a comprehensive green disaster risk management toolkit to calculate the carbon emissions and absorption quantitatively based on the unit volume of materials and processes employed in a flood risk management project. As a result of applying the developed toolkit to a about 22,300 small stream restoration projects in Korea, the total carbon emissions were estimated to be 1,158,840.7 tons of CO2, of which 89.4% of the total carbon emissions originated from concrete-related construction activities, such as cement and ready-mixed concrete pouring. As a result of evaluating the nationwide carbon absorption results of all small stream restoration projects, total absorption by 2030 is expected to be 3.0 to 10.2 times higher than carbon emissions. The comprehensive toolkits are expected to support the selection of customized processes, materials, and methods by providing a systematic approach to calculate and minimize carbon emissions, ultimately contributing to the achievement of carbon neutrality goals in flood risk management projects. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: The Flood Estimation and Forecasting Chain: Meteorological–Hydrological–Hydraulic Forecasts and Predictive Uncertainty towards Operational Decisions)
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Review

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16 pages, 991 KiB  
Review
Comprehensive Flood Risk Assessment: State of the Practice
by Neil S. Grigg
Hydrology 2023, 10(2), 46; https://doi.org/10.3390/hydrology10020046 - 10 Feb 2023
Cited by 12 | Viewed by 8051
Abstract
A comprehensive assessment of flood hazards will necessitate a step-by-step analysis, starting with hydrometeorological examinations of runoff and flow, followed by an assessment of the vulnerability of those at risk. Although bodies of knowledge about these topics are large, flood risk assessments face [...] Read more.
A comprehensive assessment of flood hazards will necessitate a step-by-step analysis, starting with hydrometeorological examinations of runoff and flow, followed by an assessment of the vulnerability of those at risk. Although bodies of knowledge about these topics are large, flood risk assessments face data challenges such as climate change, population growth, and shifting land uses. Recent studies have provided comprehensive reviews of advances in the water sciences arena, and in a complementary way, this paper reviews the state of the practice of assessing flood risk, include flood scenarios, hydrometeorology, inundation modeling, flood frequency analysis, interrelationships with water infrastructure, and vulnerability of people and places. The research base for each of these topics is extensive. Some of the tools in these areas, such as hydrologic modeling, have research advances that extend back decades, whereas others, such as numerical weather prediction, have more room to evolve. It’s clear from all studies that data is crucial along the progression from atmospheric conditions to the impact on flood victims. How data are provided and shared and how they are used by stakeholders in flood risk reduction continue to evolve. Improved availability of data and uses of emerging tools of data science and machine learning are needed to assess and mitigate flood risks. Continued the development of key tools is also required, especially to improve the capability to assemble them effectively on user platforms. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: The Flood Estimation and Forecasting Chain: Meteorological–Hydrological–Hydraulic Forecasts and Predictive Uncertainty towards Operational Decisions)
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