Submit to Water Review for Water Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Advances in Flood Forecasting and Hydrological Modeling

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (5 June 2022) | Viewed by 36822

Share This Special Issue

Special Issue Editors

Prof. Dr. Zhijia Li

E-Mail Website
Guest Editor

Department of Hydrology and Water Resources Engineering, Hohai University, Nanjing, China
Interests: hydrological modeling; water resources management; hydrologic and water resource modeling
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Cheng Yao

E-Mail Website
Guest Editor

College of Hydrology and Water Resources, Hohai University, Nanjing, China
Interests: hydrology and water resources; watershed hydrology; hydrological processes and modeling; flood forecasting and warning

Special Issue Information

Dear Colleagues,

Flood is one of the most serious natural disasters all over the world, and flood forecasting and prevention are the most important non-engineering measures for flood control and disaster mitigation. Hydrological processes are nonstationary and are subject to environmental change, which could significantly alter the frequency distribution of floods. Watershed flood simulation is a focus of flood analysis and prediction, and is a fundamental issue in flood control and disaster reduction. Recently, many studies have established intelligent algorithm precipitation-driven watershed flood hydrological and hydraulic coupled forecasting models for forecasting experiments. The aim of this Special Issue is to draw together the latest research in flood risk and hydrology simulation. We call for research papers focusing on hydrological modeling or other techniques understanding how the frequency and severity of hydrological and hydro-meteorological events are changing. This is to provide empirical bases for the formulation of appropriate strategies for enhancing flood risk reduction.

For this Special Issue of Water, we invite authors to submit research on, but not strictly limited to, the following topics:

The application of intelligent algorithms and learning in hydrology;
Flash flood simulation and prevention;
Distributed watershed hydrology models;
Prior estimation of distributed parameters;
Simulation of hydrological processes coupled with meteorology;
Research on the hydrological cycle in a changing environment;
Groundwater vulnerability in exploitation and use;
Change of hydrological characteristics of watersheds receiving human impact;
Land data assimilation, urban water security;
Hydrological analysis in scale;
The strategy of dynamically adjusting the parameter in real-time flood forecasting;
Parameter sensitivity analysis;
Spatial rationality analysis of parameters.

Prof. Dr. Zhijia Li
Prof. Dr. Cheng Yao
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. 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

water resources management
hydrological process
flood forecasting
flood disaster
hydrological modeling
catchments
extreme weather

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

21 pages, 6956 KiB

Open AccessArticle

Development and Evaluation of a Web-Based and Interactive Flood Management Tool for Awash and Omo-Gibe Basins, Ethiopia

by Surafel M. Woldegebrael, Belete B. Kidanewold and Assefa M. Melesse

Water 2022, 14(14), 2195; https://doi.org/10.3390/w14142195 - 11 Jul 2022

Cited by 4 | Viewed by 3414

Abstract

Flood risk management is used to monitor floodwater and mitigate flooding that impacts people, properties and infrastructures, and the environment. This study developed an interactive web-based “flood tool” for Awash and Omo-Gibe basins in Ethiopia to improve the flood monitoring services and facilities. The data used were real-time and seasonal rainfall-runoff forecasts, flood inundations, and other forecast products for the 2021 flood season (June to September) in a case study. Methods used were multiple scripts written in the Hypertext Markup Language (HTML) and the Visual Studio Code as a coding environment. The coefficient-of-determination (R²) and efficiency (NSE) were used to evaluate the forecast products. The R² values for selected river stations were the Awash-Hombole (0.79), Mojo (0.64), Awash-7 (0.66), Awash-Adaitu (0.62), Gibe-Tolai (0.78), and Gibe-Abelti (0.70) rivers. The R² values for Koka and Gibe-3 reservoirs inflows (water levels) forecasts were 0.97 (0.96) and 0.93 (0.99), and the NSE values were 0.89 (0.88) and 0.92 (0.95) for each reservoir, respectively. Besides, the flood inundation extents (km²) from satellite observation and model were compared for the main flood-prone areas and in agreement with very good performance. The flood tool can therefore present early warning forecast products and convey advice to decision-makers to take action for the people at risk. Full article

(This article belongs to the Special Issue Advances in Flood Forecasting and Hydrological Modeling)

► Show Figures

Figure 1

31 pages, 10322 KiB

Open AccessArticle

Modeling the 2D Inundation Simulation Based on the ANN-Derived Model with Real-Time Measurements at Roadside IoT Sensors

by Shiang-Jen Wu, Chih-Tsu Hsu, Jhih-Cyuan Shen and Che-Hao Chang

Water 2022, 14(14), 2189; https://doi.org/10.3390/w14142189 - 11 Jul 2022

Cited by 5 | Viewed by 2432

Abstract

This study aims to develop a smart model for the two-dimensional (2D) inundation simulation based on the derived artificial neural network (ANN) model with real-time measurements at the roadside IoT (Internet of Things) sensors; in detail, the flooding zones and associated area can be quantified by combining the inundation-depth estimates at the ungauged locations (defined by the virtual IoT sensor, VIOT) via the corresponding inundation-estimation equations, established using the ANN-derived model with the measurements at the IoT sensors (named SM_EID_VIOT model). Moreover, the resulting inundation-depth estimates at the ungauged locations from the proposed SM_EID_VIOT model can be improved by means of the real-time error-correction approach for the 2D inundation simulation. To demonstrate the reliability of the results from the proposed SM_EID_VIOT model, 1000 simulations of the rainfall-induced flood events within the study area of the Miaoli City of Northern Taiwan are generated as the model-training and validation datasets. Consequently, the proposed SM_EID_VIOT could estimate the inundation depths with an acceptable accuracy at the ungauged locations in time and space based on a low root mean square error (RMSE) of under 0.01 m and a high coefficient of determination (R²) of over 0.8; and it also can delineate the flooding zone to quantify the corresponding area in high reliability in terms of the precision ratio of about 0.7. Full article

(This article belongs to the Special Issue Advances in Flood Forecasting and Hydrological Modeling)

► Show Figures

Figure 1

18 pages, 3739 KiB

Open AccessEditor’s ChoiceArticle

Water Level Forecasting Using Deep Learning Time-Series Analysis: A Case Study of Red River of the North

by Vida Atashi, Hamed Taheri Gorji, Seyed Mojtaba Shahabi, Ramtin Kardan and Yeo Howe Lim

Water 2022, 14(12), 1971; https://doi.org/10.3390/w14121971 - 20 Jun 2022

Cited by 45 | Viewed by 9267

Abstract

The Red River of the North is vulnerable to floods, which have caused significant damage and economic loss to inhabitants. A better capability in flood-event prediction is essential to decision-makers for planning flood-loss-reduction strategies. Over the last decades, classical statistical methods and Machine Learning (ML) algorithms have greatly contributed to the growth of data-driven forecasting systems that provide cost-effective solutions and improved performance in simulating the complex physical processes of floods using mathematical expressions. To make improvements to flood prediction for the Red River of the North, this paper presents effective approaches that make use of a classical statistical method, a classical ML algorithm, and a state-of-the-art Deep Learning method. Respectively, the methods are seasonal autoregressive integrated moving average (SARIMA), Random Forest (RF), and Long Short-Term Memory (LSTM). We used hourly level records from three U.S. Geological Survey (USGS), at Pembina, Drayton, and Grand Forks stations with twelve years of data (2007–2019), to evaluate the water level at six hours, twelve hours, one day, three days, and one week in advance. Pembina, at the downstream location, has a water level gauge but not a flow-gauging station, unlike the others. The floodwater-level-prediction results show that the LSTM method outperforms the SARIMA and RF methods. For the one-week-ahead prediction, the RMSE values for Pembina, Drayton, and Grand Forks are 0.190, 0.151, and 0.107, respectively. These results demonstrate the high precision of the Deep Learning algorithm as a reliable choice for flood-water-level prediction. Full article

(This article belongs to the Special Issue Advances in Flood Forecasting and Hydrological Modeling)

► Show Figures

Figure 1

21 pages, 2667 KiB

Open AccessArticle

Application of the British Columbia MetPortal for Estimation of Probable Maximum Precipitation and Probable Maximum Flood for a Coastal Watershed

by Leanna M. King and Zoran Micovic

Water 2022, 14(5), 785; https://doi.org/10.3390/w14050785 - 2 Mar 2022

Cited by 1 | Viewed by 3518

Abstract

Estimation of the Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) are regulatory requirements in many jurisdictions that are used in the design of dams and assessment of existing infrastructure. The recently available British Columbia MetPortal provides regionally consistent PMP and precipitation frequency estimates across the province of British Columbia (BC). This paper proposes an approach to process and apply this data for the estimation of the PMF for watersheds across British Columbia. Guidelines are presented for selection of transposition points applicable to a watershed, and algorithms are developed for processing the geospatial probable maximum storm and precipitation frequency data. The algorithms developed are generic to multiple software and programming environments, and could also be applied in other regions where spatially and temporally intact PMP estimates are available. A detailed description of data sources and development of PMF scenario inputs is provided, as well as details of important sensitivity analyses. The methodology is applied to estimate the PMF for the Cheakamus Basin north of Squamish British Columbia. The application of the MetPortal PMP and precipitation frequency estimates, when used with a consistent PMF development methodology as proposed in this paper, will help improve the consistency of PMF estimates for watersheds across the province, offering a welcome improvement for dam owners and regulators. Full article

(This article belongs to the Special Issue Advances in Flood Forecasting and Hydrological Modeling)

► Show Figures

Figure 1

18 pages, 17238 KiB

Open AccessEditor’s ChoiceArticle

Data-Driven Flood Alert System (FAS) Using Extreme Gradient Boosting (XGBoost) to Forecast Flood Stages

by Will Sanders, Dongfeng Li, Wenzhao Li and Zheng N. Fang

Water 2022, 14(5), 747; https://doi.org/10.3390/w14050747 - 26 Feb 2022

Cited by 29 | Viewed by 7511

Abstract

Heavy rainfall leads to severe flooding problems with catastrophic socio-economic impacts worldwide. Hydrologic forecasting models have been applied to provide alerts of extreme flood events and reduce damage, yet they are still subject to many uncertainties due to the complexity of hydrologic processes and errors in forecasted timing and intensity of the floods. This study demonstrates the efficacy of using eXtreme Gradient Boosting (XGBoost) as a state-of-the-art machine learning (ML) model to forecast gauge stage levels at a 5-min interval with various look-out time windows. A flood alert system (FAS) built upon the XGBoost models is evaluated by two historical flooding events for a flood-prone watershed in Houston, Texas. The predicted stage values from the FAS are compared with observed values with demonstrating good performance by statistical metrics (RMSE and KGE). This study further compares the performance from two scenarios with different input data settings of the FAS: (1) using the data from the gauges within the study area only and (2) including the data from additional gauges outside of the study area. The results suggest that models that use the gauge information within the study area only (Scenario 1) are sufficient and advantageous in terms of their accuracy in predicting the arrival times of the floods. One of the benefits of the FAS outlined in this study is that the XGBoost-based FAS can run in a continuous mode to automatically detect floods without requiring an external starting trigger to switch on as usually required by the conventional event-based FAS systems. This paper illustrates a data-driven FAS framework as a prototype that stakeholders can utilize solely based on their gauging information for local flood warning and mitigation practices. Full article

(This article belongs to the Special Issue Advances in Flood Forecasting and Hydrological Modeling)

► Show Figures

Figure 1

20 pages, 7508 KiB

Open AccessArticle

Water Balance of Pit Lake Development in the Equatorial Region

by Edy Jamal Tuheteru, Rudy Sayoga Gautama, Ginting Jalu Kusuma, Arno Adi Kuntoro, Kris Pranoto and Yosef Palinggi

Water 2021, 13(21), 3106; https://doi.org/10.3390/w13213106 - 4 Nov 2021

Cited by 9 | Viewed by 4625

Abstract

In recent years, Indonesia has become the largest coal exporter in the world, and most of the coal is being mined by means of open-pit mining. The closure of an open-pit mine will usually leave a pit morphological landform that, in most cases, will be developed into a pit lake. One of the main issues in developing a pit lake is the understanding of the pit lake filling process. This paper discusses the hydrological model in filling the mineout void in a coal mine in Kalimantan which is located close to the equatorial line. The J-void is a mineout coal pit that is 3000 m long and 1000 m wide, with a maximum depth of 145 m. The development of the J-void pit lake after the last load of coal had been mined out experienced a dynamic process, such as backfilling activities with an overburden as well as pumping mine water from the surrounding pits. There are two components in the model, i.e., overland/subsurface and pit area. The overland zone is simulated using the Rainfall-Runoff NRECA Hydrological Model approach to determine the runoff and groundwater components, whereas the pit area is affected by direct rainfall and evaporation. The model is validated with the observation data. The main source of water in the J-void pit lake is rainwater, both from the surrounding catchment area as well as direct rainfall. As this coal mine area is characterized as a multi-pit area and, consequently, several pit lakes will be formed in the future, the result of the hydrological model is very useful in planning the future pit lakes. Full article

(This article belongs to the Special Issue Advances in Flood Forecasting and Hydrological Modeling)

► Show Figures

Figure 1

Review

Jump to: Research

22 pages, 6820 KiB

Open AccessReview

Scientometric Analysis-Based Review for Drought Modelling, Indices, Types, and Forecasting Especially in Asia

by Dan Wu, Yanan Li, Hui Kong, Tingting Meng, Zenghui Sun and Han Gao

Water 2021, 13(18), 2593; https://doi.org/10.3390/w13182593 - 20 Sep 2021

Cited by 8 | Viewed by 3896

Abstract

An extended drought period with low precipitation can result in low water availability and issues for humans, animals, and plants. Drought forecasting is critical for water resource development and management as it helps to reduce negative consequences. In this study, scientometric analysis and manual comprehensive analysis on drought modelling and forecasting are used. A scientometric analysis is used to determine the current research trend using bibliometric data and to identify relevant publication field sources with the most publications, the most frequently used keywords, the most cited articles and authors, and the countries that have made the greatest contributions to the field of water resources. This paper also tries to provide an overview of water issues, such as drought classification, drought indices, historical droughts, and their impact on Asian countries such as China, Pakistan, India, and Iran. There have been many models established for this purpose and choosing the appropriate model for study is a long procedure for researchers. An appropriate, comprehensive, pedagogical study of model ideas and historical implementations would benefit researchers by helping them to avoid overlooking viable model options, thus reducing their time spent on the topic. As a result, the goal of this paper is to review drought-forecasting approaches and recommend the best models for the Asian region. The models are divided into four categories based on their mechanisms: Regression analysis, stochastic modelling, machine learning, and dynamic modelling. The basic concepts of each approach in terms of the model’s historical use, benefits, and limitations are explained. Finally, prospects for future drought research in Asia are discussed as well as potential modelling techniques. Full article

(This article belongs to the Special Issue Advances in Flood Forecasting and Hydrological Modeling)

► Show Figures

Journal Menu

Journal Browser

Advances in Flood Forecasting and Hydrological Modeling

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (7 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI