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Advances in Flash Flood Forecasting

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

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 47763

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Special Issue Editor

Prof. Dr. Paulin Coulibaly

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Guest Editor

Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada
Interests: hydrologic modeling and forecasting; flood forecasting; hydroinformatics; hydrologic data assimilation; water monitoring network design

Special Issue Information

Dear Colleagues,

Flash flooding remains one of the most deadly and costly natural hazards for urban, highly populated cities and regions around the world. Although challenges remain in enhancing the timing, accuracy, and reliability of flash flood forecasts, significant advances have been achieved over the last decade. This Special Issue will welcome innovative contributions on the following major topics related to flash flood forecasting:

The understanding, modeling, and prediction of key drivers of flash floods, such as extreme meteorological events, snowmelt, ice jam, and dam or levee failure.
Methodologies for taking advantage of (i) emerging real-time or near-real-time products from mesoscale numerical models, radars, satellites, and in situ sensor networks, (ii) data fusion products, (iii) quantitative precipitation estimations, and (iv) land surface products, flood inundation maps, and LiDAR data.
Integrated modeling systems including hydrologic–hydraulic models, artificial intelligence-based forecasting systems, and new hybrid forecasting systems including sequential data assimilation, Bayesian processors, and information theory.

The need for enhancing flash flood forecasting for early warning cannot be overstated, and this Special Issue will bring together current and recent advances in flash flood forecasting for the water and hydrology community.

Prof. Paulin Coulibaly
Guest Editor

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Keywords

flash flood
urban flood
flash flood forecasting
quantitative precipitation estimation
radar rainfall
data fusion
sensor networks
hydrologic model
hydraulic model
machine learning

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Published Papers (9 papers)

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Research

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21 pages, 7305 KiB

Open AccessFeature PaperArticle

Understanding Uncertainty in Probabilistic Floodplain Mapping in the Time of Climate Change

by Zahra Zahmatkesh, Shasha Han and Paulin Coulibaly

Water 2021, 13(9), 1248; https://doi.org/10.3390/w13091248 - 29 Apr 2021

Cited by 7 | Viewed by 3641

Abstract

An integrated framework was employed to develop probabilistic floodplain maps, taking into account hydrologic and hydraulic uncertainties under climate change impacts. To develop the maps, several scenarios representing the individual and compounding effects of the models’ input and parameters uncertainty were defined. Hydrologic model calibration and validation were performed using a Dynamically Dimensioned Search algorithm. A generalized likelihood uncertainty estimation method was used for quantifying uncertainty. To draw on the potential benefits of the proposed methodology, a flash-flood-prone urban watershed in the Greater Toronto Area, Canada, was selected. The developed floodplain maps were updated considering climate change impacts on the input uncertainty with rainfall Intensity–Duration–Frequency (IDF) projections of RCP8.5. The results indicated that the hydrologic model input poses the most uncertainty to floodplain delineation. Incorporating climate change impacts resulted in the expansion of the potential flood area and an increase in water depth. Comparison between stationary and non-stationary IDFs showed that the flood probability is higher when a non-stationary approach is used. The large inevitable uncertainty associated with floodplain mapping and increased future flood risk under climate change imply a great need for enhanced flood modeling techniques and tools. The probabilistic floodplain maps are beneficial for implementing risk management strategies and land-use planning. Full article

(This article belongs to the Special Issue Advances in Flash Flood Forecasting)

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19 pages, 4821 KiB

Open AccessArticle

Influence of Calibration Parameter Selection on Flash Flood Simulation for Small to Medium Catchments with MISDc-2L Model

by Ngoc Tu Nguyen, Wei He, Yonghua Zhu and Haishen Lü

Water 2020, 12(11), 3255; https://doi.org/10.3390/w12113255 - 20 Nov 2020

Cited by 6 | Viewed by 2573

Abstract

It is of great challenge to accurately predict flash floods for small to medium catchments (SMC) in mountainous areas, for which parameter calibration strategies are crucial for model performance. This study investigates the influence of calibration parameter selection on flash flood simulations using a rainfall–runoff model, MISDc-2L (Modello Idrologico Semi-Distribuito in continuo–2 layers), at hourly scale for SMC in the Huai River basin of China over the 2010–2015 period. We investigated model performances under different calibration schemes, where different amounts of model parameters were selected for the calibration procedure. The model clearly performed better in the case involving calibration of partial sensitive parameters than that of a full parameter set with respect to the peaks, the hydrographs and the base-flow of flood simulation, especially after including maximum water capacity (W_max) in the calibration. This finding was consistently valid under different model calibration experiments, including single event, “split-sample” test and combined events at different flood magnitude levels. We further found that the model performed better for high magnitude flood events than medium and low ones, but clear improvements can be achieved for low and medium magnitude flood events with careful calibration parameter selection. Our study suggests that calibration parameter selection is important for flash flood event simulations with the MISDc-2L model for SMC in the Huai River basin of China; specifically, the reduction in calibration parameter amount and the inclusion of W_max in calibration remarkably improve flood simulation. Full article

(This article belongs to the Special Issue Advances in Flash Flood Forecasting)

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18 pages, 4108 KiB

Open AccessArticle

Flood Hazard Assessment in Data-Scarce Watersheds Using Model Coupling, Event Sampling, and Survey Data

by Jorge Hurtado-Pidal, Juan S. Acero Triana, Edgar Espitia-Sarmiento and Fernando Jarrín-Pérez

Water 2020, 12(10), 2768; https://doi.org/10.3390/w12102768 - 4 Oct 2020

Cited by 12 | Viewed by 6722

Abstract

The application of hydrologic and hydrodynamic models in flash flood hazard assessment is mainly limited by the availability of robust monitoring systems and long-term hydro-meteorological observations. Nevertheless, several studies have demonstrated that coupled modeling approaches based on event sampling (short-term observations) may cope with the lack of observed input data. This study evaluated the use of storm events and flood-survey reports to develop and validate a modeling framework for flash flood hazard assessment in data-scarce watersheds. Specifically, we coupled the hydrologic modeling system (HEC-HMS) and the Nays2Dflood hydrodynamic solver to simulate the system response to several storm events including one, equivalent in magnitude to a 500-year event, that flooded the City of Tena (Ecuador) on 2 September, 2017. Results from the coupled approach showed satisfactory model performance in simulating streamflow and water depths (0.40 ≤ Nash-Sutcliffe coefficient ≤ 0.95; −3.67% ≤ Percent Bias ≤ 23.4%) in six of the eight evaluated events, and a good agreement between simulated and surveyed flooded areas (Fit Index = 0.8) after the 500-year storm. The proposed methodology can be used by modelers and decision-makers for flood impact assessment in data-scarce watersheds and as a starting point for the establishment of flood forecasting systems to lessen the impacts of flood events at the local scale. Full article

(This article belongs to the Special Issue Advances in Flash Flood Forecasting)

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16 pages, 4035 KiB

Open AccessArticle

Using the Apriori Algorithm and Copula Function for the Bivariate Analysis of Flash Flood Risk

by Ming Zhong, Jiao Wang, Tao Jiang, Zhijun Huang, Xiaohong Chen and Yang Hong

Water 2020, 12(8), 2223; https://doi.org/10.3390/w12082223 - 7 Aug 2020

Cited by 10 | Viewed by 2978

Abstract

Flash flooding is a phenomenon characterized by multiple variables. Few studies have focused on the extracted variables involved in flash flood risk and the joint probability distribution of the extracted variables. In this paper, a novel methodology that integrates the Apriori algorithm and copula function is presented and used for a flood risk analysis of Arizona in the United States. Due to the various rainfall indices affecting the flash flood risk, when performing the Apriori algorithm, the accumulated 3-h rainfall and accumulated 6-h rainfall were extracted as the most fitting rainfall indices. After comparing the performance of copulas, the Frank copula was found to exhibit the best fit for the flash flood hazard; thus, it was used for a bivariate joint probability analysis. The bivariate joint distribution functions of P–Q, PA–Q, PB–Q, and D–Q were established, and the results showed an increasing trend of flash flood risk with increases in the rainfall indices and peak flow; however, the risk displayed the least significant relation with the duration of the flash flood. These results are expected to be useful for risk analysis and decision making regarding flash floods. Full article

(This article belongs to the Special Issue Advances in Flash Flood Forecasting)

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21 pages, 2814 KiB

Open AccessArticle

Performance of a PDE-Based Hydrologic Model in a Flash Flood Modeling Framework in Sparsely-Gauged Catchments

by Thomas Poméon, Niklas Wagner, Carina Furusho, Stefan Kollet and Ricardo Reinoso-Rondinel

Water 2020, 12(8), 2157; https://doi.org/10.3390/w12082157 - 30 Jul 2020

Cited by 8 | Viewed by 3551

Abstract

Modeling and nowcasting of flash floods remains challenging, mainly due to uncertainty of high-resolution spatial and temporal precipitation estimates, missing discharge observations of affected catchments and limitations of commonly used hydrologic models. In this study, we present a framework for flash flood hind- and nowcasting using the partial differential equation (PDE)-based ParFlow hydrologic model forced with quantitative radar precipitation estimates and nowcasts for a small 18.5 km² headwater catchment in Germany. In the framework, an uncalibrated probabilistic modeling approach is applied. It accounts for model input uncertainty by forcing the model with precipitation inputs from different sources, and accounts for model parameter uncertainty by perturbing two spatially uniform soil hydraulic parameters. Thus, sources of uncertainty are propagated through the model and represented in the results. To demonstrate the advantages of the proposed framework, a commonly used conceptual model was applied over the same catchment for comparison. Results show the framework to be robust, with the uncalibrated PDE-based model matching streamflow observations reasonably. The model lead time was further improved when forced with precipitation nowcasts. This study successfully demonstrates a parsimonious application of the PDE-based ParFlow model in a flash flood hindcasting and nowcasting framework, which is of interest in applications to poorly or ungauged watersheds. Full article

(This article belongs to the Special Issue Advances in Flash Flood Forecasting)

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30 pages, 13689 KiB

Open AccessArticle

A Method to Improve the Flood Maps Forecasted by On-Line Use of 1D Model

by Pasquale G. F. Filianoti, Angelo Nicotra, Antonino Labate and Demetrio A. Zema

Water 2020, 12(6), 1525; https://doi.org/10.3390/w12061525 - 27 May 2020

Cited by 4 | Viewed by 2916

Abstract

Forecasting floods in urban areas during a heavy rainfall is the aim of every early warning system. 2D-models produce the most accurate flood maps, but they are practically useless as quasi real-time tools, because their run times are comparable to times of propagation of floods. Run times of 1D-model are of tens of seconds, but their predictions lack accuracy and many useful indicators of flood severity. Our aim is the identification of the 2D-model map that is more similar to the actual map, chosen among those simulated off-line. To this aim, we produce a rough flood map of the occurring event, through a quasi real-time simulation of the rainfall-runoff using a 1D-model. Then we apply an original method, named “ranking approach”, to perform the best matching. This method is applied to the Corace torrent (Calabria, Southern Italy), using 17 synthetic hyetographs to simulate the same number of rainfall-runoff events, using 1D (SWMM) and 2D (MIKE) models. The method proves to be effective in 65% of the cases, while in 82% of cases (i.e., for 14 cases out 17), the event produced by the same ietograph falls within the third rank. Full article

(This article belongs to the Special Issue Advances in Flash Flood Forecasting)

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17 pages, 6989 KiB

Open AccessArticle

Flash Flood Early Warning Coupled with Hydrological Simulation and the Rising Rate of the Flood Stage in a Mountainous Small Watershed in Sichuan Province, China

by Huawei Tu, Xiekang Wang, Wanshun Zhang, Hong Peng, Qian Ke and Xiaomin Chen

Water 2020, 12(1), 255; https://doi.org/10.3390/w12010255 - 16 Jan 2020

Cited by 24 | Viewed by 5224

Abstract

Flash floods in mountainous areas have become more severe and frequent as a result of climate change and are a threat to public safety and social development. This study explores the application of distributed hydrological models in flash floods risk management in a small watershed in Sichuan Province, China, and aims to increase early warning lead time in mountainous areas. The Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model was used to simulate the flash flood process and analyze the variation in flood hydrographs. First, the HEC-HMS model was established based on geospatial data and the river network shape, and eight heavy rainfall events from 2010 to 2015 were used for model calibration and validation, showing that the HEC-HMS model was effective for the simulation of mountain floods in the study area. Second, with the assumption that rainfall and flood events have the same frequency, the flood hydrographs with different frequencies (p = 1%, 2%, 5%, and 10%) were calculated by the HEC-HMS model. The rising limbs of the flood hydrographs were significantly different and can be divided into three parts (0–5 h, 6–10 h, and 11–15 h). The rising rate of the flood stage for each part of the flood hydrograph increases in multiples. According to the analysis of the flood hydrographs, two critical early warning indicators with an invention patent were determined in the study: the flood stage for immediate evacuation and the rising rate. The application of the indicators in the study shows that it is feasible to advance the time of issuing an early warning signal, and it is expected that the indicators can offer a reference for flash flood early warning in the study area and other small watersheds in mountainous areas. Full article

(This article belongs to the Special Issue Advances in Flash Flood Forecasting)

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21 pages, 10958 KiB

Open AccessEditor’s ChoiceArticle

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

by Binh Thai Pham, Tran Van Phong, Huu Duy Nguyen, Chongchong Qi, Nadhir Al-Ansari, Ata Amini, Lanh Si Ho, Tran Thi Tuyen, Hoang Phan Hai Yen, Hai-Bang Ly, Indra Prakash and Dieu Tien Bui

Water 2020, 12(1), 239; https://doi.org/10.3390/w12010239 - 15 Jan 2020

Cited by 105 | Viewed by 7877

Abstract

Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management. Full article

(This article belongs to the Special Issue Advances in Flash Flood Forecasting)

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Review

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29 pages, 1851 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Recent Advances in Real-Time Pluvial Flash Flood Forecasting

by Andre D. L. Zanchetta and Paulin Coulibaly

Water 2020, 12(2), 570; https://doi.org/10.3390/w12020570 - 19 Feb 2020

Cited by 63 | Viewed by 10184

Abstract

Recent years have witnessed considerable developments in multiple fields with the potential to enhance our capability of forecasting pluvial flash floods, one of the most costly environmental hazards in terms of both property damage and loss of life. This work provides a summary and description of recent advances related to insights on atmospheric conditions that precede extreme rainfall events, to the development of monitoring systems of relevant hydrometeorological parameters, and to the operational adoption of weather and hydrological models towards the prediction of flash floods. With the exponential increase of available data and computational power, most of the efforts are being directed towards the improvement of multi-source data blending and assimilation techniques, as well as assembling approaches for uncertainty estimation. For urban environments, in which the need for high-resolution simulations demands computationally expensive systems, query-based approaches have been explored for the timely retrieval of pre-simulated flood inundation forecasts. Within the concept of the Internet of Things, the extensive deployment of low-cost sensors opens opportunities from the perspective of denser monitoring capabilities. However, different environmental conditions and uneven distribution of data and resources usually leads to the adoption of site-specific solutions for flash flood forecasting in the context of early warning systems. Full article

(This article belongs to the Special Issue Advances in Flash Flood Forecasting)

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