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Remote Sensing
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Anticipation of Flash Floods and Rainfall-Induced Hydro-Geomorphic Hazards
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Anticipation of Flash Floods and Rainfall-Induced Hydro-Geomorphic Hazards

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 11140

Special Issue Editors

Dr. Alfred J. Kalyanapu

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Tennessee Tech University, Cookeville, Box 5015, TN 38505, USA
Interests: hydrology; flood modeling; RS/GIS applications; machine learning
Dr. Venkata Reddy Keesara

E-Mail Website
Guest Editor
Civil Engineering Department, National Institute of Technology Warangal, Warangal 506004, India
Interests: watershed hydrology; flood modeling and forecasting; RS and GIS applications; soft computing techniques; machine learning algorithms
Dr. Md Nowfel Mahmud Bhuyian

E-Mail Website
Guest Editor
West Tennessee River Basin Authority, 3628 East End Drive, Humboldt, TN 38343, USA
Interests: hydrology; flood modeling GIS/RS applications; flood impact assessment

Special Issue Information

Dear Colleagues,

Floods continue to be a major cause of natural disasters worldwide. Among these floods, flash floods develop very quickly and can cause significant damage. For example, in the conterminous United States alone, between 1996 and 2017, flashfloods occurred around 69 times every week on average, causing nuisance flooding, property damage and fatalities. Most of these flash floods occurred during summer time, with an average duration of 3.5 hours, and were less frequent in winter. Similar problems related to flash floods have also been observed in different parts of the world due to an increase in extreme climate events. Recent studies on these flash floods and their associated hydro-geomorphic hazards have highlighted the application of remote sensing techniques and GIS applications for early warning systems. With these continued advances and the availability of new computing architectures, integration with machine learning and artificial intelligence will likely result in newer high-fidelity predictive models.

This Special Issue aims to summarize the state-of-the-art techniques for the detection, prediction and modeling of flash floods and rainfall-induced hydro-geomorphic hazards such as landslides, and to identify the future strategies to improve hazard mitigation and management capabilities.

Articles may address, but are not limited to, the following topics:

  • Machine learning applications for flash floods and rainfall-induced hazards;
  • Flash floods and climate change;
  • Flood susceptibility determination through RS/GIS;
  • Flood risk assessment;
  • Flash flood and food security;
  • Flood forecasting;
  • Soft computing applications in flood modeling and forecasts. 

Dr. Alfred J. Kalyanapu
Dr. Venkata Reddy Keesara
Dr. Md Nowfel Mahmud Bhuyian
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. Remote Sensing 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 2700 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 forecasting
  • remote sensing applications
  • GIS
  • machine learning techniques
  • rainfall-induced floods
  • flood risk management
  • Google Earth Engine

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Published Papers (5 papers)

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Research

16 pages, 23065 KiB  
Article
Analysis of the Low-Frequency Debris Flow Disaster Induced by a Local Rainstorm on 12 July 2022, in Pingwu County, China
by Mei Liu, Mingfeng Deng, Ningsheng Chen, Shufeng Tian and Tao Wang
Remote Sens. 2024, 16(9), 1547; https://doi.org/10.3390/rs16091547 - 26 Apr 2024
Cited by 1 | Viewed by 959
Abstract
Low-frequency debris flows often lead to severe disasters due to large energy releases and strong concealment. However, the understanding of formation conditions, movement processes, and disaster-causing mechanisms of low-frequency debris flow is still limited, especially regarding occurrences within the large catchment (>50 km [...] Read more.
Low-frequency debris flows often lead to severe disasters due to large energy releases and strong concealment. However, the understanding of formation conditions, movement processes, and disaster-causing mechanisms of low-frequency debris flow is still limited, especially regarding occurrences within the large catchment (>50 km2). This study presents a typical case of large-scale, low-frequency debris flow occurring in the Heishui catchment (102.65 km2), Pingwu County, China. The movement process, disaster characteristics, and causes of the Heishui debris flow were analyzed in detail through field investigations and remote sensing interpretation. The results indicated that the Heishui debris flow is a large-scale, low-frequency, dilute debris flow with a recurrence period of over 100 years. The debris flow was primarily initiated from the right branch gully, Longchi gully, triggered by a local rainstorm with a maximum hourly rainfall return period of over 20 years. The main cause of casualties and building damage is attributed to large energy releases from boulder blockages and outbursts that occurred in the middle part of the main channel. This led to a sudden increase in peak discharge to 1287 m3/s, with a volume of 3.5 × 105 m3 of solid materials being transported to the outlet of the gully. It is essential to enhance the identification of debris flows by comprehensively considering tributary gullies’ susceptibility and strengthening joint meteorological and hydrological monitoring networks in the middle and upper reaches within large catchments. This preliminary work contributes towards improving prevention and mitigation strategies for low-frequency debris flows occurring within large catchments. Full article
(This article belongs to the Special Issue Anticipation of Flash Floods and Rainfall-Induced Hydro-Geomorphic Hazards)
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21 pages, 47072 KiB  
Article
Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco
by Sliman Hitouri, Meriame Mohajane, Meriam Lahsaini, Sk Ajim Ali, Tadesual Asamin Setargie, Gaurav Tripathi, Paola D’Antonio, Suraj Kumar Singh and Antonietta Varasano
Remote Sens. 2024, 16(5), 858; https://doi.org/10.3390/rs16050858 - 29 Feb 2024
Cited by 11 | Viewed by 4233
Abstract
Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures and informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., [...] Read more.
Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures and informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., synthetic aperture radar (SAR) images for flood inventory preparation and integrated four machine learning models (Random Forest: RF, Classification and Regression Trees: CART, Support Vector Machine: SVM, and Extreme Gradient Boosting: XGBoost) to predict flood susceptibility in Metlili watershed, Morocco. Initially, 12 independent variables (elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power index, distance from streams, distance from roads, lithology, rainfall, land use/land cover, and normalized vegetation index) were used as conditioning factors. The flood inventory dataset was divided into 70% and 30% for training and validation purposes using a popular library, scikit-learn (i.e., train_test_split) in Python programming language. Additionally, the area under the curve (AUC) was used to evaluate the performance of the models. The accuracy assessment results showed that RF, CART, SVM, and XGBoost models predicted flood susceptibility with AUC values of 0.807, 0.780, 0.756, and 0.727, respectively. However, the RF model performed better at flood susceptibility prediction compared to the other models applied. As per this model, 22.49%, 16.02%, 12.67%, 18.10%, and 31.70% areas of the watershed are estimated as being very low, low, moderate, high, and very highly susceptible to flooding, respectively. Therefore, this study showed that the integration of machine learning models with radar data could have promising results in predicting flood susceptibility in the study area and other similar environments. Full article
(This article belongs to the Special Issue Anticipation of Flash Floods and Rainfall-Induced Hydro-Geomorphic Hazards)
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22 pages, 68043 KiB  
Article
Insight into the Characteristics and Triggers of Loess Landslides during the 2013 Heavy Rainfall Event in the Tianshui Area, China
by Xiaoyi Shao, Siyuan Ma, Chong Xu and Yueren Xu
Remote Sens. 2023, 15(17), 4304; https://doi.org/10.3390/rs15174304 - 31 Aug 2023
Cited by 7 | Viewed by 1673
Abstract
The 2013 heavy rainfall event (from June to July) in the Tianshui area triggered the most serious rainfall-induced group-occurring landslides since 1984, causing extensive casualties and economic losses. To better understand the characteristics and triggers of these loess landslides, we conducted a detailed [...] Read more.
The 2013 heavy rainfall event (from June to July) in the Tianshui area triggered the most serious rainfall-induced group-occurring landslides since 1984, causing extensive casualties and economic losses. To better understand the characteristics and triggers of these loess landslides, we conducted a detailed analysis of the landslides and relevant influencing factors. Based on the detailed rainfall-induced landslide database obtained using visual interpretation of remote sensing images before and after rainfall, the correlation between the landslide occurrence and different influencing factors such as terrain, geomorphology, geology, and rainfall condition was analyzed. This rainfall event triggered approximately 54,000 landslides with a total area of 67.9 km2, mainly consisting of shallow loess landslides with elongated type, shallow rockslides, collapses, and mudflows. The landslides exhibited a clustered distribution, with the majority concentrated in two specific areas (i.e., Niangniangba and Shetang). The abundance index of landslides was closely associated with the hillslope gradient, total rainfall, and drainage (river) density. The landslide area density (LAD) was positively correlated with these influential factors, characterized by either an exponential or a linear relationship. The Middle Devonian Shujiaba formation (D2S) was identified to be highly susceptible to landslides, and the landslide events therein accounted for 35% of the total landslide occurrences within 22% of the study area. In addition, the E-SE aspect was more prone to landslides, while the W-NW aspect exhibited a low abundance of landslides. Full article
(This article belongs to the Special Issue Anticipation of Flash Floods and Rainfall-Induced Hydro-Geomorphic Hazards)
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20 pages, 4189 KiB  
Article
Reliability of GPM IMERG Satellite Precipitation Data for Modelling Flash Flood Events in Selected Watersheds in the UAE
by Mohamed A. Hamouda, Gilbert Hinge, Henok S. Yemane, Hasan Al Mosteka, Mohammed Makki and Mohamed M. Mohamed
Remote Sens. 2023, 15(16), 3991; https://doi.org/10.3390/rs15163991 - 11 Aug 2023
Cited by 4 | Viewed by 1782
Abstract
Arid regions are prone to unprecedented extreme rainfall events that often result in severe flash floods. Using near-real-time precipitation data in hydrological modelling can aid in flood preparedness. This study analyzed rainfall data obtained from Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG [...] Read more.
Arid regions are prone to unprecedented extreme rainfall events that often result in severe flash floods. Using near-real-time precipitation data in hydrological modelling can aid in flood preparedness. This study analyzed rainfall data obtained from Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG V. 06) since 2001 to highlight recent trends of extreme rainfall indices for three selected watersheds in the UAE. Additionally, to validate the trends, the present study incorporated CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) into the analysis. Furthermore, for the first time, this study assessed the performance of the three products of IMERG in modelling flash flood events in the selected watersheds of UAE. A physical-based, fully distributed model was used to simulate the heaviest storm event. Also, a sensitivity analysis of the model’s output to variations in the input parameters was conducted using the one-factor-at-a-time method. The result of the trend analysis indicated that IMERG and CHIRPS show similar trends in both datasets, indicating agreement and reliability in their observations. However, there are a few instances where IMERG and CHIRPS show slight discrepancies in the nature of the trend. In general, the trend analysis results indicated an increasing trend of total precipitation (mm) and consecutive wet days, which suggests a rise in the risk of flash floods. The simulation of the flash flood event showed that the IMERG final product outperformed the other two products, closely matching the model output of the gauge rainfall data with mean absolute error (MAE) of 1.5, 2.37, and 0.5 for Wadi Ham, Wadi Taween, and Wadi Maidaq, respectively. The model’s performance was positively correlated with the size of the watershed. The sensitivity analysis results demonstrated that the model’s output was most sensitive to infiltration parameters. The study’s outcomes provide a good opportunity to improve near-real-time impact evaluation of flash flood events in the watersheds of the UAE. Full article
(This article belongs to the Special Issue Anticipation of Flash Floods and Rainfall-Induced Hydro-Geomorphic Hazards)
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27 pages, 13689 KiB  
Article
Multiscale Observation Product (MOP) for Temporal Flood Inundation Mapping of the 2015 Dallas Texas Flood
by Elena Sava, Guido Cervone and Alfred Kalyanapu
Remote Sens. 2023, 15(6), 1615; https://doi.org/10.3390/rs15061615 - 16 Mar 2023
Cited by 1 | Viewed by 1531
Abstract
This paper presents a new data fusion multiscale observation product (MOP) for flood emergencies. The MOP was created by integrating multiple sources of contributed open-source data with traditional spaceborne remote sensing imagery in order to provide a sequence of high spatial and temporal [...] Read more.
This paper presents a new data fusion multiscale observation product (MOP) for flood emergencies. The MOP was created by integrating multiple sources of contributed open-source data with traditional spaceborne remote sensing imagery in order to provide a sequence of high spatial and temporal resolution flood inundation maps. The study focuses on the 2015 Memorial Day floods that caused up to USD 61 million dollars of damage. The Hydraulic Engineering Center River Analysis System (HEC-RAS) model was used to simulate water surfaces for the northern part of the Trinity River in Dallas, using reservoir surcharge releases and topographic data provided by the U.S. Army Corps of Engineers. A measure of fit assessment is performed on the MOP flood maps with the HEC-RAS simulated flood inundation output to quantify spatial differences. Estimating possible flood inundation using individual datasets that vary spatially and temporally allow to gain an understanding of how much each observational dataset contributes to the overall water estimation. Results show that water surfaces estimated by MOP are comparable with the simulated output for the duration of the flood event. Additionally, contributed data, such as Civil Air Patrol, although they may be geographically sparse, become an important data source when fused with other observation data. Full article
(This article belongs to the Special Issue Anticipation of Flash Floods and Rainfall-Induced Hydro-Geomorphic Hazards)
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