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Flood Vulnerability Assessment with Hydrologic/Hydraulic Modeling and Remote Sensing Techniques

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 22120

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Special Issue Editors

Dr. Xinyi Shen

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Guest Editor

Civil and Environmental Engineering, University of Connecticut, 159 Discovery Dr., Storrs, CT 06269, USA
Interests: flood inundation modeling and observatory; Humans, Disasters, and the Built Environment; microwave remote sensing; artificial intelligence; compound flooding
Special Issues, Collections and Topics in MDPI journals

Dr. Albert J. Kettner

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Guest Editor

Institute of Arctic and Alpine Research, University of Colorado, 4001 Discovery Drive, Boulder, CO 80303, USA
Interests: remote sensing; hydrology; flood detection; flood hazards; modeling fluvial sediments; river discharge estimates; flood risk

Dr. Sagy Cohen

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Guest Editor

Department of Geography, University of Alabama, Tuscaloosa, AL 35487, USA
Interests: hydrology; geomorphology; numerical modeling; geospatial analysis; remote sensing
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Dr. Yiwen Mei

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Guest Editor

School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA
Interests: hydrologic modeling; climate signal analysis; machine learning; groundwater modeling

Special Issue Information

Dear Colleagues,

Flooding is the most common natural hazard worldwide, causing thousands of fatalities every year and having a tremendous economic impact. Future predictions indicate this will worsen because of a growing population and climate change. It is therefore important to develop and improve our knowledge in the field of flood vulnerability assessment and hazard alleviation. Multiple disciplines, including hydrometeorology, oceanology, meteorology, remote sensing, sociology, and economics, are collaborating to assess the triggers, magnitude, risk, and impact of flood hazards as well as the recovery from the hazards. Moreover, with the increasing capacity of numerical models, machine learning, big data archives, our ability to monitor, predict, and understand flood risk is improving rapidly.

Demonstrated by recent flood events with many other concurrent natural disasters, this special issue of Remote Sensing timely addresses flooding, in particular, it seeks to highlight interdisciplinary approaches to assess the complexity of flood vulnerability. This special issue includes topics such as:

Compound coastal flood risk analysis;
Flood-inundation mapping using high-resolution remote sensing and/or data fusion
The integration of high-resolution remote sensing techniques in numerical flood modeling
Artificial intelligence (AI) and citizen science in flood vulnerability assessment or flood modeling
Analysis of flood vulnerability drivers, including but not limited to climate variabilities, urbanization environmental disturbances, flood risk awareness, and social inequalities
The impact of concurrent flooding and other natural hazards such as wildfire and infectiousness

Dr. Xinyi Shen
Dr. Albert J. Kettner
Dr. Sagy Cohen
Dr. Yiwen Mei
Guest Editors

Manuscript Submission Information

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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 Risk/Vulnerability/Impact/Resilience
Remote Sensing
Hydrological/Hydrodynamic Modeling
Machine Learning
Compound/Cascading Hazards

Published Papers (7 papers)

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Research

18 pages, 6406 KiB

Open AccessArticle

A Method to Construct an Environmental Vulnerability Model Based on Multi-Source Data to Evaluate the Hazard of Short-Term Precipitation-Induced Flooding

by Hong Zhu, Jiaqi Yao, Jian Meng, Chengling Cui, Mengyao Wang and Runlu Yang

Remote Sens. 2023, 15(6), 1609; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15061609 - 15 Mar 2023

Cited by 1 | Viewed by 1812

Abstract

Flood hazards resulting from short-term severe precipitation have caused serious social and economic losses and have posed extraordinary threats to the safety of lives and property. Vulnerability, which reflects the degree of the adverse impact of flooding on a city, the sensitivity of the environment, and the extent to which rescues are possible during flooding, is one of the significant factors of the disaster risk assessment. Because of this, this paper proposes an Environmental Vulnerability Analysis Model (EVAM), based on comprehensively evaluating multi-source remote sensing data. The EVAM includes a two-stage, short-term flood vulnerability assessment. In the first stage, the flood’s areal extension and land-use classification are extracted, based on the U-NET++ network, using multi-source satellite remote sensing images. The results from the first stage are used in the second stage of vulnerability assessment. In the second stage, combining multi-source data with associated feature extraction results establishes the Exposure–Sensitivity–Adaptive capacity framework. The short-term flood vulnerability index is leveraged through the analytic hierarchy process (AHP) and the entropy method is calculated for an environmental vulnerability evaluation. This novel proposed framework for short-term flood vulnerability evaluation is demonstrated for the Henan Province. The experimental results show that the proportion of vulnerable cities in the Henan Province ranging from high to low is 22.22%, 22.22%, 38.89%, and 16.67%, respectively. The relevant conclusions can provide a scientific basis for regional flood control and risk management as well as corresponding data support for post-disaster reconstruction in disaster regions. Full article

(This article belongs to the Special Issue Flood Vulnerability Assessment with Hydrologic/Hydraulic Modeling and Remote Sensing Techniques)

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17 pages, 6838 KiB

Open AccessArticle

Impact of Tides and Surges on Fluvial Floods in Coastal Regions

by Huidi Liang and Xudong Zhou

Remote Sens. 2022, 14(22), 5779; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225779 - 16 Nov 2022

Cited by 3 | Viewed by 1807

Abstract

Fluvial floods in coastal areas are affected by tides and storm surges, while the impact is seldom quantified because the dynamics of seawater levels are often not represented in river routing models. This study established a model framework by coupling a surge model with a global hydrodynamic model at a higher spatiotemporal resolution than previous studies so that flood processes affected by seawater level fluctuation in small river basins can be investigated. Model implementation in Zhejiang Province, China, shows that the integration of dynamic seawater levels increases the stress of flooding along the Zhejiang coasts. The ocean effect varies in space, as it is much stronger in northern Zhejiang because of the lower landform and strong tidal amplification, while the mountainous rivers in southern Zhejiang are dominated by river flow regimes. Typhoon Lekima resulted in compound flood events (i.e., rainfall-induced riverine flood, tides, and surges), during which the maximum water level at the outlet of Qiantang River was 0.80 m in the default model settings with a constant downstream seawater level (i.e., 0 m), while it increased to 2.34 m (or 2.48 m) when tides (or tides and surges) were considered. The maximum increase due to tides and surges was 2.09 m and 1.45 m, respectively, while the maximum increase did not match the time of the flood peak. This mismatching indicates the need to consider different processes in physical models rather than linearly summing up different extreme water levels (i.e., river flood, tide, and surge) found in previous studies. The model framework integrating various flow processes will help to prevent risks of compound events in coastal cities in practical and future projections under different scenarios. Full article

(This article belongs to the Special Issue Flood Vulnerability Assessment with Hydrologic/Hydraulic Modeling and Remote Sensing Techniques)

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20 pages, 9494 KiB

Open AccessArticle

Sensitivity of Remote Sensing Floodwater Depth Calculation to Boundary Filtering and Digital Elevation Model Selections

by Sagy Cohen, Brad G. Peter, Arjen Haag, Dinuke Munasinghe, Nishani Moragoda, Anuska Narayanan and Sera May

Remote Sens. 2022, 14(21), 5313; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215313 - 24 Oct 2022

Cited by 3 | Viewed by 2025

Abstract

The Floodwater Depth Estimation Tool (FwDET) calculates water depth from a remote sensing-based inundation extent layer and a Digital Elevation Model (DEM). FwDET’s low data requirement and high computational efficiency allow rapid and large-scale calculation of floodwater depth. Local biases in FwDET predictions, often manifested as sharp transitions or stripes in the water depth raster, can be attributed to spatial or resolution mismatches between the inundation map and the DEM. To alleviate these artifacts, we are introducing a boundary cell smoothing and slope filtering procedure in version 2.1 of FwDET (FwDET2.1). We present an optimization analysis that quantifies the effect of differing parameterization on the resulting water depth map. We then present an extensive intercomparison analysis in which 16 DEMs are used as input for FwDET Google Earth Engine (FwDET-GEE) implementation. We compare FwDET2.1 to FwDET2.0 using a simulated flood and a large remote sensing derived flood map (Irrawaddy River in Myanmar). The results show that FwDET2.1 results are sensitive to the smoothing and filtering values for medium and coarse resolution DEMs, but much less sensitive when using a finer resolution DEM (e.g., 10 m NED). A combination of ten smoothing iterations and a slope threshold of 0.5% was found to be optimal for most DEMs. The accuracy of FwDET2.1 improved when using finer resolution DEMs except for the MERIT DEM (90 m), which was found to be superior to all the 30 m global DEMs used. Full article

(This article belongs to the Special Issue Flood Vulnerability Assessment with Hydrologic/Hydraulic Modeling and Remote Sensing Techniques)

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21 pages, 5080 KiB

Open AccessArticle

Can Satellite and Atmospheric Reanalysis Products Capture Compound Moist Heat Stress-Floods?

by Lei Gu, Ziye Gu, Qiang Guo, Wei Fang, Qianyi Zhang, Huaiwei Sun, Jiabo Yin and Jianzhong Zhou

Remote Sens. 2022, 14(18), 4611; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14184611 - 15 Sep 2022

Viewed by 1301

Abstract

Satellite-retrieved and model-based reanalysis precipitation products with high resolution have received increasing attention in recent decades. Their hydrological performance has been widely evaluated. However, whether they can be applied in characterizing the novel category of extreme events, such as compound moist heat-flood (CMHF) events, has not been fully investigated to date. The CMHF refers to the rapid transition from moist heat stress to devastating floods and has occurred increasingly frequently under the current warming climate. This study focuses on the applicability of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and the fifth generation of European Reanalysis (ERA5-Land) in simulating CMHF events over 120 catchments in China. Firstly, the precipitation accuracy of IMERG and ERA5-Land products is appraised for each catchment, using the gridded in situ meteorological dataset (CN05.1) as a baseline. Then, the ability of IMERG and ERA5-Land datasets in simulating the fraction, magnitude, and decade change of floods and CMHFs is comprehensively evaluated by forcing the XAJ and GR4J hydrological models. The results show that: (a) the IMERG and ERA5-Land perform similarly in terms of precipitation occurrences and intensity; (b) the IMERG yields discernably better performance than the ERA5-Land in streamflow simulation, with 71.7% and 50.8% of catchments showing the Kling–Gupta efficiency (KGE) higher than 0.5, respectively; (c) both datasets can roughly capture the frequency, magnitude, and their changes of floods and CMHFs in recent decades, with the IMERG exhibiting more satisfactory accuracy. Our results indicate that satellite remote sensing and atmospheric reanalysis precipitation can not only simulate individual hydrological extremes in most regions, but monitor compound events such as CMHF episodes, and especially, the IMERG satellite can yield better performance than the ERA5-Land reanalysis. Full article

(This article belongs to the Special Issue Flood Vulnerability Assessment with Hydrologic/Hydraulic Modeling and Remote Sensing Techniques)

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18 pages, 5980 KiB

Open AccessArticle

Protecting Existing Urban Green Space versus Cultivating More Green Infrastructures: Strategies Choices to Alleviate Urban Waterlogging Risks in Shenzhen

by Yun Qian, Han Wang and Jiansheng Wu

Remote Sens. 2021, 13(21), 4433; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214433 - 03 Nov 2021

Cited by 9 | Viewed by 2601

Abstract

For urban waterlogging alleviation, green infrastructures have been widely concerned. How to carry out scientific green infrastructure planning becomes an important issue in flood control and disaster relief. Based on historical media records of urban waterlogging from 2017 to 2020 and combined with variables about topography, land cover and socioeconomics, we used the Radial Basis Function Neural Network (RBFNN) to conduct urban waterlogging susceptibility assessment and simulate the risk of waterlogging in different scenarios of green land configuration in Shenzhen. The results showed that: (1) high proportions of impervious surface and population could increase the risks in Luohu and Futian districts, followed by Nanshan and Baoan districts, while high proportions of green space could effectively reduce the risks in southeastern Shenzhen; (2) urban waterlogging in Luohu and Futian districts can be alleviated by strengthening green infrastructure construction while Longgang and Longhua districts should make comprehensive use of other flood prevention methods; (3) turning existing urban green space into impervious surfaces would increase the risks of waterlogging, which is more evident in places with high proportions of green space such as Dapeng and Yantian districts. The effectiveness of green infrastructures varies in different spatial locations. Therefore, more attention should be paid to protecting existing green spaces than cultivating more green infrastructures in urban waterlogging alleviation. Full article

(This article belongs to the Special Issue Flood Vulnerability Assessment with Hydrologic/Hydraulic Modeling and Remote Sensing Techniques)

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21 pages, 3309 KiB

Open AccessArticle

Development of a Flash Flood Confidence Index from Disaster Reports and Geophysical Susceptibility

by Andrew Kruczkiewicz, Agathe Bucherie, Fernanda Ayala, Carolynne Hultquist, Humberto Vergara, Simon Mason, Juan Bazo and Alex de Sherbinin

Remote Sens. 2021, 13(14), 2764; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142764 - 14 Jul 2021

Cited by 17 | Viewed by 6375

Abstract

The analysis of historical disaster events is a critical step towards understanding current risk levels and changes in disaster risk over time. Disaster databases are potentially useful tools for exploring trends, however, criteria for inclusion of events and for associated descriptive characteristics is not standardized. For example, some databases include only primary disaster types, such as ‘flood’, while others include subtypes, such as ‘coastal flood’ and ‘flash flood’. Here we outline a method to identify candidate events for assignment of a specific disaster subtype—namely, ‘flash floods’—from the corresponding primary disaster type—namely, ‘flood’. Geophysical data, including variables derived from remote sensing, are integrated to develop an enhanced flash flood confidence index, consisting of both a flash flood confidence index based on text mining of disaster reports and a flash flood susceptibility index from remote sensing derived geophysical data. This method was applied to a historical flood event dataset covering Ecuador. Results indicate the potential value of disaggregating events labeled as a primary disaster type into events of a particular subtype. The outputs are potentially useful for disaster risk reduction and vulnerability assessment if appropriately evaluated for fitness of use. Full article

(This article belongs to the Special Issue Flood Vulnerability Assessment with Hydrologic/Hydraulic Modeling and Remote Sensing Techniques)

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24 pages, 9647 KiB

Open AccessArticle

Investigating the Role of Green Infrastructure on Urban WaterLogging: Evidence from Metropolitan Coastal Cities

by Qifei Zhang, Zhifeng Wu and Paolo Tarolli

Remote Sens. 2021, 13(12), 2341; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122341 - 15 Jun 2021

Cited by 24 | Viewed by 4373

Abstract

Urban green infrastructures (UGI) can effectively reduce surface runoff, thereby alleviating the pressure of urban waterlogging. Due to the shortage of land resources in metropolitan areas, it is necessary to understand how to utilize the limited UGI area to maximize the waterlogging mitigation function. Less attention, however, has been paid to investigating the threshold level of waterlogging mitigation capacity. Additionally, various studies mainly focused on the individual effects of UGI factors on waterlogging but neglected the interactive effects between these factors. To overcome this limitation, two waterlogging high-risk coastal cities—Guangzhou and Shenzhen, are selected to examine the effectiveness and stability of UGI in alleviating urban waterlogging. The results indicate that the impact of green infrastructure on urban waterlogging largely depends on its area and biophysical parameter. Healthier or denser vegetation (superior ecological environment) can more effectively intercept and store rainwater runoff. This suggests that while increasing the area of UGI, more attention should be paid to the biophysical parameter of vegetation. Hence, the mitigation effect of green infrastructure would be improved from the “size” and “health”. The interaction of composition and spatial configuration greatly enhances their individual effects on waterlogging. This result underscores the importance of the interactive enhancement effect between UGI composition and spatial configuration. Therefore, it is particularly important to optimize the UGI composition and spatial pattern under limited land resource conditions. Lastly, the effect of green infrastructure on waterlogging presents a threshold phenomenon. The excessive area proportions of UGI within the watershed unit or an oversized UGI patch may lead to a waste of its mitigation effect. Therefore, the area proportion of UGI and its mitigation effect should be considered comprehensively when planning UGI. It is recommended to control the proportion of green infrastructure at the watershed scale (24.4% and 72.1% for Guangzhou and Shenzhen) as well as the area of green infrastructure patches (1.9 ha and 2.8 ha for Guangzhou and Shenzhen) within the threshold level to maximize its mitigation effect. Given the growing concerns of global warming and continued rapid urbanization, these findings provide practical urban waterlogging prevention strategies toward practical implementations. Full article

(This article belongs to the Special Issue Flood Vulnerability Assessment with Hydrologic/Hydraulic Modeling and Remote Sensing Techniques)

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