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Remote Sensing Applications for Flood Forecasting and Flood Risk Management

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 26759

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Prof. Dr. Pingping Luo

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Xi’an Monitoring, Modelling and Early Warning of Watershed Spatial Hydrology International Science and Technology Cooperation Base, Chang’an University, Xi’an 710054, China
Interests: engaged in hydrology and water resources; carrying out theoretical research on extreme hydrological sequence reconstruction; forest hydrology for flood and drought disasters, and dynamic mechanism research on the impact of rainfall uncertainty on flood and drought disasters
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Dr. Ahmed Elbeltagi

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Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt
Interests: agricultural engineering; water resources management; irrigation science; water footprint; artificial intelligence
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Prof. Dr. Binaya Kumar Mishra

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School of Engineering, Faculty of Science and Technology, Pokhara University, Pokhara 33700, Nepal
Interests: water resources management; climate and ecosystem change adaptation; hydrologic and environmental modeling; applications of GIS and remote sensing
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Dr. Reza Hassanzadeh

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Institute of Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran
Interests: geohazards and natural disasters’ risk; vulnerability and resilience in urban areas using geographical information system (GIS); remote sensing (RS); spatial modelling
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Prof. Dr. Van-Thanh-Van Nguyen

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Department of Civil Engineering, McGill University, Montreal, QC H3A 0G4, Canada
Interests: climate change impact and adaptation in water resources; modelling of hydrologic extremes; watershed modelling for sustainable water resource development
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Dr. Baofu Li

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Guest Editor

School of Geography and Tourism, Qufu Normal University, Qufu, China
Interests: climate change; climate variability; water resources; drought; arid and semi-arid areas; meteorology; hydrology; geography
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Special Issue Information

Dear Colleagues,

The world has suffered from an increased frequency of flood disasters under the changing climate, and the economic losses caused by flood disasters are rapidly increasing. To adapt to the climate change and the frequent natural disasters, our global researchers need to pay additional attention to the disaster prevention, mitigation, and relief capabilities. Flooding has become the main restriction factor for the sustainable development of human society and economy. Consequently, flood forecasting and flood risk management have always been the focus of intensive research effort around the world. In order to address this critical research challenge, remote sensing technology has been used to monitor the status and evolution of floods and to provide reference data for improving the flood emergency response capability and disaster risk management level.

With the aggravation of climate change, floods have caused huge economic losses and endanger the safety of cities. Therefore, urban flood monitoring and early warning, flood loss prediction and flood risk response measures are particularly important in flood risk management and flood resilient cities. Remote sensing technology can monitor and simulate the occurrence and development of flood disasters, thus providing an important reference for flood disaster prediction and effective flood control. Based on various remote sensing spatial information, the relevant evaluation and analysis models are constructed, the flood disaster and drought degree are scientifically evaluated, the disaster is accurately predicted, and the early warning information is sent out in time, which provides a reliable reference for flood control and drought relief. Thus, better disaster prevention and mitigation effects can be achieved, reducing the loss of lives and economic property and promoting the rapid development of the global social economy.

The proposed Special Issue focuses on popularizing the latest research results related to the applications of remote sensing technology in the field of flood risk prediction and management, so as to reduce the impacts of flood disasters and to ensure the sustainable development of urban and river basins and the economy, society, and the environment. Through remote sensing inversion simulation, this Special Issue aims to put forward reasonable ideas for urban and river flood risk response measures. This issue attempts to use related methods in hydrologic modeling and forecasting and water resources planning and management, including, but not limited to, remote sensing inversion simulation, empirical methods, and sustainable development.

Prof. Dr. Pingping Luo
Dr. Ahmed Elbeltagi
Prof. Dr. Binaya Kumar Mishra
Dr. Reza Hassanzadeh
Prof. Dr. Van-Thanh-Van Nguyen
Dr. Baofu Li
Guest Editors

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Keywords

flood forecasting
remote sensing and GIS
risk identification
dynamic simulation
food control
arid area
hydrological modelling
urban stormwater management
climate change
flood damage assessment
adaptation and mitigation
integrated water resource management
policy and strategies
flood-resilient cities
water quality
urban planning
watershed spatial hydrology

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Published Papers (9 papers)

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Research

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24 pages, 13789 KiB

Open AccessArticle

A Study of the Effect of DEM Spatial Resolution on Flood Simulation in Distributed Hydrological Modeling

by Hengkang Zhu and Yangbo Chen

Remote Sens. 2024, 16(16), 3105; https://doi.org/10.3390/rs16163105 - 22 Aug 2024

Viewed by 709

Abstract

Watershed hydrological modeling methods are currently the predominant approach for flood forecasting. Digital elevation model (DEM) data, a critical input variable, significantly influence the accuracy of flood simulations, primarily due to their resolution. However, there is a paucity of research exploring the relationship between DEM resolution and flood simulation accuracy. This study aims to investigate this relationship by examining three watersheds of varying scales in southern Jiangxi Province, China. Utilizing the Liuxihe model, a new-generation physically based distributed hydrological model (PBDHM), we collected and collated data, including DEM, land use, soil type, and hourly flow and rainfall data from monitoring stations, covering 22 flood events over the last decade, to conduct model calibration and flood simulation. DEM data were processed into seven resolutions, ranging from 30 m to 500 m, to analyze the impact of DEM resolution on flood simulation accuracy. The results are as follows. (1) The Nash–Sutcliffe efficiency coefficients for the entire set of flood events were above 0.75, demonstrating the Liuxihe model’s strong applicability in this region. (2) The DEM resolution of the Anhe and Dutou watersheds lost an average of 7.9% and 0.8% accuracy when increasing from 30 m to 200 m, with further losses of 37.9% and 10.7% from 200 m to 300 m. Similarly, the Mazhou watershed showed an average of 8.4% accuracy loss from 30 m to 400 m and 20.4% from 400 m to 500 m. These results suggest a threshold where accuracy sharply declines as DEM resolution increases, and this threshold rises with watershed scale. (3) Parameter optimization in the Liuxihe model significantly enhanced flood simulation accuracy, effectively compensating for the reduction in accuracy caused by increased DEM resolution. (4) The optimal parameters for flood simulation varied with different DEM resolutions, with significant changes observed in riverbed slope and river roughness, which are highly sensitive to DEM resolution. (5) Changes in DEM resolution did not significantly impact surface flow production. However, the extraction of the water system and the reduction in slope were major factors contributing to the decline in flood simulation accuracy. Overall, this study elucidates that there is a threshold range of DEM resolution that balances data acquisition efficiency and computational speed while satisfying the basic requirements for flood simulation accuracy. This finding provides crucial decision-making support for selecting appropriate DEM resolutions in hydrological forecasting. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Flood Forecasting and Flood Risk Management)

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28 pages, 42651 KiB

Open AccessArticle

A Novel Flood Risk Analysis Framework Based on Earth Observation Data to Retrieve Historical Inundations and Future Scenarios

by Kezhen Yao, Saini Yang, Zhihao Wang, Weihang Liu, Jichong Han, Yimeng Liu, Ziying Zhou, Stefano Luigi Gariano, Yongguo Shi and Carlo Jaeger

Remote Sens. 2024, 16(8), 1413; https://doi.org/10.3390/rs16081413 - 16 Apr 2024

Viewed by 2024

Abstract

Global warming is exacerbating flood hazards, making the robustness of flood risk management a critical issue. Without considering future scenarios, flood risk analysis built only on historical knowledge may not adequately address the coming challenges posed by climate change. A comprehensive risk analysis framework based on both historical inundations and future projections to tackle uncertainty is still lacking. In this view, a scenario-based, data-driven risk analysis framework that for the first time integrates recent historical floods and future risk trends is here presented, consisting of flood inundation-prone and high-risk zones. Considering the Poyang Lake Eco-Economic Zone (PLEEZ) in China as the study area, we reproduced historical inundation scenarios of major flood events by using Sentinel-1 imagery from 2015 to 2021, and used them to build the risk baseline model. The results show that 11.7% of the PLEEZ is currently exposed to the high-risk zone. In the SSP2-RCP4.5 scenario, the risk would gradually decrease after peaking around 2040 (with a 19.3% increase in high-risk areas), while under the traditional fossil fuel-dominated development pathway (SSP5-RCP8.5), the risk peak would occur with a higher intensity about a decade earlier. The attribution analysis results reveal that the intensification of heavy rainfall is the dominant driver of future risk increase and that the exploitation of unused land such as wetlands induces a significant increase in risk. Finally, a hierarchical panel of recommended management measures was developed. We hope that our risk analysis framework inspires newfound risk awareness and provides the basis for more effective flood risk management in river basins. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Flood Forecasting and Flood Risk Management)

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23 pages, 7359 KiB

Open AccessArticle

Spatiotemporal Variation and Causes of Typical Extreme Precipitation Events in Shandong Province over the Last 50 Years

by Jie Liu, Baofu Li and Mengqiu Ma

Remote Sens. 2024, 16(7), 1283; https://doi.org/10.3390/rs16071283 - 5 Apr 2024

Cited by 1 | Viewed by 1002

Abstract

In this study, based on hourly ERA5 reanalysis data from July to September, from 1971 to 2020, for Shandong Province, we used mathematical statistical analysis, the Mann–Kendall nonparametric statistical test, cluster analysis, and other methods to extract and analyze the spatiotemporal variation characteristics and causes of typical extreme precipitation events. The results indicated the following: (1) The total number and duration of precipitation events show a nonsignificant upward trend, while the average and extreme rainfall intensities show a nonsignificant downward trend. (2) Extreme precipitation events are primarily concentrated in Qingdao, Jinan, Heze, and Binzhou, with fewer events occurring in central Shandong Province. (3) Extreme precipitation events are classified into four types (namely, patterns I, II, III, and IV). Pattern I exhibits two rain peaks, with the primary rain peak occurring after the secondary rain peak. Similarly, pattern II also displays two rain peaks, with equivalent rainfall amounts for both peaks. In contrast, pattern III has multiple, evenly distributed rain peaks. Finally, pattern IV shows a rain peak during the first half of the precipitation event. Pattern I has the highest occurrence probability (46%), while pattern IV has the lowest (7%). (4) The spatial distributions of the different rain patterns are similar, with most being found in the eastern coastal and western regions. (5) Extreme precipitation events result from interactions between large-scale circulation configurations and mesoscale convective systems. The strong blocking situation and significant circulation transport at middle and low latitudes in East Asia, along with strong convergent uplift, abnormally high specific humidity, and high-water-vapor convergence centers, play crucial roles in supporting large-scale circulation systems and triggering mesoscale convective systems. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Flood Forecasting and Flood Risk Management)

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19 pages, 18397 KiB

Open AccessArticle

Spatiotemporal Information Mining for Emergency Response of Urban Flood Based on Social Media and Remote Sensing Data

by Hui Zhang, Hao Jia, Wenkai Liu, Junhao Wang, Dehe Xu, Shiming Li and Xianlin Liu

Remote Sens. 2023, 15(17), 4301; https://doi.org/10.3390/rs15174301 - 31 Aug 2023

Cited by 5 | Viewed by 1868

Abstract

The emergency response is crucial in preventing and mitigating urban floods. Both remote sensing and social media data offer distinct advantages in large-scale flood monitoring and near-real-time flood monitoring. However, current research lacks a thorough exploration of the application of social media data and remote sensing imagery in the urban flood emergency response. To address this issue, this paper, while extracting disaster information based on social media data, deeply mines the spatiotemporal distribution characteristics and dynamic spatial accessibility of rescue points. Furthermore, SAR imagery and social media data for monitoring urban flooding are compared. This study took the Zhengzhou 7.20 urban flood as a case study and created a methodological framework to quickly extract flood disaster information (flood, landslide, and rescue points) using these two types of data; spatiotemporal analysis and random forest classification were also conducted to mine valuable information. Temporally, the study revealed that disaster information did not increase proportionally with the amount of rainfall during the rainfall process. Spatially, specific regions with higher susceptibility to flooding, landslides, and rescue points were identified, such as the central region characterized by low drainage standards and high-density urban areas, as well as the eastern region with low-lying terrain. Moreover, this study examined the spatial accessibility of rescue resources in real flood scenarios and found that their service coverage varied throughout the day during and after the disaster. In addition, social media excelled in high-density urban areas’ flood point extraction, while SAR performed better in monitoring floods at the edges of low-density urban areas and large water bodies, allowing them to complement each other, to a certain extent. The findings of this study provide scientific reference value for the optimal selection of rescue paths and the allocation of resources in the emergency response to urban floods caused by extreme rainstorms. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Flood Forecasting and Flood Risk Management)

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26 pages, 70881 KiB

Open AccessArticle

Urban Flood Dynamic Risk Assessment Based on Typhoon Rainfall Process: A Case Study of Typhoon “Lupit” (2109) in Fuzhou, China

by Xiaochen Qin, Yilong Wu, Tianshu Lin and Lu Gao

Remote Sens. 2023, 15(12), 3116; https://doi.org/10.3390/rs15123116 - 14 Jun 2023

Cited by 7 | Viewed by 2482

Abstract

Flood disasters caused by typhoon rainfall seriously threaten regional social and economic development. Accurately assessing the risk of typhoons and their secondary disasters is a great challenge in disaster prevention and reduction. To address this, the city of Fuzhou, Fujian Province, which was severely affected by Typhoon “Lupit” (2109), was selected as a case study. A typhoon rainfall flood disaster system including four components (the disaster-causing factor, disaster-pregnant environment, disaster-bearing body, and disaster prevention and reduction capacity) was constructed. A typhoon-rainfall process comprehensive intensity index (TPCI) based on different time scales within the typhoon process was developed to accurately evaluate the flood risk. The TPCI represented the disaster-causing factors of rainfall intensity, duration, and concentration features. Geographical similarity and random forest (RF) were applied to screen 23 typical indices for an urban flood disaster risk assessment model. The results indicated that the TPCI based on a 6 h precipitation simulation at a 24 h time scale was highly effective in highlighting the role of short-term precipitation in the typhoon process. A total of 66.5% of the floodplain area had a medium-grade or higher TPCI value, while 32.5% of the area had a low-grade TPCI. Only 1% of the flooded areas were not identified, which indicated that the TPCI could accurately capture the risk of typhoon rainfall. The urban flood disaster risk assessment model comprehensively considered socioeconomic and natural environment conditions. High-risk areas were identified as regions with extreme precipitation and dense populations. The dynamic evaluation results accurately described the spatiotemporal differences in the flood disaster risk. A period of extreme precipitation lagged the landfall time of Typhoon “Lupit”, causing the proportion of areas above the medium–high-risk threshold of flood disasters to rapidly increase from 8.29% before the landfall of the typhoon to 23.57% before its demise. The high-risk areas of flood disasters were mainly distributed in the towns of Shangjie, Nanyu, and Gaishan, which was consistent with the observed disasters. These study findings could contribute to the development of effective measures for disaster prevention and reduction, and improve the resilience of urban areas to typhoon disasters. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Flood Forecasting and Flood Risk Management)

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17 pages, 2851 KiB

Open AccessArticle

Impact of Urbanization on Regional Rainfall-Runoff Processes: Case Study in Jinan City, China

by Yanjun Zhao, Jun Xia, Zongxue Xu, Yunfeng Qiao, Jianming Shen and Chenlei Ye

Remote Sens. 2023, 15(9), 2383; https://doi.org/10.3390/rs15092383 - 1 May 2023

Cited by 7 | Viewed by 2904

Abstract

Rapid urbanization has altered the regional hydrological processes, posing a great challenge to the sustainable development of cities. The TVGM-USWM model, a new urban hydrological model considering the nonlinear rainfall-runoff relationship and the flow routing in an urban drainage system, was developed in this study. We employed this model in the Huangtaiqiao drainage basin of Jinan City, China, and examined the impact of land cover changes due to urbanization on rainfall-runoff processes. Two urbanization scenarios were set up in the TVGM-USWM model during the design rainfall events with different return periods. Results showed that (1) the TVGM-USWM model demonstrated good applicability in the study area, and the R_NS values of the flood events are all greater than 0.75 in both calibration and validation periods; (2) the proportion of impervious areas increased from 44.65% in 1990 to 71.00% in 2020, and urbanization played a leading role in the process of land cover change and manifested itself as a circular extensional expansion; and (3) urbanization showed a significant amplifying effect on the design flood processes, particularly for relatively big floods with small frequency, and the impact of urbanization on the time-to-peak of the design flood gradually decreased as the frequency of the design rainfall decreased. The results of this study can provide technical support for flood mitigation and the construction of a sponge city in Jinan City. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Flood Forecasting and Flood Risk Management)

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20 pages, 4767 KiB

Open AccessArticle

Application of a New Hybrid Deep Learning Model That Considers Temporal and Feature Dependencies in Rainfall–Runoff Simulation

by Feng Zhou, Yangbo Chen and Jun Liu

Remote Sens. 2023, 15(5), 1395; https://doi.org/10.3390/rs15051395 - 1 Mar 2023

Cited by 14 | Viewed by 3368

Abstract

Runoff forecasting is important for water resource management. Although deep learning models have substantially improved the accuracy of runoff prediction, the temporal and feature dependencies between rainfall–runoff time series elements have not been effectively exploited. In this work, we propose a new hybrid deep learning model to predict hourly streamflow: SA-CNN-LSTM (self-attention, convolutional neural network, and long short-term memory network). The advantages of CNN and LSTM in terms of data extraction from time series data are combined with the self-attention mechanism. By considering interdependences of the rainfall–runoff sequence between timesteps and between features, the prediction performance of the model is enhanced. We explored the performance of the model in the Mazhou Basin, China; we compared its performance with the performances of LSTM, CNN, ANN (artificial neural network), RF (random forest), SA-LSTM, and SA-CNN. Our analysis demonstrated that SA-CNN-LSTM demonstrated robust prediction with different flood magnitudes and different lead times; it was particularly effective within lead times of 1–5 h. Additionally, the performance of the self-attention mechanism with LSTM and CNN alone, respectively, was improved at some lead times; however, the overall performance was unstable. In contrast, the hybrid model integrating CNN, LSTM, and the self-attention mechanism exhibited better model performance and robustness. Overall, this study considers the importance of temporal and feature dependencies in hourly runoff prediction, then proposes a hybrid deep learning model to improve the performances of conventional models in runoff prediction. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Flood Forecasting and Flood Risk Management)

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17 pages, 4712 KiB

Open AccessArticle

Flood Frequency Analysis Using Mixture Distributions in Light of Prior Flood Type Classification in Norway

by Lei Yan, Liying Zhang, Lihua Xiong, Pengtao Yan, Cong Jiang, Wentao Xu, Bin Xiong, Kunxia Yu, Qiumei Ma and Chong-Yu Xu

Remote Sens. 2023, 15(2), 401; https://doi.org/10.3390/rs15020401 - 9 Jan 2023

Cited by 3 | Viewed by 2126

Abstract

The fundamental assumption of flood frequency analysis is that flood samples are generated by the same flood generation mechanism (FGM). However, flood events are usually triggered by the interaction of meteorological factors and watershed properties, which results in different FMGs. To solve this problem, researchers have put forward traditional two-component mixture distributions (TCMD-T) without clearly linking each component distribution to an explicit FGM. In order to improve the physical meaning of mixture distributions in seasonal snow-covered areas, the ratio of rainfall to flood volume (referred to as rainfall–flood ratio, RF) method was used to classify distinct FGMs. Thus, the weighting coefficient of each component distribution was determined in advance in the rainfall–flood ratio based TCMD (TCMD-RF). TCMD-RF model was applied to 34 basins in Norway. The results showed that flood types can be clearly divided into rain-on-snow-induced flood, snowmelt-induced flood and rainfall-induced flood. Moreover, the design flood and associated uncertainties were also estimated. It is found that TCMD-RF model can reduce the uncertainties of design flood by 20% compared with TCMD-T. The superiority of TCMD-RF is attributed to its clear classification of FGMs, thus determining the weighting coefficients without optimization and simplifying the parameter estimation procedure of mixture distributions. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Flood Forecasting and Flood Risk Management)

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Review

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35 pages, 3127 KiB

Open AccessReview

BIM–GIS Integrated Utilization in Urban Disaster Management: The Contributions, Challenges, and Future Directions

by Yu Cao, Cong Xu, Nur Mardhiyah Aziz and Syahrul Nizam Kamaruzzaman

Remote Sens. 2023, 15(5), 1331; https://doi.org/10.3390/rs15051331 - 27 Feb 2023

Cited by 29 | Viewed by 8732

Abstract

In the 21st Century, disasters have severe negative impacts on cities worldwide. Given the significant casualties and property damage caused by disasters, it is necessary for disaster management organizations and the public to enhance urban disaster management. As an effective method, BIM (Building Information Modeling)–GIS (Geographic Information System) integration can significantly improve urban disaster management. Despite the significance of BIM–GIS integration, there is rarely the adoption of BIM–GIS integration in urban disaster management, which significantly hinders the development of the quality and efficiency of urban disaster management. To enhance urban disaster management and reduce the negative impact caused by disasters, this study is developed to perform a systematic review of the utilization of BIM–GIS integration in urban disaster management. Through the systematic review, the capabilities of BIM–GIS integration in disaster prevention and mitigation, disaster response, and post-disaster recovery are reviewed and analyzed. Moreover, the data acquisition approaches, interoperability, data utilization and analysis methods, and future directions of BIM–GIS integrated utilization in the disaster management process are also discussed and analyzed. Through this study, the public and urban disaster managers can effectively familiarize themselves with and utilize the capabilities of BIM–GIS integration in urban disaster management, thereby improving the urban disaster management efficiency and the survival rate of disaster victims worldwide. For BIM and GIS software developers, this study can support them to familiarize themselves with the methods and trends of BIM–GIS integrated utilization in urban disaster management and thus optimize the development of software for BIM and GIS. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Flood Forecasting and Flood Risk Management)

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