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Remote Sensing in Hydrometeorology and Natural Hazards
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Remote Sensing in Hydrometeorology and Natural Hazards

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: 31 January 2026 | Viewed by 5638

Special Issue Editors

Prof. Dr. Peng Sun

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Guest Editor
School of Geography and Toursim, Anhui Normal University, Wuhu 241002, China
Interests: remote sensing hydrology; climatic and hydrological extremes
Special Issues, Collections and Topics in MDPI journals
Dr. Linyao Dong

E-Mail
Guest Editor
Changjiang River Scientific Research Institute, Changjiang Water Resources Commission, Wuhan 430010, China
Interests: remote sensing; machine learning; flash flood monitoring and simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Rui Yao

E-Mail Website
Guest Editor
School of Geography and Toursim, Anhui Normal University, Wuhu 241002, China
Interests: remote sensing; extreme climate events; Geographic modeling; flood simulation

Special Issue Information

Dear Colleagues,

Hydrometeorology is essential for understanding the interactions between the atmosphere and terrestrial water systems. This understanding underpins the efforts to address the global challenges related to water resource management, climate change, and environmental sustainability. Moreover, as remote sensing data are extensively utilized across meteorology, hydrology, and disaster science, integrating remote sensing technologies into hydrometeorology is crucial for fostering sustainable water resource management and mitigating the impacts of climate change.

Although hydrometeorological extremes and natural disasters have significant social and economic impacts, the physical processes and underlying mechanisms driving these events remain poorly understood. Additionally, the social dynamics and consequences associated with natural disasters also require a thorough investigation and clear identification. Therefore, this Special Issue on “Remote Sensing in Hydrometeorology and Natural Hazards” aims to innovative the applications of remote sensing technologies within hydrometeorology and natural hazards. By presenting studies that utilize remote sensing for sustainable water management, climate resilience, and environmental conservation, this Issue aspires to enhance our understanding and ability to address critical global challenges. Hence, we invite researchers, practitioners, and policymakers to contribute their insights and findings, fostering collaboration and knowledge exchange to support a sustainable and resilient future.

Prof. Dr. Peng Sun
Dr. Linyao Dong
Dr. Rui 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. 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

  • warming climate
  • remote sensing
  • hydrological cycle
  • hydrological hazards
  • hydrometeorology
  • climatic and hydrological extremes

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Published Papers (6 papers)

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Research

25 pages, 7970 KiB  
Article
Bayesian Model Averaging for Satellite Precipitation Data Fusion: From Accuracy Estimation to Runoff Simulation
by Shaowei Ning, Yang Cheng, Yuliang Zhou, Jie Wang, Yuliang Zhang, Juliang Jin and Bhesh Raj Thapa
Remote Sens. 2025, 17(7), 1154; https://doi.org/10.3390/rs17071154 - 25 Mar 2025
Viewed by 308
Abstract
Precipitation plays a vital role in the hydrological cycle, directly affecting water resource management and influencing flood and drought risk prediction. This study proposes a Bayesian Model Averaging (BMA) framework to integrate multiple precipitation datasets. The framework enhances estimation accuracy for hydrological simulations. [...] Read more.
Precipitation plays a vital role in the hydrological cycle, directly affecting water resource management and influencing flood and drought risk prediction. This study proposes a Bayesian Model Averaging (BMA) framework to integrate multiple precipitation datasets. The framework enhances estimation accuracy for hydrological simulations. The BMA framework synthesizes four precipitation products—Climate Hazards Group Infrared Precipitation with Station (CHIRPS), the fifth-generation ECMWF Atmospheric Reanalysis (ERA5), Global Satellite Mapping of Precipitation (GSMaP), and Integrated Multi-satellitE Retrievals (IMERG)—over China’s Ganjiang River Basin from 2008 to 2020. We evaluated the merged dataset’s performance against its constituent datasets and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) at daily, monthly, and seasonal scales. Evaluation metrics included the correlation coefficient (CC), root mean square error (RMSE), and Kling–Gupta efficiency (KGE). The Variable Infiltration Capacity (VIC) hydrological model was further applied to assess how these datasets affect runoff simulations. The results indicate that the BMA-merged dataset substantially improves precipitation estimation accuracy when compared with individual inputs. The merged product achieved optimal daily performance (CC = 0.72, KGE = 0.70) and showed superior seasonal skill, notably reducing biases in autumn and winter. In hydrological applications, the BMA-driven VIC model effectively replicated observed runoff patterns, demonstrating its efficacy for regional long-term predictions. This study highlights BMA’s potential for optimizing hydrological model inputs, providing critical insights for sustainable water management and risk reduction in complex basins. Full article
(This article belongs to the Special Issue Remote Sensing in Hydrometeorology and Natural Hazards)
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23 pages, 23795 KiB  
Article
Sentinel-2 Reveals Record-Breaking Po River Shrinking Due to Severe Drought in 2022
by Federico Filipponi, Giulia Colazzo, Erica Vassoney, Claudio Comoglio and Gianluca Filippa
Remote Sens. 2025, 17(6), 1070; https://doi.org/10.3390/rs17061070 - 18 Mar 2025
Viewed by 504
Abstract
Monitoring inland waters is of critical importance for the effective and sustainable management of water resources, especially under climate change scenarios. This paper introduces a satellite-based approach for river monitoring using optical multispectral data. Time series of percentage water content, derived by the [...] Read more.
Monitoring inland waters is of critical importance for the effective and sustainable management of water resources, especially under climate change scenarios. This paper introduces a satellite-based approach for river monitoring using optical multispectral data. Time series of percentage water content, derived by the normalized difference water index (NDWI) calculated for each satellite acquisition, are aggregated at monthly timesteps to generate monthly water frequencies. Then, the river dynamics are evaluated by comparing each month with the previous one and with the average conditions of the same month in previous years. The ability of the method to investigate hydromorphological processes over time is demonstrated with the case study of the record-breaking Po River shrinking due to the severe 2022 drought in northern Italy, through the analysis of Copernicus Sentinel-2 satellite acquisitions. Earth observation data analysis is complemented with metrics generated from in situ river discharge measurements, including the coefficient of variation and the Streamflow Drought Index (SDI), to provide a more comprehensive understanding of the severity and variability of the hydrological drought throughout the year 2022. The findings demonstrate the satellite-based observation capabilities in monitoring surface waters, thereby stimulating the development of operational services like hydromorphological assessment. Full article
(This article belongs to the Special Issue Remote Sensing in Hydrometeorology and Natural Hazards)
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18 pages, 12108 KiB  
Article
The Potential Impact of the Three Gorges Reservoir on Regional Extreme Precipitation—A Sensitivity Study
by Ya Huang, Weihua Xiao and Yuyan Zhou
Remote Sens. 2025, 17(4), 670; https://doi.org/10.3390/rs17040670 - 16 Feb 2025
Viewed by 381
Abstract
Understanding the potential impact of the Three Gorges Reservoir (TGR) on regional extreme precipitation and its mechanisms is critical for the safe operation of the reservoir and the efficient management of regional water resources. This study uses the regional climate model RegCM4 to [...] Read more.
Understanding the potential impact of the Three Gorges Reservoir (TGR) on regional extreme precipitation and its mechanisms is critical for the safe operation of the reservoir and the efficient management of regional water resources. This study uses the regional climate model RegCM4 to conduct a double-nested simulation experiment (50 km to 10 km) from 1989 to 2012, evaluated against the CN5.1 observation dataset. Sensitivity experiments with three different lake area ratios (0%, 20% and 100%) were performed using the sub-grid partitioning method in the Community Land Model Version 4.5 to analyze the spatiotemporal distribution, intensity, and frequency of precipitation under varying TGR water areas. The results show that with a 20% lake area ratio, precipitation slightly decreases, but the impact on extreme precipitation indices is not statistically significant. However, with a 100% lake area ratio, significant decreases in both total and extreme precipitation indices occur. The reduction is primarily driven by the formation of anomalous mountain-valley circulation between the TGR and surrounding mountains, which leads to atmospheric subsidence and reduced convective activity. These findings indicate that while the TGR has a negligible impact on extreme precipitation under its current configuration, the exaggerated sensitivity experiments reveal potential mechanisms and localized effects. This research enhances the understanding of the TGR’s influence on regional extreme precipitation and provides valuable insights for water resource management and reservoir operation. Full article
(This article belongs to the Special Issue Remote Sensing in Hydrometeorology and Natural Hazards)
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27 pages, 9460 KiB  
Article
Data Uncertainty of Flood Susceptibility Using Non-Flood Samples
by Yayi Zhang, Yongqiang Wei, Rui Yao, Peng Sun, Na Zhen and Xue Xia
Remote Sens. 2025, 17(3), 375; https://doi.org/10.3390/rs17030375 - 23 Jan 2025
Viewed by 699
Abstract
Flood susceptibility provides scientific support for flood prevention planning and infrastructure development by identifying and assessing flood-prone areas. The uncertainty posed by non-flood sample datasets remains a key challenge in flood susceptibility mapping. Therefore, this study proposes a novel sampling method for non-flood [...] Read more.
Flood susceptibility provides scientific support for flood prevention planning and infrastructure development by identifying and assessing flood-prone areas. The uncertainty posed by non-flood sample datasets remains a key challenge in flood susceptibility mapping. Therefore, this study proposes a novel sampling method for non-flood points. A flood susceptibility model is constructed using a machine learning algorithm to examine the uncertainty in flood susceptibility due to non-flood point selection. The influencing factors of flood susceptibility are analyzed through interpretable models. Compared to non-flood datasets generated by random sampling with the buffer method, the non-flood dataset constructed using the spatial range identified by the frequency ratio model and sampling method of one-class support vector machine achieves higher accuracy. This significantly improves the simulation accuracy of the flood susceptibility model, with an accuracy increase of 24% in the ENSEMBLE model. (2) In constructing the flood susceptibility model using the optimal non-flood dataset, the ENSEMBLE learning algorithm demonstrates higher accuracy than other machine learning methods, with an AUC of 0.95. (3) The northern and southeastern regions of the Zijiang River Basin have extremely high flood susceptibility. Elevation and drainage density are identified as key factors causing high flood susceptibility in these areas, whereas the southwestern region exhibits low flood susceptibility due to higher elevation. (4) Elevation, slope, and drainage density are the three most important factors affecting flood susceptibility. Lower values of elevation and slope and higher drainage density correlate with higher flood susceptibility. This study offers a new approach to reducing uncertainty in flood susceptibility and provides technical support for flood prevention and disaster mitigation in the basin. Full article
(This article belongs to the Special Issue Remote Sensing in Hydrometeorology and Natural Hazards)
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15 pages, 2542 KiB  
Article
Flood Risk Analysis of Urban Agglomerations in the Yangtze River Basin Under Extreme Precipitation Based on Remote Sensing Technology
by Haichao Li, Dawen Yang, Zhenduo Zhu, Yanqi Wei, Yuliang Zhou, Hiroshi Ishidaira, Nii Amarquaye Commey and Han Cheng
Remote Sens. 2024, 16(22), 4289; https://doi.org/10.3390/rs16224289 - 17 Nov 2024
Viewed by 1440
Abstract
Flooding is the most pervasive hydrological disaster globally. This study presents a comprehensive analysis of torrential rain and flood characteristics across three major urban agglomerations (CY, MRYR, and YRD) in the Yangtze River Basin from 1991 to 2020. Utilizing satellite-derived microwave SSM/I data [...] Read more.
Flooding is the most pervasive hydrological disaster globally. This study presents a comprehensive analysis of torrential rain and flood characteristics across three major urban agglomerations (CY, MRYR, and YRD) in the Yangtze River Basin from 1991 to 2020. Utilizing satellite-derived microwave SSM/I data and CHIRPS precipitation datasets, this study examines the impacts of urbanization and climate change on flood risk patterns. The results showed: (1) In 1998, the MRYR had the highest flood risk due to heavy rainfall and poor flood control, but by 2020, risk shifted to the CY with rapid urbanization and more rainfall, while the YRD maintained the lowest risk due to advanced flood control. (2) The relationship between impervious surface area and flood risk varied by region. The CY showed a negative correlation (−0.41), suggesting effective flood mitigation through topography and infrastructure; the MRYR had a slight positive correlation (0.12), indicating increased risks from urban expansion; and the YRD’s weak negative correlation (−0.18) reflected strong flood control systems. This research underscores the imperative of strategic urban planning and effective water resource management to mitigate future flood risks and contributes valuable insights to ongoing efforts in flood disaster prevention and control within the Yangtze River Basin. Full article
(This article belongs to the Special Issue Remote Sensing in Hydrometeorology and Natural Hazards)
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22 pages, 14747 KiB  
Article
Observed Changes and Projected Risks of Hot–Dry/Hot–Wet Compound Events in China
by Yifan Zou and Xiaomeng Song
Remote Sens. 2024, 16(22), 4208; https://doi.org/10.3390/rs16224208 - 12 Nov 2024
Viewed by 1305
Abstract
Compound extreme events can cause serious impacts on both the natural environment and human beings. This work aimed to explore the changes in compound drought–heatwave and heatwave–extreme precipitation events (i.e., CDHEs and CHPEs) across China using daily-scale gauge-based meteorological observations, and to examine [...] Read more.
Compound extreme events can cause serious impacts on both the natural environment and human beings. This work aimed to explore the changes in compound drought–heatwave and heatwave–extreme precipitation events (i.e., CDHEs and CHPEs) across China using daily-scale gauge-based meteorological observations, and to examine their future projections and potential risks using the Coupled Model Intercomparison Project (CMIP6) under the shared socioeconomic pathway (SSP) scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5). The results show the following: (1) The frequencies of CDHEs and CHPEs across China showed a significant increasing trend from 1961 to 2020, with contrasting trends between the first half and second half of the period (i.e., a decrease from 1961 to 1990 and an increase from 1991 to 2020). Similar trends were observed for four intensity levels (i.e., mild, moderate, severe, and extreme) of CDHEs and CHPEs. (2) All the frequencies under three SSP scenarios will show increasing trends, especially under higher emission scenarios. Moreover, the projected intensities of CDHEs and CHPEs will gradually increase, especially for higher levels. (3) The exposure of the population (POP) and Gross Domestic Product (GDP) will be concentrated mainly in China’s coastal areas. The GDP exposures to the CDHEs and CHPEs will reach their highest values for SSP5-8.5, while the POP exposure will peak for SSP2-4.5 and SSP5-8.5, respectively. Our findings can offer scientific and technological support to actively mitigate future climate change risks. Full article
(This article belongs to the Special Issue Remote Sensing in Hydrometeorology and Natural Hazards)
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