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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 22701

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Special Issue Editors

Prof. Dr. Joo-Heon Lee

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Guest Editor

Department of Civil Engineering, Joongbu University, Goyang 10279, Korea
Interests: remote sensing of hydro-meteorology; drought monitoring and forecasting; climate change adaptation; eco hydrology; statistical hydrology
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jong-Suk Kim

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Guest Editor

State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
Interests: hydroclimatology; hydrosystem modeling; flood/drought frequency analysis; climate variability and change; tropical meteorology; environmental assessment; risk management
Special Issues, Collections and Topics in MDPI journals

Dr. Young Hun Jung

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Guest Editor

Construction and Disaster Prevention Engineering, Kyungpook National University, Sangju, Gyeongbuk 37224, Korea
Interests: hydrologic modeling; flood inundation; hydrologic data; soil carbon sequestration; uncertainty analysis

Dr. Chanyang Sur

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Guest Editor

Earth System Science Interdisciplinary Center, University of Maryland, College Park 20740, MD, USA
Interests: meteorological / agricultural / hydrological / ecological drought analysis; drought risk assessment; drought forecasting / outlook using climate change scenario; water and carbon circulation; analysis of hydrometeorological variables (evapotranspiration & soil moisture); data assimilation; hydrologic modeling; land information system (LIS)

Special Issue Information

Dear Colleagues,

Extreme hydrometeorological events that occur naturally threaten and cause harm to lives and livelihoods and result in billions of dollars of damage worldwide every year. Their environmental impacts are equally catastrophic. Human activities may help prevent hydrometeorological hazards from turning into disasters but, in many situations, they may also exacerbate their impacts, e.g., through excessive development in coastal areas that increase risk exposure and community vulnerability. Moreover, climate change may be responsible for the increasing frequency and magnitude of atmospheric patterns that lead to more frequent and intense hydrometeorological disasters (e.g., severe storms, floods, and droughts).

This Special Issue will focus on “Remote sensing of Hydrologic Extremes”. We welcome novel research, reviews, and opinion pieces covering all related topics, including flood/drought monitoring, risk management and policy, hydrometeorological extremes and its impact on human-environment systems, frequency analysis, and vulnerability assessment for adaptation to climate change. Specific topics include, but are not limited to:

Flood/drought, risk management, and policy: decision making under uncertainty
Hydrometeorological extremes and its impact on human-environment systems
Regional and nonstationary frequency analysis of extreme events
Detection and prediction of hydrometeorological extremes with observational and model-based approaches
Vulnerability and impact assessment for adaptation to climate change

Prof. Dr. Joo-Heon Lee
Prof. Dr. Jong-Suk Kim
Dr. Young Hun Jung
Dr. Chanyang Sur
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

Remote Sensing
Hydro-meteorological extremes
Flood/Drought
Frequency analysis
Risk management
Vulnerability and impact assessment
Climate change impacts & Adaptation

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Published Papers (7 papers)

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Research

17 pages, 6395 KiB

Open AccessArticle

Seasonal Precipitation Variability and Non-Stationarity Based on the Evolution Pattern of the Indian Ocean Dipole over the East Asia Region

by Jong-Suk Kim, Sun-Kwon Yoon, Sang-Myeong Oh and Hua Chen

Remote Sens. 2021, 13(9), 1806; https://doi.org/10.3390/rs13091806 - 6 May 2021

Cited by 2 | Viewed by 2238

Abstract

Non-linear behavioral links with atmospheric teleconnections were identified between the Indian Ocean Dipole (IOD) mode and seasonal precipitation over East Asia (EA) using statistical models. The analysis showed that the lower the lag time, the higher the correlation; more than a two-fold correlation for non-linear regression with a kernel density estimator than for the linear regression method. When the IOD peaked, a pattern of significant reductions in seasonal precipitation during the negative IOD period occurred throughout the Korean Peninsula (KP). The occurrence of the positive IOD was in line with the El Niño phenomenon and generated greater seasonal precipitation than only the positive IOD, which takes place from March to May. This change occurred more in the cold tongue El Niño than the warm pool El Niño, inducing much higher spring precipitation throughout the KP. When negative IODs and La Niña coincided, there was slightly greater precipitation from March to May compared to the sole occurrence of negative IODs. In positive (negative) IOD years, there was anti-cyclonic (cyclonic) circulation in the South China Sea (SCS), helping to transport moisture to EA. The composite precipitation anomalies in the positive (negative) IOD years show above (below) normal precipitation in southern China. In contrast, other parts of the EA experienced drier (humid) signals than normal years. In positive IOD years, the anti-cyclonic circulation strength of the Bay of Bengal and the SCS continued until autumn and spring of the following year. This shows possible remote connections between climate events related to the tropical Indian Ocean and variations in precipitation over EA. Full article

(This article belongs to the Special Issue Remote Sensing of Hydro-Meteorology)

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20 pages, 11985 KiB

Open AccessArticle

Capacity of the PERSIANN-CDR Product in Detecting Extreme Precipitation over Huai River Basin, China

by Shanlei Sun, Jiazhi Wang, Wanrong Shi, Rongfan Chai and Guojie Wang

Remote Sens. 2021, 13(9), 1747; https://doi.org/10.3390/rs13091747 - 30 Apr 2021

Cited by 11 | Viewed by 2049

Abstract

Assessing satellite-based precipitation product capacity for detecting precipitation and linear trends is fundamental for accurately knowing precipitation characteristics and changes, especially for regions with scarce and even no observations. In this study, we used daily gauge observations across the Huai River Basin (HRB) during 1983–2012 and four validation metrics to evaluate the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) capacity for detecting extreme precipitation and linear trends. The PERSIANN-CDR well captured climatologic characteristics of the precipitation amount- (PRCPTOT, R85p, R95p, and R99p), duration- (CDD and CWD), and frequency-based indices (R10mm, R20mm, and Rnnmm), followed by moderate performance for the intensity-based indices (Rx1day, R5xday, and SDII). Based on different validation metrics, the PERSIANN-CDR capacity to detect extreme precipitation varied spatially, and meanwhile the validation metric-based performance differed among these indices. Furthermore, evaluation of the PERSIANN-CDR linear trends indicated that this product had a much limited and even no capacity to represent extreme precipitation changes across the HRB. Briefly, this study provides a significant reference for PERSIANN-CDR developers to use to improve product accuracy from the perspective of extreme precipitation, and for potential users in the HRB. Full article

(This article belongs to the Special Issue Remote Sensing of Hydro-Meteorology)

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18 pages, 7920 KiB

Open AccessArticle

Integrated Drought Monitoring and Evaluation through Multi-Sensor Satellite-Based Statistical Simulation

by Jong-Suk Kim, Seo-Yeon Park, Joo-Heon Lee, Jie Chen, Si Chen and Tae-Woong Kim

Remote Sens. 2021, 13(2), 272; https://doi.org/10.3390/rs13020272 - 14 Jan 2021

Cited by 15 | Viewed by 3664

Abstract

To proactively respond to changes in droughts, technologies are needed to properly diagnose and predict the magnitude of droughts. Drought monitoring using satellite data is essential when local hydrogeological information is not available. The characteristics of meteorological, agricultural, and hydrological droughts can be monitored with an accurate spatial resolution. In this study, a remote sensing-based integrated drought index was extracted from 849 sub-basins in Korea’s five major river basins using multi-sensor collaborative approaches and multivariate dimensional reduction models that were calculated using monthly satellite data from 2001 to 2019. Droughts that occurred in 2001 and 2014, which are representative years of severe drought since the 2000s, were evaluated using the integrated drought index. The Bayesian principal component analysis (BPCA)-based integrated drought index proposed in this study was analyzed to reflect the timing, severity, and evolutionary pattern of meteorological, agricultural, and hydrological droughts, thereby enabling a comprehensive delivery of drought information. Full article

(This article belongs to the Special Issue Remote Sensing of Hydro-Meteorology)

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14 pages, 16376 KiB

Open AccessArticle

Statistical Prediction of Typhoon-Induced Rainfall over China Using Historical Rainfall, Tracks, and Intensity of Typhoon in the Western North Pacific

by Jong-Suk Kim, Anxiang Chen, Junghwan Lee, Il-Ju Moon and Young-Il Moon

Remote Sens. 2020, 12(24), 4133; https://doi.org/10.3390/rs12244133 - 17 Dec 2020

Cited by 20 | Viewed by 4013

Abstract

Typhoons or mature tropical cyclones (TCs) can affect inland areas of up to hundreds of kilometers with heavy rains and strong winds, along with landslides causing numerous casualties and property damage due to concentrated precipitation over short time periods. To reduce these damages, it is necessary to accurately predict the rainfall induced by TCs in the western North Pacific Region. However, despite dramatic advances in observation and numerical modeling, the accuracy of prediction of typhoon-induced rainfall and spatial distribution remains limited. The present study offers a statistical approach to predicting the accumulated rainfall associated with typhoons based on a historical storm track and intensity data along with observed rainfall data for 55 typhoons affecting the southeastern coastal areas of China from 1961 to 2017. This approach is shown to provide an average root mean square error of 51.2 mm across 75 meteorological stations in the southeast coastal area of China (ranging from 15.8 to 87.3 mm). Moreover, the error is less than 70 mm for most stations, and significantly lower in the three verification cases, thus demonstrating the feasibility of this approach. Furthermore, the use of fuzzy C-means clustering, ensemble averaging, and corrections to typhoon intensities, can provide more accurate rainfall predictions from the method applied herein, thus allowing for improvements to disaster preparedness and emergency response. Full article

(This article belongs to the Special Issue Remote Sensing of Hydro-Meteorology)

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33 pages, 6282 KiB

Open AccessArticle

Evaluation of Multi-Satellite Precipitation Datasets and Their Error Propagation in Hydrological Modeling in a Monsoon-Prone Region

by Jie Chen, Ziyi Li, Lu Li, Jialing Wang, Wenyan Qi, Chong-Yu Xu and Jong-Suk Kim

Remote Sens. 2020, 12(21), 3550; https://doi.org/10.3390/rs12213550 - 30 Oct 2020

Cited by 24 | Viewed by 3285

Abstract

This study comprehensively evaluates eight satellite-based precipitation datasets in streamflow simulations on a monsoon-climate watershed in China. Two mutually independent datasets—one dense-gauge and one gauge-interpolated dataset—are used as references because commonly used gauge-interpolated datasets may be biased and unable to reflect the real performance of satellite-based precipitation due to sparse networks. The dense-gauge dataset includes a substantial number of gauges, which can better represent the spatial variability of precipitation. Eight satellite-based precipitation datasets include two raw satellite datasets, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Climate Prediction Center MORPHing raw satellite dataset (CMORPH RAW); four satellite-gauge datasets, Tropical Rainfall Measuring Mission 3B42 (TRMM), PERSIANN Climate Data Record (PERSIANN CDR), CMORPH bias-corrected (CMORPH CRT), and gauge blended datasets (CMORPH BLD); and two satellite-reanalysis-gauge datasets, Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS). The uncertainty related to hydrologic model physics is investigated using two different hydrological models. A set of statistical indices is utilized to comprehensively evaluate the precipitation datasets from different perspectives, including detection, systematic, random errors, and precision for simulating extreme precipitation. Results show that CMORPH BLD and MSWEP generally perform better than other datasets. In terms of hydrological simulations, all satellite-based datasets show significant dampening effects for the random error during the transformation process from precipitation to runoff; however, these effects cannot hold for the systematic error. Even though different hydrological models indeed introduce uncertainties to the simulated hydrological processes, the relative hydrological performance of the satellite-based datasets is consistent in both models. Namely, CMORPH BLD performs the best, which is followed by MSWEP, CMORPH CRT, and TRMM. PERSIANN CDR and CHIRPS perform moderately well, and two raw satellite datasets are not recommended as proxies of gauged observations for their worse performances. Full article

(This article belongs to the Special Issue Remote Sensing of Hydro-Meteorology)

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18 pages, 17351 KiB

Open AccessArticle

Remote Sensing-Based Rainfall Variability for Warming and Cooling in Indo-Pacific Ocean with Intentional Statistical Simulations

by Jong-Suk Kim, Phetlamphanh Xaiyaseng, Lihua Xiong, Sun-Kwon Yoon and Taesam Lee

Remote Sens. 2020, 12(9), 1458; https://doi.org/10.3390/rs12091458 - 4 May 2020

Cited by 4 | Viewed by 2640

Abstract

This study analyzed the sensitivity of rainfall patterns in South China and the Indochina Peninsula (ICP) using statistical simulations of observational data. Quantitative changes in rainfall patterns over the ICP were examined for both wet and dry seasons to identify hotspots sensitive to ocean warming in the Indo-Pacific sector. The rainfall variability was amplified by combined and/or independent effects of the El Niño–Southern Oscillation and the Indian Ocean Dipole (IOD). During the years of El Niño and a positive phase of the IOD, rainfall is less than usual in Thailand, Cambodia, southern Laos, and Vietnam. Conversely, during the years of La Niña and a negative phase of the IOD, rainfall throughout the ICP is above normal, except in parts of central Laos, northern Vietnam, and South China. This study also simulated the change of ICP rainfall in the wet and dry seasons with intentional IOD changes and verified IOD-sensitive hotspots through quantitative analysis. The results of this study provide a clear understanding both of the sensitivity of regional precipitation to the IOD and of the potential future impact of statistical changes regarding the IOD in terms of understanding regional impacts associated with precipitation in changing climates. Full article

(This article belongs to the Special Issue Remote Sensing of Hydro-Meteorology)

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19 pages, 9239 KiB

Open AccessArticle

Spatial Downscaling of MODIS Chlorophyll-a with Genetic Programming in South Korea

by Hamid Mohebzadeh, Junho Yeom and Taesam Lee

Remote Sens. 2020, 12(9), 1412; https://doi.org/10.3390/rs12091412 - 30 Apr 2020

Cited by 9 | Viewed by 3473

Abstract

Chlorophyll-a (Chl-a) is one of the major indicators for water quality assessment and recent developments in ocean color remote sensing have greatly improved the ability to monitor Chl-a on a global scale. The coarse spatial resolution is one of the major limitations for most ocean color sensors including Moderate Resolution Imaging Spectroradiometer (MODIS), especially in monitoring the Chl-a concentrations in coastal regions. To improve its spatial resolution, downscaling techniques have been suggested with polynomial regression models. Nevertheless, polynomial regression has some restrictions, including sensitivity to outliers and fixed mathematical forms. Therefore, the current study applied genetic programming (GP) for downscaling Chl-a. The proposed GP model in the current study was compared with multiple polynomial regression (MPR) to different degrees (2^nd-, 3^rd-, and 4^th-degree) to illustrate their performances for downscaling MODIS Chl-a. The obtained results indicate that GP with R² = 0.927 and RMSE = 0.1642 on the winter day and R² = 0.763 and RMSE = 0.5274 on the summer day provides higher accuracy on both winter and summer days than all the applied MPR models because the GP model can automatically produce appropriate mathematical equations without any restrictions. In addition, the GP model is the least sensitive model to the changes in the input parameters. The improved downscaling data provide better information to monitor the status of oceanic and coastal marine ecosystems that are also critical for fisheries and fishing farming. Full article

(This article belongs to the Special Issue Remote Sensing of Hydro-Meteorology)

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