Special Issue "Modeling and Monitoring Climate Extremes and Impacts on Natural-Human Systems"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology and Hydrogeology".

Deadline for manuscript submissions: closed (30 June 2020).

Special Issue Editors

Prof. Dr. Hyungjun Kim
Website
Guest Editor
The University of Tokyo, Japan
Interests: climate forcing and land feedback; coupled natural–human systems and sustainable development; remote sensing hydrology; big data–model integration
Special Issues and Collections in MDPI journals
Prof. Dr. Yadu Pokhrel
Website
Guest Editor
Dr. John T. Reager
Website
Guest Editor
Jet Propulsion Laboratory/NASA, USA
Interests: application of satellite gravimetry for terrestrial hydrology; influence of subsurface water storage on hydrologic extremes; global water cycle variability and sea level rise
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

During recent years, the book-keeping records of extreme events and disasters have been replaced year by year. Because of significant advances over the past decade in modeling and remote sensing capacity for climate–hydrology–human interactions, our understanding of the causes and impacts of such extreme events and disasters has improved considerably.

This Special Issue aims to solicit original scientific contributions from the broader community related to climate and atmospheric sciences, hydrology, and remote sensing, on the following topics: (1) The variability of climate forcing and hydrological feedback; (2) the detection/attribution of extreme events, and impact assessment; (3) the modeling of interactions between nature and human society; and (4) remote sensing hydrology, and data–model integration.

Studies that focus on modeling and/or monitoring behaviors as coupled natural–human systems against extreme climatic perturbation from multi-scale perspectives are particularly encouraged, but studies related to the general areas of climate and hydrological extremes, climate change and impact assessments, sustainability science, numerical model development, and the development of remote sensing algorithms are equally welcome.

Prof. Dr. Hyungjun Kim
Prof. Dr. Yadu Pokhrel
Dr. John T. Reager
Prof. Jin-Ho Yoon
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 papers will be 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. Water is an international peer-reviewed open access monthly 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 1800 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

  • Extreme events
  • Natural and human systems
  • Hydrological modeling
  • Remote sensing hydrology

Published Papers (7 papers)

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Research

Open AccessArticle
Network Modeling and Dynamic Mechanisms of Multi-Hazards—A Case Study of Typhoon Mangkhut
Water 2020, 12(8), 2198; https://doi.org/10.3390/w12082198 - 05 Aug 2020
Abstract
Coastal areas are home to billions of people and assets that are prone to natural disasters and climate change. In this study, we established a disaster network to assess the multi-hazards (gale and heavy rain) of typhoon disasters, specifically Typhoon Mangkhut of 2018 [...] Read more.
Coastal areas are home to billions of people and assets that are prone to natural disasters and climate change. In this study, we established a disaster network to assess the multi-hazards (gale and heavy rain) of typhoon disasters, specifically Typhoon Mangkhut of 2018 in coastal China, by applying the methodology of a bipartite network in both time dimension and spatial dimension. In this network, the edge set and adjacent matrix are based on the connection between an hour and a city with a multi-hazards impact that includes gales and heavy rain. We analyze the characteristics and structure of this disaster network and assess the multi-hazards that arose from Typhoon Mangkhut in different areas. The result shows that there are 14 cities in the core area and 21 cities in the periphery area, based on core–periphery classification in the disaster network. Although more damage area belongs to the periphery area, the percentage of the population affected by the typhoon and direct economic loss in GDP in the core area was 69.68% and 0.22% respectively, which is much higher than in the periphery area (55.58% and 0.06%, respectively) The core area suffered more from multi-hazards and had more disaster loss. This study shows that it is feasible to assess multiple hazards with a disaster network based on the bipartite network. Full article
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Open AccessArticle
Using GRanD Database and Surface Water Data to Constrain Area–Storage Curve of Reservoirs
Water 2020, 12(5), 1242; https://doi.org/10.3390/w12051242 - 27 Apr 2020
Abstract
Basic information on global reservoirs is well documented in databases such as GRanD (Global Reservoir and Dam) and ICOLD (International Commission on Large Dams). However, though playing a critical role in estimating reservoir storage variations from remote sensing or hydrological models, area–storage curves [...] Read more.
Basic information on global reservoirs is well documented in databases such as GRanD (Global Reservoir and Dam) and ICOLD (International Commission on Large Dams). However, though playing a critical role in estimating reservoir storage variations from remote sensing or hydrological models, area–storage curves of reservoirs are not conveniently obtained nor publicly shared. In this paper, we combine the GRanD database and Landsat-based global surface water extent (GSW) data to derive area–storage curves of reservoirs. The reported storage capacity in the GRanD database and water surface area from GSW data were used to constrain the area–storage curve. The proposed method has the potential to derive area–storage curves of reservoirs larger than 1 km2 archived in the GRanD database. The derived curves are validated with in situ reservoir data collected in US and China, and the results show that in situ records are well captured by the derived curves both in large and small reservoirs with various shapes. The derived area–storage curves could be employed to advance global monitoring or modeling of reservoir storage dynamics. Full article
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Open AccessArticle
Multi-Indicator Evaluation for Extreme Precipitation Events in the Past 60 Years over the Loess Plateau
Water 2020, 12(1), 193; https://doi.org/10.3390/w12010193 - 10 Jan 2020
Cited by 1
Abstract
The unique characteristics of topography, landforms, and climate in the Loess Plateau make it especially important to investigate its extreme precipitation characteristics. Daily precipitation data of Loess Plateau covering a period of 1959–2017 are applied to evaluate the probability features of five precipitation [...] Read more.
The unique characteristics of topography, landforms, and climate in the Loess Plateau make it especially important to investigate its extreme precipitation characteristics. Daily precipitation data of Loess Plateau covering a period of 1959–2017 are applied to evaluate the probability features of five precipitation indicators: the amount of extreme heavy precipitation (P95), the days with extreme heavy precipitation, the intensity of extreme heavy precipitation (I95), the continuous dry days, and the annual total precipitation. In addition, the joint risk of different combinations of precipitation indices is quantitatively evaluated based on the copula method. Moreover, the risk and severity of each extreme heavy precipitation factor corresponding to 50-year joint return period are achieved through inverse derivation process. Results show that the precipitation amount and intensity of the Loess Plateau vary greatly in spatial distribution. The annual precipitation in the northwest region may be too concentrated in several rainstorms, which makes the region in a serious drought state for most of the year. At the level of 10-year return period, more than five months with no precipitation events would occur in the Northwest Loess Plateau. While, P95 or I95 events of 100-year level may be encountered in a 50-year return period and in the southeastern region, which means there are foreseeable long-term extreme heavy precipitation events. Full article
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Open AccessArticle
Encounter Probability and Risk of Flood and Drought under Future Climate Change in the Two Tributaries of the Rao River Basin, China
Water 2020, 12(1), 104; https://doi.org/10.3390/w12010104 - 27 Dec 2019
Abstract
Extreme hydrometeorological events have far-reaching impacts on our daily life and may occur more frequently with rising global temperatures. The probability of the concurrence of these extreme events in the upper reaches of the river network is of particular importance for the lower [...] Read more.
Extreme hydrometeorological events have far-reaching impacts on our daily life and may occur more frequently with rising global temperatures. The probability of the concurrence of these extreme events in the upper reaches of the river network is of particular importance for the lower reaches, which is referred to as the encounter probability of extreme events, and may have even stronger socio-economic impacts. In this study, the Rao River basin in China is selected as an example to explore the encounter probability and risk of future flood and drought based on the encounter probability model. The reference period was 1971–2000, and the future prediction periods were 2020–2049 and 2070–2099. The calibrated and validated statistical downscaling model (SDSM) was used to generate future daily precipitation and daily mean temperature. The calibrated and validated Xin’anjiang model was used to predict future daily mean streamflow in the basin. In addition, the encounter probability model was established using the joint distribution of occurrence dates and magnitudes of daily mean streamflow to investigate the encounter probabilities of flood and drought under future climate change. Results show that, for flood occurrence dates, the encounter probability during the flood season would decrease in the two future periods while the dates would generally be earlier. For flood magnitudes, the encounter probability of the two tributaries’ floods and the probability of flood at each tributary would decrease (e.g., the encounter probability with the same-frequency of 100-years would reduce by 53% to 95%), which indicates reduced risk of future major floods in the study area. For drought occurrence dates, the encounter probability during the non-flood season would decrease. For drought magnitudes, the encounter probability would decrease (e.g., the encounter probability with the same-frequency of 100-years would reduce by 18% to 33%), even though the probability of future drought at each tributary would increase. Such analyses provide important probabilistic information to help us prepare for the upcoming extreme events. Full article
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Open AccessArticle
Changes in the Risk of Extreme Climate Events over East Asia at Different Global Warming Levels
Water 2019, 11(12), 2535; https://doi.org/10.3390/w11122535 - 30 Nov 2019
Cited by 1
Abstract
Limiting the global temperature increase to a level that would prevent “dangerous anthropogenic interference with the climate system” is the focus of intergovernmental climate negotiations, and the cost-benefit analysis to determine this level requires an understanding of how the risk associated with climate [...] Read more.
Limiting the global temperature increase to a level that would prevent “dangerous anthropogenic interference with the climate system” is the focus of intergovernmental climate negotiations, and the cost-benefit analysis to determine this level requires an understanding of how the risk associated with climate extremes varies with different warming levels. We examine daily extreme temperature and precipitation variances with continuous global warming using a non-stationary extreme value statistical model based on the Coupled Model Intercomparison Project Phase 5 (CMIP5). Our results show the probability of extreme warm and heavy precipitation events over East Asia (EA) will increase, while that of cold extremes over EA will decrease as global warming increases. A present-day 1-in-20-year heavy precipitation extreme in EA is projected to increase to 1.3, 1.6, 2.5, and 3.4 times more frequently of the current climatology, at the global mean warming levels of 1.5 °C, 2 °C, 3 °C, and 4 °C above the preindustrial era, respectively. Moreover, the relative changes in probability are larger for rarer events. These results contribute to an improved understanding of the future risk associated with climate extremes, which helps scientists create mitigation measures for global warming and facilitates policy-making. Full article
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Open AccessArticle
Assessing the Impacts of Extreme Climate Events on Vegetation Activity in the North South Transect of Eastern China (NSTEC)
Water 2019, 11(11), 2291; https://doi.org/10.3390/w11112291 - 01 Nov 2019
Abstract
Extreme climate events frequently exert serious effects on terrestrial vegetation activity. However, these effects are still uncertain in widely distributed areas with different climate zones. Transect analysis is important to understand how terrestrial vegetation responds to climate change, especially extreme climate events, by [...] Read more.
Extreme climate events frequently exert serious effects on terrestrial vegetation activity. However, these effects are still uncertain in widely distributed areas with different climate zones. Transect analysis is important to understand how terrestrial vegetation responds to climate change, especially extreme climate events, by substituting space for time. In this paper, seven extreme climate indices and the Normalized Difference Vegetation Index (NDVI) are employed to examine changes in the extreme climate events and vegetation activity. To reduce the uncertainty of the NDVI, two satellite-derived NDVI datasets, including the third generation Global Inventory Monitoring and Modeling System (GIMMS-3g) NDVI dataset and the NDVI from the National Oceanic and Atmospheric Administration (NOAA) satellites on Star Web Servers (SWS), were employed to capture changes in vegetation activity. The impacts of climate extremes on vegetation activity were then assessed over the period of 1982–2012 using the North–South Transect of Eastern China (NSTEC) as a case. The results show that vegetation activity was overall strengthened from 1982 to 2012 in the NSTEC. In addition, extreme high temperature events revealed an increased trend of approximately 5.15 days per decade, while a weakened trend (not significant) was found in extreme cold temperature events. The strengthened vegetation activities could be associated with enhanced extreme high temperature events and weakened extreme cold temperature events over the past decades in most of the NSTEC. Despite this, inversed changes were also found locally between vegetation activity and extreme climate events (e.g., in the Northeast Plain). These phenomena could be associated with differences in vegetation type, human activity, as well as the combined effects of the frequency and intensity of extreme climate events. This study highlights the importance of accounting for the vital roles of extreme climate effects on vegetation activity. Full article
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Open AccessArticle
Integrated Real-Time Flood Forecasting and Inundation Analysis in Small–Medium Streams
Water 2019, 11(5), 919; https://doi.org/10.3390/w11050919 - 01 May 2019
Cited by 2
Abstract
This study presents the application of an adaptive neuro-fuzzy inference system (ANFIS) and one dimensional (1-D) and two dimensional (2-D) hydrodynamic models to improve the problems of hydrological models currently used for flood forecasting in small–medium streams of South Korea. The optimal combination [...] Read more.
This study presents the application of an adaptive neuro-fuzzy inference system (ANFIS) and one dimensional (1-D) and two dimensional (2-D) hydrodynamic models to improve the problems of hydrological models currently used for flood forecasting in small–medium streams of South Korea. The optimal combination of input variables (e.g., rainfall and water level) in ANFIS was selected based on a statistical analysis of the observed and forecasted values. Two membership functions (MFs) and two ANFIS rules were determined by the subtractive clustering (SC) approach in the processes of training and checking. The developed ANFIS was applied to Jungrang Stream and water levels for six lead times (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 hour) were forecasted. Based on point forecasted water levels by ANFIS, 1-D section flood forecast and 2-D spatial inundation analysis were carried out. This study demonstrated that the proposed methodology can forecast flooding based only on observed rainfall and water level without extensive physical and topographic data, and can be performed in real-time by integrating point- and section flood forecasting and spatial inundation analysis. Full article
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