Special Issue "Hydro-Meteorological Hazards under Climate Change"

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

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editor

Prof. Dr. Shuo Wang
E-Mail Website
Guest Editor
The Hong Kong Polytechnic University, Hong Kong, China
Interests: hydrology; hydroclimatology; hydroclimatic extremes; climate projection; uncertainty quantification

Special Issue Information

Dear Colleagues,

Climate change is one of the most significant global challenges of the 21st century. The major concern of climate change is the resulting increase in hydro-meteorological hazards such as tropical cyclones, droughts, floods, and heatwaves, which has caused serious disruption with widespread socio-economic impacts. Therefore, there is an urgent need to better understand the causes, impacts, and mitigation measures of hydro-meteorological hazards under climate change in order to reduce the loss of life and damage to property.

The aim of this Special Issue is to gather contributions on hydro-meteorological extremes studies. The contributions to this Special Issue will encompass a broad spectrum of topics, including, but not limited to:

  • Novel approaches to identify hydro-meteorological hazards;
  • New observation and modeling tools to understand hydro-meteorological hazards;
  • Improvement of hydro-meteorological forecasting across various temporal and spatial scales;
  • Assessment of climate change impacts on hydro-meteorological hazards;
  • Projection of hydro-meteorological hazards and potentially devastating consequences;
  • Development of mitigation measures of hydro-meteorological disasters;
  • Quantification of uncertainties in hydro-meteorological hazard assessment.

Prof. Dr. Shuo Wang
Guest Editor

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 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 2000 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

  • hydrology
  • weather and climate extremes
  • hydro-meteorological hazards
  • hydroclimatic projections
  • climate downscaling
  • uncertainty quantification
  • big data analytics

Published Papers (3 papers)

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Research

Open AccessArticle
Projection of Climate Change and Consumptive Demands Projections Impacts on Hydropower Generation in the São Francisco River Basin, Brazil
Water 2021, 13(3), 332; https://doi.org/10.3390/w13030332 - 29 Jan 2021
Viewed by 600
Abstract
Climate change impacts may influence hydropower generation, especially with the intensification of extreme events and growing demand. In this study, we analyzed future hydroelectric generation using a set of scenarios considering both climate change and consumptive demands in the São Francisco River Basin. [...] Read more.
Climate change impacts may influence hydropower generation, especially with the intensification of extreme events and growing demand. In this study, we analyzed future hydroelectric generation using a set of scenarios considering both climate change and consumptive demands in the São Francisco River Basin. This project will increase consumptive demands for the coming decades. Five models from the recently released Coupled Model Intercomparison Project Phase 6 and two scenarios, SSP2-4.5 and SSP5-8.5, were considered to estimate climate change projections. The affluent natural flows, regulated flows, and the hydroelectric energy generated were estimated for four multi-purpose reservoirs considering all existing and new demands. The conjunction of scenarios indicated a possible significant reduction in water availability, increased consumptive demands, especially for irrigation, and reduced power generation. Only at the Sobradinho hydroelectric plant, the decrease ranged from −30% to −50% for the period 2021 to 2050 compared to the historical period (1901 to 2000). The results can provide insights into future energy generation and water resources management in the basin. Full article
(This article belongs to the Special Issue Hydro-Meteorological Hazards under Climate Change)
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Open AccessArticle
Comparative Analysis of Climate Change Impacts on Meteorological, Hydrological, and Agricultural Droughts in the Lake Titicaca Basin
Water 2021, 13(2), 175; https://doi.org/10.3390/w13020175 - 13 Jan 2021
Cited by 1 | Viewed by 1129
Abstract
The impact of climate change on droughts in the Lake Titicaca, Desaguadero River, and Lake Poopo basins (TDPS system) within the Altiplano region was evaluated by comparing projected 2034–2064 and observed 1984–2014 hydroclimate time series. The study used bias-corrected monthly climate projections from [...] Read more.
The impact of climate change on droughts in the Lake Titicaca, Desaguadero River, and Lake Poopo basins (TDPS system) within the Altiplano region was evaluated by comparing projected 2034–2064 and observed 1984–2014 hydroclimate time series. The study used bias-corrected monthly climate projections from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), under the Representative Concentration Pathway 8.5 (RCP8.5) emission scenarios. Meteorological, agricultural, and hydrological droughts were analyzed from the standardized precipitation, standardized soil moisture, and standardized runoff indices, respectively, the latter two estimated from a hydrological model. Under scenarios of mean temperature increases up to 3 °C and spatially diverse precipitation changes, our results indicate that meteorological, agricultural, and hydrological droughts will become more intense, frequent, and prolonged in most of the TDPS. A significant increase in the frequency of short-term agricultural and hydrological droughts (duration of 1–2 months) is also projected. The expected decline in annual rainfall and the larger evapotranspiration increase in the southern TDPS combine to yield larger projected rises in the frequency and intensity of agricultural and hydrological droughts in this region. Full article
(This article belongs to the Special Issue Hydro-Meteorological Hazards under Climate Change)
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Open AccessArticle
Tropical Cyclone Intensity Change Prediction Based on Surrounding Environmental Conditions with Deep Learning
Water 2020, 12(10), 2685; https://doi.org/10.3390/w12102685 - 25 Sep 2020
Viewed by 729
Abstract
Tropical cyclone (TC) motion has an important impact on both human lives and infrastructure. Predicting TC intensity is crucial, especially within the 24 h warning time. TC intensity change prediction can be regarded as a problem of both regression and classification. Statistical forecasting [...] Read more.
Tropical cyclone (TC) motion has an important impact on both human lives and infrastructure. Predicting TC intensity is crucial, especially within the 24 h warning time. TC intensity change prediction can be regarded as a problem of both regression and classification. Statistical forecasting methods based on empirical relationships and traditional numerical prediction methods based on dynamical equations still have difficulty in accurately predicting TC intensity. In this study, a prediction algorithm for TC intensity changes based on deep learning is proposed by exploring the joint spatial features of three-dimensional (3D) environmental conditions that contain the basic variables of the atmosphere and ocean. These features can also be interpreted as fused characteristics of the distributions and interactions of these 3D environmental variables. We adopt a 3D convolutional neural network (3D-CNN) for learning the implicit correlations between the spatial distribution features and TC intensity changes. Image processing technology is also used to enhance the data from a small number of TC samples to generate the training set. Considering the instantaneous 3D status of a TC, we extract deep hybrid features from TC image patterns to predict 24 h intensity changes. Compared to previous studies, the experimental results show that the mean absolute error (MAE) of TC intensity change predictions and the accuracy of the classification as either intensifying or weakening are both significantly improved. The results of combining features of high and low spatial layers confirm that considering the distributions and interactions of 3D environmental variables is conducive to predicting TC intensity changes, thus providing insight into the process of TC evolution. Full article
(This article belongs to the Special Issue Hydro-Meteorological Hazards under Climate Change)
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