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Hydro-Meteorological Hazards under Climate Change

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 40494

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Special Issue Editor

Dr. Shuo Wang

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Guest Editor

Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
Interests: water resources; climate change; hydrology; hydroclimatology; weather and climate extremes

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

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Keywords

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

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Published Papers (5 papers)

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Research

26 pages, 8368 KiB

Open AccessArticle

Using the WWF Water Risk Filter to Screen Existing and Projected Hydropower Projects for Climate and Biodiversity Risks

by Jeffrey J. Opperman, Rafael R. Camargo, Ariane Laporte-Bisquit, Christiane Zarfl and Alexis J. Morgan

Water 2022, 14(5), 721; https://doi.org/10.3390/w14050721 - 24 Feb 2022

Cited by 12 | Viewed by 13879

Abstract

Climate change is predicted to drive various changes in hydrology that can translate into risks for river ecosystems and for those who manage rivers, such as for hydropower. Here we use the WWF Water Risk Filter (WRF) and geospatial analysis to screen hydropower projects, both existing (2488 dams) and projected (3700 dams), for a variety of risks at a global scale, focusing on biodiversity risks, hydrological risks (water scarcity and flooding), and how those hydrological risks may shift with climate change, based on three scenarios. Approximately 26% of existing hydropower dams and 23% of projected dams are within river basins that currently have medium to very high risk of water scarcity; 32% and 20% of the existing and projected dams, respectively, are projected to have increased risk by 2050 due to climate change. For flood risk, 75% of existing dams and 83% of projected dams are within river basins with medium to very high risk, and the proportion of hydropower dams in basins with the highest levels of flood risk is projected to increase by nearly twenty times (e.g., from 2% to 36% of dams). In addition, a large proportion of existing (76%) and projected hydropower dams (93%) are located in river basins with high or very high freshwater biodiversity importance. This is a high-level screening, intended to elucidate broad patterns of risk to increase awareness, highlight trends, and guide more detailed studies. Full article

(This article belongs to the Special Issue Hydro-Meteorological Hazards under Climate Change)

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28 pages, 9458 KiB

Open AccessArticle

Stochastic Flood Risk Assessment under Climate Change Scenarios for Toronto, Canada Using CAPRA

by Daniela Rincón, Juan Felipe Velandia, Ioannis Tsanis and Usman T. Khan

Water 2022, 14(2), 227; https://doi.org/10.3390/w14020227 - 13 Jan 2022

Cited by 11 | Viewed by 5043

Abstract

Amongst all natural disasters, floods have the greatest economic and social impacts worldwide, and their frequency is expected to increase due to climate change. Therefore, improved flood risk assessment is important for implementing flood mitigation measures in urban areas. The increasing need for quantifying the impacts of flooding have resulted in the development of methods for flood risk assessment. The aim of this study was to quantify flood risk under climate change scenarios in the Rockcliffe area within the Humber River watershed in Toronto, Canada, by using the Comprehensive Approach to Probabilistic Risk Assessment (CAPRA) method. CAPRA is a platform for stochastic disaster risk assessment that allows for the characterization of uncertainty in the underlying numerical models. The risk was obtained by integrating the (i) flood hazard, which considered future rainfall based on the Representative Concentration Pathways (RCPs 2.6, 4.5, 6.0, and 8.5) for three time periods (short-term: 2020–2049, medium-term: 2040–2069, and long-term: 2070–2099); (ii) exposed assets within a flood-prone region; (iii) vulnerability functions, which quantified the damage to an asset at different hazard levels. The results revealed that rainfall intensities are likely to increase during the 21st century in the study area, leading to an increase in flood hazards, higher economic costs, and social impacts for the majority of the scenarios. The highest impacts were found for the climate scenario RCP 8.5 for the long-term period and the lowest for RCP 4.5 for the short-term period. The results from this modeling approach can be used for planning purposes in a floodplain management study. The modeling approach identifies critical areas that need to be protected to mitigate future flood risks. Higher resolution climate change and field data are needed to obtain detailed results required for a final design that will mitigate these risks. Full article

(This article belongs to the Special Issue Hydro-Meteorological Hazards under Climate Change)

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25 pages, 7621 KiB

Open AccessEditor’s ChoiceArticle

Projection of Climate Change and Consumptive Demands Projections Impacts on Hydropower Generation in the São Francisco River Basin, Brazil

by Marx Vinicius Maciel da Silva, Cleiton da Silva Silveira, José Micael Ferreira da Costa, Eduardo Sávio Passos Rodrigues Martins and Francisco das Chagas Vasconcelos Júnior

Water 2021, 13(3), 332; https://doi.org/10.3390/w13030332 - 29 Jan 2021

Cited by 21 | Viewed by 3899

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. 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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24 pages, 5783 KiB

Open AccessEditor’s ChoiceArticle

Comparative Analysis of Climate Change Impacts on Meteorological, Hydrological, and Agricultural Droughts in the Lake Titicaca Basin

by Ricardo Zubieta, Jorge Molina-Carpio, Wilber Laqui, Juan Sulca and Mercy Ilbay

Water 2021, 13(2), 175; https://doi.org/10.3390/w13020175 - 13 Jan 2021

Cited by 23 | Viewed by 8676

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 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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16 pages, 2253 KiB

Open AccessArticle

Tropical Cyclone Intensity Change Prediction Based on Surrounding Environmental Conditions with Deep Learning

by Xin Wang, Wenke Wang and Bing Yan

Water 2020, 12(10), 2685; https://doi.org/10.3390/w12102685 - 25 Sep 2020

Cited by 33 | Viewed by 4511

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 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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