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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Water Management".

Deadline for manuscript submissions: closed (30 May 2021) | Viewed by 12703

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Special Issue Editors

Dr. Eun-Sung Chung

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Guest Editor

Department of Civil Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea
Interests: climate change; hydrologic modeling; multicriteria decision making method; robust decision making; urban hydrology; water resources management
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Dr. Shamsuddin Shahid

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Guest Editor

Department of Hydraulics & Hydrology, Faculty of Civil Engineering, M46-332, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia
Interests: water resources ranagement; groundwater hydrology; climate variability and changes; climate change impacts on hydrological processes and water resources; droughts and water scarcity; hydrological data analysis and modeling; and hydrological hazards and risk mapping
Special Issues, Collections and Topics in MDPI journals

Dr. Kamal Ahmed

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Guest Editor

Department of Water Resources Management, Lasbella University of Water, Agriculture and Marine Sciences, Pakistan
Interests: hydrological disasters; climate change variability and changes; adaptation and mitigation to climate change; climate downscaling and projections

Special Issue Information

Dear Colleagues,

Global-warming-induced climate change has become an inevitable reality all over the world. Rising temperature and changing precipitation pattern have changed the hydrological processes and made all kinds of hydrological hazards (floods, droughts, water stress, water pollution, soil erosion, groundwater depletion and ecological disturbance, etc.) more recurrent and severe in most parts of the globe. Many scientists have concluded that the stationarity of the global climate is doubtful, and we must carefully prepare for the unexpected impact of climate change through various pathways. The shared socioeconomic pathways (SSPs) scenarios for the IPCC 6th assessment report have been newly developed by combining the scenarios of greenhouse gas emissions with the scenarios of socioeconomic changes and climate policies. Many research centers have already generated future climate projections based on SSP scenarios. This Special Issue will report on state-of-the-art techniques and their global applications for the assessment of impacts and adaptation to climate change on hydrology and water resources. Reporting possible changes in hydrological hazards at local, regional, and global scales under different climate change scenarios, particularly SSP scenarios and the adaptation measures that should be taken to mitigate the impacts under new scenarios will be the major focus of this issue. Although there have been plenty of articles on this theme for the past several decades, the subjects related to climate change impacts and adaptation to hydrology should be continuously studied due to their importance. It is also important to reanalyze the existing data and update the previous findings based on recent scenarios.

Prof. Eun-Sung Chung
Prof. Shamsuddin Shahid
Dr. Kamal Ahmed
Guest Editors

Manuscript Submission Information

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Keywords

climate change
climate change scenarios
drought
ecology
flood
general circulation model
hydrology
shared socioeconomic pathways
water quality

Published Papers (4 papers)

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Research

14 pages, 1761 KiB

Open AccessArticle

Agriculture Adaptation Options for Flood Impacts under Climate Change—A Simulation Analysis in the Dajia River Basin

by Hsin-Chi Li, Yi-Hua Hsiao, Chia-Wei Chang, Yung-Ming Chen and Lee-Yaw Lin

Sustainability 2021, 13(13), 7311; https://doi.org/10.3390/su13137311 - 30 Jun 2021

Cited by 7 | Viewed by 3118

Abstract

Adaptation to climate change has become an important matter of discussion in the world in response to the growing rate of global warming. In recent years, many countries have gradually adopted adaption strategies to climate change, with the aim of reducing the impact of climate variabilities. Taiwan is in a geographical location that is prone to natural disasters and is thus very vulnerable to climate change. To explore an appropriate method for Taiwan to adapt to climate change, this study took Dajia River Basin as the simulation site to explore the potential climate change impact in the area. An impact study was conducted to identify the trend of flooding under climate change scenarios. We used the SOBEK model to simulate downstream inundation caused by the worst typhoon event of the 20th century (1979–2003) and for typhoon events that might occur at the end of the 21st century (2075–2099) in Taiwan, according to the climate change scenario of representative concentration pathways 8.5 (RCP8.5) and dynamical downscaling rainfall data. Agricultural lands were found to be the most affected areas among all land types, and the flooded area was forecast to increase by 1.89 times by the end of 21st century, when compared to the end of 20th century. In this study, upland crops, which are affected the most by flooding, were selected as the adaptation targets for this site and multiple engineering and non-engineering options were presented to reduce the potential climate change impacts. With respect to the results, we found that all adaptation options, even when considering the cost, yield higher benefits than the “do-nothing” option. Among the adaptation options presented for this site, utilizing engineering methods with non-engineering methods show the best result in effectively reducing the impact of climate change, with the benefit-to-cost ratio being around 1.16. This study attempts to explore useful and effective assessment methods for providing sound scientific and economic evidence for the selection of adequate adaption options for flood impacts in agriculture in the planning phase. Full article

(This article belongs to the Special Issue Climate Change Impacts on Hydrology, Water Quality and Ecology)

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16 pages, 2487 KiB

Open AccessArticle

Projection of Water Availability and Sustainability in Nigeria Due to Climate Change

by Mohammed Sanusi Shiru, Shamsuddin Shahid and Inhwan Park

Sustainability 2021, 13(11), 6284; https://doi.org/10.3390/su13116284 - 02 Jun 2021

Cited by 16 | Viewed by 3112

Abstract

This study projects water availability and sustainability in Nigeria due to climate change. This study used Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage data (TWS), Global Precipitation Climatology Center (GPCC) precipitation data and Climate Research Unit (CRU) temperature data. Four general circulation models (GCMs) of the Coupled Model Intercomparison Project 5 were downscaled using the best of four downscaling methods. Two machine learning (ML) models, RF and SVM, were developed to simulate GRACE TWS data for the period 2002–2016 and were then used for the projection of spatiotemporal changes in TWS. The projected TWS data were used to assess the spatiotemporal changes in water availability and sustainability based on the reliability–resiliency–vulnerability (RRV) concept. This study revealed that linear scaling was the best for downscaling over Nigeria. RF had better performance than SVM in modeling TWS for the study area. This study also revealed there would be decreases in water storage during the wet season (June–September) and increases in the dry season (January–May). Decreases in projected water availability were in the range of 0–12 mm for the periods 2010–2039, 2040–2069, and 2070–2099 under RCP2.6 and in the range of 0–17 mm under RCP8.5 during the wet season. Spatially, annual changes in water storage are expected to increase in the northern part and decrease in the south, particularly in the country’s southeast. Groundwater sustainability was higher during the period 2070–2099 under all RCPs compared to the other periods and this can be attributed to the expected increases in rainfall during this period. Full article

(This article belongs to the Special Issue Climate Change Impacts on Hydrology, Water Quality and Ecology)

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20 pages, 4013 KiB

Open AccessArticle

Volatility in Rainfall and Predictability of Droughts in Northwest Bangladesh

by Mohammad Ahsan Uddin, ASM Maksud Kamal, Shamsuddin Shahid and Eun-Sung Chung

Sustainability 2020, 12(23), 9810; https://doi.org/10.3390/su12239810 - 24 Nov 2020

Cited by 5 | Viewed by 2619

Abstract

This study was conducted to evaluate the variability, trends, volatility, and transition patterns of rainfall in drought-prone northwest Bangladesh. Daily rainfall recorded at five stations for the period 1959–2018 were used for this purpose. Non-parametric tests of variability changes, a modified Mann–Kendall trend test, innovative trend analysis (ITA), a generalized autoregressive conditional heteroscedasticity (GARCH)–jump model, and a Markov chain (MC) were used to assess the variability changes, trends, volatility, and transitions in rainfall to understand the possibility of the persistence of droughts and their predictability. The results showed an overall decrease of variability in annual and seasonal rainfall, but an increase in mean pre-monsoon rainfall and a decrease in mean monsoon rainfall. This caused a decrease in pre-monsoon droughts, but few changes in monsoon droughts. The ITA and rainfall anomaly analysis revealed high temporal variability and, thus, rapid shifts in rainfall regimes, which were also supported by the volatility dynamics and time-varying jumps from the GARCH–jump model and the rapid changes in drought index from the MC analysis. Therefore, the lack of drought in recent years cannot be considered as an indicator of declining droughts in the region. Full article

(This article belongs to the Special Issue Climate Change Impacts on Hydrology, Water Quality and Ecology)

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19 pages, 5368 KiB

Open AccessArticle

Uncertainty Analysis of Monthly Precipitation in GCMs Using Multiple Bias Correction Methods under Different RCPs

by Young Hoon Song, Eun-Sung Chung and Mohammed Sanusi Shiru

Sustainability 2020, 12(18), 7508; https://doi.org/10.3390/su12187508 - 11 Sep 2020

Cited by 26 | Viewed by 3057

Abstract

This study quantified the uncertainties in historical and future average monthly precipitation based on different bias correction methods, General Circulation Models (GCMs), Representative Concentration Pathways (RCPs), projection periods, and locations within the study area (i.e., the coastal and inland areas of South Korea). The GCMs were downscaled using deep learning, random forest, and nine quantile mapping bias correction methods for 22 gauge stations in South Korea. Data from the Korean Meteorology Administration (1970–2005) were used as the reference data in this study. Two statistical measures, the standard deviation and interquartile range, were used to quantify the uncertainties. The probability distribution density was used to assess the similarity/variation in rainfall distributions. For the historical period, the uncertainty in the selection of bias correction methods was greater than that in the selection of GCMs, whereas the opposite pattern was observed for the projection period. The projection period had the lowest level of uncertainty in the selection of RCP scenarios, and for the future, the uncertainly related to the time period was slightly lower than that for the other sources but was much greater than that for the RCP selection. In addition, it was clear that the level of uncertainty of inland areas is much lower than that of coastal areas. The uncertainty in the selection of the GCMs was slightly greater than that in the selection of the bias correction method. Therefore, the uncertainty in the selection of coastal areas was intermediate between the selection of bias correction methods and GCMs. This paper contributes to an improved understanding of the uncertainties in climate change projections arising from various sources. Full article

(This article belongs to the Special Issue Climate Change Impacts on Hydrology, Water Quality and Ecology)

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