Special Issue "The Drought Risk Analysis, Forecasting, and Assessment 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: closed (31 May 2020).

Special Issue Editor

Prof. Tae-Woong Kim
Website
Guest Editor
Hayang University, Department of Civil & Environmental Engineering, Kuri, South Korea
Interests: data-driven modelling for hydrological extremes; hydrological time series analysis and forecasting; hydrosystems reliability and risk analysis

Special Issue Information

Dear Colleagues,

During the last few decades, drought risk assessment and forecasting have faced rapid expansion, not only from a theoretical point of view but also in terms of affecting many application areas under climate change. The framework of drought risk analysis provides a unified and coherent approach to solve inference and decision-making problems under uncertainty due to climate change, such as hydro-meteorological modeling, drought frequency estimation, hybrid models of forecasting, and water resources management. As such, we would expect climate change to have a profound impact on drought risk and water resources.

Prof. Tae-Woong Kim
Guest Editor

Manuscript Submission Information

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Keywords

  • Drought risk assessment
  • Drought forecasting
  • Bayesian approaches to risk assessment
  • Hybrid models for forecasting

Published Papers (11 papers)

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Editorial

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Open AccessEditorial
Drought Risk Analysis, Forecasting and Assessment under Climate Change
Water 2020, 12(7), 1862; https://doi.org/10.3390/w12071862 - 29 Jun 2020
Abstract
Climate change is undoubtedly one of the world’s biggest challenges in the 21st century. Drought risk analysis, forecasting and assessment are facing rapid expansion, not only from theoretical but also practical points of view. Accurate monitoring, forecasting and comprehensive assessments are of the [...] Read more.
Climate change is undoubtedly one of the world’s biggest challenges in the 21st century. Drought risk analysis, forecasting and assessment are facing rapid expansion, not only from theoretical but also practical points of view. Accurate monitoring, forecasting and comprehensive assessments are of the utmost importance for reliable drought-related decision-making. The framework of drought risk analysis provides a unified and coherent approach to solving inference and decision-making problems under uncertainty due to climate change, such as hydro-meteorological modeling, drought frequency estimation, hybrid models of forecasting and water resource management. This Special Issue will provide researchers with a summary of the latest drought research developments in order to identify and understand the profound impacts of climate change on drought risks and water resources. The ten peer-reviewed articles collected in this Special Issue present novel drought monitoring and forecasting approaches, unique methods for drought risk estimation and creative frameworks for environmental change assessment. These articles will serve as valuable references for future drought-related disaster mitigations, climate change interconnections and food productivity impacts. Full article
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Research

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Open AccessArticle
Comprehensive Drought Assessment Using a Modified Composite Drought index: A Case Study in Hubei Province, China
Water 2020, 12(2), 462; https://doi.org/10.3390/w12020462 - 09 Feb 2020
Cited by 1
Abstract
Under the background of global climate change, accurate monitoring and comprehensive assessment of droughts are of great practical significance to sustain agricultural development. Considering multiple causes and the complexity of the occurrence of drought, this paper employs multiple input variables, i.e., precipitation, temperature, [...] Read more.
Under the background of global climate change, accurate monitoring and comprehensive assessment of droughts are of great practical significance to sustain agricultural development. Considering multiple causes and the complexity of the occurrence of drought, this paper employs multiple input variables, i.e., precipitation, temperature, evaporation, and surface water content to construct a modified composite drought index (MCDI) using a series of mathematical calculation methods. The derived MCDI was calculated as a multivariate drought index to measure the drought conditions and verify its accuracy in Hubei Province in China. Compared with the existing multivariate drought index, i.e., meteorological drought composite index (CI), there was a high level of correlation in monitoring drought events in Hubei Province. Moreover, according to the drought historical record, the significant drought processes monitored by the MCDI were consistent with actual drought conditions. Furthermore, temporal and spatial analysis of drought in Hubei Province was performed based on the monitoring results of the MCDI. This paper generalizes the development of the MCDI as a new method for comprehensive assessments of regional drought. Full article
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Open AccessArticle
Effect of Climate Change on Maize Yield in the Growing Season: A Case Study of the Songliao Plain Maize Belt
Water 2019, 11(10), 2108; https://doi.org/10.3390/w11102108 - 10 Oct 2019
Cited by 2
Abstract
Based on the 1965–2017 climate data of 18 meteorological stations in the Songliao Plain maize belt, the Coupled Model Intercomparision Project (CMIP5) data, and the 1998–2017 maize yield data, the drought change characteristics in the study area were analyzed by using the standardized [...] Read more.
Based on the 1965–2017 climate data of 18 meteorological stations in the Songliao Plain maize belt, the Coupled Model Intercomparision Project (CMIP5) data, and the 1998–2017 maize yield data, the drought change characteristics in the study area were analyzed by using the standardized precipitation evapotranspiration index (SPEI) and the Mann–Kendall mutation test; furthermore, the relationship between meteorological factors, drought index, and maize climate yield was determined. Finally, the maize climate yields under 1.5 °C and 2.0 °C global warming scenarios were predicted. The results revealed that: (1) from 1965 to 2017, the study area experienced increasing temperature, decreasing precipitation, and intensifying drought trends; (2) the yield of the study area showed a downward trend from 1998 to 2017. Furthermore, the climate yield was negatively correlated with temperature, positively correlated with precipitation, and positively correlated with SPEI-1 and SPEI-3; and (3) under the 1.5 °C and the 2.0 °C global warming scenarios, the temperature and the precipitation increased in the maize growing season. Furthermore, under the studied global warming scenarios, the yield changes predicted by multiple regression were −7.7% and −15.9%, respectively, and the yield changes predicted by one-variable regression were −12.2% and −21.8%, respectively. Full article
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Open AccessArticle
Hydrologic Risk Assessment of Future Extreme Drought in South Korea Using Bivariate Frequency Analysis
Water 2019, 11(10), 2052; https://doi.org/10.3390/w11102052 - 30 Sep 2019
Cited by 5
Abstract
Recently, climate change has increased the frequency of extreme weather events. In South Korea, extreme droughts are frequent and cause serious damage. To identify the risk of extreme drought, we need to calculate the hydrologic risk using probabilistic analysis methods. In particular, future [...] Read more.
Recently, climate change has increased the frequency of extreme weather events. In South Korea, extreme droughts are frequent and cause serious damage. To identify the risk of extreme drought, we need to calculate the hydrologic risk using probabilistic analysis methods. In particular, future hydrologic risk of extreme drought should be compared to that of the control period. Therefore, this study quantitatively assessed the future hydrologic risk of extreme drought in South Korea according to climate change scenarios based on the representative concentration pathway (RCP) 8.5. A threshold level method was applied to observation-based rainfall data and climate change scenario-based future rainfall data to identify drought events and extract drought characteristics. A bivariate frequency analysis was then performed to estimate the return period considering both duration and severity. The estimated return periods were used to calculate and compare hydrologic risks between the control period and the future. Results indicate that the average duration of drought events for the future was similar with that for the control period, however, the average severity increased in most future scenarios. In addition, there was decreased risk of maximum drought events in the Yeongsan River basin in the future, while there was increased risk in the Nakdong River basin. The median of risk of extreme drought in the future was calculated to be larger than that of the maximum drought in the control period. Full article
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Open AccessArticle
Stochastic Model for Drought Forecasting in the Southern Taiwan Basin
Water 2019, 11(10), 2041; https://doi.org/10.3390/w11102041 - 29 Sep 2019
Cited by 4
Abstract
The global rainfall pattern has changed because of climate change, leading to numerous natural hazards, such as drought. Because drought events have led to many disasters globally, it is necessary to create an early warning system. Drought forecasting is an important step toward [...] Read more.
The global rainfall pattern has changed because of climate change, leading to numerous natural hazards, such as drought. Because drought events have led to many disasters globally, it is necessary to create an early warning system. Drought forecasting is an important step toward developing such a system. In this study, we utilized the stochastic, autoregressive integrated moving average (ARIMA) model to predict drought conditions based on the standardized precipitation index (SPI) in southern Taiwan. We employed data from 1967 to 2006 to train the model and data from 2007 to 2017 for model validation. The results showed that the coefficients of determination (R2) were over 0.80 at each station, and the root-mean-square error and mean absolute error were sufficiently low, indicating that the ARIMA model is effective and adequate for our stations. Finally, we employed the ARIMA model to forecast future drought conditions from 2019 to 2022. The results yielded relatively low SPI values in southern Taiwan in future summers. In summary, we successfully constructed an ARIMA model to forecast drought. The information in this study can act as a reference for water resource management. Full article
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Open AccessArticle
Attribution Analysis of Hydrological Drought Risk Under Climate Change and Human Activities: A Case Study on Kuye River Basin in China
Water 2019, 11(10), 1958; https://doi.org/10.3390/w11101958 - 20 Sep 2019
Cited by 1
Abstract
This study conducted quantitative diagnosis on the impact of climate change and human activities on drought risk. Taking the Kuye river basin (KRB) in China as the research area, we used variation point diagnosis, simulation of precipitation and runoff, drought risk assessment, and [...] Read more.
This study conducted quantitative diagnosis on the impact of climate change and human activities on drought risk. Taking the Kuye river basin (KRB) in China as the research area, we used variation point diagnosis, simulation of precipitation and runoff, drought risk assessment, and attribution quantification. The results show that: (1) the annual runoff sequence of KRB changed significantly after 1979, which was consistent with the introduction of large-scale coal mining; (2) under the same drought recurrence period, the drought duration and severity in the human activity stage were significantly worse than in the natural and simulation stages, indicating that human activities changed the drought risk in this area; and (3) human activities had little impact on drought severity in the short duration and low recurrence period, but had a greater impact in the long duration and high recurrence period. These results provide scientific guidance for the management, prevention, and resistance of drought; and guarantee sustainable economic and social development in the KRB. Full article
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Open AccessArticle
Analysis of Drought-Sensitive Areas and Evolution Patterns through Statistical Simulations of the Indian Ocean Dipole Mode
Water 2019, 11(6), 1302; https://doi.org/10.3390/w11061302 - 23 Jun 2019
Cited by 1
Abstract
In this study, we investigated extreme droughts in the Indochina peninsula and their relationship with the Indian Ocean Dipole (IOD) mode. Areas most vulnerable to drought were analyzed via statistical simulations of the IOD based on historical observations. Results of the long-term trend [...] Read more.
In this study, we investigated extreme droughts in the Indochina peninsula and their relationship with the Indian Ocean Dipole (IOD) mode. Areas most vulnerable to drought were analyzed via statistical simulations of the IOD based on historical observations. Results of the long-term trend analysis indicate that areas with increasing spring (March–May) rainfall are mainly distributed along the eastern coast (Vietnam) and the northwestern portions of the Indochina Peninsula (ICP), while Central and Northern Laos and Northern Cambodia have witnessed a reduction in spring rainfall over the past few decades. This trend is similar to that of extreme drought. During positive IOD years, the frequency of extreme droughts was reduced throughout Vietnam and in the southwestern parts of China, while increased drought was observed in Cambodia, Central Laos, and along the coastline adjacent to the Myanmar Sea. Results for negative IOD years were similar to changes observed for positive IOD years; however, the eastern and northern parts of the ICP experienced reduced droughts. In addition, the results of the statistical simulations proposed in this study successfully simulate drought-sensitive areas and evolution patterns of various IOD changes. The results of this study can help improve diagnostic techniques for extreme droughts in the ICP. Full article
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Open AccessArticle
Seasonal Drought Pattern Changes Due to Climate Variability: Case Study in Afghanistan
Water 2019, 11(5), 1096; https://doi.org/10.3390/w11051096 - 25 May 2019
Cited by 23
Abstract
We assessed the changes in meteorological drought severity and drought return periods during cropping seasons in Afghanistan for the period of 1901 to 2010. The droughts in the country were analyzed using the standardized precipitation evapotranspiration index (SPEI). Global Precipitation Climatology Center rainfall [...] Read more.
We assessed the changes in meteorological drought severity and drought return periods during cropping seasons in Afghanistan for the period of 1901 to 2010. The droughts in the country were analyzed using the standardized precipitation evapotranspiration index (SPEI). Global Precipitation Climatology Center rainfall and Climate Research Unit temperature data both at 0.5° resolutions were used for this purpose. Seasonal drought return periods were estimated using the values of the SPEI fitted with the best distribution function. Trends in climatic variables and SPEI were assessed using modified Mann–Kendal trend test, which has the ability to remove the influence of long-term persistence on trend significance. The study revealed increases in drought severity and frequency in Afghanistan over the study period. Temperature, which increased up to 0.14 °C/decade, was the major factor influencing the decreasing trend in the SPEI values in the northwest and southwest of the country during rice- and corn-growing seasons, whereas increasing temperature and decreasing rainfall were the cause of a decrease in SPEI during wheat-growing season. We concluded that temperature plays a more significant role in decreasing the SPEI values and, therefore, more severe droughts in the future are expected due to global warming. Full article
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Open AccessArticle
Future Hydrological Drought Risk Assessment Based on Nonstationary Joint Drought Management Index
Water 2019, 11(3), 532; https://doi.org/10.3390/w11030532 - 14 Mar 2019
Cited by 4
Abstract
As the environment changes, the stationarity assumption in hydrological analysis has become questionable. If nonstationarity of an observed time series is not fully considered when handling climate change scenarios, the outcomes of statistical analyses would be invalid in practice. This study established bivariate [...] Read more.
As the environment changes, the stationarity assumption in hydrological analysis has become questionable. If nonstationarity of an observed time series is not fully considered when handling climate change scenarios, the outcomes of statistical analyses would be invalid in practice. This study established bivariate time-varying copula models for risk analysis based on the generalized additive models in location, scale, and shape (GAMLSS) theory to develop the nonstationary joint drought management index (JDMI). Two kinds of daily streamflow data from the Soyang River basin were used; one is that observed during 1976–2005, and the other is that simulated for the period 2011–2099 from 26 climate change scenarios. The JDMI quantified the multi-index of reliability and vulnerability of hydrological drought, both of which cause damage to the hydrosystem. Hydrological drought was defined as the low-flow events that occur when streamflow is equal to or less than Q80 calculated from observed data, allowing future drought risk to be assessed and compared with the past. Then, reliability and vulnerability were estimated based on the duration and magnitude of the events, respectively. As a result, the JDMI provided the expected duration and magnitude quantities of drought or water deficit. Full article
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Open AccessArticle
Changes in Future Drought with HadGEM2-AO Projections
Water 2019, 11(2), 312; https://doi.org/10.3390/w11020312 - 12 Feb 2019
Cited by 4
Abstract
The standardized precipitation index (SPI)—a meteorological drought index—uses various reference precipitation periods. Generally, drought projections using future climate change scenarios compare reference SPIs between baseline and future climates. Here, future drought was projected based on reference precipitation under the baseline climate to quantitatively [...] Read more.
The standardized precipitation index (SPI)—a meteorological drought index—uses various reference precipitation periods. Generally, drought projections using future climate change scenarios compare reference SPIs between baseline and future climates. Here, future drought was projected based on reference precipitation under the baseline climate to quantitatively compare changes in the frequency and severity of future drought. High-resolution climate change scenarios were produced using HadGEM2-AO General Circulation Model (GCM) scenarios for Korean weather stations. Baseline and future 3-month cumulative precipitation data were fitted to gamma distribution; results showed that precipitation of future climate is more than the precipitation of the baseline climate. When future precipitation was set as that of the baseline climate instead of the future climate, results indicated that drought intensity and frequency will decrease because the non-exceedance probability for the same precipitation is larger in the baseline climate than in future climate. However, due to increases in regional precipitation variability over time, some regions with opposite trends were also identified. Therefore, it is necessary to understand baseline and future climates in a region to better design resilience strategies and mechanisms that can help cope with future drought. Full article
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Open AccessArticle
Atmospheric Teleconnection-Based Extreme Drought Prediction in the Core Drought Region in China
Water 2019, 11(2), 232; https://doi.org/10.3390/w11020232 - 30 Jan 2019
Cited by 4
Abstract
This paper aims to improve the predictability of extreme droughts in China by identifying their relationship with atmospheric teleconnection patterns (ATPs). Firstly, a core drought region (CDR) is defined based on historical drought analysis to investigate possible prediction methods. Through the investigation of [...] Read more.
This paper aims to improve the predictability of extreme droughts in China by identifying their relationship with atmospheric teleconnection patterns (ATPs). Firstly, a core drought region (CDR) is defined based on historical drought analysis to investigate possible prediction methods. Through the investigation of the spatial-temporal characteristics of spring drought using a modified Mann–Kendall test, the CDR is found to be under a decadal drying trend. Using principal component analysis, four principal components (PCs), which explain 97% of the total variance, are chosen out of eight teleconnection indices. The tree-based model reveals that PC1 and PC2 can be divided into three groups, in which extreme spring drought (ESD) frequency differs significantly. The results of Poisson regression on ESD and PCs showed good predictive performance with R-squared value larger than 0.8. Furthermore, the results of applying the neural networks for PCs showed a significant improvement in the issue of under-estimation of the upper quartile group in ESD, with a high coefficient of determination of 0.91. This study identified PCs of large-scale ATPs that are candidate parameters for ESD prediction in the CDR. We expect that our findings can be helpful in undertaking mitigation measures for ESD in China. Full article
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