Advances in Drought Monitoring, Simulation and Prediction

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Climatology".

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 21247

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Climate Prediction Center, NCEP/NWS/NOAA/U.S. Department of Commerce, 5830, University Research Court, NCWCP, College Park, MD 20740, USA
Interests: drought; hurricanes; reanalysis; teleconnections; climate forecast/change; weeks 3+4
Department of Geography, Michigan State University, East Lansing, MI 48824, USA
Interests: hydroclimatology; interaction of land and atmospheric scales; variability and predictability of climate; varations in the hydrological processes; resource management
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Special Issue Information

Dear Colleagues,

Drought is a destructive natural hazard with significant impacts on agriculture, water supply, food security and even human health. These impacts will likely become even more important in the future, especially in the ongoing global climate change context. Unlike other natural disasters, droughts are due primarily, but not only, to the lack of seasonal rainfall, and may evolve quickly anywhere, over a period of month or a season and can last for several months to seasons, and possibly even much longer, with sometimes no specific end in sight. On the other hand, recently, a new quickly evolving “flash drought” has appeared in literature and has caught everyone’s attention, even though there has been no clear accepted definition for it.  Overall, the generic term “drought” means different things to different people, as it exhibits itself and it impacts differently in meteorological, agricultural, and hydrological communities.  The real cost of climate change on global humanity and financial markets has not been fully researched yet. However, drought can be very costly, as can be seen from an NCDC (National Climatic Data Center, US) study, which reports that, of the more than 90 “Billion-Dollar-plus” weather- and climate-related disasters from 1980 to 2008; 14 of them were drought- and wildfire-related. The most recent drought and wildfire calamity during 2019 in eastern Australia had devastating consequences and is the worst natural disaster in the country’s history, with severe impacts on many fronts, including human, economic, wildlife, etc.

While (low) precipitation and (high) temperature are the two fundamental contributory factors to any drought, the importance of soil moisture, evaporation, transpiration, runoff, and other related factors and indices cannot be ignored. Over the last few decades, many exciting advances have been made around the world in monitoring, detection and forecasting of drought, including the more recent use of neural networks, machine learning and modern artificial intelligence methods incorporating the many new available drought relevant data sets. In this new Special Issue of the journal Atmosphere from MDPI (Multidisciplinary Digital Publishing Institute), the pioneer in open access journals, we are looking forward to receiving original papers that document “Advances in Drought Monitoring, Simulation and Prediction".  Submissions will appear in publication upon peer review. We hope that this Special Issue, upon completion, will showcase the state of the art in drought research from authors around the world.  The Special Issue is now open for submissions. 

Dr. Muthuvel Chelliah
Dr. Lifeng Luo
Guest Editors

Manuscript Submission Information

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Keywords

  • drought
  • precipitation
  • rainfall
  • snowfall
  • high temperatures
  • soil moisture
  • evaporation
  • evapotranspiration
  • runoff
  • climate change

Published Papers (7 papers)

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22 pages, 4169 KiB  
Article
Changes of Extreme Agro-Climatic Droughts and Their Impacts on Grain Yields in Rain-Fed Agricultural Regions in China over the Past 50 Years
by Min Liu, Ying Guo, Yanfang Wang and Jing Hao
Atmosphere 2022, 13(1), 4; https://doi.org/10.3390/atmos13010004 - 21 Dec 2021
Cited by 5 | Viewed by 2687
Abstract
Climate change and climate extremes and their impacts on agriculture, water resources, and ecosystems have become important issues globally. Agricultural sustainability and food security are facing unprecedented challenges due to the increasing occurrence of extreme climatic events, including, notably, extreme droughts in recent [...] Read more.
Climate change and climate extremes and their impacts on agriculture, water resources, and ecosystems have become important issues globally. Agricultural sustainability and food security are facing unprecedented challenges due to the increasing occurrence of extreme climatic events, including, notably, extreme droughts in recent years in China. In this study, a threshold determination model of extreme agro-climatic droughts (EADs) was built based on the cumulative probability distribution functions (CDF) of an agricultural drought index—the consecutive days without available precipitation (CDWAP). The CDWAP was established by combining meteorological data with the characteristics of cropping patterns and the water requirement in different growing periods of crops. The CDF of CDWAP was obtained based on the relationship of CDWAP and its occurrence frequency. Based on the model, the spatial pattern of the thresholds of EADs and the threshold exceedance time series of EADs in 500 meteorological stations were obtained, and then changes in the frequencies and intensities of EADs in China and their impacts on grain yields in rain-fed regions during the past 50 years were analyzed. The results follow: (1) The threshold value of EADs in China gradually increased from southeast to northwest. The stations of the highest value were located in the Northwest China, with the CDWAP more than 60 days, while the lowest value was in the middle reaches of the Yangzi River, with the CDWAP less than 16 days. (2) The frequencies and intensities of the EADs increased mostly in the east areas of the Hu Huanyong line, which was also the main agricultural production region in China. The North China (NC) and Southwest China (SW) regions showed the highest increasing rates of the EADs; their frequencies and intensities were 11.3% and 2.2%, respectively, for the NC region, and 9.3% and 2.7%, respectively, for the SW region. (3) Case studies in the NC, SW, and SE regions indicated that there was a negative correlation between grain yields and EAD frequency and intensity; i.e., the low grain yields often occurred in the year with relatively higher frequency or/and stronger intensity of EADs. The correlation coefficients of grain yield and EAD were generally greater than that of merely extreme climatic droughts; therefore, the study of EAD is necessary when researching the impacts of extreme drought events on grain yield. Full article
(This article belongs to the Special Issue Advances in Drought Monitoring, Simulation and Prediction)
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22 pages, 9735 KiB  
Article
Future Projection of Drought Vulnerability over Northeast Provinces of Iran during 2021–2100
by Iman Babaeian, Atefeh Erfani Rahmatinia, Alireza Entezari, Mohammad Baaghideh, Mohammad Bannayan Aval and Maral Habibi
Atmosphere 2021, 12(12), 1704; https://doi.org/10.3390/atmos12121704 - 20 Dec 2021
Cited by 4 | Viewed by 2895 | Correction
Abstract
Future projection of drought vulnerability is vital for northern provinces of Iran, including North Khorasan, Khorasan-Razavi, and South Khorasan, due to the highly dependent of their economy on agriculture. The study is motivated by the fact that no research has been conducted to [...] Read more.
Future projection of drought vulnerability is vital for northern provinces of Iran, including North Khorasan, Khorasan-Razavi, and South Khorasan, due to the highly dependent of their economy on agriculture. The study is motivated by the fact that no research has been conducted to project the future Drought Vulnerability Index (DVI). DVI consist of three components of exposure, sensitivity, and adaptation capacity. More exposure levels of drought, higher sensitivity value, and lower adaptation capacity lead to a higher amount of vulnerability. Combined ERA-Interim-observation meteorological data, CMIP5 models under RCP4.5 and RCP8.5 scenarios, and national census data are used to estimate DVI in the past and future periods. CanESM2, GFDL-ESM2M, and CNRM-CM5 General Circulation Model (GCM) are selected from CMIP5 based on Taylor diagram results. The delta-change technique was selected for statistical downscaling of GCM outputs because it is most widely used. The study period is regarded as 1986–2005 as observation and four future 20-years periods during 2021–2100. Results indicated that the dissipation of the class of “very low” vulnerability is eminent in the near future period of 2021–2040 under the RCP4.5 scenario, and all provinces would experience a new worse class of “very high” vulnerability at 2081–2100, both under RCP4.5 and RCP8.5 scenarios. Full article
(This article belongs to the Special Issue Advances in Drought Monitoring, Simulation and Prediction)
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18 pages, 3738 KiB  
Article
A Study on the Appropriateness of the Drought Index Estimation Method Using Damage Data from Gyeongsangnamdo, South Korea
by Youngseok Song and Moojong Park
Atmosphere 2021, 12(8), 998; https://doi.org/10.3390/atmos12080998 - 2 Aug 2021
Cited by 3 | Viewed by 1891
Abstract
Drought is one of the disasters that causes the most extensive and severe damage. Therefore, drought prevention must be performed for administrative districts at the national level rather than the individual level. This study proposes a drought index estimation method for Gyeongsangnamdo, South [...] Read more.
Drought is one of the disasters that causes the most extensive and severe damage. Therefore, drought prevention must be performed for administrative districts at the national level rather than the individual level. This study proposes a drought index estimation method for Gyeongsangnamdo, South Korea that evaluates its appropriateness through a comparison with damage data over several years. The standardized precipitation index (SPI) by duration was used as the drought index that was estimated for 13 rainfall stations located inside and outside Gyeonsangnam-do using the Thiessen method and cluster analysis. The SPI of Gyeongsangnamdo by duration based on the Thiessen method and cluster analysis for the years when drought damage occurred was compared with an SPI value of −2.0, which is the extreme drought condition, to determine its appropriateness. For the evaluation of the appropriateness, the performance indicators of the mean absolute deviation (MAD), mean squared error (MSE), and root mean square error (RMSE) were used. The analysis results showed that SPI by duration based on the cluster analysis method was more appropriate for damage data over many years than that based on the Thiessen method. Full article
(This article belongs to the Special Issue Advances in Drought Monitoring, Simulation and Prediction)
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17 pages, 4598 KiB  
Article
Spatial–Temporal Assessment of Historical and Future Meteorological Droughts in China
by Rucun Han, Zhanling Li, Zhanjie Li and Yuanyuan Han
Atmosphere 2021, 12(6), 787; https://doi.org/10.3390/atmos12060787 - 18 Jun 2021
Cited by 17 | Viewed by 2538
Abstract
Drought is a natural phenomenon in which the natural amount of water in an area is below the normal level. It has negative impacts on production in numerous industries and people’s lives, especially in the context of climate change. Investigating the spatial–temporal variation [...] Read more.
Drought is a natural phenomenon in which the natural amount of water in an area is below the normal level. It has negative impacts on production in numerous industries and people’s lives, especially in the context of climate change. Investigating the spatial–temporal variation of drought is of great importance in water resource allocation and management. For a better understanding of how drought has changed in China from 1961 to 2020 and will change in the future period of this century (2021–2100), a spatial–temporal assessment of drought based on the standardized precipitation evapotranspiration index (SPEI) was carried out. The trends and characteristics (number, duration, and severity) of historical and future droughts in China were evaluated based on 12-month SPEI by employing the Mann–Kendall test, Sen’s slope and run theory. The similarities, differences, and spatial–temporal evolution of droughts in these two periods were analyzed. The results showed that in the historical period the number of droughts decreased gradually from the south of China to the north. Less frequent drought but with longer duration and stronger severity occurred in the northeast and the northern areas. In the future period, most parts of China are projected to suffer more severe droughts with longer duration, especially for Northeast China, North China, Qinghai–Tibetan Plateau, and Southwest China. The likely increasing severity and duration of droughts in most areas of China in the future makes it very necessary to formulate the corresponding drought prevention and relief strategies to reduce the possible losses caused by droughts. Full article
(This article belongs to the Special Issue Advances in Drought Monitoring, Simulation and Prediction)
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16 pages, 10268 KiB  
Article
Development of a Flash Drought Intensity Index
by Jason A. Otkin, Yafang Zhong, Eric D. Hunt, Jordan I. Christian, Jeffrey B. Basara, Hanh Nguyen, Matthew C. Wheeler, Trent W. Ford, Andrew Hoell, Mark Svoboda and Martha C. Anderson
Atmosphere 2021, 12(6), 741; https://doi.org/10.3390/atmos12060741 - 9 Jun 2021
Cited by 36 | Viewed by 6040
Abstract
Flash droughts are characterized by a period of rapid intensification over sub-seasonal time scales that culminates in the rapid emergence of new or worsening drought impacts. This study presents a new flash drought intensity index (FDII) that accounts for both the unusually rapid [...] Read more.
Flash droughts are characterized by a period of rapid intensification over sub-seasonal time scales that culminates in the rapid emergence of new or worsening drought impacts. This study presents a new flash drought intensity index (FDII) that accounts for both the unusually rapid rate of drought intensification and its resultant severity. The FDII framework advances our ability to characterize flash drought because it provides a more complete measure of flash drought intensity than existing classification methods that only consider the rate of intensification. The FDII is computed using two terms measuring the maximum rate of intensification (FD_INT) and average drought severity (DRO_SEV). A climatological analysis using soil moisture data from the Noah land surface model from 1979–2017 revealed large regional and interannual variability in the spatial extent and intensity of soil moisture flash drought across the US. Overall, DRO_SEV is slightly larger over the western and central US where droughts tend to last longer and FD_INT is ~75% larger across the eastern US where soil moisture variability is greater. Comparison of the FD_INT and DRO_SEV terms showed that they are strongly correlated (r = 0.82 to 0.90) at regional scales, which indicates that the subsequent drought severity is closely related to the magnitude of the rapid intensification preceding it. Analysis of the 2012 US flash drought showed that the FDII depiction of severe drought conditions aligned more closely with regions containing poor crop conditions and large yield losses than that captured by the intensification rate component (FD_INT) alone. Full article
(This article belongs to the Special Issue Advances in Drought Monitoring, Simulation and Prediction)
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14 pages, 5222 KiB  
Article
Feasibility of Calculating Standardized Precipitation Index with Short-Term Precipitation Data in China
by Dongdong Zuo, Wei Hou, Hao Wu, Pengcheng Yan and Qiang Zhang
Atmosphere 2021, 12(5), 603; https://doi.org/10.3390/atmos12050603 - 6 May 2021
Cited by 17 | Viewed by 2599
Abstract
At present, high-resolution drought indices are scarce, and this problem has restricted the development of refined drought analysis to some extent. This study explored the possibility of calculating the standardized precipitation index (SPI) with short-term precipitation sequences in China, based on data from [...] Read more.
At present, high-resolution drought indices are scarce, and this problem has restricted the development of refined drought analysis to some extent. This study explored the possibility of calculating the standardized precipitation index (SPI) with short-term precipitation sequences in China, based on data from 2416 precipitation observation stations covering the time period from 1961 to 2019. The result shows that it is feasible for short-sequence stations to calculate SPI index, based on the spatial interpolation of the precipitation distribution parameters of the long-sequence station. Error analysis denoted that the SPI error was small in east China and large in west China, and the SPI was more accurate when the observation stations were denser. The SPI error of short-sequence sites was mostly less than 0.2 in most areas of eastern China and the consistency rate for the drought categories was larger than 80%, which was lower than the error using the 30-year precipitation samples. Further analysis showed that the estimation error of the distribution parameters β and q was the most important cause of SPI error. Two drought monitoring examples show that the SPI of more than 50,000 short-sequence sites can correctly express the spatial distribution of dry and wet and have refined spatial structure characteristics. Full article
(This article belongs to the Special Issue Advances in Drought Monitoring, Simulation and Prediction)
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2 pages, 604 KiB  
Correction
Correction: Babaeian et al. Future Projection of Drought Vulnerability over Northeast Provinces of Iran during 2021–2100. Atmosphere 2021, 12, 1704
by Iman Babaeian, Atefeh Erfani Rahmatinia, Alireza Entezari, Mohammad Baaghideh, Mohammad Bannayan Aval and Maral Habibi
Atmosphere 2022, 13(3), 365; https://doi.org/10.3390/atmos13030365 - 22 Feb 2022
Viewed by 875
Abstract
Error in Figure Legend [...] Full article
(This article belongs to the Special Issue Advances in Drought Monitoring, Simulation and Prediction)
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