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Atmosphere
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Drought Monitoring, Prediction and Impacts
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Drought Monitoring, Prediction and Impacts

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

Deadline for manuscript submissions: 3 February 2025 | Viewed by 6881

Special Issue Editor

Prof. Dr. Muhammad Abrar Faiz

E-Mail Website
Guest Editor
1. School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150030, China
2. Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea
Interests: climatology; climate extremes; climate change; drought; hydrology; water resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, climate change is the most debated issue worldwide, not only affecting our lives but also posing a serious threat to future generations. Anthropogenic activities are consistently modifying the climate system, which has resulted in altered precipitation patterns, elevated atmospheric carbon dioxide, melting of snow covers, and most importantly an increase in global temperature. Recently, droughts in various regions of the world have highlighted the dangers of a lack of domestic animal feed, forcing farmers to import grain for their animals. This Special Issue aims to showcase the latest advancements in the monitoring and prediction of droughts to enhance our understanding of their complex dynamics and mitigate their adverse impacts.

The purpose of this Special Issue is to assemble cutting-edge research contributions from the global scientific community, fostering collaboration and knowledge exchange in the field of drought monitoring, simulation, and prediction. By disseminating innovative approaches and methodologies, this Special Issue aims to enhance the accuracy of drought prediction, support sustainable water resource management, and contribute to the development of effective strategies to mitigate the adverse impacts of droughts on society and the environment. We encourage researchers to submit their original research articles, reviews, and case studies to this Special Issue. Contributions that incorporate multidisciplinary approaches and data-driven methodologies will be especially welcome. Together, let us address the challenges of droughts and strive to build a more resilient and sustainable future.

This Special Issue seeks high-quality research papers that cover a broad spectrum of topics related to drought monitoring, simulation, and prediction. Potential areas of interest include remote sensing and monitoring, climate models and simulation, data assimilation and fusion, drought prediction, and early warning systems, hydrological and agricultural impacts, adaptation and mitigation strategies, uncertainty, and risk assessment.

Prof. Dr. Muhammad Abrar Faiz
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 submissions that pass pre-check are 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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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

  • droughts monitoring
  • simulation and prediction
  • impacts
  • remote sensing
  • climate models
  • evapotranspiration
  • drought indices
  • data assimilation
  • early warning systems
  • hydrological impacts
  • agricultural impacts
  • adaptation strategies
  • water resources
  • sustainable development

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Published Papers (5 papers)

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Research

23 pages, 5658 KiB  
Article
Investigating Hydrological Drought Characteristics in Northeastern Thailand in CMIP5 Climate Change Scenarios
by Sornsawan Chatklang, Piyapong Tongdeenok and Naruemol Kaewjampa
Atmosphere 2024, 15(9), 1136; https://doi.org/10.3390/atmos15091136 - 19 Sep 2024
Viewed by 644
Abstract
In this study, we analyzed the predictions of hydrological droughts in the Lam Chiang Kri Watershed (LCKW) by using the Soil and Water Assessment Tool (SWAT) and streamflow data for 2010–2021. The objective was to assess the streamflow drought index (SDI) for 5-, [...] Read more.
In this study, we analyzed the predictions of hydrological droughts in the Lam Chiang Kri Watershed (LCKW) by using the Soil and Water Assessment Tool (SWAT) and streamflow data for 2010–2021. The objective was to assess the streamflow drought index (SDI) for 5-, 10-, 25-, and 50-year return periods (RPs) in 2029 and 2039 in two representative concentration pathway (RCP) scenarios: the moderate climate change scenario (RCP 4.5) and the high-emission scenario (RCP 8.5). The SWAT model showed high accuracy (R2 = 0.82, NSE = 0.78). In RCP4.5, streamflow is projected to increase by 34.74% for 2029 and 18.74% for 2039, while in RCP8.5, a 37.06% decrease is expected for 2029 and 55.84% for 2039. A historical analysis indicated that there were frequent short-term droughts according to SDI-3 (3-month-period index), particularly from 2014 to 2015 and 2020 to 2021, and severe droughts according to SDI-6 (6-month-period index) in 2015 and 2020. The RCP8.5 projections indicate worsening drought conditions, with critical periods from April to June. A wavelet analysis showed that there is a significant risk of severe hydrological drought in the LCKW. Drought characteristic analysis indicated that high-intensity events occur with low frequency in the 50-year RP. Conversely, high-frequency droughts with lower intensity are observed in RPs of less than 50 years. The results of this study highlight an increase in severe drought risk in high emission scenarios, emphasizing the need for water management. Full article
(This article belongs to the Special Issue Drought Monitoring, Prediction and Impacts)
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21 pages, 4775 KiB  
Article
Critical Drought Characteristics: A New Concept Based on Dynamic Time Period Scenarios
by Ahmad Abu Arra, Mehmet Emin Birpınar, Şükrü Ayhan Gazioğlu and Eyüp Şişman
Atmosphere 2024, 15(7), 768; https://doi.org/10.3390/atmos15070768 - 27 Jun 2024
Cited by 3 | Viewed by 1348
Abstract
In research on monitoring drought events, analysis is often carried out using a single period as a reference. On the other hand, changing this default period in drought calculations causes the drought index values obtained from research to differ. As a gap in [...] Read more.
In research on monitoring drought events, analysis is often carried out using a single period as a reference. On the other hand, changing this default period in drought calculations causes the drought index values obtained from research to differ. As a gap in the literature, this point highlights the necessity of investigating the effect of various time periods on drought characteristics. It underscores the need to propose a new concept and methodology to address this gap effectively. This research aims to analyze critical drought characteristics through dynamic time period scenarios. For the first time in the literature, drought indices and potential and critical characteristics were analyzed for various (dynamic) time periods. Drought analysis was carried out for 13 time period scenarios with 10-year intervals from a meteorological station in Durham (1872–2021) by changing the initial time condition using the Standardized Precipitation Index (SPI). The results showed that in addition to the similarities, there are significant differences between drought characteristics. For example, in some time period scenarios, a drought event was recorded during a specific period, while in other scenarios (S5–S7, S10–S13), no drought was detected during the same period, like in SPI 1. Additionally, for SPI 12, the drought duration varied significantly, lasting between 20 and 29 months, and for SPI 6, the drought duration varied between 3 and 13 months. Regarding the intensity, SPI 1 ranged between −0.89 and −1.33, indicating a 33% difference, and the SPI 3 intensity ranged between −1.08 and −1.91, indicating a 50% increase in intensity. This research significantly contributes to the field by providing a novel approach using dynamic time period scenarios to determine critical drought characteristics, offering valuable insights for water resource management, drought mitigation planning, and design purposes. Full article
(This article belongs to the Special Issue Drought Monitoring, Prediction and Impacts)
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18 pages, 4366 KiB  
Article
Deep Learning for Flash Drought Detection: A Case Study in Northeastern Brazil
by Humberto A. Barbosa, Catarina O. Buriti and T. V. Lakshmi Kumar
Atmosphere 2024, 15(7), 761; https://doi.org/10.3390/atmos15070761 - 26 Jun 2024
Cited by 1 | Viewed by 1511
Abstract
Flash droughts (FDs) pose significant challenges for accurate detection due to their short duration. Conventional drought monitoring methods have difficultly capturing this rapidly intensifying phenomenon accurately. Machine learning models are increasingly useful for detecting droughts after training the models with data. Northeastern Brazil [...] Read more.
Flash droughts (FDs) pose significant challenges for accurate detection due to their short duration. Conventional drought monitoring methods have difficultly capturing this rapidly intensifying phenomenon accurately. Machine learning models are increasingly useful for detecting droughts after training the models with data. Northeastern Brazil (NEB) has been a hot spot for FD events with significant ecological damage in recent years. This research introduces a novel 2D convolutional neural network (CNN) designed to identify spatial FDs in historical simulations based on multiple environmental factors and thresholds as inputs. Our model, trained with hydro-climatic data, provides a probabilistic drought detection map across northeastern Brazil (NEB) in 2012 as its output. Additionally, we examine future changes in FDs using the Coupled Model Intercomparison Project Phase 6 (CMIP6) driven by outputs from Shared Socioeconomic Pathways (SSPs) under the SSP5-8.5 scenario of 2024–2050. Our results demonstrate that the proposed spatial FD-detecting model based on 2D CNN architecture and the methodology for robust learning show promise for regional comprehensive FD monitoring. Finally, considerable spatial variability of FDs across NEB was observed during 2012 and 2024–2050, which was particularly evident in the São Francisco River Basin. This research significantly contributes to advancing our understanding of flash droughts, offering critical insights for informed water resource management and bolstering resilience against the impacts of flash droughts. Full article
(This article belongs to the Special Issue Drought Monitoring, Prediction and Impacts)
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14 pages, 5068 KiB  
Article
Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China
by Zixuan Chen, Guojie Wang, Xikun Wei, Yi Liu, Zheng Duan, Yifan Hu and Huiyan Jiang
Atmosphere 2024, 15(2), 155; https://doi.org/10.3390/atmos15020155 - 25 Jan 2024
Cited by 2 | Viewed by 1414
Abstract
Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with [...] Read more.
Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. In this study, we employed a deep learning approach to predict drought in the Fenhe River (FHR) basin, taking into account the meteorological conditions of surrounding regions. We used the daily SAPEI (Standardized Antecedent Precipitation Evapotranspiration Index) as the drought evaluation index. Our results demonstrate the effectiveness of the CNN model in predicting drought events 1~10 days in advance. We evaluated the predictions made by the model; the average Nash–Sutcliffe efficiency (NSE) between the predicted and true values for the next 10 days was 0.71. While the prediction accuracy slightly decreased with longer prediction lengths, the model remained stable and effective in predicting heavy drought events that are typically difficult to predict. Additionally, key meteorological variables for drought predictions were identified, and we found that training the CNN model with these key variables led to higher prediction accuracy than training it with all variables. This study approves an effective deep learning approach for daily drought prediction, particularly when considering the meteorological conditions of surrounding regions. Full article
(This article belongs to the Special Issue Drought Monitoring, Prediction and Impacts)
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22 pages, 3007 KiB  
Article
Teleconnections of Atmospheric Circulations to Meteorological Drought in the Lancang-Mekong River Basin
by Lei Fan, Yi Wang, Chenglin Cao and Wen Chen
Atmosphere 2024, 15(1), 89; https://doi.org/10.3390/atmos15010089 - 10 Jan 2024
Cited by 1 | Viewed by 1207
Abstract
The Lancang-Mekong River Basin (LMRB) is one of the major transboundary basins globally, facing ongoing challenges due to flood and drought disasters. Particularly in the past two decades, the basin has experienced an increased frequency of meteorological drought events, posing serious threats to [...] Read more.
The Lancang-Mekong River Basin (LMRB) is one of the major transboundary basins globally, facing ongoing challenges due to flood and drought disasters. Particularly in the past two decades, the basin has experienced an increased frequency of meteorological drought events, posing serious threats to the local socio-economic structures and ecological systems. Thus, this study aimed to analyze the meteorological drought characteristics in the LMRB and identify the impact and correlation of atmospheric circulation on the meteorological drought in the basin. Specifically, the different levels of meteorological drought events were defined using the Run Theory based on the seasonal and annual SPEI from 1980 to 2018. The time lag correlation between meteorological drought events and the EI Nino-Southern Oscillation (ENSO), Arctic Oscillation (AO), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO), were analyzed in the LMRB. Our results indicated that, from a temporal perspective, the period from November to April of the following year was particularly prone to meteorological droughts in the basin. In terms of spatial distribution, the primary agricultural regions within the basin, including Thailand, Eastern Cambodia, and Vietnam, were highly susceptible to meteorological droughts. Further analysis revealed a teleconnection between drought events in the LMRB and atmospheric circulation factors. The sensitivity of the basin’s drought timing to its response decreased in the order of the ENSO > AO > NAO > PDO. In general, the ENSO had the most substantial influence on drought events in the basin, with the strongest response relationship, while the upper reaches of the basin displayed the most significant response to the AO; the occurrence and progression of meteorological droughts in this area synchronized with the AO. These findings enhance our understanding of drought-prone areas in the LMRB, including the meteorological factors and driving mechanisms involved. This information is valuable for effectively mitigating and managing drought risks in the region. Full article
(This article belongs to the Special Issue Drought Monitoring, Prediction and Impacts)
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