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Climate-Driven Droughts: Pathways to Resilience in Line with SDG13

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 796

Special Issue Editors


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Guest Editor
School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150030, China
Interests: precipitation; extremes; climate change; drought; hydrological modeling
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Guest Editor
National Water and Energy Center, United Arab Emirates University, Al Ain, United Arab Emirates
Interests: water resources; climate change impact assessment; remote sensing applications in weather and climate

Special Issue Information

Dear Colleagues,

Under the background of the United Nations’ Sustainable Development Goals (SDGs), this particular Issue, "Climate-Driven Droughts: Pathways to Resilience in Line with SDG13", explores the growing challenges of drought under climate change and its implications for sustainable development, focusing on SDG13 (Climate Action). This Special Issue concerns the scientific and socio-economic aspects of droughts, addressing how climate variability exacerbates drought frequency and intensity across various regions. By presenting novel research, the aim of this Special Issue is to define strategies for drought mitigation, enhance adaptation, and build resilience in line with SDG13 targets.

This Issue will cover a wide range of topics, including drought monitoring, propagation, modeling, and the impact of climate change on water resources. It will also explore policy frameworks, tools, and technologies contributing to sustainable drought management. Moreover, this Issue will complement the existing literature by integrating the latest findings on climate-driven droughts and offering interdisciplinary approaches that bridge scientific research and policy development. It will contribute to sustainability by providing solutions to manage drought in a changing climate, helping communities adapt, and promoting long-term environmental resilience.

We invite contributions that advance our understanding of drought and climate change, providing actionable insights for sustainable development.

Prof. Dr. Muhammad Abrar Faiz
Dr. Faisal Baig
Guest Editors

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. Sustainability is an international peer-reviewed open access semimonthly 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

  • climate
  • extremes
  • SDG 13
  • droughts
  • drought propagation
  • sustainability

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Published Papers (1 paper)

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Research

29 pages, 11499 KiB  
Article
Evolution Characteristics and Influencing Factors of Agricultural Drought Resilience: A New Method Based on Convolutional Neural Networks Combined with Ridge Regression
by Chenyi Jiang, Liangliang Zhang, Dong Liu, Mo Li, Xiaochen Qi, Tianxiao Li and Song Cui
Sustainability 2025, 17(11), 4808; https://doi.org/10.3390/su17114808 - 23 May 2025
Viewed by 257
Abstract
To enhance the precision of regional agricultural drought resilience evaluation, a convolutional neural network optimized with Adam with weight decay (AdamW–CNN) was constructed. Based on local agricultural economic development regulations and utilizing the Driving Force–Pressure–State–Impact–Response (DPSIR) conceptual model, sixteen indicators of agricultural drought [...] Read more.
To enhance the precision of regional agricultural drought resilience evaluation, a convolutional neural network optimized with Adam with weight decay (AdamW–CNN) was constructed. Based on local agricultural economic development regulations and utilizing the Driving Force–Pressure–State–Impact–Response (DPSIR) conceptual model, sixteen indicators of agricultural drought resilience were selected. Subsequently, data preprocessing was conducted for Qiqihar City, Heilongjiang Province, China, which encompasses an area of 42,400 km2. The drought resilience was accurately assessed based on the developed AdamW–CNN model from 2000 to 2021 in the study area. The key driving factors behind the spatiotemporal evolution of drought resilience were identified using gray relational analysis, and the future evolution trend of agricultural drought resilience was revealed through Ridge regression analysis improved by the Kepler optimization algorithm (KOA–Ridge). The results indicated that the agricultural drought resilience in Qiqihar City exhibited a trend of initial fluctuations, followed by a significant increase in the middle phase, and then stable development in the later stage. Precipitation, investment in the primary industry, grain output per unit of cultivated area, per capita cultivated land area, and the proportion of effective irrigation area were the primary driving factors in the study area. By simulating the drought resilience index of four typical regions and analyzing its evolution, it was found that the AdamW–CNN model, combined with the KOA–Ridge model, has greater advantages over the RMSProp-CNN model and the CNN model in terms of fit, stability, reliability, and evaluation accuracy. These findings provide a robust model for measuring agricultural drought resilience, offering valuable insights for regional drought prevention and management. Full article
(This article belongs to the Special Issue Climate-Driven Droughts: Pathways to Resilience in Line with SDG13)
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