CAS-Climate: CAREER: Analytical methods for understanding and predicting agricultural flash droughts in a changing climate

CAS-气候：职业：了解和预测气候变化下农业突发干旱的分析方法

• 批准号: 2144293
• 负责人: Di Tian
• 金额: $ 57.47万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144293&HistoricalAwards=false
• 关键词: CAS; Climate; CAREER; Analytical; methods

Flash drought is an extreme hydro-climate event characterized by sudden onset, rapid intensification, and devastating impact on communities. It rapidly depletes soil moisture, posing significant water and heat stresses for plant growth and agricultural productions over non-irrigated lands. Flash drought is challenging to predict because of its fast onset and development, and complex land-ocean-atmosphere factors that contribute to or affect their formation. This project of interlinked activities study the causes and predictability of flash droughts, improve their forecasts and projections, assess their impact on climate change, and promote education and outreach. The proposed research and educational programs will benefit society, especially agricultural communities vulnerable to droughts, and pave the way towards better-informed drought management, climate adaptation and improved resilience.The research will disentangle underlying drivers of agricultural flash droughts using machine learning-based causal inference analysis, develop and evaluate agricultural flash drought forecasts at the sub-seasonal timescale using deep learning approaches, and assess changes in agricultural flash drought under contemporary and future climate, based on coupled general circulation models large ensembles. These research objectives will be integrated with an education plan focusing on developing and implementing innovative lessons on drought through the 4-H (“head, heart, hands, and health”) program, conducting workshops on sub-seasonal forecasts and decision making with agricultural stakeholders through the climate learning network, mentoring undergraduate and graduate students, and involving graduate students in educational activities besides research. Deliverables from this project will contribute to an improved understanding and predictions of droughts at the regional scale and will provide a framework for analyses of a broader class of extreme climate events, which will be broadly applicable at different locations around the world. This award is co-funded by the Hydrologic Sciences and Climate and Large-Scale Dynamics programs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

暴旱是一种极端水文气候事件，其特点是突然发生，迅速加剧，并对社区造成破坏性影响。它迅速耗尽土壤水分，对非灌溉土地上的植物生长和农业生产造成严重的水和热压力。由于突发干旱的发生和发展迅速，以及促成或影响其形成的复杂的陆地-海洋-大气因素，预测突发干旱具有挑战性。这一相互关联的活动项目研究突发干旱的原因和可预测性，改进其预测和预测，评估其对气候变化的影响，并促进教育和宣传。拟议的研究和教育计划将造福社会，特别是易受干旱影响的农业社区，并为更明智的干旱管理、气候适应和提高复原力铺平道路。该研究将使用基于机器学习的因果推理分析来解开农业闪电干旱的潜在驱动因素，使用深度学习方法开发和评估亚季节时间尺度的农业闪电干旱预测，并基于耦合的大气环流模型大集合，评估当前和未来气候下农业暴旱的变化。这些研究目标将与一项教育计划相结合，该计划侧重于通过4-H（“头、心、手和健康”）方案制定和实施关于干旱的创新课程，通过气候学习网络与农业利益攸关方举办关于亚季节预报和决策的讲习班，指导本科生和研究生，并让研究生参与研究之外的教育活动。这一项目的成果将有助于更好地了解和预测区域范围内的干旱，并将为分析更广泛的极端气候事件提供一个框架，该框架将广泛适用于世界各地。该奖项由水文科学、气候和大规模动力学项目共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Projected mid-century rainfall erosivity under climate change over the southeastern United States

气候变化下美国东南部本世纪中叶的降雨侵蚀力预测

• DOI: 10.1016/j.scitotenv.2022.161119
• 发表时间: 2023
• 期刊: Science of The Total Environment
• 影响因子: 9.8
• 作者: Takhellambam, Bijoychandra S.;Srivastava, Puneet;Lamba, Jasmeet;McGehee, Ryan P.;Kumar, Hemendra;Tian, Di
• 通讯作者: Tian, Di

On deep learning-based bias correction and downscaling of multiple climate models simulations

• DOI: 10.1007/s00382-022-06277-2
• 发表时间: 2022-04
• 期刊: Climate Dynamics
• 影响因子: 4.6
• 作者: Fang Wang;D. Tian

County level calibration strategy to evaluate peanut irrigation water use under different climate change scenarios

• DOI: 10.1016/j.eja.2022.126693
• 发表时间: 2023-02
• 期刊: European Journal of Agronomy
• 影响因子: 5.2
• 作者: Xiaoxing Zhen;Weige Huo;Di Tian;Qiong Zhang;A. Sanz‐Saez;Chen Chen-Chen;W. Batchelor

Synergistic contributions of climate and management intensifications to maize yield trends from 1961 to 2017

• DOI: 10.1088/1748-9326/acb27f
• 发表时间: 2023-01
• 期刊: Environmental Research Letters
• 影响因子: 6.7
• 作者: H. Medina;D. Tian

Customized deep learning for precipitation bias correction and downscaling

• DOI: 10.5194/gmd-16-535-2023
• 发表时间: 2023-01
• 期刊: Geoscientific Model Development
• 影响因子: 5.1
• 作者: Fang Wang;D. Tian;M. Carroll