CAREER: Predicting Climate Impacts on Irrigated Agriculture

该项目将评估气候和供水对未来灌溉农业生产的影响。通过灌溉提高农业产量是解决全球饥饿问题的复杂方案的重要组成部分。灌溉可以使边际土地适合农业，使现有农田更具生产力。然而，灌溉受到其大量用水的限制。确定灌溉减少全球饥饿的潜力需要对气候、农业供水和作物生产等多种因素有透彻的了解。该项目将通过促进对灌溉农业生产力的主要制约因素的了解，改进对灌溉农田气候影响的预测。因此，它将制定加强未来灌溉管理的战略。知识、数据和模型将直接广泛传播，并通过与农业模型相互比较和改进项目和堪萨斯地质调查局的合作广泛传播。该项目还将为处于STEM关键阶段的学生增加自然地理、气候科学、数值模拟和数据分析方面的知识。此外，研究人员将创建一个教学模型，使高中生能够探索气候和水资源对作物生产的影响，为教学模型开发教育者专业发展课程，并为打算主修STEM领域的不同研究生和本科生提供研究经验。直接模拟供水对产量影响的灌溉农业预测很少，通常对作物生长和灌溉的描述过于简单化。该项目将通过四个主要努力解决这一关键不足：(1)构建一个将气候和水文数据与能够模拟有限自动灌溉的作物模型连接起来的建模框架；(2)使用报告的产量和卫星获取的蒸散、土壤水分和植被指数数据来评估和改进建模框架；(3)强制使用气候和供水情景的建模框架；以及(4)探索灌溉情景和建模假设中的作物水分利用和生产。这些努力将为气候、水和农业之间的三个重要问题提供答案：(1)水资源短缺对灌溉农业生产的影响是什么？目前和未来？(2)哪些灌溉管理措施可以在不损害产量的情况下减少用水量，并最终减少缺水对灌溉作物生产的影响？(3)卫星数据如何改进灌溉农业生产的作物模型模拟？该项目将专注于美国的灌溉玉米、大豆、水稻和小麦，灌溉约占美国消费用水的90%，农业生产的1200亿美元。然而，该项目开发的数据集、模型和方法将提供改善全球灌溉农业预测的机会。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project will assess the effects of climate and water supply on future irrigated agricultural production. Increasing agricultural production through irrigation is an essential part of the complex solution to global hunger. Irrigation can make marginal land suitable for agriculture and existing croplands more productive. However, irrigation is limited by its massive use of water. Determining the potential of irrigation to reduce global hunger requires a thorough understanding of multiple factors such as climate, agricultural water supply, and crop production. This project will improve predictions of climate impacts on irrigated croplands by advancing the understanding of key constraints on irrigated agricultural productivity. As a result, it will develop strategies for enhancing future irrigation management. Knowledge, data, and models will be widely disseminated both directly and through collaborations with the Agricultural Model Intercomparison and Improvement Project and Kansas Geological Survey. This project will also increase knowledge of physical geography, climate science, numerical modeling, and data analysis for students at critical STEM stages. Additionally, the investigator will create a teaching model that enables high-school students to explore the impacts of climate and water resources on crop production, develop an educator professional development course for the teaching model, and provide research experiences for diverse graduate and undergraduate students intending to major in a STEM field.Irrigated agricultural projections that directly simulate the impacts of water supply on yield are few, and typically have simplistic representations of crop growth and irrigation. This project will address this key deficiency through four main efforts: (1) Construct a modeling framework that connects climate and hydrologic data with a crop model capable of simulating limited automatic irrigation; (2) Evaluate and improve the modeling framework using reported yields and satellite-derived evapotranspiration, soil moisture, and vegetation index data; (3) Force the modeling framework with climate and water supply scenarios; and (4) Explore crop water use and production across irrigation scenarios and modeling assumptions. These efforts will provide answers to three important questions at the interface of climate, water, and agriculture: (1) What are the effects of water supply shortages on irrigated agricultural production?currently and in the future? (2) Which irrigation management practices can reduce water use without harming yields, and ultimately decrease the impacts of water scarcity on irrigated crop production? (3) How can satellite data improve crop model simulations of irrigated agricultural production? This project will focus on irrigated corn, soybean, rice, and wheat in the United States, where irrigation is responsible for approximately 90% of consumptive water use and $120 billion of agricultural production. However, datasets, models, and methods developed by this project will provide opportunities to improve projections of irrigated agriculture globally.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.