INFEWS/T1: A Modeling Framework to Understand the coupling of Food, Energy, and Water in the Teleconnected Corn and Cotton Belts

INFEWS/T1：了解远程连接的玉米和棉花带中食品、能源和水耦合的建模框架

基本信息

批准号：
1639327
负责人：
Xin-Zhong Liang
金额：
$ 300万
依托单位：
University of Maryland, College Park
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1639327&HistoricalAwards=false
关键词：
INFEWS T1 Modeling Framework Understand

项目摘要

The resilience of U.S. agriculture is significantly impacted by increasing climate extremes, growing population demands, and evolving land use. This project will develop, evaluate, and apply a model of the coupled Food, Energy, and Water (FEW) systems across the Corn and Cotton Belts of the Midwest, Southeast and Great Plains. The study will evaluate food crops currently grown in these Belts as well as the potential for growing bioenergy crops on marginal land. The project will: (1) develop a FEW model framework that represents the coupled nature of food and bioenergy production and water and its responses to environmental forcings and human interventions; (2) enhance understanding of key feedback mechanisms within FEW systems; (3) determine potential thresholds in FEW systems that would indicate damage to the resilience of U.S. agriculture and water resources. The project will provide training for PhD students and postdoctoral researchers in transdisciplinary research where cross-fertilization of ideas is central. Building upon existing partnerships with Minority-Serving Institutions, the project will also involve minority students through summer internships. This project will integrate state-of-the-art knowledge and modeling across the climate, hydrologic, agronomic, biogeochemical, engineering, and economic sciences. The project will use this integrated model to evaluate the coupled nature of food and bioenergy production, water quantity and quality, and climate and hydrologic processes in response to environmental forcings and human interventions. This activity will advance the transdisciplinary science by building diverse systems knowledge to address critical issues of vulnerability, resilience and sustainability. Specifically, the project will develop a scale-dependent approach for coupling, predicting and applying the interactive water, carbon, and nitrogen cycle processes with agriculture, water resources, the biofuels industry and economic practices. This will enable the larger-scale impacts of external forcings (e.g., increasing climate extremes and population demands) to be directly coupled to the impacts of human interventions (e.g., infrastructure and management) and decision level information. It will improve the scientific basis for decisions in sustainable agricultural food and bioenergy practices, water resources management and pollution assessment, hydroengineering design, and adaptation and mitigation strategies.

美国农业的恢复力受到日益严重的极端气候、不断增长的人口需求和不断变化的土地利用的显着影响。该项目将开发、评估和应用中西部、东南部和大平原玉米和棉花带的耦合食品、能源和水 (FEW) 系统模型。该研究将评估这些地带目前种植的粮食作物以及在贫瘠土地上种植生物能源作物的潜力。该项目将：（1）开发一个 FEW 模型框架，代表粮食和生物能源生产与水的耦合性质及其对环境强迫和人类干预的响应； (2) 加强对 FEW 系统内关键反馈机制的理解； (3) 确定 FEW 系统中可能表明美国农业和水资源恢复力受到损害的潜在阈值。该项目将为博士生和博士后研究人员提供以思想交叉为核心的跨学科研究培训。在与少数族裔服务机构现有合作伙伴关系的基础上，该项目还将让少数族裔学生参与暑期实习。该项目将整合气候、水文、农艺、生物地球化学、工程和经济科学领域的最先进知识和模型。该项目将利用这一综合模型来评估粮食和生物能源生产、水量和质量以及气候和水文过程对环境强迫和人类干预的耦合性质。这项活动将通过建立多样化的系统知识来解决脆弱性、复原力和可持续性的关键问题，从而推进跨学科科学。具体来说，该项目将开发一种与规模相关的方法，用于耦合、预测和应用交互式水、碳和氮循环过程与农业、水资源、生物燃料工业和经济实践。这将使外部强迫（例如，日益增加的极端气候和人口需求）的更大规模影响能够直接与人类干预（例如，基础设施和管理）和决策级信息的影响相结合。它将改善可持续农业食品和生物能源实践、水资源管理和污染评估、水利工程设计以及适应和缓解战略等决策的科学基础。

项目成果

期刊论文数量（32）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Developing the Coupled CWRF‐FVCOM Modeling System to Understand and Predict Atmosphere‐Watershed Interactions Over the Great Lakes Region

开发耦合 CWRF - FVCOM 建模系统以了解和预测五大湖地区大气 - 流域相互作用

DOI：
10.1029/2020ms002319
发表时间：
2020-12
期刊：
Journal of Advances in Modeling Earth Systems
影响因子：
6.8
作者：
Sun, Lei;Liang, Xin‐Zhong;Xia, Meng
通讯作者：
Xia, Meng

Nitrate loading projection is sensitive to freeze-thaw cycle representation

硝酸盐负荷预测对冻融循环表示敏感

DOI：
10.1016/j.watres.2020.116355
发表时间：
2020-11
期刊：
Water Research
影响因子：
12.8
作者：
Wang, Qianfeng;Qi, Junyu;Li, Jia;Cole, Jefferson;Waldhoff, Stephanie T.;Zhang, Xuesong
通讯作者：
Zhang, Xuesong

Understanding and Reducing Warm and Dry Summer Biases in the Central United States: Analytical Modeling to Identify the Mechanisms for CMIP Ensemble Error Spread

了解并减少美国中部温暖干燥的夏季偏差：通过分析模型确定 CMIP 集合误差传播机制

DOI：
10.1175/jcli-d-22-0255.1
发表时间：
2023-04
期刊：
Journal of Climate
影响因子：
4.9
作者：
Sun, Chao;Liang, Xin
通讯作者：
Liang, Xin

The long-term trend and production sensitivity change in the US ozone pollution from observations and model simulations

通过观测和模型模拟得出的美国臭氧污染的长期趋势和生产敏感性变化

DOI：
10.5194/acp-20-3191-2020
发表时间：
2020-03-17
期刊：
Atmospheric Chemistry and Physics
影响因子：
6.3
作者：
Hao He;Xin‐Zhong Liang;Chao Sun;Z. Tao;D. Tong
通讯作者：
D. Tong

Food insecurity and compound environmental shocks in Nepal: Implications for a changing climate

尼泊尔的粮食不安全和复合环境冲击：对气候变化的影响

DOI：
10.1016/j.worlddev.2021.105511
发表时间：
2021-09
期刊：
World Development
影响因子：
6.9
作者：
Randell, Heather;Jiang, Chengsheng;Liang, Xin;Murtugudde, Raghu;Sapkota, Amir
通讯作者：
Sapkota, Amir