Multi-scale Monitoring and Modeling of Land Use and Climate Change Impacts on the Terrestrial Hydrologic Cycle: Implications for the Great Lakes Basin

土地利用和气候变化对陆地水文循环影响的多尺度监测和建模：对五大湖盆地的影响

• 批准号: 0911642
• 负责人: David Hyndman
• 金额: $ 24.36万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0911642&HistoricalAwards=false
• 关键词: Multi; scale; Monitoring; Modeling; Land

Multi-scale Monitoring and Modeling of Land Use and Climate Change Impacts on the Terrestrial Hydrologic Cycle in the Great Lakes BasinThis award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Vadose-zone soil moisture is an important driver of processes in agricultural, hydrological, ecological, and climate systems, yet the detailed nature of plant water use across ranges of scales is often poorly characterized. With projected changes in climate and land use (including afforestation, urbanization, and agricultural intensification), there is a critical need to understand the likely impacts on the hydrologic cycle and ecosystem health. Important hydrological and biophysical processes are not adequately characterized with point estimates, and models of root-water uptake are generally unable to accurately predict such changes. Our objectives are to: 1) quantify multi-scale dynamics of vegetation-water interactions across different land cover types to improve predictive capabilities of hydrologic models, and 2) explore the impacts of land use and climate changes on local to regional scale hydrologic fluxes. To explore the likely effects of projected changes in climate and land cover, we propose to use time-lapse electrical resistivity imaging and a novel coupling of a fully-integrated terrestrial hydrology model with a dynamic vegetation growth model to study managed and natural sites along a climate gradient across a range of soils. Our research will provide: 1) improved knowledge and predictive capability of short- and long-term processes that drive the terrestrial water cycle, 2) root-zone moisture and root-development data that will improve coupled land surface and climate models, and 3) quantitative information about implications of land use and climate changes across a range of scales.

土地利用和气候变化对五大湖流域陆地水文循环影响的多尺度监测和建模该奖项根据2009年美国复苏和再投资法案（公法111-5）资助。 包气带土壤水分是农业、水文、生态和气候系统过程的重要驱动力，但在不同尺度范围内植物水分利用的详细性质往往特征不佳。随着气候和土地利用（包括植树造林、城市化和农业集约化）的预计变化，迫切需要了解对水文循环和生态系统健康的可能影响。重要的水文和生物物理过程没有充分的特点与点估计，根系吸水模型一般无法准确预测这种变化。我们的目标是：1）量化不同土地覆盖类型的植被-水相互作用的多尺度动态，以提高水文模型的预测能力; 2）探索土地利用和气候变化对地方到区域尺度水文通量的影响。为了探索气候和土地覆盖的预计变化可能产生的影响，我们建议使用延时电阻率成像和一个完全集成的陆地水文模型与动态植被生长模型的新耦合，研究管理和自然的网站沿着气候梯度在一系列土壤。我们的研究将提供：1）提高知识和短期和长期过程的预测能力，推动陆地水循环，2）根区水分和根发育数据，将改善耦合的土地表面和气候模型，和3）关于土地利用和气候变化的影响在一系列尺度的定量信息。