CMG RESEARCH: Predictive Modeling of Thermally Driven Hydrologic Fluxes for Land and Atmosphere Interactions Across Space and Time Scales

土壤水分在许多环境和水文气候应用中起着关键作用。 事实上，土壤湿度对于理解陆地和大气交换是如此重要，以至于2007年凤凰号火星使命包括专门设计用于测量土壤温度、热特性和含水量的设备。 认识到土壤湿度作为一个重要的状态变量的重要性，2007年NRC地球科学十年调查报告建议将卫星使命土壤湿度主动/被动（SMAP）用于全球土壤湿度测绘。 还有其他正在进行的全球任务，如AQUA卫星上的C-ban AMSR-E传感器，它提供了地球顶部1厘米的土壤湿度？的皮肤与两个轨道每日覆盖在美国南部大平原常规测量遥感无法捕捉快速变化的近地表土壤，热量和水的传输过程，尽管他们的重要性，了解陆地表面水文。 正因为如此，对数学模型的需求，以估计这种可变性和预测大规模的行为比以往任何时候都更加迫切。 建议的工作重点是发展预测多尺度模型的土壤水分动态的基础上，液体和蒸汽流耦合热传输。 这些模型考虑了不同空间和时间尺度上复杂的非线性相互作用。 在精细尺度上的不确定性通过概率模型来建模。 拟议的方法旨在方便地纳入不同尺度的现有动态数据，因此，将为即将进行的空间飞行任务的地面验证提供更好的指导，这些飞行任务涉及测量异质地下环境的土壤湿度和温度。

Soil moisture plays a critical role in many environmental and hydroclimatic applications. In fact, soil moisture is so central to understanding land and atmosphere exchange that the 2007 Phoenix Mars Mission included devices specifically designed to measure soil temperature, thermal properties, and water content. Recognizing the importance of soil moisture as a vital state variable, the 2007 NRC Earth Science Decadal Survey report recommended the satellite mission Soil Moisture Active/Passive (SMAP) for global soil moisture mapping. There are other ongoing global missions, such as the C-ban AMSR-E sensor on the AQUA satellite, which provides soil moisture of the top 1cm of the earth?s skin with two orbital daily coverages in the Southern Great Plains of the U.S. Routine measurements by remote sensing are unable to capture rapidly shifting near-surface soil, heat, and water transport processes despite their importance to understanding land surface hydrology. Because of that, the need for mathematical models to estimate such variabilities and to predict large-scale behavior is more imperative than ever. The proposed work focuses on developing predictive multiscale models for soil moisture dynamics based on liquid and vapor flow coupled with heat transport. These models take into account complex nonlinear interactions at different spatial and temporal scales. Uncertainties at the fine scale are modeled via probabilistic models. The proposed methodologies are designed to readily incorporate available dynamic data at different scales and, thus, will provide an improved guidance for ground validations of upcoming space missions concerning measurements of soil moisture and temperature for heterogeneous subsurface environments.