Quantifying Controls on Snow Distribution in the Sierra Nevada Using Ground-based and Remotely Sensed Observations within an Ensemble Kalman Smoother

使用集成卡尔曼平滑器内的地面和遥感观测来量化对内华达山脉雪分布的控制

• 批准号: 1032295
• 负责人: Noah Molotch
• 金额: $ 21.62万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1032295&HistoricalAwards=false
• 关键词: Quantifying; Controls; Snow; Distribution; Sierra

Climate in the semi-arid Western U.S. exhibits considerable inter-annual variability; and the temporal and spatial distributions of precipitation, snowmelt, soil moisture, evapotranspiration and other hydrologic processes are sensitive to this variability. Sensitivity to climate change varies across gradients of physiography (e.g. elevation, vegetative community structure, and latitude) but the drivers and degree of this sensitivity in different mountainous landscapes are not fully comprehended. Similarly, the impact of these changes on basin-scale snowpack water storage cannot be determined because observations are not distributed across a range of elevations and other physiographic conditions that control snow distribution. As a result, statistical interpolation models of these scarce observations inadequately represent spatial patterns of snow accumulation. For nearly three decades, remotely sensed observations of snow cover depletion have been used to forecast seasonal snowmelt runoff and indirectly seasonal snow accumulation integrated over a watershed. The use of these data to reconstruct snow accumulation is based on the simple concept that deeper snow takes more time (or energy) to melt than shallower snow. More recently advances in remote sensing have enabled sub-pixel detection of snow cover depletion and the development of pixel-specific snow accumulation reconstruction models. The scarcity of ground-based observations needed to evaluate model performance and refine algorithms has restricted reconstruction modeling studies to small headwater catchments. Similarly, reconstruction techniques have not been used to resolve the temporal variability in snow distribution during the accumulation season. To address these inadequacies the proposed research will synergistically develop new observing and modeling systems for estimating the spatial distribution of snow accumulation. The proposed work will address questions related to temporal and spatial variability in snow distribution patterns in the central Sierra Nevada Mountains. A new method of snowfall estimation will be developed in which an Ensemble Kalman Smoother will be used to assimilate new remotely sensed snow measurement capabilities into physically based mass and energy balance models. Densely distributed clusters of ultrasonic snow depth sensors spanning the elevational gradients of the seasonally snow covered portions of the Sierra Nevada will be used to develop this new technique by accounting for both sub-grid variability and the spatial representativeness of the ground observations. The combination of these new modeling and measurement capabilities will enable new understanding of snow accumulation processes and have the potential to fundamentally change the way in which point observations are distributed over rugged terrain.

美国西部半干旱地区的气候表现出相当大的年际变化；降水、融雪、土壤水分、蒸散发等水文过程的时空分布对这一变率较为敏感。不同地形（如海拔、植被群落结构和纬度）对气候变化的敏感性不同，但不同山地景观对气候变化敏感性的驱动因素和程度尚未完全了解。同样，这些变化对流域尺度积雪储水量的影响也无法确定，因为观测值没有分布在一系列海拔高度和控制积雪分布的其他地理条件上。因此，这些稀少观测数据的统计插值模型不能充分反映积雪的空间格局。近三十年来，积雪损耗的遥感观测已被用于预测季节性融雪径流和间接季节性积雪累积。使用这些数据来重建积雪是基于一个简单的概念，即较深的雪比较浅的雪融化需要更多的时间（或能量）。最近遥感技术的进展使亚像素探测积雪损耗和开发特定像素的积雪重建模型成为可能。由于缺乏评估模型性能和改进算法所需的地面观测资料，因此重建模型研究仅限于小型水源集水区。同样，重建技术也未用于解决积雪季节雪分布的时间变异性。为了解决这些不足，建议的研究将协同开发新的观测和建模系统，以估计积雪的空间分布。拟议的工作将解决与内华达山脉中部积雪分布模式的时空变化有关的问题。将开发一种新的降雪估计方法，其中使用集成卡尔曼平滑将新的遥感降雪测量能力吸收到基于物理的质量和能量平衡模型中。通过考虑亚网格变异性和地面观测的空间代表性，内华达山脉季节性积雪覆盖部分的高度梯度上密集分布的超声波雪深传感器集群将用于开发这种新技术。这些新的建模和测量能力的结合将使人们对积雪过程有新的认识，并有可能从根本上改变在崎岖地形上点观测分布的方式。