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

美国西部半干旱地区的气候具有显著的年际变化，降水、融雪、土壤水分、蒸散和其他水文过程的时空分布对这种变化敏感。 对气候变化的敏感性在自然地理的梯度（例如海拔，植被群落结构和纬度）上各不相同，但在不同的山地景观中这种敏感性的驱动因素和程度并没有完全理解。 同样，这些变化对流域尺度积雪蓄水量的影响也无法确定，因为观测数据没有分布在控制积雪分布的海拔和其他自然地理条件的范围内。 因此，这些稀缺的观测数据的统计插值模型不足以代表积雪的空间格局。 近三十年来，遥感观测积雪覆盖枯竭已被用来预测季节性融雪径流和间接的季节性积雪积累集成在一个分水岭。 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需要更多的时间（或能量）来融化。 最近，遥感技术的进步使亚像素检测积雪枯竭和像素特定的积雪重建模型的发展。 评估模型性能和改进算法所需的地面观测的稀缺性限制了对小型水源集水区的重建建模研究。 同样，重建技术还没有被用来解决在积雪季节的雪分布的时间变化。 为了解决这些不足之处，拟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。 拟议的工作将解决有关的时间和空间变化的雪分布模式在中部的内华达州山脉的问题。 将开发一种新的降雪量估计方法，其中将使用Englance Kalman Smooter将新的遥感测雪能力纳入基于物理的质量和能量平衡模型。 密集分布的超声波雪深传感器集群跨越海拔梯度的季节性积雪覆盖的部分的内华达州将被用来开发这一新技术，占两个子网格的变化和空间代表性的地面观测。 这些新的建模和测量功能的结合将使人们能够对积雪过程有新的理解，并有可能从根本上改变点观测在崎岖地形上的分布方式。

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.