Snow Surface Roughness - Data Collection, Geostatistical Analysis, Relationship to Meteorologic Observations, and Relevance to Snow Hydrologic Models

这项拟议工作的目标是研究冬末和融化季节的雪面粗糙度，将这些变化与天气条件联系起来，并评估表面粗糙度的变化对水文响应模型的影响。雪面粗糙度对地表-大气交换至关重要，包括调查几个尺度上的积雪融化；地面能量交换；积雪中的融水通量，以及冬季风的输送和积雪覆盖的侵蚀。雪面粗糙度对雪面遥感回波信号也有影响。到目前为止，表面粗糙度通常被估计为根据边界层中的流动条件估计的粗糙度长度。在拟议的工作中，我们计划(1)用专门为此目的设计的仪器通过直接测量来评估雪面粗糙度/微地形，(2)对雪面类型进行地质统计学分类，(3)将雪面粗糙度参数与气象和微气象时间序列数据联系起来，(4)提供真实的(=测量的)粗糙度表征作为水文模型的输入：(A)作为常用模型的输入的粗糙度长度和(B)包括相关长度、特征长度、高度和地表特征及其各向异性的间距在内的多维参数，将使用冰川粗糙度传感器(GRS)测量雪面状况，并将在科罗拉多州前沿山脉的Niwot Ridge收集动态GPS(全球定位系统)数据。这是一个长期生态研究(LTER)项目的所在地。作为LTER和相关项目的一部分收集的水文和环境数据将为直接测量表面粗糙度的重要性提供经验评估。专门为表面特征开发的地统计学方法将在数据分析中应用和改进。地统计学表面特征利用特征向量，其中分量捕捉高分辨率的形态属性，并有助于雪面类型的分类。

0001514HerzfeldObjectives of the proposed work are to study the snow surface roughness during late winter and melting season, to relate these changes to weather conditions, and to assess the influence of changes in surface roughness on hydrologic response models.Snow surface roughness is critical to surface - atmosphere exchanges, including the investigation of snowmelt at several scales; surface energy exchange; meltwater flux in the snowpack, and wind transport and erosion of the snow cover in winter. Snow surface roughness also influences Remote Sensing return signals from the snow surface. To date surface roughness has usually been estimated as the roughness length, estimated from flow conditions in the boundary layer.In the proposed work we plan to (1) evaluate snow surface roughness/microtopography by direct measurement with an instrument especially designed for this purpose,(2) classify snow surface types geostatistically,(3) relate snow surface roughness parameters to meteorologic and micrometerologic time series data and (4) provide realistic (= measured) roughness characterization as input for hydrologic models: (a) roughness length as input for commonly used models and (b) multidimensional parameters including correlation length, characteristic length, height and spacing of surface features and their anisotropies, which will require an adaptation of snow hydrological models.Snow surface conditions will be measured with the Glacier Roughness Sensor (GRS), and kinematic GPS (Global Positioning System) data will be collected at Niwot Ridge, Colorado Front Range. This is the site of a Long-Term Ecological Research (LTER) Project. Hydrologic and environmental data collected as part of the LTER and associated programs will provide the empirical evaluation of the importance of measuring surface roughness directly.Geostatistical methods developed especially for surface characterization will be applied and refined in the data analysis. Geostatistical surface characterization utilizes a feature vector where components capture high-resolution-morphologic properties and facilitate a classification of snow surface types.