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

雪表面粗糙度 - 数据收集、地统计分析、与气象观测的关系以及与雪水文模型的相关性

• 批准号: 0001514
• 负责人: Ute Herzfeld
• 金额: $ 7.44万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-06-01 至 2002-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0001514&HistoricalAwards=false
• 关键词: Snow; Surface; Roughness; Data; Collection

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.

0001514 Herzanche建议的工作目标是研究在冬季后期和融化季节的雪表面粗糙度，将这些变化与天气条件联系起来，并评估表面粗糙度变化对水文响应模型的影响。积雪中的融水通量，以及冬季积雪的风输送和侵蚀。 雪面粗糙度也会影响雪面的遥感回波信号。 迄今为止，表面粗糙度通常被估计为粗糙度长度，从边界层中的流动条件估计。在拟议的工作中，我们计划（1）评估雪表面粗糙度/微地形直接测量仪器专门为此目的设计，（2）分类雪表面类型地质统计学，（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）提供现实的作为水文模型输入的（=实测）粗糙度特征：（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，将用冰川粗糙度传感器（GRS）测量雪面状况，并将在科罗拉多Front Range的尼沃特岭收集全球定位系统（GPS）动态数据。 这是一个长期生态研究（LTER）项目的所在地。 作为LTER和相关计划的一部分收集的水文和环境数据将提供直接测量表面粗糙度重要性的经验评估，在数据分析中将应用和完善专门为表面表征开发的地质统计学方法。 地质统计学表面表征利用特征向量，其中组件捕获高分辨率形态学特性并促进雪表面类型的分类。