Absolute Change in April 1 Snow Water Equivalent (CONUS) (Image Service)

The April Snow Water Equivalent data were modeled using the spatial analog models of Luce et al. [https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844] (see also https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from 1975-2005. To simulate historical and future gridded snow, the models ingest gridded winter cumulative precipitation and winter average temperature (from the MACAv2-Metdata dataset). We used the MACAv2-metdata monthly precipitation and minimum and maximum temperature datasets for the period 2071-2090 (RCP8.5). Average temperature was calculated as the arithmetic mean of minimum and maximum temperature datasets. Average temperature was averaged over the winter months (November through March) and precipitation was summed over the winter months. More information on the project associated with this dataset is available from the U.S. Forest Service Rocky Mountain Research Station.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.本四月雪水当量（Snow Water Equivalent）数据采用Luce等人[https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844]提出的空间类比模型进行建模（另可参考https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752）。该模型基于美国西部积雪遥测（Snowpack Telemetry, SNOTEL）站点的降水与积雪数据，以及1975-2005年的气温数据构建。为模拟历史与未来网格化积雪，模型输入网格化冬季累积降水量与冬季平均气温数据（数据来源于MACAv2-Metdata数据集）。本研究使用了2071-2090年典型浓度路径8.5（RCP8.5）情景下的MACAv2-metdata月降水量、最低与最高气温数据集。平均气温由最低与最高气温数据集的算术平均值计算得到，随后对冬季月份（11月至次年3月）的平均气温取均值，对冬季月份的降水量进行求和。关于本数据集关联项目的更多信息，可从美国林业局落基山研究站获取。本记录取自接入https://data.gov目录的美国农业部（USDA）企业数据清单。本记录包含以下资源：ISO-19139元数据、ArcGIS Hub数据集、ArcGIS地理服务（ArcGIS GeoService）。如需完整信息，请访问https://data.gov。