Daily time series of surface water input from rainfall, rain on snow, and snowmelt for the Conterminous United States from 1990 to 2023, as well as annual series of input seasonality, precipitation seasonality, and average rainfall, rain on snow, and snowmelt rates

This data release contains daily gridded data reflecting surface water input from rainfall, rain on snow (mixed), and snowmelt for the conterminous United States for water years 1990 to 2023 (1990/10/01 to 2023/09/30). This release also contains annual estimates of gridded input seasonality (an index reflecting whether surface water input occurs within a concentrated period or is equally distributed throughout the year), precipitation seasonality, average snowmelt, rainfall and rain on snow rates, and finally, annual totals of each input type. Average snowmelt, rainfall and rain on snow rates were computed using days where values were greater than zero. Daily data were generated using precipitation input from the gridMET dataset (Abatzoglou, 2013) and the University of Arizona snow water equivalent product (Broxton et al., 2019).

Abatzoglou, J. T. (2013), Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol., 33: 121–131.

Broxton, P., X. Zeng, and N. Dawson. (2019). Daily 4 km Gridded SWE and Snow Depth from Assimilated In-Situ and Modeled Data over the Conterminous US, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/0GGPB220EX6A.

本数据集发布内容包含1990至2023水文年（water year，即1990年10月1日至2023年9月30日）美国本土（conterminous United States）范围内的逐日网格化数据（gridded data），该数据反映了降雨、雪面降雨（rain on snow，混合型）及融雪三类来源的地表水源输入。本次发布还涵盖了多项年度估算值：网格化输入季节性指数（用于表征地表水源输入是集中于特定时段还是全年均匀分布）、降水季节性、平均融雪速率、降雨速率及雪面降雨速率，以及每类输入类型的年度总量。其中，平均融雪、降雨及雪面降雨速率仅基于日值大于零的日期计算得到。本逐日数据集基于gridMET数据集（Abatzoglou，2013）与亚利桑那大学雪水当量（snow water equivalent）产品（Broxton et al.，2019）的降水输入生成。

Abatzoglou, J. T. (2013). 面向生态应用与建模的网格化地表气象数据集研制. 国际气候学杂志, 33: 121–131.

Broxton, P., X. Zeng, & N. Dawson. (2019). 基于美国本土同化原位观测与模拟数据生成的每日4公里网格化雪水当量与雪深（版本1）. 美国科罗拉多州博尔德市：美国国家航空航天局（NASA）国家雪冰数据中心分布式主动存档中心. https://doi.org/10.5067/0GGPB220EX6A.