Assessment of a Long-Term High-Resolution Hydroclimatic Dataset for the U.S. Midwest

作者：Dev Niyogi, Elin M. Jacobs, Xing Liu, Anil Kumar, L. L. Biehl, P. Suresh C. Rao · 发表于：Earth Interactions · 年份：2017 · DOI：10.1175/ei-d-16-0022.1 · 被引用次数：7 · 研究领域：Soil Moisture and Remote Sensing、Hydrology and Watershed Management Studies、Climate change and permafrost

Abstract A new, high-resolution (4 km), gridded land surface dataset produced with the Land Information System (LIS) is introduced, and the first set of synthesis of key hydroclimatic variables is reported. The dataset is produced over a 33-yr time period (1980–2012) for the U.S. Midwest with the intent to aid the agricultural community in understanding hydroclimatic impacts on crop production and decision-making in operational practices. While approximately 20 hydroclimatic variables are available through the LIS dataset, the focus here is on soil water content, soil temperature, and evapotranspiration. To assess the performance of the model, the LIS dataset is compared with in situ hydrometeorological observations across the study domain and with coarse-resolution reanalysis products [NARR, MERRA, and NLDAS-2 (phase 2 of the North American Land Data Assimilation System)]. In agricultural regions such as the U.S. Midwest, finescale hydroclimatic mapping that links the regional scale to the field scale is necessary. The new dataset provides this link as an intermediate-scale product that links point observations and coarse gridded datasets. In general, the LIS dataset compares well with in situ observations and coarser gridded products in terms of both temporal and spatial patterns, but cases of strong disagreement exist particularly in areas with sandy soils. The dataset is made available to the broader research community as an effort to fill the gap in spatial hydroclimatic...