The Purdue Agro-climatic (PAC) dataset for the U.S. Corn Belt: Development and initial results

作者：Xing Liu, Elin M. Jacobs, Anil Kumar, L. L. Biehl, Jeff Andresen, Dev Niyogi · 发表于：Climate Risk Management · 年份：2016 · DOI：10.1016/j.crm.2016.10.005 · 被引用次数：9 · 研究领域：Climate change impacts on agriculture、Climate variability and models、Plant Water Relations and Carbon Dynamics

This study is a result of a project titled “Useful to Usable (U2U): Transforming Climate Variability and Change Information for Cereal Crop Producers”. This paper responds to the project goal to improve farm resiliency and profitability in the U.S. Corn Belt region by transforming existing meteorological dataset into usable knowledge and tools for the agricultural community. A high-resolution agro-climatic dataset that covers the U.S. Corn Belt was built for the U2U project based on a Land Data Assimilation System (LDAS) framework. This data referred to as the Purdue Agro-climatic (PAC) dataset is a gridded, continuous dataset suitable for agroclimatic and crop model studies over the U.S. Corn Belt. The dataset was created at 4 km, sub-daily spatiotemporal resolution and covers the period of 1981–2014. The dataset includes a range of variables such as daily maximum/minimum temperature, solar radiation, rainfall, evapotranspiration (ET), multilevel soil moisture and soil temperatures. The data were compared to field measurements from Ameriflux and the Soil Climate Analysis Network (SCAN), and with coarser but widely used atmospheric regional reanalysis data products. Validations indicate an overall good agreement between this dataset and field measurements. The agreement is particularly high for radiation and temperature parameters and lesser for rainfall and soil moisture data. Despite the differences with observations, the data show improvements over the coarser resolution p...