A Land Information System (LIS) Based Dataset for Regional Agro-Hydro-Climatic Assessments over the U.S. Corn Belt.

This study is a result of a USDA sponsored project titled Useful to Usable (U2U): “Transforming Climate Variability and Change Information for Cereal Crop Producers”. The objective of this project is to improve farm resilience and profitability in the U.S. Corn Belt region by transforming existing meteorological dataset into usable knowledge and tools for the agricultural community.

This paper describes a high-resolution (4 km, daily, 1981-2014) agro-hydro-climatic dataset that covers the U.S. Corn Belt that was built for the U2U project based on Land Data Assimilation System (LDAS) and NASA Land Information System (LIS). This new dataset include daily maximum/minimum temperature, solar radiation, rainfall, Evapotranspiration (ET), 4-layer soil moisture, soil temperature and etc. We validated our dataset with observed data from Ameriflux and the Soil Climate Analysis Network (SCAN), and with coarser reanalyses. Validations indicate strong agreement between this dataset and field measurements. Specially, for solar radiation and soil moisture, this dataset has better performance than model calculations, which are normally used in crop models. For soil moisture, this dataset is in agreement with other products during the growing season, but is able to capture fine scale features more accurately.  

 By using this new high-resolution dataset. We conducted multiple agro-hydro-climatic analyses: 1) Evaluation of the meteorological and agronomic impacts of climate variability due to El Niño–Southern Oscillation (ENSO) on the Corn Belt; 2) Developing a modeling framework to simulate gridded crop yields, aiming to provide high-resolution regional crop modeling products. 3) Analysis of regional soil water variability based on a hydrologic framework, with focus on drought assessment.    

 The reliability of the new agro-hydro-climatic dataset show good potential to simulate regional corn yield with climate projections. This high-resolution dataset will be made available for the wider community, where we believe it can fill gaps in observed data records and increase accessibility for the agricultural sector.