1082 An Agro-hydro-climatic dataset for the U.S. Corn Belt: Development, Validations and Applications

Wednesday, 25 January 2017
4E (Washington State Convention Center )
Xing Liu, Purdue Univ., West Lafayette, IN; and E. Jacobs, L. L. Biehl, A. Kumar, J. Andresen, and D. Niyogi

As part of a project titled “Useful to Usable (U2U): Transforming Climate Variability and Change Information for Cereal Crop Producers”, the goal of this study to transform existing meteorological dataset and modeling products into a easily accessible agro-hydro-climactic dataset for assessing the agricultural impacts of climate variability and climate change.

In this study, a high-resolution agro-hydro-climatic dataset that covers the U.S. Corn Belt was developed based on a Noah model based Land Data Assimilation System (LDAS) framework. This dataset is at 4 km, daily spatiotemporal resolution and over the period of 1981-2015. The dataset provides daily maximum/minimum temperature, solar radiation, rainfall, evapotranspiration (ET), multi-level soil moisture and soil temperatures. It is a gridded, continuous dataset suitable for regional agro-climatic, hydro-climatic assessments and crop-modeling studies. The data were compared to field oberavtions from Ameriflux and the Soil Climate Analysis Network (SCAN), and with coarser but widely used atmospheric regional reanalysis data products (North American Regional Reanalysis (NARR), Modern-Era Retrospective analysis for Research and Applications (MERRA)). Results indicate this dataset has an overall good agreement with field observations, especially for radiation and temperature parameters. For rainfall and soil moisture, this dataset shows less agreement. The dataset also presents improvements over the coarser resolution products and other available models.

We applied this dataset for agro-climatic and hydro-climatic assessments, as well as conducting regional crop model studies with the Hybrid-Maize model. Results highlight the values of this dataset in filling gaps in observed data records and in regional crop modeling. The high accessibility of this dataset also indicates potential in wider applications.

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