Location: Sustainable Water Management Research
Project Number: 6066-13000-005-026-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Jul 1, 2021
End Date: Jun 30, 2025
This study aims at investigating climate states and climate changes observed and projected from 1981 to 2060 that are causatively associated with water resource dynamics in the region of Mississippi Embayment Aquifer. The objectives of the study are to produce 1) a historical climate data set at 4 km resolution from 1982 to 2015, and 2) a projected climate data set at 4 km resolution for 2016 to 2060 produced using a variety of weather research forecasting models (WRF).
In this project, both advanced statistical analysis and dynamic downscaling climate modeling are used to construct a historical dataset and a projected climate dataset from 1981 to 2060. The historical climate data (observed) will be area-weighted averaged into a crop-report-district (CRD) level after data homogeneity test passed and data quality assured. The projected climate data will be modeled by Weather Research & Forecasting Models (WRF). We will use 18 global circulation model (GCM) data and downscale into 4 km × 4 km grids for the Mississippi Embayment Aquifer region. All data sets including surface precipitation, maximum temperature, minimum temperature, relative humidity, wind speed, and solar radiation integrated on a daily basis at a CRD level. Changes of reference evapotranspiration (ET), drought index, and vapor pressure deficit (VPD) will be delivered from 1981 to 2060 on both high spatial and temporal resolutions. The WRF model developed for the Ogallala Aquifer region will be used as a starting point for modeling the Mississippi Embayment Aquifer. Statistical analysis of climate states and changes associated with the water demands and waters supplies for the Mississippi Delta region will be used to refine the model. Models for both stabilized and high levels of green house gas emissions will be developed as will reference evapotranspiration, drought index, and vapor pressure deficit data sets.