Improving irrigation and groundwater parameterizations in the Community Land Model (CLM) using in-situ observations and satellite data

In this study, we use in-situ observations and satellite data of soil moisture and groundwater to improve irrigation and groundwater parameterizations in the version 4.5 of the Community Land Model (CLM). The irrigation application trigger, which is based on the soil moisture deficit mechanism, is enhanced by integrating soil moisture observations and the data from the Soil Moisture Active Passive (SMAP) mission which is available since 2015. Further, we incorporate different irrigation application mechanisms based on schemes used in various other land surface models (LSMs) and carry out a sensitivity analysis using point simulations at two different irrigated sites in Mead, Nebraska where data from the AmeriFlux observational network are available. We then conduct regional simulations over the entire High Plains region and evaluate model results with the available irrigation water use data at the county-scale. Finally, we present results of groundwater simulations by implementing a simple pumping scheme based on our previous studies. Results from the implementation of current irrigation parameterization used in various LSMs show relatively large difference in vertical soil moisture content profile (e.g., 0.2 mm3/mm3) at point scale which is mostly decreased when averaged over relatively large regions (e.g., 0.04 mm3/mm3 in the High Plains region). It is found that original irrigation module in CLM 4.5 tends to overestimate the soil moisture content compared to both point observations and SMAP, and the results from the improved scheme linked with the groundwater pumping scheme show better agreement with the observations.