Application of high-resolution meteorological data from NCAM-WRF to characterize agricultural drought in small-scale farmlands based on soil moisture deficit

We discussed the applicability of high-resolution meteorological data simulated by the NCAM-Weather Research and Forecasting (NCAM-WRF) model to investigate spatially distributed soil-moisture deficits in site-scale farmland areas. A gridded soil water budget model was developed to utilize the 90 m NCAM-WRF meteorological data to predict soil moisture content (SMC) at multiple depths. The applicability of the NCAM-WRF climatic variables to predict SMC was evaluated by comparing the SMC estimates with in-situ observations at the monitoring site. We used the Quantile Mapping (QM) method to correct the biases of NCAM-WRF precipitation outputs. The SMC estimates derived from the newly developed soil water budget model showed a good agreement with observations, and we proved that the bias-corrected NCAM-WRF precipitation data could improve the predictability of the temporal evolution of SMCs. For characterizing agricultural drought during the crop growing season, we presented a novel approach to estimate the magnitude, duration, and severity of agricultural drought events based on crop's critical pressure head. We mapped the distribution of SMC, soil matric potential (SMP), and drought severity at the 90-m resolution, and the results showed that applying the NCAM-WRF climatic variables to the modeling of SMC/SMP profiles can lead to drought characterization on site scale while accounting for the spatial variability of rainfall and other climatic variables.