A Case Study of the Impact of Land-Atmosphere Coupling on a Persistent Regional Drought in Northeastern Kansas and Northern Missouri

Drought simulation is still a crucial problem as a part of understanding hydrometeorological processes. Previous studies have shown the impact of land-atmosphere coupling on understanding the triggering of convection and both positive and negative soil moisture-precipitation (SM-P) feedbacks and its implications to drought. However, there are still large uncertainties in simulating precipitation in many models. The physical mechanisms embedded in models also have influence on drought predictions. Therefore, understanding and modeling land surface interaction during the development of drought is important to separating modeling uncertainties of surface fluxes from large scale forcing. Our previous research of evaluating precipitation from the NASA Unified WRF model (NU-WRF) model over CONUS from 1999 to 2004 showed a poor precipitation skill in summer (June to August) for most of the US. This result was thought to be due to poor local scale convection since the winter shows higher skill and is more likely due to synoptic scale events. A recent localized drought in Northeastern Kansas and Northern Missouri developed over the spring and summer of 2018. This localized drought developed from a larger drought pattern starting from winter of 2017 and provides an interesting test case for exploring the impact of land-atmosphere interactions on drought development, persistence and recovery. In particular, this seemingly isolated drought is useful to analyze local drought metrics with minimal influence of large-scale atmospheric drought patterns. To study this event, we will run several land surface models (including the Noah and Noah-MP LSMs) over these regions as well as coupled land-atmosphere seasonal simulations using the NU-WRF to analyze the land-atmosphere interaction contributing to drought evolution during this period and compare it to observations. The result will be presented in terms of providing insights to uncertainty of model precipitation and its implication for drought prediction.