Toward Improving Predictability of Extreme Hydrometeorological Events in the Northern High Plains: Multi-scale Climate vs Land Surface Hydrology Modeling

Our goal is to investigate predictability of hydrometeorological extreme events in relation with surface water availability in the Northern High Plains. Hydrometeorological extreme events are considered the most costly natural phenomena. As a key component of weather and climate systems, observing or modeling water availability contributes to better understand the interdependence water-climate. However, this understanding, still in early stage, is challenged when water deficits and surpluses prevail at particular areas or in particular timespans, affecting ecosystems and agro-ecosystems' sustainability. In consequence, we still grapple to identify what sources of predictability could be added to flood and drought forecasts in areas such as the NHP. To identify the possible benefit of multi-scale climate modeling and seasonal hydrological forecast on flood and drought predictability on the NHP, we implement the use the Ocean Land Atmospheric Model (OLAM) and the Variable Infiltration capacity Model (VIC). OLAM is characterized by a dynamic core with a global geodesic grid with hexagonal (and variably refined) mesh cells and a finite volume discretization of the full compressible Navier Stokes equations, a cut-grid cell method for topography (that reduces error in computational gradient computation and anomalous vertical dispersion). On the other hand, VIC is a semi-distributed land-surface hydrology model forced by precipitation, minimum and maximum temperature and wind speed. Our hypothesis is that multi-scale climate forecast will be more sensitive to initial conditions than hydrologic forecast. To test this hypothesis we simulate precipitation during identified historical flood and drought events in the NHP (i.e. 2011-2012 years). We initialized OLAM with CFS-data 1-10 days previous to a flooding event (as initial conditions) to explore (1) short-term and high-resolution and (2) long-term and coarse-resolution simulations of flood and drought events, respectively. On the other hand, the ensemble streamflow prediction (ESP, consistent multiple 1-year simulations initialized with a single initial condition) is based on the use of VIC. VIC is forced with observational data regridded to 1/16th degree resolution obtained from the Sub-continental Observation Dataset, developed from climatological stations in Canada, US, and Mexico for the 1950-2013 timespan. While floods are assessed during a maximum of 15-days refined-mesh simulations, droughts are evaluated during 12 months. Simulated precipitation will be compared with the Sub-continental Observation Dataset, a gridded 1/16th degree resolution data obtained from climatological stations in Canada, US, and Mexico. Hydrologic simulations will be compared with USGS's historical streamflow data. This in-progress research will ultimately contribute to integrate OLAM and VIC models and improve predictability of extreme hydrometeorological events.