Multi-model Future Hydroclimate Projections using Downscaled CMIP6 over the Conterminous United States

Accurate knowledge of long-term hydrometeorological trends forms a critical basis for future water resources planning, mitigation and adaptation strategies. In addition, extreme events can directly govern the regional water surplus/shortages and thereby making it important to understand their spatiotemporal variations in hydroclimate impact assessment. In this study, we generate an ensemble of future hydroclimate projections for the conterminous United States (CONUS) using the latest generation of climate projections from the sixth Coupled Model Intercomparison Project (CMIP6). Downscaling of temperature and precipitation is conducted using the double bias correction constructed analogues (DBCCA) approach with the new Daymet version 4 (Daymet4) as the training data set. We conduct CONUS-scale hydrologic modeling using two calibrated distributed hydrologic models (PRMS and VIC) driven by Daymet4 observation and downscaled CMIP6 under SSP585 emission scenario for both 1980-2019 historic baseline and 2020-2059 future periods. Using monthly and annual simulated runoff as a metric, we investigate the trend and anomaly of water availability at each CONUS 8-digit hydrologic unit (HUC8). This analysis is complemented by evaluating changes in extreme runoff regimes by analyzing high runoff and low runoff, defined as 95^th and 5^th percentile of daily runoff time series, respectively. The trends in runoff variations are explained by evaluating other hydrologic processes such as evapotranspiration and snowmelt to provide a deeper understanding of variations in hydrologic cycle. The multi-model ensemble approach employed in this study allows us to better capture the uncertainties associated with hydrologic impact assessment and provide insights for informed decisions to water resource managers.