Co-developing CONUS-wide current and future hydroclimate projections to support US agency water security initiatives

The US Secure Water Act of 2010 requires several US agencies to report to Congress every five years on future water-related mission vulnerabilities. Over the last 15 years, 21st century climate projection datasets from the Coupled Model Intercomparison Projects (CMIP) have been downscaled and used to drive hydrologic and streamflow scenarios across the Contiguous United States (CONUS).  The resulting datasets form input for federal and state agency planning, guidance and policy, for water resources applications from watershed to regional scales, and for the climate-water research community. The advent of CMIP6 has triggered the co-development of new, updated hydrologic modeling for future hydroclimate impact projections, which is proceeding via a multi-agency effort that integrates researchers with stakeholders from US federal water, climate and energy agencies. The effort has lately spurred interest in a related trans-boundary joint hydroclimate science effort between the US and Canada. This effort uses the process-oriented SUMMA land/hydrology model and mizuRoute channel routing model, which have been configured for CONUS and adjoining watersheds at a USGS HUC12 (and MERIT-Hydro) watershed resolution, a contrast to earlier grid-based modeling approaches. Several hundred CMIP6 future climate scenarios are being downscaled to drive future hydrologic assessments that are tailored to water agency planning needs.

This work necessitated the creation of new strategies to upgrade existing capabilities in continental-scale process-based hydrological modelling and projections, which have been undermined by poor calibration in prior iterations. Notable innovations included a powerful new large-sample parameter estimation approach based on machine-learning (ML) emulators; creating extended (CAMELS-like) large-sample catchment datasets for model calibration and validation (using both natural and reconstructed historical streamflow observations); creating a new CONUS-wide multi-decadal high-resolution surface meteorological (forcing) dataset, derived using ML methods; and the use of water management guided performance metrics to inform model training and evaluation. This presentation summarizes the new CMIP6 hydroclimate dataset initiative, and highlights the critical role of integrated researcher-stakeholder engagement in achieving fit-for-purpose and actionable large-domain hydrology outcomes.