A framework for improving land-surface hydrologic process representation in CLM over California

To address the Climate Prediction Project for the Americas (CPPA), formerly known as the Global Energy and Water Cycle Experiment (GEWEX) American Prediction Project (GAPP) research priority: “Climate-based hydrologic and water management applications at regional scales,” this proposal has selected California as a data-rich, high population, and scientifically productive study region. California’s wdater supply systems are straining to keep up with economic growth and urban development. The groundwater resource—which accounts for 30-40% of the water California uses — is diminishing at a rate of millions of acre-feet per year. The state has experienced two major droughts and three major floods since 1980’s, and California continues to grow and build. Combined, these regional changes pose an urgent need for accurate models and reliable predictions of key hydrologic processes of regional climate change and guidance for California’s water management responses.

The UC Irvine Center for Hydrometeorology and Remote Sensing (CHRS) proposes to respond specifically to the call for “Efforts of development and improvement of integrated (i.e., coupled snow, surface water, soil moisture and groundwater) hydrologic models…data assimilations, model evaluations against high-resolution datasets, and parameterization of water management (e.g. irrigation, reservoir storage, and release, groundwater withdrawal etc.) for use in basin- to continental-scale models.” To address this challenge, proposed integrated hydrological models would include: 1) a remote sensing satellite-based snow model, 2) a modified Community Land Model (CLM) Land Surface Model (LSM), and 3) a two-dimensional MODFLOW (The USGS Modular ground-Water Model—the Ground-Water Flow Process). In addition to simulating the basic hydrologic processes, these coupled models will aim specifically to improve estimations of interactive mechanisms: snow cover and Snow Water Equivalent (SWE) estimation using in-situ and satellite data; the partition of snow water into runoff, soil moisture and groundwater recharge; parameterization of seasonal irrigation and groundwater pumping over the state, and groundwater discharge/recharge to/from river flows.

Among only a few U.S. states that have accumulated decades of ground-based water records, California maintains extensive networks for regular hydrological measurements. Taking advantage of California’s wealth of historical and current data, we propose to use long-term data for model parameter calibration and sequential data assimilation techniques to substantially improve model performance. Calibrating model parameters using information from long-term observations will optimize values for key model parameters, reducing model uncertainty. The sequential data assimilation technique—the Sequential Bayesian Filter with Monte Carlo implementation (SBF-MC) will integrate near real-time hydrological measurements from satellites (e.g. top layer soil moisture) and/or ground sites (e.g. stream gauge data and groundwater well monitoring) to both improve the predictions of model state variables and quantify prediction uncertainty.