UC Berkeley

Modeling groundwater is challenging: it is not readily visible and is difficult to measure, with limited sets of observations available. Even though groundwater models can reproduce water table and head variations, considerable drift in modeled land surface states can nonetheless result from partially known geologic structure, errors in the input forcing fields, and imperfect Land Surface Model (LSM) parameterizations. These models frequently have biased results that are very different from observations. While many hydrologic groups are grappling with developing better models to resolve these issues, it is also possible to make models more robust through data assimilation of observation groundwater data. The goal of this project is to develop a methodology for high-resolution land surface model runs over large spatial region and improve hydrologic modeling through observation data assimilation, and then to apply this methodology to improve groundwater monitoring and banking.

The high-resolution LSM modeling in this dissertation shows that model physics performs well at these resolutions and actually leads to better modeling of water/energy budget terms. The overarching goal of assimilation methodology is to resolve the critical issue of how to improve groundwater modeling in LSMs that lack sub-surface parameterizations and also run them on global scales. To achieve this, the research in this dissertation has been divided into three parts. The first goal was to run a commonly used land surface model at hyper resolution (1 km or finer) and show that this improves the modeling results without breaking the model. The second goal was to develop an observation data assimilation methodology to improve the high-resolution model. The third was to show real-world applications of this methodology.

The need for improved accuracy is currently driving the development of hyper-resolution land surface models that can be implemented at a continental scale with resolutions of 1 km or finer. In Chapter 2, I describe our research incorporating fine-scale grid resolutions and surface data into the National Center for Atmospheric Research (NCAR) Community Land Model (CLM v4.0) for simulations at 1 km, 25 km, and 100 km resolution using 1 km soil and topographic information. Multi-year model runs were performed over the southwestern United States, including the entire state of California and the Colorado River basin. Results show changes in the total amount of CLM-modeled water storage and in the spatial and temporal distributions of water in snow and soil reservoirs, as well as in surface fluxes and energy balance. We also demonstrate the critical scales at which important hydrological processes--such as snow water equivalent, soil moisture content, and runoff--begin to more accurately capture the magnitude of the land water balance for the entire domain. This proves that grid resolution itself is also a critical component of accurate model simulations, and of hydrologic budget closure.

To inform future model progress, we compare simulation outputs to station and gridded observations of model fields. Although the higher grid resolution model is not driven by high-resolution forcing, grid resolution changes alone yield significant reductions in the Root Mean Square Error (RMSE) between model outputs and actual observations: the RMSE decreases by more than 35% for soil moisture, 36% for terrestrial water storage anomaly, 34% for sensible heat, and 12% for snow water equivalent. The results of a 100 m resolution simulation over a spatial sub-domain indicate that parameters such as drainage, runoff, and infiltration are significantly impacted when hillslope scales of ~100 meters or finer are considered. We further show how limitations in the current model physics, including no lateral flow between grid cells, can affect model simulation accuracy.

The results presented in Chapter 2 are encouraging, but also highlight the limitations in improving an LSM by only increasing spatial resolution of the model and the surface datasets. As was shown with the water table depth analysis, increasing model resolution cannot compensate for parameterization errors and lack of sub-surface information in CLM. However, this problem can be solved by providing additional information to the model in the form of water table depth via data assimilation.

In Chapter 3, I discuss the development and verification of a methodology for assimilating observed groundwater depth measurements from multiple wells into the high spatial resolution LSM. A kriging-based interpolation technique is employed to overcome the problem of spatially and temporally sparse observations, and the interpolated data is assimilated into the CLM4.0 at 1 km resolution in a test region in northern California. Direct insertion and Ensemble Adjusted Kalman Filter (EAKF) based techniques are used for assimilation with direct insertion, producing better results and demonstrating major improvement in the simulation of water table depth. The Linear Pearson correlation coefficient between the observed well data and the assimilated model is 0.810, as opposed to only 0.107 for the non-assimilated model. This improvement is most significant where the water table depth is greater than 5 m. Assimilation also improves soil moisture content, especially in the dry season when the water table is at its lowest. Other variables, including sensible heat flux, ground evaporation, infiltration, and runoff are also analyzed in order to quantify the effect of this assimilation methodology. Though the changes in these variables seem small, they can be very important in coupled models, and the improved simulation of groundwater in the assimilated model can explain the changes in these results.

This assimilation technique has been designed for use in regions with sparse and varied observation data, and it can be easily adapted to work in LSMs with a functional groundwater component. This gives us the capability to better model groundwater for the recent past and present, and also the potential to apply climate projections to probabilistically predict groundwater for future climate-change scenarios.

We have collaborated with Wellintel Inc. to implement our methodology on the ground using their observation data. We are in the process of setting up our model over a large region along the central California coast, where for the past few months Wellintel has implemented a pilot with measurements based on its water table depth measuring devices. The aim of this collaboration is to provide users with actionable water table depth data in and around their properties for the past, present, and near future. We are combining this methodology with Wellintel data to create a groundwater-management and groundwater-banking monitoring tool.

This is the first time that groundwater assimilation has been simulated in a high-resolution LSM, and as such this project provides proof-of-concept and application of a unique methodology that can be run at hyper resolution with data assimilation. The assimilation method is a very powerful tool that researchers can now apply to model land surface parameters much better than previously.