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. (Abstract shortened by UMI.)
|Advisor:||Miller, Norman L.|
|Commitee:||Baldocchi, Dennis D., Chiang, John, Rubin, Yoram|
|School:||University of California, Berkeley|
|School Location:||United States -- California|
|Source:||DAI-B 76/08(E), Dissertation Abstracts International|
|Subjects:||Geography, Hydrologic sciences|
|Keywords:||Assimilation, Groundwater, Hydrology, Hyper resolution model, Land surface model|
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