UC Irvine

Soil moisture is a vital component of the hydrologic cycle. It modulates the partitioning of infiltration and runoff as well as controls surface moisture and energy fluxes. Until recent years, it has also been one of the most overlooked components because of the difficulty to effectively measure and model it at scales most relevant to the hydrologic community. With a rise of new observation technologies and a growth in modeling efforts, this dissertation aims to investigate and establish the most useful ways to incorporate observed soil moisture information into a conceptually-based distributed hydrologic model developed for operational streamflow prediction by the U.S. National Weather Service. A common platform of soil saturation ratios for comparison between observations and model simulations of soil moisture is established using past observations mindfully combined with calculations of soil hydraulic properties based on readily available characteristics from soil surveys. Data assimilation using an Ensemble Kalman Filter is used to update conceptual model storages with observed volumetric soil moisture. The assimilation is modified to address underdispersiveness that occurs following a precipitation event and after prolonged dry periods. A second filtering step is developed to spread innovation at pixels collocated with observations to model pixels that are not “observed.” The use of soil moisture observations as a tool for calibration is also investigated, and a new two-step hybrid calibration scheme that utilizes both soil moisture observations and observed streamflow is introduced. The effects of soil moisture data assimilation and soil moisture-based calibration on simulated streamflow are also analyzed.

Development of soil saturation ratio comparison efforts and data assimilation techniques are conducted in the Russian River Basin in northern California. Both in situ soil moisture probes and Soil Moisture Ocean Salinity (SMOS) surface soil moisture estimates are tested in the data assimilation experiments. The soil moisture-based calibration development takes place in the Turkey River Basin in northeastern Iowa, and is further tested in the Russian River Basin. Results show that while both uses of soil moisture are effective in improving estimates of the soil moisture state, the calibration scheme is more stable and necessary to improve simulated streamflow.