Soil moisture is a critical variable for water and energy cycle. It determines the partitioning of available energy into sensible, latent and ground heat fluxes, as well as the partitioning of incoming precipitation into surface runoff and infiltration. The prediction of soil moisture pattern at high spatial resolution, however, is challenging. This project aims to answer the following questions: (1) Can we predict the observed high resolution soil moisture pattern (100-101 m) using numerical models? (2) What data and modeling techniques are needed to resolve fine scale land surface heterogeneities using numerical models? and (3) Is national soil database sufficient for high resolution modeling? The model used in this project is a coupled land surface hydrologic model, Flux-PIHM, which adds a land surface balance scheme to the Penn State Integrated Hydrologic Model (PIHM). Flux-PIHM has been implemented at the Shale Hills watershed (0.08 km2) in central Pennsylvania with an average grid size of 150 m2. The locally measured soil maps, soil parameters, and tree map, as well as LiDAR topographic data have been synthesized into the Flux- PIHM model to provide soil, land cover, and topography inputs.
Calibrated only using watershed-scale data (i.e., discharge and surface heat fluxes) and point measurements (i.e., soil moisture and water table depth at one location), and driven by spatially uniform forcing data, Flux-PIHM is able to resolve the observed hill-slope scale (101 m) soil moisture pattern at the watershed owing to the spatially-distributed physically-based hydrologic component, especially the simulation of horizontal groundwater flow. The model successfully reproduces the seasonal change of soil moisture, and resolves the observed soil moisture pattern. This ability of Flux-PIHM to resolve hill-slope patterns is unique relative to current land surface models, and is especially significant for high-resolution simulations at small watersheds, which represent a large areal fraction of many landscapes.
To test the important factors that drive the fine-scale soil moisture pattern, different Flux-PIHM runs using different combinations of soil maps, soil parameters, and bedrock depths are performed. While different test cases are all able to provide good agreement with catchment-scale observations and point observations, the predicted soil moisture patterns are considerably different among different test cases. Model results show that the prediction of soil moisture distribution is strongly driven by the input soil maps and soil parameters, as well as topography (surface elevation and bedrock depth); water table position is determined by surface topography and bedrock depth. Results suggest that using the filed measured soil maps and parameters significantly improves the predicted soil moisture pattern, compared with using the national database (SSURGO) 30-m resolution product.
Shi, Y., Baldwin, D.C., Davis, K.J., Yu, X., Duffy, C., Lin, H. (2013): Resolving the High Resolution Soil Moisture Pattern at the Shale Hills Watershed Using a Land Surface Hydrologic Model. Abstract H23F-1332 presented at 2013 Fall Meeting, AGU, San Francisco, CA, 9-13 Dec..
This Paper/Book acknowledges NSF CZO grant support.