Abstract

Coupled models of the land surface and the subsurface, which incorporate hydrologic components into LSMs and couple the deeper subsurface with the atmosphere, may yield significant improvements in both short-term climate forecasting and flood/drought forecasting. A fully-coupled land surface hydrologic model, Flux-PIHM, is developed by incorporating a land-surface scheme into the Penn State Integrated Hydrologic Model (PIHM). The land-surface scheme is mainly adapted from the Noah LSM, which is widely used in mesoscale atmospheric models and has undergone extensive testing. Because PIHM is capable of simulating lateral water flow and deep groundwater, Flux-PIHM is able to represent both the link between groundwater and the surface energy balance as well as some of the land surface heterogeneities caused by topography.

Flux-PIHM has been implemented at the Shale Hills watershed (0.08 km2) in central Pennsylvania. Observations of discharge, water table depth, soil moisture, soil temperature, and sensible and latent heat fluxes in June and July 2009 are used to manually calibrate Flux-PIHM. Model predictions from 1 March to 1 December 2009 are evaluated. Model predictions of discharge, soil moisture, water table depth, sensible and latent heat fluxes, and soil temperature show good agreements with observations. The discharge prediction is comparable to state-of-the-art conceptual models implemented at similar watersheds. Comparisons of model predictions between Flux-PIHM and the original hydrologic model PIHM show that the inclusion of the complex surface energy balance simulation only brings slight improvement in hourly model discharge predictions. Flux-PIHM does improve the evapotranspiration prediction at hourly scale, the prediction of total annual discharge, and also improves the predictions of some peak discharge events, especially after extended dry periods. Model results reveal that annual average sensible and latent heat fluxes are strongly correlated with water table depth, and the correlation is especially strong for the model grids near the river.

To simplify the procedure of model calibration, a Flux-PHIM data assimilation system is developed by incorporating the ensemble Kalman filter (EnKF) into Flux-PIHM. This is the first parameter estimation using EnKF for a physically-based hydrologic model. Both synthetic and real data experiments are performed at the Shale Hills watershed to test the capability of EnKF in parameter estimation. Six model parameters selected from a model parameter sensitivity test are estimated. In the synthetic experiments, synthetic observations of discharge, water table depth, soil moisture, land surface temperature, sensible and latent heat fluxes, and transpiration are assimilated into the system. Results show that EnKF is capable of accurately estimating model parameter values for Flux-PIHM. The estimated parameter values are very close to the true parameter values. Synthetic experiments are also performed to test the efficiency of assimilating different observations. It is found that discharge, soil moisture, and land surface temperature (or sensible and latent heat fluxes) are the most critical observations for Flux-PIHM calibration.  In real data experiments, in situ observations of discharge, water table depth, soil moisture, and sensible and latent heat fluxes are assimilated. Results show that for five out of the six parameters, the EnKF-estimated parameter values are very close to the manually-calibrated parameter values. The predictions using EnKF-estimated parameters and manually-calibrated parameters are also similar. Thus the results demonstrate that, given a limited number of site-specific observations, an automated calibration method (EnKF) can be used to optimize Flux-PIHM for watersheds like Shale Hills.

Citation

Shi, Y. (2012): Development of a land surface hydrologic modeling and data assimilation system for the study of subsurface-land surface interaction. Doctor of Philosophy, Meteorology, The Pennsylvania State University, p. 214..

This Paper/Book acknowledges NSF CZO grant support.