Search Results

You are looking at 1 - 2 of 2 items for :

  • Author or Editor: Todd M. Crawford x
  • Journal of Hydrometeorology x
  • Refine by Access: All Content x
Clear All Modify Search
Todd M. Crawford
,
David J. Stensrud
,
Toby N. Carlson
, and
William J. Capehart

Abstract

The Soil Hydrology Model (SHM) was modified, and daily simulations of soil volumetric water content were made at 38 Oklahoma Mesonet sites for July 1997. These model results were compared with soil moisture observations made at the mesonet sites at depths of 5, 25, 60, and 75 cm. This work is believed to be the first time that a hydrological model has been evaluated with in situ soil moisture measurements over such an extensive area spanning several climate zones.

Comparisons of time series between the observed and modeled domain-averaged volumetric water content at 5 cm revealed similar phase and amplitude changes, a coefficient of determination (R 2) of 0.64, and small mean bias and root-mean-square errors (MBE and rmse) of 0.03 and 0.09, respectively. At 25, 60, and 75 cm, the model performance was slightly worse, with R 2 values between 0.27 and 0.40, MBE between −0.01 and 0.02, and rmse between 0.11 and 0.13. The model response to changes in soil water at these levels was sluggish, possibly because of, among other things, a lack of ability to model preferential downward water flow through cracks in the soil.

The results of this study suggest that SHM can be used effectively to initialize 5-cm soil moisture values in numerical prediction models. At deeper soil levels, however, the relatively small R 2 values and negligible MBE suggest that the model may be better suited for initializing a regionally averaged soil moisture value rather than unique gridbox values. These results illustrate the difficulty in using point measurements to validate a hydrological model, especially deeper in the soil where moisture values are more dependent on soil properties (which can vary sharply over small distances) and are less dependent on recent rainfall.

Full access
Todd M. Crawford
,
David J. Stensrud
,
Franz Mora
,
James W. Merchant
, and
Peter J. Wetzel

Abstract

The Parameterization for Land–Atmosphere–Cloud Exchange (PLACE) module is used within the Fifth-Generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) to determine the importance of individual land surface parameters in simulating surface temperatures. Sensitivity tests indicate that soil moisture and the coverage and thickness of green vegetation [as manifested by the values of fractional green vegetation coverage (fVEG) and leaf area index (LAI)] have a large effect on the magnitudes of surface sensible heat fluxes. The combined influence of LAI and fVEG is larger than the influence of soil moisture on the partitioning of the surface energy budget. Values for fVEG, albedo, and LAI, derived from 1-km-resolution Advanced Very High Resolution Radiometer data, are inserted into PLACE, and changes in model-simulated 1.5-m air temperatures in Oklahoma during July of 1997 are documented. Use of the land cover data provides a clear improvement in afternoon temperature forecasts when compared with model runs with monthly climatological values for each land cover type. However, temperature forecasts from MM5 without PLACE are significantly more accurate than those with PLACE, even when the land cover data are incorporated into the model. When only the temperature observations above 37°C are analyzed, however, the simulations from the high-resolution land cover dataset with PLACE significantly outperform MM5 without PLACE. Previous land surface models have simply used (at best) climatological values of these crucial land cover parameters. The ability to improve model simulations of surface energy fluxes and the resultant temperatures in a diagnostic sense provides promise for future attempts at ingesting satellite-derived land cover data into numerical models. These model improvements would likely be most helpful in predictions of extreme temperature events (during drought or extremely wet conditions) for which current numerical weather prediction models often perform poorly. The potential value of real-time land cover information for model initialization is substantial.

Full access