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- Author or Editor: Toby N. Carlson x
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Abstract
Rapid soil-surface drying, which is called “decoupling,” accompanied by an increase in near-surface air temperature and sensible heat flux, is typically confined to the top 1–2 cm of the soil, while the deeper layers remain relatively moist. Because decoupling depends also on a precise knowledge of fractional vegetation cover, soil properties, and soil water content, an accurate knowledge of these parameters is essential for making good predictions of temperature and humidity. Accordingly, some simulations centered on the Atmospheric Radiation Measurement Program Cloud and Radiation Test Bed Southern Great Plains site in Kansas and Oklahoma using a high-resolution substrate layer (Simulator for Hydrology and Energy Exchange at the Land Surface), the Fifth-Generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model, and derived and default values for soil water content and fractional vegetation cover are presented. In so doing, the following points are made: 1) decoupling occurs only within certain threshold ranges of soil water content that are closely related to the soil type and 2) a knowledge of fractional vegetation cover derived from concurrent observations is necessary for capturing the spatial variation in rapid soil drying in forecast models.
Abstract
Rapid soil-surface drying, which is called “decoupling,” accompanied by an increase in near-surface air temperature and sensible heat flux, is typically confined to the top 1–2 cm of the soil, while the deeper layers remain relatively moist. Because decoupling depends also on a precise knowledge of fractional vegetation cover, soil properties, and soil water content, an accurate knowledge of these parameters is essential for making good predictions of temperature and humidity. Accordingly, some simulations centered on the Atmospheric Radiation Measurement Program Cloud and Radiation Test Bed Southern Great Plains site in Kansas and Oklahoma using a high-resolution substrate layer (Simulator for Hydrology and Energy Exchange at the Land Surface), the Fifth-Generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model, and derived and default values for soil water content and fractional vegetation cover are presented. In so doing, the following points are made: 1) decoupling occurs only within certain threshold ranges of soil water content that are closely related to the soil type and 2) a knowledge of fractional vegetation cover derived from concurrent observations is necessary for capturing the spatial variation in rapid soil drying in forecast models.
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.
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.
