Editorial Type:
Article
Article Type:
Research Article

Comparison of Four Different Stomatal Resistance Schemes Using FIFE Observations

Devdutta S. Niyogi Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina

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Sethu Raman Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina

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Print Publication:
01 Jul 1997
DOI:
https://doi.org/10.1175/1520-0450(1997)036<0903:COFDSR>2.0.CO;2
Page(s):
903–917
Restricted access

Abstract

Stomatal resistance (Rs) calculation has a major impact on the surface energy partitioning that influences diverse boundary layer processes. Present operational limited area or mesoscale models have the Jarvis-type parameterization, whereas the microscale and the climate simulation models prefer physiological schemes for estimating Rs. The pivotal question regarding operational mesoscale models is whether an iterative physiological scheme needs to be adopted ahead of the analytical Jarvis-type formulation.

This question is addressed by comparing the ability of three physiological schemes along with a typical Jarvis-type scheme for predicting Rs using observations made during FIFE. The data used is typical of a C4-type vegetation, predominant in regions of high convective activity such as the semiarid Tropics and the southern United States grasslands. Data from three different intensive field campaigns are analyzed to account for vegetation and hydrological diversity.

It is found that the Jarvis-type approach has low variance in the outcome due to a poor feedback for the ambient changes. The physiological models, on the other hand, are found to be quite responsive to the external environment. All three physiological schemes have a similar performance qualitatively, which suggests that the vapor pressure deficit approach or the relative humidity descriptor used in the physiological schemes may not yield different results for routine meteorological applications. For the data considered, the physiological schemes had a consistently better performance compared to the Jarvis-type scheme in predicting Rs outcome. All four schemes can, however, provide a reasonable estimate of the ensemble mean of the samples considered. A significant influence of the seasonal change in the minimum Rs in the Jarvis-type scheme was also noticed, which suggests the use of nitrogen-based information for improving the performance of the Jarvis-type scheme. A possible interactive influence of soil moisture on the capabilities of the four schemes is also discussed. Overall, the physiological schemes performed better under higher moisture availability.

Corresponding author address: Prof. Sethu Raman, Dept. of MEAS, North Carolina State University, Raleigh, NC 27695-8208.

Abstract

Stomatal resistance (Rs) calculation has a major impact on the surface energy partitioning that influences diverse boundary layer processes. Present operational limited area or mesoscale models have the Jarvis-type parameterization, whereas the microscale and the climate simulation models prefer physiological schemes for estimating Rs. The pivotal question regarding operational mesoscale models is whether an iterative physiological scheme needs to be adopted ahead of the analytical Jarvis-type formulation.

This question is addressed by comparing the ability of three physiological schemes along with a typical Jarvis-type scheme for predicting Rs using observations made during FIFE. The data used is typical of a C4-type vegetation, predominant in regions of high convective activity such as the semiarid Tropics and the southern United States grasslands. Data from three different intensive field campaigns are analyzed to account for vegetation and hydrological diversity.

It is found that the Jarvis-type approach has low variance in the outcome due to a poor feedback for the ambient changes. The physiological models, on the other hand, are found to be quite responsive to the external environment. All three physiological schemes have a similar performance qualitatively, which suggests that the vapor pressure deficit approach or the relative humidity descriptor used in the physiological schemes may not yield different results for routine meteorological applications. For the data considered, the physiological schemes had a consistently better performance compared to the Jarvis-type scheme in predicting Rs outcome. All four schemes can, however, provide a reasonable estimate of the ensemble mean of the samples considered. A significant influence of the seasonal change in the minimum Rs in the Jarvis-type scheme was also noticed, which suggests the use of nitrogen-based information for improving the performance of the Jarvis-type scheme. A possible interactive influence of soil moisture on the capabilities of the four schemes is also discussed. Overall, the physiological schemes performed better under higher moisture availability.

Corresponding author address: Prof. Sethu Raman, Dept. of MEAS, North Carolina State University, Raleigh, NC 27695-8208.

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