JULY1997 903NIYOGI AND RAMANq 1997 American Meteorological SocietyComparison of Four Different Stomatal Resistance Schemes Using FIFE ObservationsDEVDUTTAS. NIYOGI ANDSETHURAMANDepartment of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina(Manuscript received 11 July 1996, in final form 12 November 1996)ABSTRACTStomatal resistance (Rs) calculation has a major impact on the surface energy partitioning that influencesdiverse boundary layer processes. Present operational limited area or mesoscale models have the Jarvis-typeparameterization, whereas the microscale and the climate simulation models prefer physiological schemes forestimating Rs. The pivotal question regarding operational mesoscale models is whether an iterative physiologicalscheme 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 Rsusing observations made during FIFE. The data used is typical of a C4-typevegetation, predominant in regions of high convective activity such as the semiarid Tropics and the southernUnited States grasslands. Data from three different intensive field campaigns are analyzed to account for veg-etation and hydrological diversity.It is found that the Jarvis-type approach has low variance in the outcome due to a poor feedback for theambient changes. The physiological models, on the other hand, are found to be quite responsive to the externalenvironment. All three physiological schemes have a similar performance qualitatively, which suggests that thevapor pressure deficit approach or the relative humidity descriptor used in the physiological schemes may notyield different results for routine meteorological applications. For the data considered, the physiological schemeshad a consistently better performance compared to the Jarvis-type scheme in predicting Rsoutcome. All fourschemes can, however, provide a reasonable estimate of the ensemble mean of the samples considered. Asignificant influence of the seasonal change in the minimum Rsin 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.1. IntroductionVarious planetary boundary layer (PBL) and generalcirculation models (GCMs) are linked with soil–vege-tation–atmosphere transfer (SVAT) schemes. Some ofthese land surface parameterizations presentlyemployedin PBL and GCM studies include Deardorff (1978),BATS (Dickinson et al. 1986), Avissar et al. (1985),SiB (Sellers et al. 1986), Wetzel and Chang (1988),Noilhan and Planton (1989), Acs (1994), Bosilovich andSun (1995), Viterbo and Beljaars (1995), Pleim and Xiu(1995), and Alapaty et al. (1996a). These models havevarying degrees of complexity when describing the en-ergy partitioning at the surface—that is, at the soil andat the vegetation. In addition to these ‘‘operational’’ or‘‘meteorological’’ schemes, physiologically intensivemodels for the terrestrial biosphere–atmosphere inter-actions also exist (see Farquhar and Sharkey 1982).Some of these include Farquhar et al. (1980), Ball etal. (1987), Meyers and Paw U (1987), Lynn and CarlsonCorresponding author address: Prof. Sethu Raman, Dept. ofMEAS, North Carolina State University, Raleigh, NC 27695-8208.E-mail: [email protected](1990), Raupach (1991), Collatz et al. (1991, 1992),Kim and Verma (1991), Baldocchi (1992, 1994), Jacobs(1994), Dougherty et al. (1994), SiB2 (Sellers et al.1996), Cox et al. (1996), and IBIS (Foley et al. 1996).Although other approaches such as those used by Mon-teith (1995a,b) and Makela et al. (1996) seem to providepromising insight for understanding the SVAT strategyfrom observations, they are still evolving and have notbeen incorporated in weather or climate simulation mod-els yet.One of the principal differences between the ‘‘me-teorological’’ and the ‘‘physiological’’ approach of theSVAT parameterization is the manner in which the sto-matal response is modeled. The stomatal response,quantified as stomatal resistance (or conductance), is ameasure of the difficulty (or ease) for the vegetation totranspire. Change in the transpiration alters the evapo-transpirative/latent heat flux, which due to the surfaceenergy balance constraints in the modeling perspectivemodifies the sensible heat flux realizations (cf. Alapatyet al. 1996a; Jarvis and McNaughton 1986; DeBruin1983). The possible impact of the stomatal resistancechanges on the coupled atmospheric processes, arequantified in Figs. 1a–c (adopted from Niyogi 1996).904 VOLUME36JOURNAL OF APPLIED METEOROLOGYFIG. 1. (a) Resulting top soil moisture (10-cm-thick layer) variationfor changes in the initial Rs minvalues. In the figure, 30, 60, and 120refer to the Rs minvalues (s m21) considered. (b) Same as in (a) exceptfor surface sensible heat flux. Notice the peak values for the extremecases (30 and 120 s m21) differ by almost a factor of 2. (c) Same asin (a) except for turbulent heat flux profiles. Sensible heat fluxes areplotted on the left and are denoted by SHF, while the latent heat fluxesare on the right and are denoted by LHF.The results are based on a one-dimensional simulationoutput with a meteorological stomatal resistance (Rs)parameterization (Noilhan and Planton 1989; Alapatyet al. 1996a) using FIFE [First ISLSCP (InternationalSatellite Land Surface Climatology Project) Field Ex-periment] observations [see Pleim and Xiu (1995) andAlapaty et al. (1996) for the planetary boundary layermodel initialization and domain details]. The sensitivityof Rsestimation in the boundary layer turbulent energyflux partitioning and hydrological budgeting is dis-played by varying the minimum Rsfrom 30, 60, and120sm21. Figure 1a shows the change in the predictedtop 10-cm-thick surface-layer soil moisture for the threeresistances. Around 1300 LT, the top soil moisture forthe highest Rsis nearly half of that for the lowest Rs.Similarly, there is a considerable impact on the energyflux estimations (Figs. 1b,c). Once again, the highestresistance has the highest sensible heat flux values (Fig.1b), which influences other boundary layer parameterssuch as