Estimation of the Minimum Canopy Resistance for Croplands and Grasslands Using Data from the 2002 International H2O Project

Vegetated surfaces, such as grasslands and croplands, constitute a significant portion of the earth’s surface and play an important role in land–atmosphere exchange processes. This study focuses on one important parameter used in describing the exchange of moisture from vegetated surfaces: the minimum canopy resistance (rcmin ). This parameter is used in the Jarvis canopy resistance scheme that is incorporated into the Noah and many other land surface models. By using an inverted form of the Jarvis scheme, rcmin is determined from observational data collected during the 2002 International H2O Project (IHOP_2002). The results indicate that rcmin is highly variable both site to site and over diurnal and longer time scales. The mean value at the grassland sites in this study is 96 s m 1 while the mean value for the cropland (winter wheat) sites is one-fourth that value at 24 s m 1 . The mean rcmin for all the sites is 72 s m 1 with a standard deviation of 39 s m 1 . This variability is due to both the empirical nature of the Jarvis scheme and a combination of changing environmental conditions, such as plant physiology and plant species composition, that are not explicitly considered by the scheme. This variability in rcmin has important implications for land surface modeling where rcmin is often parameterized as a constant. For example, the Noah land surface model parameterizes rcmin for the grasslands and croplands types in this study as 40 s m 1 . Tests with the coupled Weather Research and Forecasting (WRF)–Noah model indicate that the using the modified values of rcmin from this study improves the estimates of latent heat flux; the difference between the observed and modeled moisture flux decreased by 50% or more. While land surface models that estimate transpiration using Jarvis-type relationships may be improved by revising the rcmin values for grasslands and croplands, updating the rcmin will not fully account for the variability in rcmin observed in this study. As such, it may be necessary to replace the Jarvis scheme currently used in many land surface and numerical weather prediction models with a physiologically based estimate of the canopy resistance.

[1]  Daewon W. Byun,et al.  Simulation of Atmospheric Boundary Layer Processes Using Local- and Nonlocal-Closure Schemes , 1997 .

[2]  J. Lhomme.  Stomatal control of transpiration: Examination of the Jarvis‐type representation of canopy resistance in relation to humidity , 2001 .

[3]  D. Deaven,et al.  The Regional Analysis System for the Operational “Early” Eta Model: Original 80-km Configuration and Recent Changes , 1995 .

[4]  J. D. Tarpley,et al.  The multi‐institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system , 2004 .

[5]  I. E. Woodrow,et al.  A Model Predicting Stomatal Conductance and its Contribution to the Control of Photosynthesis under Different Environmental Conditions , 1987 .

[6]  K. Alapaty,et al.  Comparison of four different stomatal resistance schemes using FIFE data. Part II: Analysis of terrestrial biospheric-atmospheric interactions , 1998 .

[7]  J. Wallace,et al.  Evaporation from sparse crops‐an energy combination theory , 2007 .

[8]  D. Vidal-Madjar,et al.  Estimation of stomatal resistance and canopy evaporation during the HAPEX-MOBILHY experiment , 1992 .

[9]  Evelyne Richard,et al.  An investigation of mesoscale flows induced by vegetation inhomogeneities using an evapotranspiration model calibrated against HAPEX-MOBILHY data. , 1989 .

[10]  E. Mlawer,et al.  Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave , 1997 .

[11]  J. D. Tarpley,et al.  Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model , 2003 .

[12]  R. Pielke.  Influence of the spatial distribution of vegetation and soils on the prediction of cumulus Convective rainfall , 2001 .

[13]  S. Carpenter,et al.  Global Consequences of Land Use , 2005, Science.

[14]  J. Dudhia,et al.  A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes , 2006 .

[15]  M. Raupach,et al.  Maximum conductances for evaporation from global vegetation types , 1995 .

[16]  J. D. Tarpley,et al.  Surface radiation budgets in support of the GEWEX Continental‐Scale International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), including the North American Land Data Assimilation System (NLDAS) project , 2003 .

[17]  J. Dudhia.  Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model , 1989 .

[18]  Lawrence B. Flanagan,et al.  Seasonal and interannual variation in evapotranspiration, energy balance and surface conductance in a northern temperate grassland , 2002 .

[19]  George Burba,et al.  Seasonal and interannual variability in evapotranspiration of native tallgrass prairie and cultivated wheat ecosystems , 2005 .

[20]  R. Sage,et al.  Low‐temperature photosynthetic performance of a C4 grass and a co‐occurring C3 grass native to high latitudes , 2004 .

[21]  Fei Chen,et al.  Development and Evaluation of a Coupled Photosynthesis-Based Gas Exchange Evapotranspiration Model (GEM) for Mesoscale Weather Forecasting Applications , 2009 .

[22]  T. Sharkey,et al.  Stomatal conductance and photosynthesis , 1982 .

[23]  J. Alfieri,et al.  Influence of Land Cover and Soil Moisture on the Horizontal Distribution of Sensible and Latent Heat Fluxes in Southeast Kansas during IHOP_2002 and CASES-97 , 2007 .

[24]  Reinder Ronda,et al.  Representation of the canopy conductance in modeling the surface energy budget for low vegetation , 2001 .

[25]  R. G. Allen,et al.  Evapotranspiration estimates under deficient water supplies , 1996 .

[26]  Sharon Zhong,et al.  Urban and land surface effects on the 30 July 2003 mesoscale convective system event observed in the southern Great Plains , 2006 .

[27]  Sethu Raman,et al.  Comparison of Four Different Stomatal Resistance Schemes Using FIFE Observations , 1997 .

[28]  A..  A new convective adjustment scheme. Part I: Observational and theoretical basis , 2006 .

[29]  Local Initiation of Deep Convection on the Canadian Prairie Provinces , 2004 .

[30]  Luis S. Pereira,et al.  Modelling surface resistance from climatic variables , 2000 .

[31]  Gilles Boulet,et al.  Stomatal control of transpiration: Examination of Monteith's Formulation of canopy resistance , 1998 .

[32]  R. Raddatz.  Moisture recycling on the Canadian Prairies for summer droughts and pluvials from 1997 to 2003 , 2005 .

[33]  C. Potter,et al.  Feedbacks of Vegetation on Summertime Climate Variability over the North American Grasslands. Part I: Statistical Analysis , 2006 .

[34]  M. Gleason,et al.  Forecasting Site-Specific Leaf Wetness Duration for Input to Disease-Warning Systems. , 2006, Plant disease.

[35]  E. Cooter,et al.  Sensitivity of the National Oceanic and Atmospheric Administration multilayer model to instrument error and parameterization uncertainty , 2000 .

[36]  Z. Janjic.  The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes , 1994 .

[37]  P. Blanken,et al.  NCAR/CU Surface, Soil, and Vegetation Observations during the International H2O Project 2002 Field Campaign , 2007 .

[38]  S. Planton,et al.  A Simple Parameterization of Land Surface Processes for Meteorological Models , 1989 .

[39]  David J. Stensrud,et al.  Influences of a Winter Wheat Belt on the Evolution of the Boundary Layer , 2005 .

[40]  Dong-Jun Seo,et al.  The WSR-88D rainfall algorithm , 1998 .

[41]  Steven E. Koch,et al.  An Overview of the International H2O Project (IHOP_2002) and Some Preliminary Highlights , 2004 .

[42]  R. Oren,et al.  Carbon dioxide and water vapor exchange in a warm temperate grassland , 2003, Oecologia.

[43]  J. Stewart,et al.  Preliminary modelling of transpiration from the fife site in Kansas , 1989 .

[44]  J. Stewart.  Modelling surface conductance of pine forest , 1988 .

[45]  D. O. Hall,et al.  The global carbon sink: a grassland perspective , 1998 .

[46]  R. Raddatz.  Evidence for the influence of agriculture on weather and climate through the transformation and management of vegetation: Illustrated by examples from the Canadian Prairies , 2007 .

[47]  Fei Chen,et al.  Effect of Land–Atmosphere Interactions on the IHOP 24–25 May 2002 Convection Case , 2006 .

[48]  Chun-Ta Lai,et al.  Evapotranspiration from a wet temperate grassland and its sensitivity to microenvironmental variables , 2005 .

[49]  G. Fogg.  The state and movement of water in living organisms. , 1966, Journal of the Marine Biological Association of the United Kingdom.

[50]  I. R. Cowan,et al.  Stomatal conductance correlates with photosynthetic capacity , 1979, Nature.

[51]  Bhaskar J. Choudhury,et al.  Synergism of multispectral satellite observations for estimating regional land surface evaporation , 1994 .

[52]  J. Dudhia,et al.  A Revised Approach to Ice Microphysical Processes for the Bulk Parameterization of Clouds and Precipitation , 2004 .

[53]  E. Schulze,et al.  Relationships among Maximum Stomatal Conductance, Ecosystem Surface Conductance, Carbon Assimilation Rate, and Plant Nitrogen Nutrition: A Global Ecology Scaling Exercise , 1994 .

[54]  Peter D. Blanken,et al.  Energy balance and canopy conductance of a boreal aspen forest: Partitioning overstory and understory components , 1997 .

[55]  Zavisa Janjic,et al.  Comments on “Development and Evaluation of a Convection Scheme for Use in Climate Models” , 2000 .

[56]  Shashi B. Verma,et al.  Modeling canopy stomatal conductance in a temperate grassland ecosystem , 1991 .

[57]  David Yates,et al.  Variability in the Environmental Factors Driving Evapotranspiration from a Grazed Rangeland during Severe Drought Conditions , 2007 .

[58]  G. Collatz,et al.  Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer , 1991 .

[59]  A. Keys,et al.  Photosynthetic responses of three C4 grasses of different metabolic subtypes to water deficit. , 2007, Functional plant biology : FPB.

[60]  G. J. Collatz,et al.  Comparison of Radiative and Physiological Effects of Doubled Atmospheric CO2 on Climate , 1996, Science.

[61]  J. Norman,et al.  Correcting eddy-covariance flux underestimates over a grassland , 2000 .

[62]  K. Alapaty,et al.  Uncertainty in the Specification of Surface Characteristics, Part ii: Hierarchy of Interaction-Explicit Statistical Analysis , 1999 .

[63]  T. W. Horst,et al.  Description and Evaluation of the Characteristics of the NCAR High-Resolution Land Data Assimilation System , 2007 .

[64]  D. Charles-Edwards,et al.  The Influence of Temperature on Photosynthesis and Transpiration in ten Temperate Grass Varieties Grown in four Different Environments , 1971 .

[65]  G. Meehl,et al.  The Importance of Land-Cover Change in Simulating Future Climates , 2005, Science.

[66]  Jerry L. Hatfield,et al.  Canopy Resistance as Affected by Soil and Meteorological Factors in Potato , 2004 .

[67]  A. Betts,et al.  A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX and arctic air‐mass data sets , 1986 .

[68]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity , 2001 .

[69]  J. Monteith.  Evaporation and environment. , 1965, Symposia of the Society for Experimental Biology.

[70]  G. Farquhar,et al.  A hydromechanical and biochemical model of stomatal conductance , 2003 .

[71]  P. Jarvis.  The Interpretation of the Variations in Leaf Water Potential and Stomatal Conductance Found in Canopies in the Field , 1976 .

[72]  R. Pielke,et al.  An overview of regional land-use and land-cover impacts on rainfall , 2007 .