Soil Vegetation Atmosphere Transfer Research Articles

Soil Water Evaluation Using a Hydrologic Model and Calibrated Sensor Network

Studies show that it may be possible to combine satellite‐derived soil water maps with soil–vegetation–atmosphere transfer (SVAT) models to obtain spatially distributed, temporally continuous information on vadose zone water contents. However, before this method can be instituted, it is essential to determine the ability of a SVAT model to simulate vadose zone soil water contents. A study was designed to evaluate the simultaneous heat and water (SHAW) model by comparing its soil water predictions with measured soil water contents collected by electrical resistance sensors (ERS) during the Monsoon ′90 multidisciplinary field experiment. ERS collected hourly soil water measurements at 5‐, 15‐, and 30‐cm depths in a shrub‐dominated site [Larrea tridentada (Sessé & Moc. ex DC.) Coville] with large bare interspace areas. Data collected by the ERS were calibrated to time domain reflectometer (TDR) sensor measurements placed adjacent to the ERS using an in situ calibration technique. Results indicated that the SHAW model overestimated soil water at each depth by 0.02 m3 m−3 under bare soil and underestimated soil water at each depth under shrub cover by 0.02 m3 m−3 The ability of the model to simulate ERS water content values gives it the potential to be periodically updated with remotely sensed data to predict vadose zone soil water content over large areas at high temporal resolutions.

Multi-objective conditioning of a simple SVAT model

Abstract. It has previously been argued that current Soil Vegetation Atmosphere Transfer (SVAT) models are over-parameterised given the calibration data typically available. Using the Generalised Likelihood Uncertainty Estimation (GLUE) methodology, multiple feasible model parameter sets are here conditioned on latent heat fluxes and then additionally on the sensible and ground heat fluxes at a single site in Amazonia. The model conditioning schemes were then evaluated with a further data set collected at the same site according to their ability to reproduce the latent, sensible and ground heat fluxes. The results indicate that conditioning the model on only the latent heat flux component of the energy balance does not constrain satisfactorily the predictions of the other components of the energy balance. When conditioning on all heat flux objectives, significant additional constraint of the feasible parameter space is achieved with a consequent reduction in the predictive uncertainty. There are still, however, many parameter sets that adequately reproduce the calibration/validation data, leading to significant predictive uncertainty. Surface temperature measurements, whilst also subject to uncertainty, may be employed usefully in a multi-objective calibration of SWAT models.

Continuous long-term measurements of soil–plant–atmosphere variables at an agricultural site

It is a major challenge in modern science to decrease the uncertainty in predictions of global climate change. One of the largest uncertainties in present-day global climate models resides with the understanding of processes in the soil–vegetation–atmosphere-transfer (SVAT) system. Continuous, long-term data are needed to correctly quantify balances of water, energy and CO2 in this system and to correctly model them. It is the objective of this paper to demonstrate how a combined system of existing sensor, computer, and network technologies could be set up to provide continuous and reliable long-term SVAT-process data from an agricultural site under almost all weather conditions.A long-term climate-monitoring system within the framework of NOPEX was set up in 1993–1994 at the Marsta Meteorological Observatory (MMO). It is situated in a flat agricultural area where annual crops are cultivated on a heavy clay soil. It has successfully monitored relevant states and fluxes in the system, such as atmospheric fluxes of momentum, heat, water vapour and CO2, atmospheric profiles of wind speed, direction, and temperature, short- and long-wave radiation, soil temperature, soil-water contents, groundwater levels, and rainfall and snow depth. System uptime has been more than 90% for most of its components during the first 5 years of operation.Results from the first 5 years of operation has proven MMO to be an ideal site for intercomparison and intercalibration of radiometers and fast turbulence sensors, and for evaluation of other sensors, e.g., rain gauges. The long time series of radiation data have been valuable to establish numerical limits for a set of quality-control flags. MMO has served as a boundary-layer research station and results from NOPEX campaigns show how the dimensionless wind gradient depends not only on the traditional stability parameter z/L but also on the height of the convective boundary layer. Measurements at the observatory grounds and a neighbouring field show a considerable variability in surface properties, which must be accounted for when assessing budgets of heat and other scalars. Questions concerning long-term calibration plans, maintenance of sensors and data-collection system, and continuous development of the computer network to keep it up to date are, however, only partly of interest as a research project in itself. It is thus difficult to get it funded from usual research-funding agencies.The full value of data generated by the MMO system can best be appreciated after a decade or more of continuous operation. Main uses of the data would be to evaluate how SVAT models handle the natural variability of climate conditions, quantification of water, carbon and energy budgets during various weather conditions, and development of new parameterisation schemes in global and regional climate models.

Wetland occurrence in relation to topography: a test of topographic indices as moisture indicators

Information about the spatial distributions of soil moisture or groundwater levels is needed for aggregation of soil–vegetation–atmosphere–transfer (SVAT) models. The possibility of predicting wetness distributions in catchments from topographic data was investigated using topographic indices, notably the TOPMODEL index. The indices were calculated from commercially available gridded data (50×50m2) over two areas with contrasting topography: a catchment (Nåsten) in the low-relief NOPEX region in southern Sweden and a group of catchments in a hilly area (Kassjöån) in central Sweden. The occurrence of mires, assumed to represent the extreme wetness end of the wetness spectrum, was used as field data.The frequency distributions of topographic indices for mire and non-mires were clearly different in Kassjöån, although there was a large overlapping, whereas the two distributions were very similar in Nåsten. Prediction of mires from topographic indices was meaningful only in Kassjöån. Although it gave poor results in terms of fractions of successfully predicted mire cells out of the observed mire cells, the general spatial patterns of mires were fairly well simulated. One important reason for the failure of the indices to predict mires in Nåsten, and probably also to predict other wetness classes, is that the spatial resolution in the index calculation was coarser than typical length scales of the topographic features in this catchment, being only a few tenths of meters.The importance of geologic conditions in modifying the topographic control over the wetness is exemplified from the obtained relationship between topographic indices and mire occurrence.

Continuous long-term measurements of soil-plant-atmosphere variables at a forest site

It is a major challenge in modern science to decrease the uncertainty in predictions of global climate change. One of the largest uncertainties in present-day global climate models resides with the understanding of processes in the soil-vegetation-atmosphere-transfer (SVAT) system. Continuous, long-term data are needed in order to correctly quantify balances of water, energy and CO2 in this system and to correctly model it. It is the objective of this paper to demonstrate how a combined system of existing sensor, computer, and network technologies could be set up to provide continuous and reliable long-term SVAT-process data from a forested site under almost all environmental conditions.The Central Tower Site (CTS) system was set up in 1993–1994 in a 25m high boreal forest growing on a highly heterogeneous till soil with a high content of stones and blocks. It has successfully monitored relevant states and fluxes in the system, such as atmospheric fluxes of momentum, heat, water vapour and CO2, atmospheric profiles of temperature, water vapour, CO2, short-and long-wave radiation, heat storage in soil and trees, sap-flow and a variety of ecophysiological properties, soil-water contents and tensions, and groundwater levels, rainfall and throughfall. System uptime has been more than 90% for most of its components during the first 5 years of operation.Results from the first 5 years of operation include e.g., budgets for energy, water and CO2, information on important but rarely occurring events such as evaporation from snow-covered canopies, and reactions of the forest to extreme drought. The carbon budget shows that the forest may be a sink of carbon although it is still growing. The completeness of the data has made it possible to test the internal consistency of SVAT models. The pioneering set-up at the CTS has been adopted by a large number of SVAT-monitoring sites around the world. Questions concerning tower maintenance, long-term calibration plans, maintenance of sensors and data-collection system, and continuous development of the computer network to keep it up to date are, however, only partly of interest as a research project in itself. It is thus difficult to get it funded from usual research-funding agencies.The full value of data generated by the CTS system can best be appreciated after a decade or more of continuous operation. Main uses of the data would be to evaluate how SVAT models handle the natural variability of climate conditions, quantification of water, carbon and energy budgets during various weather conditions, and development of new parameterisation schemes in global and regional climate models.

Conditioning a multiple‐patch SVAT Model using uncertain time‐space estimates of latent heat fluxes as inferred from remotely sensed data

It has been shown that the calibration of soil vegetation‐atmosphere transfer (SVAT) models is inherently uncertain, even when data are available over a relatively limited homogeneous area. The representation of subgrid‐scale variability of fluxes is not easily achieved because of the lack of information available about appropriate parameter distributions and their covariance. However, remote sensing of thermal surface responses offers the possibility of obtaining distributed estimates of surface fluxes. In this paper, multiple Landsat‐Thematic Mapper (TM) images of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site are used to derive uncertain estimates of the land surface–atmosphere sensible and latent fluxes over a period of time. Employing a framework based on fuzzy set theory, the parameter space representing all feasible parameterizations of a SVAT model are examined with respect to these image estimates. Areal weightings for a number of functional types of flux behavior are then derived through which the temporal evolution of surface fluxes can be estimated.

MUREX: a land-surface field experiment to study the annual cycle of the energy and water budgets

Abstract. The MUREX (monitoring the usable soil reservoir experimentally) experiment was designed to provide continuous time series of field data over a long period, in order to improve and validate the Soil-vegetation-Atmosphere Transfer (SVAT) parameterisations employed in meteorological models. Intensive measurements were performed for more than three years over fallow farmland in southwestern France. To capture the main processes controlling land-atmosphere exchanges, the local climate was fully characterised, and surface water and energy fluxes, vegetation biomass, soil moisture profiles, surface soil moisture and surface and soil temperature were monitored. Additional physiological measurements were carried out during selected periods to describe the biological control of the fluxes. The MUREX data of 1995, 1996, and 1997 are presented. Four SVAT models are applied to the annual cycle of 1995. In general, they succeed in simulating the main features of the fallow functioning, although some shortcomings are revealed.Key words. Hydrology (evapotranspiration; soil moisture; water-energy interactions).

Uncertainty in the Specification of Surface Characteristics, Part ii: Hierarchy of Interaction-Explicit Statistical Analysis

The uncertainty in the specification of surface characteristics in soil-vegetation- atmosphere-transfer (SVAT) schemes within planetary boundary-layer (PBL) or mesoscale models is addressed. The hypothesis to be tested is whether the errors in the specification of the individual parameters are accumulative or whether they tend to balance each other in the overall sense for the system. A hierarchy of statistical applications is developed: classical one-at-a-time (OAT) approach, level 1; linear analysis of variance (ANOVA), level 1.5; fractional factorial (FF), or level 2; two-factor interaction (TFI) technique, or level 2.5; and a non-linear response surface methodology (RSM), or level 3. Using the First ISLSCP Field Experiment (FIFE) observations for June 6, 1987 as the initial condition for a SVAT scheme dynamically coupled to a PBL model, the interactions between uncertainty errors are analyzed. A secondary objective addresses the temporal changes in the uncertainty pattern using data for morning, afternoon, and evening conditions. It is found that the outcome from the level 1 OAT-like studies can be considered as the limiting uncertainty values for the majority of mesoscale cases. From the higher-level analyses, it is concluded that for most of the moderate surface scenarios, the effective uncertainty from the individual parameters is balanced and thus lowered. However, for the extreme cases, such as near wilting or saturation soil moisture, the uncertainties add up synergistically and these effects can be even greater than those from the outcomes of the OAT-like studies. Thus, parameter uncertainty cannot be simply related to its deviation alone, but is also dependent on other parameter settings. Also, from the temporal changes in the interaction pattern studies, it is found that, for the morning case soil texture is the important parameter, for afternoon vegetation parameters are crucial, while for the evening case soil moisture is capable of propagating maximum uncertainty in the SVAT processes. Finally, a generic hypothesis is presented that an appropriate question for analysis has to be rephrased from the previous 'which parameters are significant?’ to 'what scenarios make a particular parameter significant?’

Estimation of Evapotranspiration and Photosynthesis by Assimilation of Remote Sensing Data into SVAT Models

Estimation of evapotranspiration and photosynthesis from remote sensing data frequently use soil–vegetation–atmosphere transfer models (SVAT models). These models compute energy and mass transfers using descriptions of turbulent, radiative, and water exchanges, as well as a description of stomatal control in relation with water vapor transfers and photosynthesis. Remote sensing data may provide information that is useful for driving SVAT models (e.g., surface temperature, surface soil moisture, canopy structure, solar radiation absorption, or albedo). Forcing or recalibration methods may be employed to combine remote sensing data and SVAT models. In this article a review of SVAT models and remote sensing estimation of energy and mass fluxes is presented. Examples are given based on our work on two different SVAT models. Eventually, some of the difficulties in the combined use of multispectral remote sensing data and SVAT models are discussed.

Spatial distribution of Sahelian land surface properties from airborne POLDER multiangular observations

This paper presents the spatial distribution of land surface parameters in southwestern Niger, a region composed mainly of shrub and grassland fallows, millet crop, and tiger bush. The regional patterns of the surface albedo, the leaf area index, the fractional vegetation cover, and the fraction of absorbed photosynthetically active radiation are estimated through the growing season from airborne POLDER (Polarization and Directionality of Earth Reflectances) data acquired during the Hydrologic Atmospheric Pilot Experiment in Sahel (HAPEX‐Sahel). The retrieval of these parameters is via a bidirectional reflectance model, appropriate vegetation indices, and Sun‐view geometries. Comparison of the POLDER‐derived surface parameters with airborne and ground measurements shows that the procedure generally performs well, enhancing the ability to constrain soil‐vegetation‐atmosphere transfer (SVAT) models in the Sahel area by providing spatially averaged and updated information. This will enable a more valid assessment of the role of the land surface in determining the Sahelian climate, with a better determination of the scaling effect of surface processes. Although the algorithms described in this work rely primarily on multiangular observations, such as those provided by spaceborne POLDER data sets, they should be useful in a number of remote sensing applications.

The European critical levels for ozone: improving their usage

The European critical levels (CLs) for ozone (O 3) to protect crops, natural and semi-natural vegetation, as well as forest trees, are expressed as an Accumulated exposure Over a Threshold of 40 ppb (AOT40). In principle, this exposure index should represent the O 3 concentrations at the upper boundary of the quasi-laminar layer of the plant canopy. However, in reality, those values cannot be measured and, therefore, must be estimated by micrometeorological models. Nevertheless, inappropriate calculation of AOT40 for ambient conditions using O 3 levels actually measured at some reference height above the canopy leads to predictions of unrealistic crop yield losses. At the present time, CL to protect crops from long-term effects and yield losses, is based on open-top chamber experiments, mainly with spring wheat. In addition to concerns associated with the experimental methodologies used in these studies, a correct application of CL should include simulation of phenological stages of a representative wheat canopy. The present paper describes a model for simulation of leaf area index and canopy height development, based on algorithms adopted from a widely validated agrometeorological model of the German Weather Service. Because, O 3 concentrations at the upper boundary of the quasi-laminar layer of the crop canopy are not unambiguouly connected with plant stomatal uptake, a correction of the actually simulated concentrations is needed to provide toxicologically effective O 3 concentrations (effective AOT40). A comparison of results from the application of effective AOT40, with the observations of yield by the farmers, suggests that the estimated crop losses using the effective dose are within the bounds of probability. However, at the present time, for plants other than wheat, the data base is too small to derive meaningful and reliable effective dose–response relationships. Taking into account the definition of AOT40, soil–vegetation–atmosphere-transfer models must be generally applied. Future research efforts should address the important need for flux-orientated concepts which lead to a derivation of critical absorbed doses for O 3 to protect vegetation (critical loads).

An assessment of effective land surface parameterisation in regional-scale water balance studies

Numerical experiments have been carried out with a soil–vegetation–atmosphere transfer (SVAT) model to study the impact of spatial variability in soil and land surface parameters on regional-scale water balance components. A statistical-dynamical approach has been used to account for the spatial variability of the selected parameters and to determine the seasonal evolution of the impact on the water budget. Point data are used to derive probability-density functions for the thickness of the permeable top soil layer, coefficients for the water retention and hydraulic conductivity curves, the leaf area index and the minimum stomatal resistance. These distributions are incorporated in the model’s mathematical framework in order to generate univariate distributions (sensitivity patterns) for evaporation, transpiration, total evaporation and runoff. The means of these univariate distributions of outputs yield catchment-scale averages. Next, the study obtains catchment-scale evaporation estimates by running simulations with aggregated parameters obtained as statistical descriptors of parameter distributions. The difference between the catchment-scale averages and values obtained with aggregated parameters describes the non-linear response of the model to spatial variability of the particular parameter. Finally, the study also investigates several effective parameters based on recently described hydrometeorological aggregation rules. Results show significant differences in sensitivity patterns between individual parameters and between seasons.

Modelling the water and energy balances of Amazonian rainforest and pasture using Anglo-Brazilian Amazonian climate observation study data

A soil–vegetation–atmosphere–transfer model, SWAPS, is introduced. The model is based on existing models for two-layer evaporation and energy balance, interception evaporation and unsaturated soil moisture transport. The model includes a physically based parameterisation for the soil surface resistance to evaporation (as well as a bio-physical vegetation surface resistance). SWAPS is tested against long-term soil moisture data and shorter term flux data from three sites from the Anglo-Brazilian Amazonian Climate Observation Study. Two pasture sites with widely different soil hydraulic properties, and a rainforest site have been used. Flux data are available at these sites under both wet and dry conditions. The model appears to perform well in reproducing the longer term soil moisture as well as surface fluxes. The interception loss for the forest site calculated over the long term agrees with other studies for the Amazonian forest. For the pasture sites the interception loss is less than, but similar to, that of the forest.

Functional similarity in landscape scale SVAT modelling

Abstract. In this study, it is shown that the complexity of Soil Vegetation Atmosphere Transfer (SVAT) models leads to an equifinality of functional behaviour - many parameterizations from many areas of the parameter space lead to very similar responses. Individual parameters derived by calibration (i.e. model inversion) against limited measurements are, therefore, highly uncertain. Due to the non-linear internal behaviour of SVAT models, aggregation of uncertainly known parameter fields to parameterize landscape scale variability in surface fluxes will yield highly uncertain predictions. A disaggregation approach suggested by Beven (1995) requires that the land surface be represented by a linear sum of a number of representative parameterizations or functional types. This study explores the nature of the parameter space in terms of a simple definition of functional behaviour. Parameter interactions producing similar predicted behaviours are investigated through application of Principal Component Analyses. These reveal the lack of a dominant global interaction indicating the presence of highly complex parameter interactions throughout the feasible parameter space.

MUREX: a land-surface field experiment to study the annual cycle of the energy and water budgets

The MUREX (monitoring the usable soil reservoir experimentally) experiment was designed to provide continuous time series of field data over a long period, in order to improve and validate the Soil-vegetation-Atmosphere Transfer (SVAT) parameterisations employed in meteorological models. Intensive measurements were performed for more than three years over fallow farmland in southwestern France. To capture the main processes controlling land-atmosphere exchanges, the local climate was fully characterised, and surface water and energy fluxes, vegetation biomass, soil moisture profiles, surface soil moisture and surface and soil temperature were monitored. Additional physiological measurements were carried out during selected periods to describe the biological control of the fluxes. The MUREX data of 1995, 1996, and 1997 are presented. Four SVAT models are applied to the annual cycle of 1995. In general, they succeed in simulating the main features of the fallow functioning, although some shortcomings are revealed.Key words. Hydrology (evapotranspiration; soil moisture; water-energy interactions).

Estimating the root-zone soil moisture from the combined use of time series of surface soil moisture and SVAT modelling

This work investigates the possibility to retrieve the root zone soil moisture from the combined use of time series of surface soil moisture, that can be estimated from microwave remote sensing instruments, and Soil-Vegetation-Atmosphere-Transfer (SVAT) modeling. The analysis is based on the ISBA surface scheme and two data sets acquired over' soybean crops, in 1989 and 1990. They include very detailed measurements of soil and vegetation characteristics, mass and energy transfers in the soil-plant-atmosphere system and crop remote sensing signatures in the thermal and microwave domains during a 2- or 3-month period. The 3-month experiment in 1989, made it possible to investigate the accuracy of the soil reservoir retrievals, as a function of the time period and repetitivity of the surface measurements included in the retrieval process. This work contributes to a better definition of the requirements for the use of remotely-sensed microwave observations of surface soil moisture. This is a crucial problem, if we consider the increasing potential of microwave remote sensing technology and the fundamental needs in atmospheric and hydrological models for water transfer characterization in soil.

The influence of a Soil–Vegetation–Atmosphere Transfer scheme on the simulated climate of LLNL/UCLA AGCM

The sensitivity of the Lawrence Livermore National Laboratory (LLNL)/University of California, Los Angeles (UCLA) Atmospheric General Circulation Model (AGCM) to coupling with a Soil–Vegetation–Atmosphere Transfer (SVAT) scheme is investigated in this paper. The SVAT leads to general warming of the climate over continents in association with enhanced sensible heat fluxes. The wetness bias of AGCM is reduced drastically with reductions in latent heat fluxes and precipitation. There is reduction in cloudiness when the SVAT is coupled which results in increased solar radiation at the surface. Upward long wave radiation increases in association with increases in land surface temperature. Although the coupling of SVAT improves surface fluxes of heat and water vapor, these changes are mainly offset by changes in solar and infrared radiative fluxes. Global mean energy balance at the surface and the top of atmosphere is improved marginally.

Land-surface characterization in climate models: biome-based parameter inference is not equivalent to local direct estimation

Soil-Vegetation-Atmosphere Transfer (SVAT) schemes in atmospheric general circulation models (AGCMs) require land-surface information. SVATs need this information to simulate the interactions between the atmosphere and the biosphere, in general, and to determine how the radiation absorbed by the surface is partitionned into sensible and latent heat, in particular. When investigating future climates, parameters can either be inferred from the tabulated, average characteristics of predicted biomes or computed locally, i.e. cell by cell. A null hypothesis is formulated to test if AGCMs can distinguish between the two approaches: “For AGCMs, biome-based parameter inference is equivalent to cell by cell estimation”. The hypothesis is tested with a terrestrial biosphere model (TBM), the Ecological ModUle (EMU). EMU uses a set of primary and composite generic vegetation types to calculate local vegetation characteristics and to identify dominant assemblages. For the test, EMU is run with two different climate scenarios: a present climate and the Climate Change scenario for an equivalent doubling of the atmospheric concentration of carbon dioxide generated with the United Kingdom Meteorological Office AGCM. Results indicate that the two approaches are not equivalent. Consequently, the null hypothesis is rejected. Moreover, the differences in vegetation characteristics are of a magnitude which should have noticeable impacts on exchanges of energy, water, and momentum between the atmosphere and the surface. This suggests that estimating vegetation locally should increase the credibility of AGCM investigations of Global Change.

The Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) phase 2(c) Red–Arkansas River basin experiment:: 3. Spatial and temporal analysis of water fluxes

The water-balance components of 16 Soil–Vegetation Atmospheric Transfer (SVAT) schemes were evaluated by comparing predicted and observed streamflow, predicted evapotranspiration and evapotranspiration inferred from an atmospheric moisture budget analysis, and soil moisture storage changes for a seven-year period (1980–1986) using data from the Red–Arkansas River basins of the Southern Great Plains of the USA. The evaluations support the following suggestions: (a) The mean annual runoff of all models follows, at least generally, the strong climatic East–West gradient of precipitation, although most models predict too much runoff in the dry part of the basin. (b) The mean monthly storage change tends to be underestimated, even though all models capture reasonably well the seasonality of the evapotranspiration. (c) The wide range of conceptualizations used for generation of surface and subsurface runoff strongly affect runoff generation on seasonal, and shorter, time scales. Model responses to summer precipitation ranged from almost no summer runoff (one model) to the (more common) situation of persistent overprediction of summer runoff, especially in the driest part of the basin. (d) All models tended to underpredict evapotranspiration in summer and overpredict in winter. (e) Model-derived mean seasonal cycles of changes in soil moisture storage are qualitatively similar to those inferred from observations, but most models do not predict the decrease in April soil moisture storage and the increase in October that is inferred from observations.

Which type of soil–vegetation–atmosphere transfer scheme is needed for general circulation models: a proposal for a higher–order scheme

Recent investigations on various aspects of land–atmosphere interactions at the microscale and the mesoscale are summarized, for the purpose of reflecting on the type of Soil–Vegetation–Atmosphere Transfer (SVAT) schemes, needed for General Circulation Models (GCMs). It appears that, among all land–surface characteristics considered in advanced SVAT schemes, only stomatal conductance, soil–surface wetness, leaf area index, surface roughness, and albedo play a major role inthe redistribution of energy at the ground surface. However, the relationship between these characteristics and the surface heat and momentum fluxes is strongly nonlinear. Furthermore, landscape discontinuities resulting from spatial heterogeneity of these characteristics, can induce atmospheric mesoscale circulations, which have a strong impact on the structure of the planetary boundary layer, clouds, and precipitation. These findings imply that appropriate SVAT schemes for GCMs need to provide higher statistical moments and characteristic length scales of the spatial distribution of the above mentioned five land–surface characteristics. The conceptual features and equations of a higher–order SVAT scheme are described here. This new SVAT scheme will probably not be able to mimic diurnal variations of land–surface fluxes at a precise location, but is expected to provide appropriate surface forcings, which trigger nonlinear atmospheric responses (including clouds and precipitation) at the microscale and the mesoscale. Furthermore, this new SVAT scheme is designed to directly use remotely–sensed parameters, which are not converted into pseudo plant and soil characteristics. Thus, this scheme is particularly convenient for data assimilation.