Soil Vegetation Atmosphere Transfer Scheme Research Articles

Soil temperature response in Korea to a changing climate using a land surface model

The land surface processes play an important role in weather and climate systems through its regulation of radiation, heat, water and momentum fluxes. Soil temperature (ST) is one of the most important parameters in the land surface processes; however, there are few extensive measurements of ST with a long time series in the world. According to the CLImatology of Parameters at the Surface (CLIPS) methodology, the output of a trusted Soil-Vegetation- Atmosphere Transfer (SVAT) scheme can be utilized instead of observations to investigate the regional climate of interest. In this study, ST in South Korea is estimated in a view of future climate using the output from a trusted SVAT scheme — the University of TOrino model of land Process Interaction with Atmosphere (UTOPIA), which is driven by a regional climate model. Here characteristic changes in ST are analyzed under the IPCC A2 future climate for 2046-2055 and 2091-2100, and are compared with those under the reference climate for 1996-2005. The UTOPIA results were validated using the observed ST in the reference climate, and the model proved to produce reasonable ST in South Korea. The UTOPIA simulations indicate that ST increases due to environmental change, especially in air temperature (AT), in the future climate. The increment of ST is proportional to that of AT except for winter. In wintertime, the ST variations are different from region to region mainly due to variations in snow cover, which keeps ST from significant changes by the climate change.

Spatial‐Scale Prediction of the SVAT Soil Hydraulic Variables Characterizing Stratified Soils on the Tibetan Plateau from an EnKF Analysis of SAR Soil Moisture

In highly stratified soils as on the Tibetan Plateau, uncertainty associated with a vertical profile of soil and hydraulic properties largely restricts the performance of Soil Vegetation Atmosphere Transfer (SVAT) model. In lieu of commonly used pedotransfer functions (PTFs) or artificial neural networks (ANNs), soil hydraulic properties in this study were inverted from an Ensemble Kalman filter (EnKF) analysis of Synthetic Aperture Radar (SAR) surface soil moisture. The calibrated SVAT scheme using inverted soil hydraulic variables C1 and θgeq was better matched with in situ field measurements than the uncalibrated SVAT scheme using soil maps–based PTFs on a local point scale. It was shown that the inverse calibration of two soil hydraulic variables solved the forecast bias (underestimation) in surface soil moisture due to the assumption of vertical homogeneity and the site‐specificity of empirical PTFs. Additionally, at a SAR spatial scale, the calibrated SVAT scheme appropriately captured a high vertical gradient between surface and subsurface soil moisture, while the uncalibrated SVAT scheme could not. This suggests that it is possible to infer the SVAT soil hydraulic variables that are the main error source in SVAT scheme from the SAR soil moisture data assimilation analysis.

The Regional Climate Impact of a Realistic Future Deforestation Scenario in the Congo Basin

Abstract The demand for agricultural land in the Congo basin is expected to yield substantial deforestation over the coming decades. Although several studies exist on the climatological impact of deforestation in the Congo basin, deforestation scenarios that are implemented in climate models are generally crude. This study aims to refine current impact assessments by removing the primary forest according to an existing spatially explicit scenario, and replacing it by successional vegetation typically observed for the Congo basin. This is done within the Consortium for Small-Scale Modeling (COSMO) model in climate mode (COSMO-CLM), a regional climate model at 25-km grid spacing coupled to a state-of-the-art soil–vegetation–atmosphere transfer scheme (Community Land Model). An evaluation of the model shows good performance compared to in situ and satellite observations. Model integrations indicate that the deforestation, expected for the middle of the twenty-first century, induces a warming of about 0.7°C. This is about half the greenhouse gas–induced surface warming in this region, given an intermediate emission scenario (A1B) with COSMO-CLM driven by the ECHAM5 global climate model. This shows the necessity of taking into account deforestation to obtain realistic future climate projections. The deforestation-induced warming can be attributed to reduced evaporation, but this effect is mitigated by increased albedo and increased sensible heat loss to the atmosphere. Precipitation is also affected: as a consequence of surface warming resulting from deforestation, a regional heat low develops over the rain forest region. Resulting low-level convergence causes a redistribution of moisture in the boundary layer and a stabilization of the atmospheric column, thereby reducing convection intensity and hence precipitation by 5%–10% in the region of the heat low.

Inversion of soil hydraulic properties from the DEnKF analysis of SMOS soil moisture over West Africa

The application of Soil-Vegetation-Atmosphere-Transfer (SVAT) scheme into the estimation of soil moisture profile in semi-arid regions is largely constrained by a scarcity of spatially distributed soil and hydraulic property information. Especially, on a large scale in very dry and sandy soils or other extreme conditions, it is difficult to accurately map soil and hydraulic properties with soil maps-based Pedo-Transfer Functions (PTFs), because PTFs are usually semi-empirically defined for specific sites. One strategy to overcome this limitation is to employ satellite data for a purpose of calibration. This paper provides an operational framework of inverting the SVAT soil hydraulic variables from the deterministic ensemble Kalman filter (DEnKF) analysis of Soil Moisture and Ocean Salinity (SMOS) surface soil moisture product. This inverse calibration was first verified with the Analyses Multidisciplinaires de la Mousson Africaine (AMMA) super site data representative of a single grid cell (0.25°) of satellite data. At this local scale, the results demonstrated that the mis-estimation problems of soil surface variable C1 and equilibrium soil moisture θgeq were successfully solved after calibration, demonstrating a better agreement with the field measurement of soil moisture profile than the SMOS product and un-calibrated SVAT scheme using soil maps-based PTFs. On the meso scale, the calibrated SVAT scheme using inverted surface variables appropriately captured a non-linear relationship between surface and root zone soil moisture by showing a typical soil moisture profile in dry climates, where dry surface soil moisture is spatially consistent with rainfall events, but wet root zone soil moisture shows low correlations with surface soil moisture distributions and rainfall events. In contrast, the un-calibrated SVAT scheme using soil maps-based PTFs significantly overestimated surface soil moisture and rainfall effect. This approach suggests several operational merits in that there is no need to heavily rely on empirically defined PTFs or recalibrate land surface parameters for different land surface conditions, and this can be applied even when parameter measurements are unavailable or highly uncertain.

Validation and comparison of two soil‐vegetation‐atmosphere transfer models for tropical Africa

This study aims to compare and validate two soil‐vegetation‐atmosphere‐transfer (SVAT) schemes: TERRA‐ML and the Community Land Model (CLM). Both SVAT schemes are run in standalone mode (decoupled from an atmospheric model) and forced with meteorological in‐situ measurements obtained at several tropical African sites. Model performance is quantified by comparing simulated sensible and latent heat fluxes with eddy‐covariance measurements. Our analysis indicates that the Community Land Model corresponds more closely to the micrometeorological observations, reflecting the advantages of the higher model complexity and physical realism. Deficiencies in TERRA‐ML are addressed and its performance is improved: (1) adjusting input data (root depth) to region‐specific values (tropical evergreen forest) resolves dry‐season underestimation of evapotranspiration; (2) adjusting the leaf area index and albedo (depending on hard‐coded model constants) resolves overestimations of both latent and sensible heat fluxes; and (3) an unrealistic flux partitioning caused by overestimated superficial water contents is reduced by adjusting the hydraulic conductivity parameterization. CLM is by default more versatile in its global application on different vegetation types and climates. On the other hand, with its lower degree of complexity, TERRA‐ML is much less computationally demanding, which leads to faster calculation times in a coupled climate simulation.

Assessing the soil texture-specific sensitivity of simulated soil moisture to projected climate change by SVAT modelling

Climate change is assumed to have a regionally specific impact on the soil moisture regime. The impact of climate change on soil moisture can be expected to depend on the soil texture. Because soil moisture observations are not available operationally, models can be used to predict these changes. In this study, a soil vegetation atmosphere transfer scheme (SVAT) was applied to virtual soil columns to assess the soil texture-specific sensitivity of simulated soil moisture to projected climate change. For each of the 31 soil texture classes of the German soil texture classification, long-term simulations were carried out based on the observed and scenario-based climate data representing five different climate regions in Germany. The simulation results indicate that soil moisture regimes considerably differ from region to region and among different soil texture classes. Different soil texture classes showed different sensitivities of soil moisture with respect to the projected climate change. While the differences in soil moisture between the current conditions and the climate scenarios were largest for silt soils, they were smallest for clay soils for both continental and humid climates. Sand and loam soils behaved intermediately, showing a moderate sensitivity. The results also showed that the soil texture-specific sensitivity of soil moisture to climate change was largest for soils that were not affected by groundwater (no capillary rise). Differences between soil texture classes decreased with an increasing influence of groundwater. In contrast, increasing vegetation density, rooting depths and transpiration demand induced the sensitivity of soil moisture to climate change, except in continental climates. This study indicates that validated, physically based soil hydrological models serve as suitable tools to assess the response of soil moisture to changing climate conditions. Based on virtual soil columns, modelling experiments systematically reveal soil texture-dependent sensitivities that cannot readily be identified in real world studies due to the limited availability and accessibility of the wide spectrum of different soil textures.

Coupled Vegetation and Soil Moisture Dynamics Modeling in Heterogeneous and Sloping Terrains

Two vegetation dynamic models (VDMs) were coupled to a soil–vegetation–atmosphere transfer scheme aimed at simulating water, heat, and CO2 fluxes and to a three‐dimensional unsaturated flow and heat diffusion model allowing the study of heterogeneous soils and vegetation. The VDMs differ in reproducing gross photosynthesis. One was adapted from a daily model, while the other is a more complex model, needing the continuous monitoring of CO2 concentration and a larger number of parameters, although it is useful for assessing feedback due to an increase in CO2 concentration. The two coupled models were validated on a half‐hour time step through leaf area index (LAI), ground energy, and water flux measurements performed during the summer of 2007 and 2009 in an alfalfa (Medicago sativa L.) field in the Mediterranean (southern Italy) and during the spring of 2001 in a predominantly C3 grass covered field (California). Furthermore, the models were used at the field scale in numerical experiments for assessing the effects of model dimensionality on evapotranspiration, CO2 flux, and LAI in the presence of flat heterogeneous or sloping homogeneous terrains using both the fully three‐dimensional soil water flow model and a simpler model allowing only vertical flow. For the flat terrain experiment, one‐dimensional modeling generally provided significant differences in the simulated quantities with respect to the three‐dimensional model, even though in some cases similar results were found. For the sloping terrain, one‐dimensional modeling proved quite unsuitable for analyzing slope effects, while three‐dimensional modeling allowed detailed description of different degrees of slope interactions between soil moisture dynamics and surface fluxes for different slope angles.

Development and Evaluation of a Coupled Photosynthesis-Based Gas Exchange Evapotranspiration Model (GEM) for Mesoscale Weather Forecasting Applications

Abstract Current land surface schemes used for mesoscale weather forecast models use the Jarvis-type stomatal resistance formulations for representing the vegetation transpiration processes. The Jarvis scheme, however, despite its robustness, needs significant tuning of the hypothetical minimum-stomatal resistance term to simulate surface energy balances. In this study, the authors show that the Jarvis-type stomatal resistance/transpiration model can be efficiently replaced in a coupled land–atmosphere model with a photosynthesis-based scheme and still achieve dynamically consistent results. To demonstrate this transformative potential, the authors developed and coupled a photosynthesis, gas exchange–based surface evapotranspiration model (GEM) as a land surface scheme for mesoscale weather forecasting model applications. The GEM was dynamically coupled with a prognostic soil moisture–soil temperature model and an atmospheric boundary layer (ABL) model. This coupled system was then validated over different natural surfaces including temperate C4 vegetation (prairie grass and corn field) and C3 vegetation (soybean, fallow, and hardwood forest) under contrasting surface conditions (such as different soil moisture and leaf area index). Results indicated that the coupled model was able to realistically simulate the surface fluxes and the boundary layer characteristics over different landscapes. The surface energy fluxes, particularly for latent heat, are typically within 10%–20% of the observations without any tuning of the biophysical–vegetation characteristics, and the response to the changes in the surface characteristics is consistent with observations and theory. This result shows that photosynthesis-based transpiration/stomatal resistance models such as GEM, despite various complexities, can be applied for mesoscale weather forecasting applications. Future efforts for understanding the different scaling parameterizations and for correcting errors for low soil moisture and/or wilting vegetation conditions are necessary to improve model performance. Results from this study suggest that the GEM approach using the photosynthesis-based soil vegetation atmosphere transfer (SVAT) scheme is thus superior to the Jarvis-based approaches. Currently GEM is being implemented within the Noah land surface model for the community Weather Research and Forecasting (WRF) Advanced Research Version Modeling System (ARW) and the NCAR high-resolution land data assimilation system (HRLDAS), and validation is under way.

Monitoring groundwater variation by satellite and implications for in-situ gravity measurements

In order to establish a new technique for monitoring groundwater variations in urban areas, the applicability of precise in-situ gravity measurements and extremely high precision satellite gravity data via GRACE (Gravity Recovery and Climate Experiment) was tested. Using the GRACE data, regional scale water mass variations in four major river basins of the Indochina Peninsula were estimated. The estimated variations were compared with Soil–Vegetation–Atmosphere Transfer Scheme (SVATS) models with a river flow model of 1) globally uniform river velocity, 2) river velocity tuned by each river basin, 3) globally uniform river velocity considering groundwater storage, and 4) river velocity tuned by each river basin considering groundwater storage. Model 3) attained the best fit to the GRACE data, and the model 4) yielded almost the same values. This implies that the groundwater plays an important role in estimating the variation of total terrestrial storage. It also indicates that tuning river velocity, which is based on the in-situ measurements, needs further investigations in combination with the GRACE data. The relationships among GRACE data, SVATS models, and in-situ measurements were also discussed briefly.

Sensitivity of a soil-vegetation-atmosphere-transfer scheme to input data resolution and data classification

This paper presents modelling of the effects of input data resolution and classification of a regionally applied soil-vegetation-atmosphere-transfer (SVAT) scheme. Most SVAT schemes were developed at local scales but often are applied at regional scales to simulate regional water balances and to predict effects of environmental changes on catchment hydrology. Applying models at different scales requires investigating sensitivity to the available input data. In this study, investigated input data include soil maps, vegetation classifications, topographic information and weather data of varying temporal and spatial resolutions. Target quantities are simulated water fluxes such as evapotranspiration rates, groundwater recharge and runoff generation rates. Model sensitivity is estimated with respect to water balances and water flows, focusing on different time periods (months, years). The soil vegetation atmosphere transfer scheme SIMULAT is applied to two different catchments representing different environments where data sets of varying data quality and resolution are available. Results show that, on an annual time scale, SIMULAT is most sensitive to aggregation of soil information and mis-classification in vegetation data. On the monthly time scale, SIMULAT is also very sensitive to disaggregation of precipitation data. The sensitivity to spatial distribution of land-use data and spatio-temporal resolution of weather data is low. Based on the investigations, a ranking of the sensitivity of the model to resolution and classification of different input data sets is proposed. Minimum requirements concerning data resolution for regional scale SVAT applications are derived.

The Radiative Effect of a Fir Canopy on a Snowpack

Abstract Models of snow processes in areas of possible large-scale change need to be site independent and physically based. Here, the accumulation and ablation of the seasonal snow cover beneath a fir canopy has been simulated with a new physically based snow–soil vegetation–atmosphere transfer scheme (Snow-SVAT) called SNOWCAN. The model was formulated by coupling a canopy optical and thermal radiation model to a physically based multilayer snow model. Simple representations of other forest effects were included. These include the reduction of wind speed and hence turbulent transfer beneath the canopy, sublimation of intercepted snow, and deposition of debris on the surface. This paper tests this new modeling approach fully at a fir site within Reynolds Creek Experimental Watershed, Idaho. Model parameters were determined at an open site and subsequently applied to the fir site. SNOWCAN was evaluated using measurements of snow depth, subcanopy solar and thermal radiation, and snowpack profiles of temperature, density, and grain size. Simulations showed good agreement with observations (e.g., fir site snow depth was estimated over the season with r 2 = 0.96), generally to within measurement error. However, the simulated temperature profiles were less accurate after a melt–freeze event, when the temperature discrepancy resulted from underestimation of the rate of liquid water flow and/or the rate of refreeze. This indicates both that the general modeling approach is applicable and that a still more complete representation of liquid water in the snowpack will be important.

Evaporation over a Heterogeneous Land Surface: EVA_GRIPS and the LITFASS-2003 Experiment—An Overview

The Evaporation at Grid/Pixel Scale (EVA_GRIPS) project was realised in order to determine the area-averaged evaporation over a heterogeneous land surface at the scale of a grid box of a regional numerical weather prediction or climate model, and at the scale of a pixel of a satellite image. EVA_GRIPS combined surface-based and airborne measurements, satellite data analysis, and numerical modelling activities. A mesoscale field experiment, LITFASS-2003, was carried out in the heterogeneous landscape around the Meteorological Observatory Lindenberg (MOL) of the German Meteorological Service in May and June, 2003. The experiment was embedded in the comprehensive, operational measurement program of the MOL. Experimental determination of surface fluxes on a variety of spatial scales was achieved by employing micrometeorological flux stations, scintillometers, a combination of ground-based remote sensing instruments, and the Helipod, a turbulence probe carried by a helicopter. Surface energy fluxes were also derived from satellite data. Modelling work included the use of different Soil–Vegetation–Atmosphere Transfer schemes, a large-eddy simulation model and three mesoscale atmospheric models. The paper gives an overview on the background of EVA_GRIPS, and on the measurements and meteorological conditions during LITFASS-2003. A few general results are discussed.

ALPINE3D: a detailed model of mountain surface processes and its application to snow hydrology

AbstractCurrent models of snow cover distribution, soil moisture, surface runoff and river discharge typically have very simple parameterizations of surface processes, such as degree‐day factors or single‐layer snow cover representation. For the purpose of reproducing catchment runoff, simple snowmelt routines have proven to be accurate, provided that they are carefully calibrated specifically for the catchment they are applied to. The use of more detailed models is, however, useful to understand and quantify the role of individual surface processes for catchment hydrology, snow cover status and soil moisture distribution.We introduce ALPINE3D, a model for the high‐resolution simulation of alpine surface processes, in particular snow processes. The model can be driven by measurements from automatic weather stations or by meteorological model outputs. As a preprocessing alternative, specific high‐resolution meteorological fields can be created by running a meteorological model. The core three‐dimensional ALPINE3D modules consist of a radiation balance model (which uses a view‐factor approach and includes shortwave scattering and longwave emission from terrain and tall vegetation) and a drifting snow model solving a diffusion equation for suspended snow and a saltation transport equation. The processes in the atmosphere are thus treated in three dimensions and are coupled to a distributed (in the hydrological sense of having a spatial representation of the catchment properties) one‐dimensional model of vegetation, snow and soil (SNOWPACK) using the assumption that lateral exchange is small in these media. The model is completed by a conceptual runoff module. The model can be run with a choice of modules, thus generating more or less detailed surface forcing data as input for runoff generation simulations. The model modules can be run in a parallel (distributed) mode using a GRID infrastructure to allow computationally demanding tasks. In a case study from the Dischma Valley in eastern Switzerland, we demonstrate that the model is able to simulate snow distribution as seen from a NOAA advanced very high‐resolution radiometer image. We then analyse the sensitivity of simulated snow cover distribution and catchment runoff to the use of different surface process descriptions. We compare model runoff simulations with runoff data from 10 consecutive years. The quantitative analysis shows that terrain influence on the radiation processes has a significant influence on catchment hydrology dynamics. Neglecting the role of vegetation and the spatial variability of the soil, on the other hand, had a much smaller influence on the runoff generation dynamics. We conclude that ALPINE3D is a valuable tool to investigate surface dynamics in mountains. It is currently used to investigate snow cover dynamics for avalanche warning and permafrost development and vegetation changes under climate change scenarios. It could also serve to test the output of simpler soil–vegetation–atmosphere transfer schemes used in larger scale climate or meteorological models and to create accurate soil moisture assessments for meteorological and flood forecasting. Copyright © 2006 John Wiley & Sons, Ltd.

A physically based distributed subsurface–surface flow dynamics model for forested mountainous catchments

AbstractThis study was designed to develop a physically based hydrological model to describe the hydrological processes within forested mountainous river basins. The model describes the relationships between hydrological fluxes and catchment characteristics that are influenced by topography and land cover. Hydrological processes representative of temperate basins in steep terrain that are incorporated in the model include intercepted rainfall, evaporation, transpiration, infiltration into macropores, partitioning between preferential flow and soil matrix flow, percolation, capillary rise, surface flow (saturation‐excess and return flow), subsurface flow (preferential subsurface flow and baseflow) and spatial water‐table dynamics. The soil–vegetation–atmosphere transfer scheme used was the single‐layer Penman–Monteith model, although a two‐layer model was also provided. The catchment characteristics include topography (elevation, topographic indices), slope and contributing area, where a digital elevation model provided flow direction on the steepest gradient flow path. The hydrological fluxes and catchment characteristics are modelled based on the variable source‐area concept, which defines the dynamics of the watershed response. Flow generated on land for each sub‐basin is routed to the river channel by a kinematic wave model. In the river channel, the combined flows from sub‐basins are routed by the Muskingum–Cunge model to the river outlet; these comprise inputs to the river downstream. The model was applied to the Hikimi river basin in Japan. Spatial decadal values of the normalized difference vegetation index and leaf area index were used for the yearly simulations. Results were satisfactory, as indicated by model efficiency criteria, and analysis showed that the rainfall input is not representative of the orographic lifting induced rainfall in the mountainous Hikimi river basin. Also, a simple representation of the effects of preferential flow within the soil matrix flow has a slight significance for soil moisture status, but is insignificant for river flow estimations. Copyright © 2005 John Wiley & Sons, Ltd.

Comment on “Optimized stomatal conductance of vegetated land surfaces and its effects on simulated productivity and climate” by A. Kleidon

[1] Using a simple dynamic vegetation model coupled to an intermediate complexity climate model Kleidon [2004] suggests that an optimum stomatal conductance exists at which vegetation productivity is maximized. It is suggested that this maximum is the result of two competing processes: 1) the increased supply of CO2 with increased stomatal conductance and 2) increased cloud cover, associated with higher latent heat fluxes, that reduces sunlight. Here, it is argued that this conclusion is an artifact of the simple vegetation model used. Kleidon’s vegetation model does not take into account that diffused radiation increases productivity so in his model an increase in cloud cover always affects productivity adversely due to decrease in available radiation. Also, transpiration, interception, and soil evaporation are not calculated separately with the result that a decrease in stomatal conductance always leads to a decrease in total evapotranspiration, while more complex models suggest that decrease in transpiration can be compensated by an increase in soil evaporation. [2] It is well established that vegetation maximizes carbon gain per unit water loss at the leaf [Cowan and Farquhar, 1977] as well as the ecosystem [Mediavilla and Escudero, 2003] scale by adjusting stomatal conductance and photosynthesis rates. In has also been suggested [Huxman et al., 2004] that as the supply of moisture is reduced the rain use efficiency (RUE, productivity per unit rain) approaches some maximum value that is independent of the vegetation structure. However, the suggestion by Kleidon [2004] that plants maintain an optimum stomatal conductance to control latent heat fluxes and thus cloud cover is questionable. [3] The concept of optimum stomatal conductance as a balance between CO2 uptake and increasing cloud cover to obtain maximum vegetation productivity is questionable because: 1) an increase in cloud cover can actually increase vegetation productivity via diffused radiation; 2) a decrease in transpiration and evaporation of canopy intercepted water is compensated by increase in soil evaporation; and 3) cloud cover is largely controlled by the large scale atmospheric circulation and not by local transpiration. These three points are briefly discussed below. [4] Several researchers have demonstrated that plant productivity is enhanced in the presence of clouds due to an increase in the diffused fraction of the total radiation [Hollinger et al., 1994; Baldocchi et al., 2002] although eventually the benefits of diffused radiation are outweighed by the overall decrease in solar radiation. The role of diffused radiation in enhancing net ecosystem productivity (NEP) has also been invoked to explain the decrease in the rate of growth of atmospheric CO2 after the eruption of Mount Pinatubo [Roderick et al., 2001; Arora, 2003]. The simple vegetation model used by Kleidon [2004] does not, however, make the distinction between the diffuse and direct fraction of solar radiation so that an increase in cloud cover always affects productivity adversely due to decrease in available radiation. [5] A reduction in stomatal conductance will lead to a decrease in transpiration but this may not necessarily lead to a substantial decrease in total evapotranspiration. Kleidon [2004] runs his simulations for 100 years for dynamic vegetation to reach steady state for range of stomatal conductance values. A reduction in stomatal conductance thus also leads to lower vegetation biomass and leaf area index (LAI) via lower CO2 uptake. Simulations with complex soil-vegetation-atmosphere-transfer (SVAT) schemes show that reduction in transpiration and the evaporation of intercepted water from the canopy is compensated by an increase in soil evaporation. For instance when testing the sensitivity to different leaf area indices, Tian et al. [2004] find that increasing LAI over the Amazonian region in the NCAR climate model with the CLM2 SVAT scheme does not increase latent heat fluxes significantly for exactly this reason. The model used by Kleidon [2004] does not treat transpiration and soil evaporation separately and thus a decrease in dimensionless stomatal conductance (gs) always leads to decrease in total evapotranspiration. [6] There is no observational evidence that plants are able to sense changes in clouds that occur in response to changes in local transpiration via stomatal control. More importantly, there is little evidence that local transpiration alone regulates the cloud cover. Rather, over large parts of the world, large-scale dynamics plays a dominant role in regulating the cloud amount [e.g., Wu and Moncrieff, 2001]. Although a reduction in transpirational flux at the global scale implies reduced cloud cover, plants are not expected to adjust their stomatal conductances in synchrony with each other or in a way such that they all benefit from each other’s individual stomatal adjustment. [7] That an increase in cloud cover always affects the plant productivity adversely (without considering the beneGEOPHYSICAL RESEARCH LETTERS, VOL. 32, L08708, doi:10.1029/2004GL022110, 2005

ECOCLIMAP: a global database of land surface parameters at 1 km resolution

AbstractSoil/vegetation–atmosphere transfer schemes (SVATS) require only a few primary parameters as inputs (leaf area index, vegetation fraction and albedo). The object of this paper is to describe methods used to derive these parameters from surface cover maps, climate maps and NDVI datasets. The resolution of these new surface parameters is 1 km at the global scale. The ECOCLIMAP database is available online to the research community at http://www.cnrm.meteo.fr/gmme/PROJETS/ECOCLIMAP/page_ecoclimap.htm. An application of this strategy for urban use at a finer scale (30mresolution) is presented here for the city of Marseille. Copyright © 2005 Royal Meteorological Society.

Environmental and biological controls on the seasonal variations in latent heat fluxes derived from flux data for three forest sites

One difficulty that arises when predicting canopy‐scale energy fluxes is that the parameterization of complex (bio)physical soil vegetation atmosphere transfer schemes is often only partially conditioned by the information content of the eddy covariance data commonly used for calibration, rendering subsequent predictions and extrapolations somewhat uncertain. Here we derive a functional description for daily evaporative fluxes directly from observations at the canopy scale using a nonstationary regression framework. This method is applied to 3 year blocks of eddy covariance and micrometeorological data from three different FLUXNET forest sites: Harvard Forest, USA, University of Michigan Biological Station, USA, and Hyytiälä, Finland, covering a variety of climate and vegetation conditions. The approach yields a simple three‐parameter model which is based on partitioning latent heat between equilibrium latent heat fluxes and fluxes that are under strong stomatal control and hence are related to CO2 fluxes. Despite being well defined and able to account for much of the observed variations in latent heat, predictive validation of the model emphasizes the need to account for surface and subsurface water balance in such descriptions.

A study on the effects of subgrid-scale representation of land use on the boundary layer evolution using a 1-D model

A soil–vegetation–atmosphere transfer scheme based on a one-dimensional boundary layer model has been developed to study the sensitivity of boundary layer growth on the surface temperature fields and land-use types. Full interaction between the surface and atmosphere is achieved by representing the surface and turbulent mixing processes by using a Land Air Parameterization Scheme (LAPS) and the Blackadar's local closure PBL scheme. Sensitivity tests are performed by simulating planetary boundary layer structures over different underlying surfaces, soil textures and soil moisture contents using the meteorological data for July 17, 1999 in Philadelphia, PA. Also, a simpler surface parameterization used in the MM5 model results in a significantly higher surface temperature as well as the PBL depth over the urban area than those obtained by the LAPS parameterization in which a new method, including a combination of aggregated fluxes and parameters in calculating the surface temperature, is applied.

Data‐supported robust parameterisations in land surface–atmosphere flux predictions: towards a top‐down approach

AbstractThe current tendency in ‘physically based’ soil–vegetation–atmosphere transfer (SVAT) schemes is to use increasingly complex process descriptions to predict evaporative fluxes at both patch and landscape scales. This approach does not take proper account of the heterogeneities that are evident in any landscape. A top‐down approach to sub‐grid‐scale land surface parameterization would suggest that the current complexity may not be supported by the calibration data available. By comparing three SVAT schemes of differing complexity within the generalized likelihood uncertainty estimation framework, we demonstrate the utility of simpler rather than more complex models when calibrated against flux data from various intensive field campaigns. A more robust calibration is achieved for a simple evaporative fraction approach allowing the feasible parameter ranges to be more strongly conditioned by the available data. It is argued that a top‐down (dominant mode) predictive model based on a database of such robustly estimated parameter values would result in no greater uncertainty at the scales of application than trying to form parameter sets for complex models from a variety of sources. Copyright © 2003 John Wiley & Sons, Ltd.

A Global Database of Land Surface Parameters at 1-km Resolution in Meteorological and Climate Models

Ecoclimap, a new complete surface parameter global dataset at a 1-km resolution, is presented. It is intended to be used to initialize the soil–vegetation–atmosphere transfer schemes (SVATs) in meteorological and climate models (at all horizontal scales). The database supports the “tile” approach, which is utilized by an increasing number of SVATs. Two hundred and fifteen ecosystems representing areas of homogeneous vegetation are derived by combining existing land cover maps and climate maps, in addition to using Advanced Very High Resolution Radiometer (AVHRR) satellite data. Then, all surface parameters are derived for each of these ecosystems using lookup tables with the annual cycle of the leaf area index (LAI) being constrained by the AVHRR information. The resulting LAI is validated against a large amount of in situ ground observations, and it is also compared to LAI derived from the International Satellite Land Surface Climatology Project (ISLSCP-2) database and the Polarization and Directionality of the Earth's Reflectance (POLDER) satellite. The comparison shows that this new LAI both reproduces values coherent at large scales with other datasets, and includes the high spatial variations owing to the input land cover data at a 1-km resolution. In terms of climate modeling studies, the use of this new database is shown to improve the surface climatology of the ARPEGE climate model.