SIMSPHERE: A DOWNLOADABLE SOIL/VEGETATION/ATMOSPHERE/TRANSFER (SVAT) MODEL FOR TEACHING AND RESEARCH

Simsphere, a soil/vegetation/atmosphere/transfer (SVAT) model developed at Penn State, can be downloaded from the web for use by students and researchers. In existence for several decades, Simsphere has figured in both the classroom and in research at several universities. As such, Simsphere has been supported by a knowledgeable group of academic users and has been applied in a variety of applications, such as in remote sensing of surface soil water content, and in the assessment of water and ozone stresses on plants. This paper describes the model and how it can be downloaded and run.

Modelling of water and energy exchanges over a sparse olive orchard in semi-arid areas

In the Mediterranean basin, olive orchards occupy a large fraction of agricultural lands due to its sustainability to harsh conditions, drought in particular. Since most modeling tools to simulate vegetation functioning are not meant to represent very sparse crops (i.e., rainfed olive trees have a vegetation fraction cover ranging from 2 to 15 %), computing the water needs and the vulnerability to drought of an olive orchard is a challenge. There is indeed a very high contribution of the bare soil signal to the total fluxes, and it is difficult to decipher the contribution of the tree from that of the entire surface. In this context, in an attempt to study the olive tree hydrological functioning at field scale (38 ha), an experimental site was setup and a Soil–Vegetation–Atmosphere (SVAT) model has been applied. To represent the orchard soil–plant–atmosphere interactions, a simulation with default settings was assessed using parameters derived from both the literature and ground measurements. In this default configuration, neither the predicted actual nor the potential transpiration could reach the observed transpiration acquired during the wet season (R2 = 0.67, the Root Mean Square Error (RMSE) = 5.63 mm week−1). We show that the model fails to reproduce the relevant leaf surface that transpires. To address this issue and to improve the estimate of the year-to-year variability of the olive tree transpiration, we propose guidance on how a SVAT model can be modified to more appropriately represent the hydrological functioning of a sparse orchard. Once the tree transpiration is accurately simulated (R2 = 0.93, RMSE = 1.62 mm week−1), we evaluated whether the fully coupled (single patch) or a fully uncoupled (two patch) system better reproduced the total fluxes and their components. Owing to the independent characteristics of the soil columns inherent in the assumption of the 2-patch version, the bare soil column shows a deficiency if the topsoil root extraction is not accounted for. We deduced that we cannot accurately reproduce the soil evaporation in this configuration. This study open perspectives for a better representation of water fluxes over sparse tree crops into both hydrological and SVAT models.

Ability of a soil–vegetation–atmosphere transfer model and a two-source energy balance model to predict evapotranspiration for several crops and climate conditions

The heterogeneity of Agroecosystems, in terms of hydric conditions, crop types and states, and meteorological forcing, is difficult to characterize precisely at the field scale over an agricultural landscape. This study aims to perform a sensitivity study with respect to the uncertain model inputs of two classical approaches used to map the evapotranspiration of agroecosystems: (1) a surface energy balance (SEB) model, the Two-Source Energy Balance (TSEB) model, forced with thermal infrared (TIR) data as a proxy for the crop hydric conditions, and (2) a soil–vegetation–atmosphere transfer (SVAT) model, the SEtHyS model, where hydric conditions are computed from a soil water budget. To this end, the models' skill was compared using a large and unique in situ database covering different crops and climate conditions, which was acquired over three experimental sites in southern France and Morocco. On average, the models provide 30 min estimations of latent heat flux (LE) with a RMSE of around 55 W m−2 for TSEB and 47 W m−2 for SEtHyS, and estimations of sensible heat flux (H) with a RMSE of around 29 W m−2 for TSEB and 38 W m−2 for SEtHyS. A sensitivity analysis based on realistic errors aimed to estimate the potential decrease in performance induced by the spatialization process. For the SVAT model, the multi-objective calibration iterative procedure (MCIP) is used to determine and test different sets of parameters. TSEB is run with only one set of parameters and provides acceptable performance for all crop stages apart from the early growing season (LAI < 0.2 m2 m−2) and when hydric stress occurs. An in-depth study on the Priestley–Taylor key parameter highlights its marked diurnal cycle and the need to adjust its value to improve flux partitioning between the sensible and latent heat fluxes (1.5 and 1.25 for France and Morocco, respectively). Optimal values of 1.8–2 were highlighted under cloudy conditions, which is of particular interest due to the emergence of low-altitude drone acquisition. Under developed vegetation (LAI > 0.8 m2 m−2) and unstressed conditions, using sets of parameters that only differentiate crop types is a valuable trade-off for SEtHyS. This study provides some scientific elements regarding the joint use of both approaches and TIR imagery, via the development of new data assimilation and calibration strategies.

Modelling Key Parameters Characterising Land Surface in 1D Space using the SimSphere SVAT model: Findings from its use at European Ecosystems

The present study investigates the ability of SimSphere, a Soil Vegetation Atmosphere Transfer (SVAT) model, to predict key parameters in characterising land Surface interactions. In particular, the model’s performance in predicting Net Radiation (Rnet), Latent Heat (LE), and Sensible Heat (H) was examined. For this purpose, concurrent in-situ measurements of the corresponding parameters for a total of 70 days of the year 2011 from 7 CarboEurope network sites were acquired, incorporating a variety of environmental biomes and climatic conditions in the model evaluation.

An improved perspective in the spatial representation of soil moisture: potential added value of SMOS disaggregated 1 km resolution “all weather” product

This study uses the synergy of multi-resolution soil moisture (SM) satellite estimates from the Soil Moisture Ocean Salinity (SMOS) mission, a dense network of ground-based SM measurements, and a soil–vegetation–atmosphere transfer (SVAT) model, SURFEX (externalized surface), module ISBA (interactions between soil, biosphere and atmosphere), to examine the benefits of the SMOS level 4 (SMOS-L4) version 3.0, or “all weather” high-resolution soil moisture disaggregated product (SMOS-L43.0; ∼1 km). The added value compared to SMOS level 3 (SMOS-L3; ∼25 km) and SMOS level 2 (SMOS-L2; ∼15 km) is investigated. In situ SM observations over the Valencia anchor station (VAS; SMOS calibration and validation – Cal/Val – site in Europe) are used for comparison. The SURFEX (ISBA) model is used to simulate point-scale surface SM (SSM) and, in combination with high-quality atmospheric information data, namely from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Système d'analyse fournissant des renseignements atmosphériques à la neige (SAFRAN) meteorological analysis system, to obtain a representative SSM mapping over the VAS. The sensitivity to realistic initialization with SMOS-L43.0 is assessed to simulate the spatial and temporal distribution of SSM. Results demonstrate the following: (a) All SMOS products correctly capture the temporal patterns, but the spatial patterns are not accurately reproduced by the coarser resolutions, probably in relation to the contrast with point-scale in situ measurements. (b) The potential of the SMOS-L43.0 product is pointed out to adequately characterize SM spatio-temporal variability, reflecting patterns consistent with intensive point-scale SSM samples on a daily timescale. The restricted temporal availability of this product dictated by the revisit period of the SMOS satellite compromises the averaged SSM representation for longer periods than a day. (c) A seasonal analysis points out improved consistency during December–January–February and September–October–November, in contrast to significantly worse correlations in March–April–May (in relation to the growing vegetation) and June–July–August (in relation to low SSM values < 0.1 m3 m−3 and low spatial variability). (d) The combined use of the SURFEX (ISBA) SVAT model with the SAFRAN system, initialized with SMOS-L43.0 1 km disaggregated data, is proven to be a suitable tool for producing regional SM maps with high accuracy, which could be used as initial conditions for model simulations, flood forecasting, crop monitoring and crop development strategies, among others.

Evapotranspiration monitoring based on thermal infrared data over agricultural landscapes: comparison of a simple energy budget model and a SVAT model

The overall purpose of our work is to take advantage of Thermal Infra-Red (TIR) imagery to estimate landscape evapotranspiration fluxes over agricultural areas, relying on two approaches of increasing complexity and input data needs: a Surface Energy Balance (SEB) model, TSEB, used directly at the landscape scale with TIR forcing, and the aggregation of a Soil-Vegetation-Atmosphere Transfer (SVAT) model, SEtHyS, run at high resolution (&amp;simeq;100 m) and constrained by assimilation of TIR data. Within this preliminary study, models skills are compared thanks to large in situ database covering different crops, stress and climate conditions. Domains of validity are assessed and the possible loss of performance resulting from inaccurate but realistic inputs (forcing and model parameters) due to scaling effects are quantified. The in situ data set came from 3 experiments carried out in southern France and in Morocco. On average, models provide half-hourly averaged estimations of latent heat flux (LE) with a RMSE of around 55 W m−2 for TSEB and 47 W m−2 for SEtHyS, and estimations of sensible heat flux (H) with a RMSE of around 29 W m−2 for TSEB and 38 W m−2 for SEtHyS. TSEB has been shown to be more flexible and requires one single set of parameters but lead to low performances on rising vegetation and stressed conditions. An in-depth study on the Priestley-Taylor key parameter highlights its marked diurnal cycle and the need to adjust its value to improve flux partition between sensible and latent heat fluxes (1.5 and 1.25 for south-western France and Morocco, respectively). Optimal values of 1.8 to 2 were hilighted under cloudy conditions, which is of particular interest with the emergence of low altitude drone acquisition. SEtHyS is valid in more cases while it required a finer parameters tuning and a better knowledge of surface and vegetation. This study participates to lay the ground for exploring the complementarities between instantaneous and continuous dynamic evapotranspiration mapping monitored with TIR data.