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Highlights • Combined-Source Variational Data Assimilation Scheme was evaluated over a forest savanna. • Evaluate the potential of using global meteorological forcing data to drive the model. • An error propagation analysis indicate that the model is sensitive to uncertainties in wind speed. Abstract In this study, the performance of the combined-source variational data assimilation scheme (CS-VDA) is assessed in detail using in situ heat fluxes (i.e. sensible heat (H) and latent heat (LE)) collected at a Eucalypt forest savanna of Northern Australia (Howard Springs). The CS VDA scheme estimates surface turbulent heat fluxes via assimilation of sequences of land surface temperature (LST) and meteorological data into a surface energy balance model and a dynamic model. The main objectives of this paper were to extend previous studies to a semi-arid ecosystem and to evaluate the potential of using global meteorological forcing data (GMD) to drive the CS VDA model (rather than in-situ meteorological observations). In order to study the new errors associated with the use of GMD, the effects on LE of the uncertainty in air temperature and wind speed (the two key meteorological factors that controls the total estimation error) was quantitatively characterized. Using hourly in-situ measurements as inputs, the daily-averaged LE RMSE daily was 54 W/m2, which agrees with the errors previously reported in the literature. As expected, replacing local meteorological data with GMD reduces the performance of the LE estimation (GMA: RMSE daily = 82 W/m2, GLDAS: RMSE daily = 151 W/m2). However, LE RMSE values at 8-day temporal scale for GMA are RMSE 8-days = 32 W/m2, similar to those reported in this area for other models (MODIS (MOD16A2) and Breathing Earth System Simulator (BESS)). The error propagation analysis indicate that the CS VDA model is very sensitive to uncertainties in wind speed measurements. Moreover, there are large discrepancies between in situ and GMD wind speed. These two factors combined can explain the degradation in LE estimations. In this context, our study is a first step towards the characterization of an operational daily LE estimation scheme using hourly LST observations. [ABSTRACT FROM AUTHOR]

Latent heat flux (LE) and corresponding water loss in non-moisture-limited ecosystems are well correlated to radiation and temperature. By contrast, in savannahs and arid and semi-arid lands LE is mostly driven by available water and the vegetation exerts a strong control over the rate of transpiration. Therefore, LE models that use optical vegetation indices (VIs) to represent the vegetation component (transpiration as a function of surface conductance, G s ) generally overestimate water fluxes in water-limited ecosystems. In this study, we evaluated and compared optical and passive microwave index based retrievals of G s and LE derived using the Penman-Monteith (PM) formulation over the North Australian Tropical Transect (NATT). The methodology was evaluated at six eddy covariance (EC) sites from the OzFlux network. To parameterize the PM equation for retrievals of LE (PM-G s ), a subset of G s values was derived from meteorological and EC flux observations and regressed against individual and combined satellite indices, from (1) MODIS AQUA: the Normalized Difference Water Index (NDWI) and the Enhanced Vegetation Index (EVI); and from (2) AMSR-E passive microwave: frequency index (FI), polarization index (PI), vegetation optical depth (VOD) and soil moisture (SM) products. Similarly, we combined optical and passive microwave indices (multi-sensor model) to estimate weekly G s values, and evaluated their spatial and temporal synergies. The multi-sensor approach explained 40–80% of LE variance at some sites, with root mean square errors (RMSE) lower than 20 W/m 2 and demonstrated better performance to other satellite-based estimates of LE. The optical indices represented potential G s associated with the phenological status of the vegetation (e.g. leaf area index, chlorophyll content) at finer spatial resolution. The microwave indices provided information about water availability and moisture stress (e.g. water content in leaves and shallow soil depths, atmospheric demand) at a high temporal resolution, thereby providing a scaling factor for potential G s . We applied the newly proposed G s model to estimate LE at regional scale using global meteorological data. Our derivation could be extended to continental scales providing equally robust estimates of LE in arid and semi-arid biomes. A more accurate estimation of G s and LE across different savannah classes will improve the analysis of water use efficiency under drought conditions, which is of importance to climate change studies of water, carbon and energy cycling. [ABSTRACT FROM AUTHOR]