Remote sensing of water use and water stress in the African savanna ecosystem at local scale – Development and validation of a monitoring tool

Savannas are among the most productive biomes of Africa, where they comprise half of its surface. They support wildlife, livestock, rangelands, crops, and livelihoods, playing an important socioeconomic role in rural areas. These water-limited ecosystems with seasonal water availability are highly sensitive to changes in both climate conditions, and in land-use/management practices. Although monitoring programs for African savanna water use have been established in certain areas, most of them are largely restricted to point based measurements or coarse scales, and are not fully capable to provide distributed timely information for planning purposes. In this study we develop a mechanism for monitoring the water used by African savanna from fine scale (meters) to watershed scale, integrating the effects of the water stress. Our hypothesis is that the Ecosystem Stress Index (ESI) is a valuable tool to downscale estimates of actual evapotranspiration at coarse scale, to high resolutions. To monitor savanna water fluxes in a semi-continuous way this study integrates two different ET-estimation approaches: KC-FAO56 model, integrating reflectance-based “crop” coefficients (SPOT 4 & 5 satellites), is used to derive unstressed savanna evapotranspiration (with high spatial resolution), and the two-source surface energy balance model -TSEB, integrating radiometric surface temperature (AATSR satellites) allows the determination of water stress across savannas (ESI, with low spatial resolution). The difference between estimated and observed surface fluxes derived from TSEB (RMSDLE = 53 Wm-2, RMSDH = 50 Wm-2, RMSDRn = 60 Wm-2, RMSDG = 21 Wm-2) were of the same magnitude as the uncertainties derived from the flux measurement system, being sufficiently accurate to be employed in a distributed way and on a more regular basis. The approach of ESI to downscale ET proved to be useful, and errors between estimated and observed daily ET (RMSD 0.6 mmday−1) were consistent with the results of other studies in savanna ecosystems. The modelling framework proposed provided an accurate representation of the natural landscape heterogeneity and local conditions, with the potential of providing information suitable from local to broader scales.info:eu-repo/semantics/publishedVersio

Evaluation of modeled actual evapotranspiration estimates from a land surface, empirical and satellite-based models using in situ observations from a South African semi-arid savanna ecosystem

Evapotranspiration (ET) plays a crucial role in the land-atmosphere interaction and climate variability, especially in arid and semi-arid areas. Accurate estimates of ET are important in hydrological and climate modeling. This study evaluates eight ET data products from different models used for ET estimation. The data products are classified into three main categories depending on the type of modeling approaches: namely process-based land surface model, empirical models, and satellite data derived estimates. The different model estimates are evaluated against in situ measurements from the Skukuza flux tower which is situated in a semi-arid savanna in South Africa. The correlation score and cantered root mean square error computed on monthly ET averages indicate that the satellite-derived model and land surface model estimates are closer to the observed ET signal for the Skukuza site, both in-phase and magnitude. The empirical models' outputs tend to reflect a relatively pronounced departure from observations in magnitude. The normalised mean bias computed for different seasons reveals that the estimates from all modeling approaches are close to the observed signal during the transition period (March–May) to the austral summer. In general, all models overestimate ET during summer and underestimate it in winter. A qualitative analysis of the year-to-year variation for different seasons reveals that all model estimates are qualitatively consistent with the observed seasonal pattern of the signal. Satellite and process-based land surface models (LSMs) also show a response to extremes events such as drought years. The study identifies satellite-derived model outputs as a candidate for understanding spatio-temporal variability of ET across different landscapes within the study region, and process-based models to potentially be used for climate change impact studies on ET.The Council for Scientific and Industrial Research [project number EEGC030].https://www.elsevier.com/locate/agrformethj2019Geography, Geoinformatics and Meteorolog