Ecosystems at high latitudes are under increasing stress from climate change. To understand changes in carbon fluxes, in situ measurements from eddy covariance networks are needed. However, there are large spatiotemporal gaps in the high-latitude eddy covariance network. Here we used the relative extrapolation error index in machine learning-based upscaled gross primary production as a measure of network representativeness and as the basis for a network optimization. We show that the relative extrapolation error index has steadily decreased from 2001 to 2020, suggesting diminishing upscaling errors. In experiments where we limit site activity by either setting a maximum duration or by ending measurements at a fixed time those errors increase significantly, in some cases setting the network status back more than a decade. Our experiments also show that with equal site activity across different theoretical network setups, a more spread out design with shorter-term measurements functions better in terms of larger-scale representativeness than a network with fewer long-term towers. We developed a method to select optimized site additions for a network extension, which blends an objective modeling approach with expert knowledge. Using a case study in the Canadian Arctic we show several optimization scenarios and compare these to a random site selection among reasonable choices. This method greatly outperforms an unguided network extension and can compensate for suboptimal human choices. Overall, it is important to keep sites active and where possible make the extra investment to survey new strategic locations.

Hydrological interactions between vegetation, soil, and topography are complex, and heterogeneous in semi-arid landscapes. This along with data scarcity poses challenges for large-scale modelling of vegetation-water interactions. Here, we exploit metrics derived from daily Meteosat data over Africa at ca. 5 km spatial resolution for ecohydrological analysis. Their spatial patterns are based on Fractional Vegetation Cover (FVC) time series and emphasise limiting conditions of the seasonal wet to dry transition: the minimum and maximum FVC of temporal record, the FVC decay rate and the FVC integral over the decay period. We investigate the relevance of these metrics for large scale ecohydrological studies by assessing their co-variation with soil moisture, and with topographic, soil, and vegetation factors. Consistent with our initial hypothesis, FVC minimum and maximum increase with soil moisture, while the FVC integral and decay rate peak at intermediate soil moisture. We find evidence for the relevance of topographic moisture variations in arid regions, which, counter-intuitively, is detectable in the maximum but not in the minimum FVC. We find no clear evidence for wide-spread occurrence of the “inverse texture effect”’ on FVC. The FVC integral over the decay period correlates with independent data sets of plant water storage capacity or rooting depth while correlations increase with aridity. In arid regions, the FVC decay rate decreases with canopy height and tree cover fraction as expected for ecosystems with a more conservative water-use strategy. Thus, our observation-based products have large potential for better understanding complex vegetation–water interactions from regional to continental scales.

Next to precipitation, secondary water sources emerging from shallow groundwater and lateral redistribution of soil moisture, together with soil properties modulating their accessibility are highly important in water-limited ecosystems. However, effects of these land-associated secondary inputs are not well known over large domains given the mismatch of spatial scales of processes. Here, we quantify the role of land properties on the spatial variations of seasonal decay rate of vegetation cover over water-limited regions of Africa, using machine learning. Over the study domain, 17 % of these variations are directly attributed to land properties, and 16 % are attributed to interaction effects of land properties with climate and vegetation. Locally, total land attributed variations account for more than 60 % in hotspots with different land properties like shallow groundwater, complex topography, and favourable soil properties. Our findings lend empirical evidence for the importance of local-scale secondary water inputs over large domains.