Observation-based assessment of secondary water effects on seasonal vegetation decay across Africa

Abstract

Local studies and modeling experiments suggest that shallow groundwater and lateral redistribution of soil moisture, together with soil properties, can be highly important secondary water sources for vegetation in water-limited ecosystems. However, there is a lack of observation-based studies of these terrain-associated secondary water effects on vegetation over large spatial domains. Here, we quantify the role of terrain properties on the spatial variations of dry season vegetation decay rate across Africa obtained from geostationary satellite acquisitions to assess the large-scale relevance of secondary water effects. We use machine learning based attribution to identify where and under which conditions terrain properties related to topography, water table depth, and soil hydraulic properties influence the rate of vegetation decay. Over the study domain, the machine learning model attributes about one-third of the spatial variations of vegetation decay rates to terrain properties, which is roughly equally split between direct terrain effects and interaction effects with climate and vegetation variables. The importance of secondary water effects increases with increasing topographic variability, shallower groundwater levels, and the propensity to capillary rise given by soil properties. In regions with favorable terrain properties, more than 60% of the variations in the decay rate of vegetation are attributed to terrain properties, highlighting the importance of secondary water effects on vegetation in Africa. Our findings provide an empirical assessment of the importance of local-scale secondary water effects on vegetation over Africa and help to improve hydrological and vegetation models for the challenge of bridging processes across spatial scales.

Keywords: Africa; drylands; ecohydrology; groundwater; secondary water resources; topography; vegetation decay rate; water limitation.

Figure 1

Maps of (A) model output (λ_m), in days, where larger values of λ (blue) indicate slower decay (B) residual of the model (λ−λ_m), in days, where positive values (red) indicate underestimation. Histograms of the mapped values for the entire domain are given in the main panels of all the maps with a dashed line indicating the mean values of the domain, as well as six insets to show local variability.

Figure 2

Spatial variations of the normalized importance of terrain on λ (Φ_{terrain−total}) as the output of Equation 2 where larger (blue to red) values indicate higher importance of terrain parameters. Refer to Figure 1 for plotting details.

Figure 3

Normalized importance of terrain (same as Figure 2) with change in Vector Ruggedness Measure (VRM) (A), Water Table Depth (WTD) (B), and maximum potential upwards capillary flux 1 m above water table depth (I_cap) (C). Y-axis shows the total terrain effects (Φ_{terrain−total}) even though bars are colored and annotated to show its components as direct effects (Φ_{terrain−direct}) and interaction effects with climate (Φ_{terrain−clim}) and vegetation (Φ_{terrain−veg}), using Equation 2.

Figure 4

Effects of aridity on the importance of terrain parameters (refer to Equation 2) with change in Vector Ruggedness Measure (VRM) (A), Water Table Depth (WTD) (B), and maximum potential upward capillary flux 1 meter above water table depth (I_cap) (C).