Abstract
Terrestrial vegetation response to surface water availability is important for land–atmosphere interactions. However, the current understanding of how the vegetation responds to surface water remains limited since the physical processes happening within the biosphere and hydrosphere are highly coupled. It is even more difficult to measure such interactions for the processes related to surface soil moisture (SSM)—the central variable that interacts the most intimately with vegetation—since the observations of SSM are often scarce and uneven. Here, we use the satellite observations of vegetation optical depth (VOD) and SSM to map the response time scales of vegetation to surface water anomalies. We use the stability theory to derive vegetation memory time ( ${{{\bf \tau }}_{{{\bf ReS}}}}$ ) to reveal the global pattern of vegetation memory to surface water anomalies. That is, the time vegetation takes to return back to its equilibrium when an anomaly dissipates to a certain level (e.g., the e-folding level). We also estimate the plant reactive time ( ${{{\bf \tau }}_{{{\bf ReA}}}}$ )—the time when impacts of surface anomaly reach its peak to evaluate the overall resilience of terrestrial vegetation to surface water anomalies. The results show that ${{{\bf \tau }}_{{{\bf ReS}}}}$ tends to be longer in herbaceous biomes, whereas ${{{\bf \tau }}_{{{\bf ReA}}}}$ is longer in biomes with tree cover. Such anticorrelation of ${{{\bf \tau }}_{{{\bf ReS}}}}$ and ${{{\bf \tau }}_{{{\bf ReA}}}}$ indicates that the herbaceous biomes may be more vulnerable to surface water perturbations during climate extremes. Our study provides a global quantification on vegetation—soil moisture feedbacks—enabling comparison with earth system models.
Highlights
T ERRESTRIAL vegetation and climate system are highly coupled through land–atmosphere interactions
Where V and SM represent vegetation optical depth (VOD) and surface soil moisture (SSM), respectively; A indicates the parameter matrix; elements of A indicate the sensitivity of variables at the time step t to their values of last time step; A can be obtained by using multiple linear regression (MLR) from the VOD and SSM time series; and t is the index for the time step
The estimated tau-results could be variable-dependent, for example, in some areas, the time scale is determined by SSM, whereas in other regions, they are more related to VOD since the estimation of tau-results follows Rayleigh’s maximum principle
Summary
T ERRESTRIAL vegetation and climate system are highly coupled through land–atmosphere interactions. Studies show that the phenology of the vegetation can significantly alternate surface albedo [13], [14]; diversity of plant functional traits can even regulate droughts through land– atmosphere interactions [15], [16]. In this light, investigating how the terrestrial vegetation responds to and has an impact on surface water availability will advance our understanding of the land–atmosphere coupling processes and can further infer useful information for realistic modeling in current earth system models (ESMs)
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More From: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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