Quantification of Local- and Global- Scale Hydrological and Sediment Connectivity over
Grassland and Shrubland Hillslopes
Abstract
Quantifying connectivity patterns in dryland ecosystems is crucial for
understanding how water and sediments flow across the landscape, crucial
for mitigating the impacts of climate change and land use modifications.
This study quantifies the multi-scale water-mediated connectivity within
grassland and shrubland hillslopes using a weighted, directed network
model. By integrating high-resolution elevation, vegetation data, and
modeled event-based hydrologic and sediment transport, we quantify both
structural (topology) and functional (dynamical) connectivity under
varying rainfall and soil moisture conditions.
Our findings reveal a marked increase in local connectivity metrics in
shrublands compared to grasslands, with metrics such as betweenness
centrality and the weighted length of connected pathways increasing up
to tenfold. Despite these substantial local changes, global properties
like link density and global efficiency show less drastic variations,
suggesting a robust network topology that sustains geomorphic
functionality across different vegetation states. This indicates that
although local connectivity is highly sensitive to vegetation changes,
the overall structure and functional connectivity of water and sediment
at the global scale remain relatively stable.
Functional connectivity is more strongly correlated with structural
connectivity for sediment than for water. This difference is
particularly pronounced under high rainfall conditions, yet shows little
sensitivity to variations in antecedent soil moisture, highlighting the
critical role of event-driven processes in shaping connectivity
patterns.
The study advances our quantitative understanding of how structural
changes affect functional processes in dryland ecosystems. It offers a
comprehensive framework for analyzing connectivity at multiple scales,
which can inform targeted management strategies aimed at enhancing
ecosystem resilience.