Variations in hydrological connectivity of Australian semiarid landscapes indicate abrupt changes in rainfall‐use efficiency of vegetation

Abstract

[1] Dryland vegetation frequently shows self-organized spatial patterns as mosaic-like structures of sources (bare areas) and sinks (vegetation patches) of water runoff and sediments with variable interconnection. Good examples are banded landscapes displayed by Mulga in semiarid Australia, where the spatial organization of vegetation optimizes the redistribution and use of water (and other scarce resources) at the landscape scale. Disturbances can disrupt the spatial distribution of vegetation causing a substantial loss of water by increasing landscape hydrological connectivity and consequently, affecting ecosystem function (e.g., decreasing the rainfall-use efficiency of the landscape). We analyze (i) connectivity trends obtained from coupled analysis of remotely sensed vegetation patterns and terrain elevations in several Mulga landscapes subjected to different levels of disturbance, and (ii) the rainfall-use efficiency of these landscapes, exploring the relationship between rainfall and remotely sensed Normalized Difference Vegetation Index. Our analyses indicate that small reductions in the fractional cover of vegetation near a particular threshold can cause abrupt changes in ecosystem function, driven by large nonlinear increases in the length of the connected flowpaths. In addition, simulations with simple vegetation-thinning algorithms show that these nonlinear changes are especially sensitive to the type of disturbance, suggesting that the amount of alterations that an ecosystem can absorb and still remain functional largely depends on disturbance type. In fact, selective thinning of the vegetation patches from their edges can cause a higher impact on the landscape hydrological connectivity than spatially random disturbances. These results highlight surface connectivity patterns as practical indicators for monitoring landscape health.

Pattern selection and hysteresis in the Rietkerk model for banded vegetation in semi-arid environments

“…It is now known that self-organized vegetation patterns are a characteristic feature of semi-arid regions in many parts of the world, particularly Africa [2,3], Australia [4,5] and North America [2,6,7]. On slopes, these patterns consist of stripes running parallel to the contours [8 -11].…”

Assessing vegetation structure and ANPP dynamics in a grassland–shrubland Chihuahuan ecotone using NDVI–rainfall relationships

“…Differences in soil background brightness can generate important uncertainties in relating NDVI levels to dryland vegetation, especially when vegetation cover is low and soil type is heterogeneous in space (Okin et al, 2001). Despite these uncertainties, multiple studies have demonstrated the usefulness of NDVI for examining primary production and vegetation structure in arid and semi-arid ecosystems (for example, Weiss et al, 2004;Choler et al, 2010;Moreno-de las Heras et al, 2012), and particularly in Chihuahuan landscapes with sparse vegetation (30-50 % cover) similar to those included in this study (Peters and Eve, 1995;Peters et al, 1997;Pennington and Collins, 2007;Notaro et al, 2010). We compiled decade-scale (2000-2013) series of NDVI with a 16-day compositing period from the MODIS Terra satellite (MOD13Q1 product, collection 5, approx.…”

“…Investigations of the relationships between NDVI and rainfall suggest that arid and semi-arid vegetation responds to antecedent (or preceding cumulative) precipitation rather than to immediate rainfall, since plant growth is affected by the history of available soil moisture (Al-Bakri and Suleiman, 2004;Schwinning and Sala, 2004;Evans and Geerken, 2004;Moreno-de las Heras et al, 2012). The length (or number of days) of antecedent rainfall that best explains the NDVI (or green biomass) dynamics of dryland vegetation (hereafter optimal length of rainfall accumulation, Olr) appears to be site-specific and strongly dependent on vegetation type (Evans and Geerken, 2004;Prasad et al, 2007;Garcia et al, 2010).…”

“…Remote sensing of vegetation provides a valuable source of information for landscape monitoring and forecasting of vegetation change in drylands (Okin and Roberts, 2004;Pennington and Collins, 2007;Moreno-de las Heras et al, 2012). Satellite-derived chlorophyll-sensitive vegetation indices, such as the normalized difference vegetation index (NDVI), provide important information on vegetation structure (e.g.…”

Coupled Effects of Turing and Neimark-Sacker Bifurcations on Vegetation Pattern Self-Organization in a Discrete Vegetation-Sand Model

“…Vegetation patterns have been widely observed in arid and semi-arid areas [1][2][3][4][5][6][7]. There have been many studies on the exploration of pattern self-organization mechanisms [8][9][10][11][12][13].…”