Hydrologic variability in dryland regions: impacts on ecosystem dynamics and food security

D’Odorico, Paolo; Bhattachan, Abinash

doi:10.1098/rstb.2012.0016

Cited by 95 publications

(67 citation statements)

References 133 publications

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“…Moreover, SWA shows a stronger difference between dry and wet periods, has clearer unimodal shapes along the MAP gradient and possesses higher variability along the MAP gradient. This agrees with the hypothesized higher β dynamics in regions with higher interannual variability of annual rainfall [36,37], such as SWA (Figure 3a). In addition, this suggests that a higher rain use efficiency, which has been observed during dry periods [18], might be particularly favoured by greater rainfall variability.…”

Section: Vegetation Response To Rainfall Along Map Gradientssupporting

confidence: 89%

“…However, no attempt to describe any ecosystem-specific reaction to above or below average rainfall conditions with respect to ecohydrological traits such as β has been made on a regional scale based on gridded data. Additionally, there is evidence that greater environmental heterogeneity (such as rainfall variability) affects the way ecosystems react with changing resource availability [35,36]. Lázaro-Nogal et al [37] found greater phenotypic plasticity…”

Section: Introductionmentioning

confidence: 99%

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Dryland Vegetation Functional Response to Altered Rainfall Amounts and Variability Derived from Satellite Time Series Data

Ratzmann

Gangkofner²,

Tietjen

et al. 2016

Remote Sensing

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Vegetation productivity is an essential variable in ecosystem functioning. Vegetation dynamics of dryland ecosystems are most strongly determined by water availability and consequently by rainfall and there is a need to better understand how water limited ecosystems respond to altered rainfall amounts and variability. This response is partly determined by the vegetation functional response to rainfall (β) approximated by the unit change in annual vegetation productivity per unit change in annual rainfall. Here, we show how this functional response from 1983 to 2011 is affected by below and above average rainfall in two arid to semi-arid subtropical regions in West Africa (WA) and South West Africa (SWA) differing in interannual variability of annual rainfall (higher in SWA, lower in WA). We used a novel approach, shifting linear regression models (SLRs), to estimate gridded time series of β. The SLRs ingest annual satellite based rainfall as the explanatory variable and annual satellite-derived vegetation productivity proxies (NDVI) as the response variable. Gridded β values form unimodal curves along gradients of mean annual precipitation in both regions. β is higher in SWA during periods of below average rainfall (compared to above average) for mean annual precipitation <600 mm. In WA, β is hardly affected by above or below average rainfall conditions. Results suggest that this higher β variability in SWA is related to the higher rainfall variability in this region. Vegetation type-specific β follows observed responses for each region along rainfall gradients leading to region-specific responses for each vegetation type. We conclude that higher interannual rainfall variability might favour a more dynamic vegetation response to rainfall. This in turn may enhance the capability of vegetation productivity of arid and semi-arid regions to better cope with periods of below average rainfall conditions.

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Section: Vegetation Response To Rainfall Along Map Gradientssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Dryland Vegetation Functional Response to Altered Rainfall Amounts and Variability Derived from Satellite Time Series Data

Ratzmann

Gangkofner²,

Tietjen

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Moreover, SWA shows a stronger difference between hydroclimatic periods (dry vs. wet), has clearer unimodal shapes along the MAP gradient and possesses higher spatial variability. This agrees with the hypothesized 35 higher β plasticity in regions with pronouncedly higher interannual variability in rainfall amounts (D'Odorico and Bhattachan, 2012;Lázaro-Nogal et al, 2015), such as SWA (Fig. 2b).…”

Section: Vegetation Response To Rainfall Along Map Gradientssupporting

confidence: 85%

“…Additionally, there is evidence that greater environmental heterogeneity (such as rainfall variability) affects the way ecosystems react on changing resource availability (D'Odorico and Bhattachan, 2012;Holmgren et al, 2013). Lázaro-Nogal et al (2015) found greater phenotypic plasticity being associated with higher rainfall variability in dryland plant communities which is considered an important feature for dryland ecosystems in face of future changes in rainfall.…”

mentioning

confidence: 99%

Dryland vegetation functional response to altered rainfall amounts and variability derived from satellite time series data

Ratzmann

Gangkofner²,

Tietjen

et al. 2016

Preprint

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Abstract. Vegetation net productivity is a key variable in ecosystem functioning. Understanding how its functional response to rainfall in drylands is affected by altered rainfall amounts and variability is therefore vitally important to understand consequences of climatic change for those water-limited ecosystems. Here, we show how this functional response is affected by below and above 30-year-average rainfall conditions in two arid to semi arid subtropical regions in West and South West during dry periods for mean annual precipitation < 500 mm and spatially more variable, the responses to climate for West Africa are generally low and spatially less dynamic. Those patterns follow differences in interannual rainfall amount variability (higher in South West Africa). Regional peaks of vegetation response to rainfall along mean annual precipitation are found at precipitation values with similar interannual variability in growing season length. Vegetation type (MODIS MCD12C) specific response to rainfall mostly follows observed responses along rainfall gradients leading to region specific 25 responses for each vegetation type. We conclude that higher rainfall amount variability enhances regional-scale vegetation response to rainfall plasticity and thus dryland ecosystem resilience to dry periods. Those results apply irrespective of vegetation type and thus evidence the fundamental role of rainfall variability in ecosystem functioning. Presented results moreover imply that the Sahel region (West Africa) although currently recovering from drought might be highly susceptible to future dry periods. 30

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“…It was argued that hydrological resilience was related more to vulnerability/sustainability than the reliability of water resources such as reservoir systems (e.g., [17]). Real hydrological systems (e.g., catchments) are complex [10,37,69] and have multiple attractors (steady states), which may inform how these systems may respond to and recover from perturbations such as droughts, floods, or groundwater pumping but determining multiple attractors is very challenging [20,21,33]. Selected factors (variables of concern) are often used indirectly to define or measure the resilience of hydrological systems.…”

Section: Entropy and Resilience Of Water Resourcesmentioning

confidence: 99%

Assessing Catchment Resilience Using Entropy Associated with Mean Annual Runoff for the Upper Vaal Catchment in South Africa

Ilunga

2017

Entropy

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Abstract:The importance of the mean annual runoff (MAR)-hydrological variable is paramount for catchment planning, development and management. MAR depicts the amount of uncertainty or chaos (implicitly information content) of the catchment. The uncertainty associated with MAR of quaternary catchments (QCs) in the Upper Vaal catchment of South Africa has been quantified through Shannon entropy. As a result of chaos over a period of time, the hydrological catchment behavior/response in terms of MAR could be characterized by its resilience. Uncertainty (chaos) in QCs was used as a surrogate measure of catchment resilience. MAR data on surface water resources (WR) of South Africa of 1990 (i.e., WR90), 2005 (WR2005) and 2012 (W2012) were used in this study. A linear zoning for catchment resilience in terms of water resources sustainability was defined. Regression models (with high correlation) between the relative changes/variations in MAR data sets and relative changes in entropy were established, for WR2005 and WR2012. These models were compared with similar relationships for WR90 and WR2005, previously reported. The MAR pseudo-elasticity of the uncertainty associated with MAR was derived from regression models to characterize the resilience state of QCs. The MAR pseudo-elasticity values were relatively small to have an acceptable level of catchment resilience in the Upper Vaal catchment. Within the resilience zone, it was also shown that the effect of mean annual evaporation (MAE) was negatively significant on MAR pseudo-elasticity, compared to the effect of mean annual precipitation (MAP), which was positively insignificant.

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Hydrologic variability in dryland regions: impacts on ecosystem dynamics and food security

Cited by 95 publications

References 133 publications

Dryland Vegetation Functional Response to Altered Rainfall Amounts and Variability Derived from Satellite Time Series Data

Dryland Vegetation Functional Response to Altered Rainfall Amounts and Variability Derived from Satellite Time Series Data

Dryland vegetation functional response to altered rainfall amounts and variability derived from satellite time series data

Assessing Catchment Resilience Using Entropy Associated with Mean Annual Runoff for the Upper Vaal Catchment in South Africa

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