Multi-month memory effects on early summer vegetative activity in semi-arid South Africa and their spatial heterogeneity

Richard, Yves; Martiny, Nadège; Rouault, Mathieu; Philippon, Nathalie; Tracol, Yann; Castel, Thierry

doi:10.1080/01431161.2012.692830

Cited by 10 publications

(7 citation statements)

References 35 publications

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“…Overall, Figure 6 suggest that NDVI in the two regions has different relationships with temperature and rainfall which may relate to the fact that south of L18, grassy species are more dominant and the reverse to the north. The sensitivity of NDVI to rainfall south of L18 is in line with other studies for semi‐arid African regions where water availability is a constraint for vegetation growth (Malo and Nicholson, 1990; Farrar et al, 1994; Camberlin et al, 2007; Richard et al, 2008, 2012).…”

Section: Resultssupporting

confidence: 89%

“…For temperature, the largest area of high values (mean > 25°C) occurs during OND (Figure 5g–i) and there is no obvious relationship between these patterns and NDVI. However, since previous studies indicate that variability of vegetation over southern Africa is mainly due to rainfall of the current wet season (Camberlin et al, 2007; Richard et al, 2008, 2012; Martiny et al, 2010; Good and Caylor, 2011), partial correlation analysis was performed to remove the effects of rainfall on NDVI and test for any possible temperature‐NDVI relationship. Spatially averaged over the region, the NDVI correlations with temperature were −0.34, −0.41 and −0.50 for OND, JFMA and MJJAS, respectively, whereas after the effects of rainfall are removed, the resulting partial correlations of NDVI with temperature were −0.34, −0.12 and −0.53.…”

Section: Resultsmentioning

confidence: 99%

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Relationships between NDVI, river discharge and climate in the Okavango River Basin region

Moses

Blamey

Reason

2021

Intl Journal of Climatology

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The Okavango River Basin (ORB) is a highly sensitive and biodiverse region in southern Africa whose climate, vegetation and river discharge characteristics are not well understood. This study investigated relationships between rainfall, temperature, Normalized Difference Vegetation Index (NDVI) and river discharge over the region as well as their trends and interannual variability. It is found that spatial patterns of NDVI are closely related to those of rainfall, but less so with temperature at monthly and seasonal time scales. The relationships between NDVI and rainfall/temperature differ north of 18.9°S where rainfall is higher than to its south. Typically, there are lags of 1–2‐months between NDVI and either rainfall or temperature. Also, there are large areas across the region that show significant warming trends in all seasons as well as wetting (mainly in the north). This increasing trend in surface temperature may act to worsen the impacts of extreme events such as severe drought and fire in the region. There is also pronounced interannual variability with significant correlations found with El Niño‐Southern Oscillation (ENSO), the subtropical South Indian Ocean Dipole and the Botswana High for rainfall and temperature, and for NDVI with ENSO. For the Southern Annular Mode, significant correlations were found with rainfall and temperature only in December and April. On longer time scales, focus was also placed on the wet 2006–2013 period relative to much drier 1999–2005 epoch for October–December. The wetter conditions during 2006–2013 appear related to La Niña Modoki conditions and warmer sea surface temperature near Angola as well as regional circulation differences.

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Section: Resultssupporting

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Relationships between NDVI, river discharge and climate in the Okavango River Basin region

Moses

Blamey

Reason

2021

Intl Journal of Climatology

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“…Although more rare events can be beneficial for vegetation at low precipitation and less beneficial at high precipitation 22 , the picture here is complicated by water competition between trees and grasses, which should be properly disentangled by including the changes in dynamics along a rainfall gradient, for example including the MAR-dependent relative rooting depth of tree and grasses 18 . Tree access to deep water during the dry season may generate long-term memory effects 73 , and contribute to different phenology of trees and grasses 74 , which can lead to a temporal niche-partitioning 48 , a known mechanism of species coexistence (the so-called storage-effect) 44 . Soil texture influences water availability, and it thus mediates the response of tree cover to intra-seasonal rainfall variability, with a role that can be almost as important as total yearly rainfall 9,19 .…”

Section: Discussionmentioning

confidence: 99%

Grass and tree cover responses to intra-seasonal rainfall variability vary along a rainfall gradient in African tropical grassy biomes

D’Onofrio

Sweeney

Hardenberg

et al. 2019

Sci Rep

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Although it is well known that mean annual rainfall (MAR) and rainfall seasonality have a key role in influencing the distribution of tree and grass cover in African tropical grassy biomes (TGBs), the impact of intra-seasonal rainfall variability on these distributions is less agreed upon. Since the prevalent mechanisms determining biome occurrence and distribution change with MAR, this research investigates the role of intra-seasonal rainfall variability for three different MAR ranges, assessing satellite data on grass and tree cover, rainfall and fire intervals at a sub-continental scale in sub-Saharan Africa. For MAR below 630 mm y−1, rainfall frequency had a positive relationship with grass cover; this relationship however became mostly negative at intermediate MAR (630–1200 mm y−1), where tree cover correspondingly mostly increased with rainfall frequency. In humid TGBs, tree cover decreased with rainfall intensity. Overall, intra-seasonal rainfall variability plays a role in determining vegetation cover, especially in mesic TGBs, where the relative dominance of trees and grasses has previously been largely unexplained. Importantly, the direction of the effect of intra-seasonal variability changes with MAR. Given the predicted increases in rainfall intensity in Africa as a consequence of climate change, the effects on TGBs are thus likely to vary depending on the MAR levels.

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“…; Richard et al. ). In semi‐arid environments, cycles in vegetation productivity have been linked to precipitation legacies from previous years and pulse‐reserve responses that describe accrual of biological capital (leaves, roots, seeds) in wet years that set the stage for response in following years (Reynolds et al.…”

Section: Introductionmentioning

confidence: 98%

Semi‐arid vegetation response to antecedent climate and water balance windows

Thoma

Munson

Irvine

et al. 2016

Applied Vegetation Science

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Questions: Can we improve understanding of vegetation response to water availability on monthly time scales in semi-arid environments using remote sensing methods? What climatic or water balance variables and antecedent windows of time associated with these variables best relate to the condition of vegetation? Can we develop credible near-term forecasts from climate data that can be used to prepare for future climate change effects on vegetation?Location: Semi-arid grasslands in Capitol Reef National Park, Utah, USA. Methods:We built vegetation response models by relating the normalized difference vegetation index (NDVI) from MODIS imagery in Mar-Nov 2000-2013 to antecedent climate and water balance variables preceding the monthly NDVI observations. We compared how climate and water balance variables explained vegetation greenness and then used a multi-model ensemble of climate and water balance models to forecast monthly NDVI for three holdout years.Results: Water balance variables explained vegetation greenness to a greater degree than climate variables for most growing season months. Seasonally important variables included measures of antecedent water input and storage in spring, switching to indicators of drought, input or use in summer, followed by antecedent moisture availability in autumn. In spite of similar climates, there was evidence the grazed grassland showed a response to drying conditions 1 mo sooner than the ungrazed grassland. Lead times were generally short early in the growing season and antecedent window durations increased from 3 mo early in the growing season to 1 yr or more as the growing season progressed. Forecast accuracy for three holdout years using a multi-model ensemble of climate and water balance variables outperformed forecasts made with a na€ ıve NDVI climatology. Conclusions:We determined the influence of climate and water balance on vegetation at a fine temporal scale, which presents an opportunity to forecast vegetation response with short lead times. This understanding was obtained through high-frequency vegetation monitoring using remote sensing, which reduces the costs and time necessary for field measurements and can lead to more rapid detection of vegetation changes that could help managers take appropriate actions.

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Multi-month memory effects on early summer vegetative activity in semi-arid South Africa and their spatial heterogeneity

Cited by 10 publications

References 35 publications

Relationships between NDVI, river discharge and climate in the Okavango River Basin region

Relationships between NDVI, river discharge and climate in the Okavango River Basin region

Grass and tree cover responses to intra-seasonal rainfall variability vary along a rainfall gradient in African tropical grassy biomes

Semi‐arid vegetation response to antecedent climate and water balance windows

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