Spatio-temporal vegetation dynamics and relationship with climate over East Africa

Musau, John; Patil, Sopan; Sheffield, Justin; Marshall, Michael

doi:10.5194/hess-2016-502

Cited by 20 publications

(16 citation statements)

References 66 publications

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“…The accumulated period was designated as the supposed time‐lag duration that a climatic factor might have on vegetation growth.

{ATEM}_{(m, i)} = \sum_{k = 0}^{k = i} ((), {TEM}_{m - k} - T_{base}) ((), 0 \leq i \leq normaln)

{ASOLAR}_{(m, i)} = \sum_{k = 0}^{k = i} ((), {SOLAR}_{m - k} - S_{base}) ((), 0 \leq i \leq normaln)

{APRE}_{(m, i)} = \sum_{k = 0}^{k = i} ((), {PRE}_{m - k} - P_{base}) ((), 0 \leq i \leq normaln)

where m is the month series from January 1982 to December 2014; k is the time interval backward present month; i is the supposed accumulation duration or time lag; n is the maximum of the accumulation duration, which was defined to be 3, because previous research shows that the lagged time of vegetation responses to climate at a monthly scale primarily occurs within three months (Musau et al, ; Wu et al, ); TEM, SOLAR, and PRE represent the monthly air temperature, solar radiation, and precipitation, from January 1982 to December 2014; and T base , S base , and P base equal 0 (°C), 0 (W/m 2 ), and 0 (mm). There is no addition to the accumulated value of one climatic factor when its value falls below the base value.…”

Section: Methodsmentioning

confidence: 99%

Cumulative Effects of Climatic Factors on Terrestrial Vegetation Growth

et al. 2019

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Extensive studies have focused on instantaneous and time-lag impacts of climatic factors on vegetation growth; however, the chronical and accumulative indirect impacts of antecedent climatic factors carrying over for a period of time on vegetation growth, defined as cumulative effects, are less investigated. Here we aimed to disentangle the cumulative effects of climatic factors on vegetation growth by using vegetation indexes and accumulated meteorological data. First, we investigated the explanation and fit of climate changes on vegetation variations by applying stepwise multiple linear regression with Akaike information criterion. Then, we obtained the correlation coefficients and lagged time of climatic factors on vegetation growth whereby partial correlation and time-lag effect analyses. Results showed that (i) consideration of cumulative climate effects increased the explanation and fit of climate changes on vegetation dynamics for more than 77% of vegetated surface with an average global explanation of 68.33%, which was approximately 3.35% higher than the scenario when only time-lag effects were considered; (ii) big differences exhibited in the correlation coefficients and lagged times under the scenarios with cumulative climate effects considered or not; and (iii) positive accumulated temperature (accumulated solar radiation) effects with zero (three-month) time lag dominates most mid-high latitude ecosystems, and negative accumulated temperature effects with three-month delay dominates the temperate arid and semiarid regions and tropical dry ecosystems. By comparison, accumulated precipitation had relatively complex cumulative effects on vegetation growth. We concluded that climatic factors had significant cumulative effects on vegetation growth; consideration of the cumulative effects helps us better understand the climate-vegetation interactions. Key Points: • The climatic factors had significant cumulative impacts on vegetation growth, which were varied by climatic factors and spaces • Additional consideration of the cumulative impacts increased the explanation and fit of climatic factors on plant growth • The correlation coefficients and lagged times of climatic factors on plant growth were very different if we included cumulative impacts Supporting Information: • Supporting Information S1

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“…The accumulated period was designated as the supposed time‐lag duration that a climatic factor might have on vegetation growth.

{ATEM}_{(m, i)} = \sum_{k = 0}^{k = i} ((), {TEM}_{m - k} - T_{base}) ((), 0 \leq i \leq normaln)

{ASOLAR}_{(m, i)} = \sum_{k = 0}^{k = i} ((), {SOLAR}_{m - k} - S_{base}) ((), 0 \leq i \leq normaln)

{APRE}_{(m, i)} = \sum_{k = 0}^{k = i} ((), {PRE}_{m - k} - P_{base}) ((), 0 \leq i \leq normaln)

Section: Methodsmentioning

confidence: 99%

Cumulative Effects of Climatic Factors on Terrestrial Vegetation Growth

et al. 2019

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“…Although rainfall station data are sparse in developing countries, the CHIRPS dataset performs better than coarser satellite-derived and gaugecorrected rainfall products (Beck et al 2017). The high spatial resolution of CHIRPS captures rainfall spatial variability and land heterogeneity (Musau et al 2016), which are important in this context given the ubiquity of convective rainfall in this region and the finescale of household-level perceptions.…”

Section: Matching Farmer Perceptions and Observational Datamentioning

confidence: 99%

Cognitive Biases about Climate Variability in Smallholder Farming Systems in Zambia

Waldman

Vergopolan

Attari

et al. 2019

Weather, Climate, and Society

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Given the varying manifestations of climate change over time and the influence of climate perceptions on adaptation, it is important to understand whether farmer perceptions match patterns of environmental change from observational data. We use a combination of social and environmental data to understand farmer perceptions related to rainy season onset. Household surveys were conducted with 1171 farmers across Zambia at the end of the 2015/16 growing season eliciting their perceptions of historic changes in rainy season onset and their heuristics about when rain onset occurs. We compare farmers’ perceptions with satellite-gauge-derived rainfall data from the Climate Hazards Group Infrared Precipitation with Station dataset and hyper-resolution soil moisture estimates from the HydroBlocks land surface model. We find evidence of a cognitive bias, where farmers perceive the rains to be arriving later, although the physical data do not wholly support this. We also find that farmers’ heuristics about rainy season onset influence maize planting dates, a key determinant of maize yield and food security in sub-Saharan Africa. Our findings suggest that policy makers should focus more on current climate variability than future climate change.

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“…There has also been an increase in land use intensity, especially due to increasing livestock density. These changes are characterized by the conversion of natural ecosystems into agricultural use (Muriuki et al., 2005, K'Otuto et al., 2012, Musau et al., 2016). Climate variability manifested through changing rainfall patterns is ongoing in East African regions (Conway et al., 2005, Serdeczny et al., 2015), where both shorter periods of intense rainfall and longer periods of drought have been recorded (K'Otuto et al., 2012).…”

Section: Introductionmentioning

confidence: 99%

Interactive influence of rainfall manipulation and livestock grazing on species diversity of the herbaceous layer community in a humid savannah in Kenya

et al. 2019

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Changes in rainfall regime and grazing pressure affect vegetation composition and diversity with ecological implications for savannahs. The savannah in East Africa has experienced increased livestock grazing and rainfall variability but the impacts associated with those changes on the herbaceous layer have rarely been documented. We investigated the effect of livestock grazing, rainfall manipulation and their interaction on the composition and diversity of the herbaceous community in the savannah for two years in Lambwe, Kenya. Rainfall manipulation plots were set up for vegetation sampling; these plots received either 50% more or 50% less rainfall than control plots. Simpson's diversity and Berger–Parker indices were used to determine diversity changes and dominance respectively. The frequency of species was used to compute their abundance and their life forms as determined from the literature. Grazing significantly increased species diversity through suppression of dominant species. Rainfall manipulation had no significant impact on plant diversity in fenced plots, but rainfall reduction significantly reduced diversity in grazed plots. In contrast, rainfall manipulation had no impact on dominance in either fenced or grazed plots. The interaction of grazing and rainfall manipulation is complex and will require additional survey campaigns to create a complete picture of the implications for savannah structure and composition.

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Spatio-temporal vegetation dynamics and relationship with climate over East Africa

Cited by 20 publications

References 66 publications

Cumulative Effects of Climatic Factors on Terrestrial Vegetation Growth

Cumulative Effects of Climatic Factors on Terrestrial Vegetation Growth

Cognitive Biases about Climate Variability in Smallholder Farming Systems in Zambia

Interactive influence of rainfall manipulation and livestock grazing on species diversity of the herbaceous layer community in a humid savannah in Kenya

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