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

Abstract

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.

SatVITS-Flood: Satellite Vegetation Index Time Series Flood detection model for hyperarid regions

“…The SatVITS-Flood model scheme effectively detects and correlates floods with changes derived from satellite vegetation indices. These results, aligned with previous research, demonstrate the potential of time series analysis of satellite vegetation indices in the flood monitoring and prediction (Fu & Burgher, 2015;Grodek et al, 2020;Manning et al, 2020;Moses et al, 2021). The use of NDVI as a measure of vegetation sensitivity to changes in water supply, and the selection of pixels with a high NDVI annual standard deviation closest to the riverbank, provided an effective way to identify areas likely to be affected by floods, consistent with previous studies that have used NDVI for similar related purposes Helman and Mussery, 2020).…”

“…The general idea that floods in hyperarid regions can be detected using satellite-derived spectralbased indices leans on two main observations: (1) floods have an indirect impact on the riverbank vegetation by recharging the shallow aquifers and improving water quality (Dahan et al, 2008;Grodek et al, 2020) and (2) the changes in the vegetation cover, vigor, and growth, of both ephemeral (mainly annual herbaceous) and perennial (mainly evergreen, woody) plant species, can be detected via spectral-based vegetation indices from satellites (Grodek et al, 2020;Moses et al, 2021;Normandin et al, 2022). Figure 1 illustrates an example of the indirect effect of a flood on the riverbank vegetation in a hyperarid region and how such an effect is detected in a time series of a vegetation index (VI).…”

“…Based on the abovementioned assumptions and previous observations (Grodek et al, 2020;Moses et al, 2021;Normandin et al, 2022), we used here satellite-derived VI time series metrics to develop the Satellite VI Time Series Flood detection model for hyperarid regions (SatVITS-Flood). Section 3 presents the data used to develop and evaluate the model.…”

Spatio‐temporal effects of inundation and climate on vegetation greenness dynamics in dryland floodplains

“…The results of this study underscored the importance of lag times in explaining patterns of productivity in semi‐arid floodplains. The lagged responses have been noted for other semi‐arid and arid‐zone floodplain wetlands, including the Tarim River, northwest China (Liao et al, 2020) and the Okavango River Basin in Africa (Moses et al, 2021). While rainfall in the season of measurement was an important predictor of productivity for E. largiflorens and E. camaldulensis , rainfall in the previous season was more important for D. florulenta productivity and also improved the prediction of productivity for E. largiflorens and E. camaldulensis .…”

Long term trend analysis of vegetation dynamics over Rajasthan using satellite derived enhanced vegetation index

“…Many studies have reported that temporal variation of EVI is closely associated with rainfall (Budde et al, 2004;Doi, 2001;Richard and Poccard, 1998;Wang et al, 2003;Kumari et al, 2021). Although, there is a time lag of one or two months between EVI and rainfall (Moses et al, 2021;Kumari et al, 2021).…”