Dams play a significant role in the storage, supply, and capitalization of water resources. This study analyzes the influences of land-use and climate factors on two interconnected dams, Gaborone and Bokaa dams, in the semi-arid Botswana from 2001 to 2019. Using Random Forest regression (RFR) and Vector AutoRegression (VAR) models, the monthly dam water levels were predicted based on the variabilities of rainfall and temperature, climate indices (DSLP, Aridity Index (AI), SOI and Niño 3.4) and land-use land-cover (LULC) information comprising of built-up, cropland, water, forest, shrubland, grassland and bare-land. The prediction results using the climate factors and climate indices show that for both dams, RFR was able to detect the correlations between the dam water levels with R2 of between 0.805 and 0.845 with min, average and max temperatures as the best combined predictors. Using differenced stationary datasets, VAR identified the climate indices as the suitable predictors for water levels in Gaborone and Bokaa dams with R2 of 0.929 and 0.916 respectively. VAR also detected LULC to be strongly correlated to the dam water levels. Nevertheless, LULC was considered as more significant when combined with the climate-based predictor variables. Comparatively, VAR was able to detect the interdependence between the two dams and with the other conjunctive water sources as the water levels in both dams were not significantly correlated with rainfall trends, while RFR relied on the seasonal temperature variabilities to accurately predict the fluctuations in the dam water levels.
This study assessed 30 years (1989–2019) of urban heat phenomena variations for the case study of Gaborone City (Botswana) through the analysis of the land surface temperatures (LST). The LST variability was determined using the Landsat Thermal Infrared Sensor (TIRS) band and the land surface emissivity (ελ) factor. The study investigated the influence of land-use and land-cover change (%LULC), normalized difference vegetation index (NDVI) and the normalized difference building index (NDBI) on the variability LST. For the city boundary which occupies 190.96 km2, the vegetation cover decreased by nearly 40%, built-up area increased by 38.9%, water bodies decreased by 3% and bare-land increased by approximately 4.1%, while the 30-year mean near-air temperature was observed to have increased by +2.6 ºC. The urban LST variations were observed to increase exponentially with LSTmin of -2.5 ºC – 14.4 ºC and LSTmax of 24.4 ºC – 30.2 ºC respectively from 1989-2019. Using multiple linear regression, the mean LST was observed to be inversely proportional to NDVI (-0.934) and directly proportional to NDBI (+0.949). In correlation with %LULC, the land surface temperature increased with increase in density of the built-up area and bare-land but decreased with increase in vegetation cover and water bodies. Regression of NDVI, NDBI and %LUCC indices for the prediction of LST showed their suitability in the estimation of LST in the arid urban environment with R2 of 0.996
This study investigates the use of Sentinel-2A (S2A) and Landsat-8 (L8) OLI for monitoring of turbidity in reservoir waters. Using observed in situ data from 18 sampling stations for Chebara Reservoir in Kenya, the study developed an empirical multivariate regression model for turbidity estimation from atmospherically corrected, band adjusted and spectral resolution standardized S2A and L8 bands. Best results for turbidity estimation were obtained from the regression of in situ data with B2 (blue) and B3 (green) bands as [Rrs(B2/B3)^2+Rrs((B2/B3)] for S2A and [Rrs((B3/B2)] for L8. Both S2A and L8 retrieved turbidity with high and nearly equal accuracy of R^2 < 0.75 from the visible and NIR bands, with nearly similar RMSE of 0.5 NTU and NMAE% being higher for S2A by more than 30% as compared to L8’s average NMAE% of 15%. The study shows that for both S2A and L8 sensors, and the proposed empirical regression algorithm suffices in the rapid and cost-effective quantification of turbidity inland reservoir waters. Using spatial interpolation for the visualization of the correlation between the predicted and observed turbidity, the L8 results were found to be more significant than the turbidity estimations using S2A bands.
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