Estimation of groundwater recharge response from rainfall events in a semi-arid fractured aquifer: Case study of quaternary catchment A91H, Limpopo Province, South Africa

This study attempted to delineate and map potential groundwater recharge zones of the Singida, semi-arid, fractured crystalline basement aquifer using open source remote sensing and GIS software. Various thematic maps such as lithology/hydrogeology, soil, land-cover/use, slope, lineament density, drainage density and rainfall distribution were integrated in QGIS software. Vector input layers were rasterized and resampled using QGIS wrap projection function to make sure that the grid cells are of the same size. Reclassification using SAGA and GRASS reclass algorithms in QGIS was carried out to realign the factor classes in a consistent scale, and reclassification to a scale of 1 to 5 was carried out to harmonize the results. The study identified a number of potential areas for groundwater recharge, groundwater exploration, groundwater development and potential areas for artificial groundwater recharge. Potential groundwater recharge zones for the Singida semi-arid fractured aquifer are restricted to areas with high lineament density, cultivated areas, grassland and flat to gentle slopes. The potential of groundwater recharge is also observed in areas with low drainage density. The delineated zones provide a good understanding of the potential recharge zones, which are a starting point for recharge zone protection. This blended approach can be utilized for carrying out suitability analysis using the weighted overlay analysis approach. Areas designated good and very good are recommended for artificial recharging structures as an alternative technique for enhancing groundwater recharge through rainwater harvesting. This will help to augment groundwater storage in this semi-arid environment.

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Biogas technology implementation in rural areas: a case study of Vhembe District in Limpopo Province, South Africa

10.1016/b978-0-12-822810-4.00026-9 ◽

2022 ◽

pp. 501-509

Author(s):

T.E. Rasimphi ◽

D. Tinarwo ◽

S. Ravhengani ◽

C. Sambo ◽

P. Mhlanga

Keyword(s):

South Africa ◽

Rural Areas ◽

Technology Implementation ◽

Limpopo Province ◽

Vhembe District ◽

Biogas Technology

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The Establishment of Vegetable and Fruit Markets and Nurseries: A Case Study in the Waterberg District, Limpopo Province, South Africa

Journal of Agricultural Science ◽

10.5539/jas.v6n7p38 ◽

2014 ◽

Vol 6 (7) ◽

Author(s):

P. Maponya ◽

D. Modise ◽

E. Van Heerden ◽

S. Mahlangu ◽

N. Baloyi ◽

...

Keyword(s):

South Africa ◽

Limpopo Province

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Drivers of inter-annual variability in Net Ecosystem Exchange in a semi-arid savanna ecosystem, South Africa

Biogeosciences ◽

10.5194/bg-6-251-2009 ◽

2009 ◽

Vol 6 (2) ◽

pp. 251-266 ◽

Cited By ~ 53

Author(s):

S. A. Archibald ◽

A. Kirton ◽

M. R. van der Merwe ◽

R. J. Scholes ◽

C. A. Williams ◽

...

Keyword(s):

South Africa ◽

Eddy Covariance ◽

Photosynthetically Active Radiation ◽

Ecosystem Respiration ◽

Net Ecosystem Exchange ◽

Ann Model ◽

Rainfall Events ◽

Active Radiation ◽

Annual Variability ◽

Semi Arid

Abstract. Inter-annual variability in primary production and ecosystem respiration was explored using eddy-covariance data at a semi-arid savanna site in the Kruger Park, South Africa. New methods of extrapolating night-time respiration to the entire day and filling gaps in eddy-covariance data in semi-arid systems were developed. Net ecosystem exchange (NEE) in these systems occurs as pulses associated with rainfall events, a pattern not well-represented in current standard gap-filling procedures developed primarily for temperate flux sites. They furthermore do not take into account the decrease in respiration at high soil temperatures. An artificial neural network (ANN) model incorporating these features predicted measured fluxes accurately (MAE 0.42 gC/m2/day), and was able to represent the seasonal patterns of photosynthesis and respiration at the site. The amount of green leaf area (indexed using satellite-derived estimates of fractional interception of photosynthetically active radiation fAPAR), and the timing and magnitude of rainfall events, were the two most important predictors used in the ANN model. These drivers were also identified by multiple linear regressions (MLR), with strong interactive effects. The annual integral of the filled NEE data was found to range from −138 to +155 g C/m2/y over the 5 year eddy covariance measurement period. When applied to a 25 year time series of meteorological data, the ANN model predicts an annual mean NEE of 75(±105) g C/m2/y. The main correlates of this inter-annual variability were found to be variation in the amount of absorbed photosynthetically active radiation (APAR), length of the growing season, and number of days in the year when moisture was available in the soil.

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