This study aimed to model the amount of rainfall leaving the Olifants River Catchment area as surface runoff and entering into the subsurface as infiltration (as part of these waters may contribute to the water ingress in the abandoned/closed mines) using the RINSPE model implemented in ArcView GIS 3.3. The runoff and infiltration depths and the total volumes were calculated by the model for 7 scenarios using National Land-Cover 2000 Dataset. Scenarios 1 & 2: used one inch uniform rainfall to predict the expected runoff and infiltration at any location in the catchment for average and dry antecedent moisture conditions (AMCII & AMCI); scenario 3 used annual rainfall and assumed 40 rainfall events in a year for AMCII; scenarios 4 to 6 used annual rainfall and assumed 35, 40 and 46 rainfall events in a year for AMCI, and scenario 7 assumed 40 rainfall events in a year for AMCI and excluded the catchments areas of Letaba and Shingwedzi Rivers. Scenarios 1 and 2 show that runoff and infiltration are respectively 11.36 & 59.83% and 6.16% & 65.03% of the rainfall. Scenario 3 predicted a total infiltration of 6,449.793 million cubic meters or MCM (14.46% of total rainfall); the total runoff predicted is 16,589.1 MCM (37.2% of total rainfall). Scenarios 4, 5, 6 and 7 showed infiltrations of respectively 22.24%, 24.57%, 19.3% and 22.71% of total rainfall whereas the surface runoff predicted are respectively 13,120.85, 14,776.52, 11,310.57 and 10,748.07. MCMs (29.43%, 33.14%, 25.37% and 31.49% of total rainfall). Spatially distributed runoff and infiltration maps will help to understand the amount of rainfall leaving mined areas as polluted runoff and the amount of water infiltrating into the subsurface horizons, which may later contribute to water ingress or appear as part of the acid mine drainage formed in the catchment.
Thomas, A. (2015). Modelling of Spatially Distributed Surface Runoff and Infiltration in the Olifants River Catchment/Water Management Area Using GIS. International Journal of Advanced Remote Sensing and GIS, 4(1), 828–862. https://doi.org/10.23953/cloud.ijarsg.81