Application of satellite-derived rainfall estimates to extend water resource simulation modelling in South Africa

Spatially interpolated rainfall estimates from rain-gauges are widely used as input to hydrological models, but deriving accurate estimates at appropriate space and time scales remain a major problem. In South Africa there has been a gradual decrease in the number of active rain-gauges over time. Satellite-based estimates of spatial rainfall are becoming more readily available and offer a viable substitute. The paper presents the potential of using Climate Prediction Center African daily precipitation climatology (CPCAPC) satellite-based datasets (2001-2006) to drive a Pitman hydrological model which has been calibrated using gauge-based rainfall data (1920-1990). However, if two sources of rainfall data are to be used together, it is necessary to ensure that they are compatible in terms of their statistical properties. A non-linear frequency of exceedance transformation technique was used to correct the satellite data to be more consistent with historical spatial rainfall estimates. The technique generated simulation results for the 2001 to 2006 period that were greatly improved compared to the direct use of the untransformed satellite data. While there remain some further questions about the use of satellite-derived rainfall data in different parts of the country, they do seem to have the potential to contribute to extending water resource modelling into the future.Keywords: satellite-based rainfall, hydrological model, water resources, South Afric

Application of satellite-derived rainfall estimates to extend water resource simulation modelling in South Africa

Spatially interpolated rainfall estimates from rain-gauges are widely used as input to hydrological models, but deriving accurate estimates at appropriate space and time scales remain a major problem. In South Africa there has been a gradual decrease in the number of active rain-gauges over time. Satellite-based estimates of spatial rainfall are becoming more readily available and offer a viable substitute. The paper presents the potential of using Climate Prediction Center African daily precipitation climatology (CPCAPC) satellite-based datasets (2001-2006) to drive a Pitman hydrological model which has been calibrated using gauge-based rainfall data (1920-1990). However, if two sources of rainfall data are to be used together, it is necessary to ensure that they are compatible in terms of their statistical properties. A non-linear frequency of exceedance transformation technique was used to correct the satellite data to be more consistent with historical spatial rainfall estimates. The technique generated simulation results for the 2001 to 2006 period that were greatly improved compared to the direct use of the untransformed satellite data. While there remain some further questions about the use of satellite-derived rainfall data in different parts of the country, they do seem to have the potential to contribute to extending water resource modelling into the future

Estimation of small reservoir storage capacities in Limpopo River Basin using geographical information systems (GIS) and remotely sensed surface areas: case of Mzingwane catchment

The current interest in small reservoirs stems mainly from their utilization for domestic use, livestock watering, fishing and irrigation. Rarely were small reservoirs considered in the water resources system even though they are important in water resource planning and management. The main limitation being lack of knowledge on small reservoir capacities, for the methodologies used to quantify physical parameters of reservoirs are costly, time consuming and laborious. To address this challenge an attempt has been made in this study to estimate small reservoir storage capacities using remotely sensed surface areas. A field study on 12 small reservoirs was carried out in Mzingwane catchment in Limpopo River Basin; Zimbabwe. The depths of water accompanied with their coordinates were measured; from which area and capacity were calculated for each reservoir using geographical information system based on data acquired from the field and that from satellite images. The output data was compared and a linear regression analysis was carried out to establish a power relationship between surface area and storage capacity of small reservoirs. The Pearson correlation analysis at 95% confidence interval indicated that the variances of the two surface areas (field area and image area) were not significantly different (p < 0.05). The findings from linear regression analysis (log capacity–log area) show that there exist a power relationship between remotely sensed surface areas (m^2) and storage capacities of reservoirs (m^3), with 95% variation of the storage capacity being explained by surface areas. The relationship can be used as a tool in decision-making processes in integrated water resources planning and management in the river basin. The applicability of the relationship to other catchments requires further research as well as investigating the impacts of small reservoirs in water resources available in the river basin by carrying out a hydrological modelling of the catchment