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In semi-arid catchments, hydrological modeling, water resources planning and management are hampered by insufficient spatial rainfall data which is usually derived from limited rain gauge networks. Satellite products are potential candidates to augment the limited spatial rainfall data in these areas. In this paper, the utility of the Tropical Rainfall Measuring Mission (TRMM) product (3B42 v7) is evaluated using data from the Notwane catchment in Botswana. In addition, rainfall simulations obtained from a multi-site stochastic rainfall model based on the generalised linear models (GLMs) were used as additional spatial rainfall estimates. These rainfall products were compared to the observed rainfall data obtained from six (6) rainfall stations available in the catchment for the period 1998–2012. The results show that in general the two approaches produce reasonable spatial rainfall estimates. However, the TRMM products provided better spatial rainfall estimates compared to the GLM rainfall outputs on an average, as more than 90% of the monthly rainfall variations were explained by the TRMM compared to 80% from the GLMs. However, there is still uncertainty associated mainly with limited rainfall stations, and the inability of the two products to capture unusually high rainfall values in the data sets. Despite this observation, rainfall indices computed to further assess the daily rainfall products (i.e. rainfall occurrence and amounts, length of dry spells) were adequately represented by the TRMM data compared to the GLMs. Performance from the GLMs is expected to improve with addition of further rainfall predictors. A combination of these rainfall products allows for reasonable spatial rainfall estimates and temporal (short term future) rainfall simulations from the TRMM and GLMs, respectively. The results have significant implications on water resources planning and management in the catchment which has, for the past three years, been experiencing prolonged droughts as shown by the drying of Gaborone dam (currently at a record low of 1.6% full), which is the main source of water supply to the city of Gaborone and neighbouring townships in Botswana.

Climate change is expected to alter rainfall regimes across most parts of the world. The implications of this could be more severe in arid environments where rainfall is limited and highly variable in space and time. However, lack of good quality data, of sufficient record length and spatial coverage usually restricts model development and performance geared towards assessing the effects of climate change in these areas. This paper presents an analysis of rainfall and climate data in order to determine the time of change in rainfall series and identify possible correlations between rainfall and temperature. In addition, the paper aims to make predictions of future rainfall patterns in Botswana. This is achieved by using historical rainfall and climate data from rainfall stations spread across Botswana from 1965 to 2008. In addition, large scale reanalysis data from NCAR/NCEP and El Nino Southern Oscillation (ENSO) data were used to augment the limited observed spatial climate data series when developing a rainfall model. Temperature and ENSO indices have been used to predict rainfall regimes for the present climate. Based on these, the effects of climate change were quantified using a stochastic generalised linear rainfall model (GLM) driven by outputs of global climate models (GCMs). The results indicate that temperature is a significant rainfall predictor in Botswana compared to ENSO indices.

Changes in catchment response as a result of climate change and/or variability continue to be of concern to hydrologists and water managers and thus identification and study of changes in rainfall as a major input into the hydrologic system remains pivotal in water resources management and planning for reliable input into modelling catchment response systems. An attempt has been made in this study to analyse long-term rainfall data obtained from five rain gauges (Kabompo, Kasempa, Mwinilunga, Mongu and Kaoma) located in the headstream regions of the Zambezi river basin in Zambia and to determine if these time series belonged to similar regime, have had any significant trends and if there was any homogeneity in trends among stations. To detect change in regime, the data were separately subjected to intervention analysis (using Cumulative Summation or CUSUM technique) and step change analysis (using rank-sum test) and subsequently the trend in each of the time series were determined using Mann–Kendall-statistic. The analyses undertaken to identify possibility of any intervention due to either natural and/or manmade causes, through the CUSUM technique, did not show signs of any major interventions except for Mongu station, where a change in regime was observed around 1980. This was even confirmed through step change analysis using the rank-sum test. Though the five stations showed marginal downward trends, these were not significant. Even, the test of homogeneity in trends observed at different stations showed homogeneity between them. Based on these findings, it was concluded that the rainfall data in the entire sub-basin belonged to a similar climate regime and the rainfall data for the entire period could be used for developing rainfall-response relationship except for Mongu, where the time series from 1981 onwards appear to have been subjected human/instrumental errors and needs to be investigated and updated.

Information on rainfall magnitude at specified recurrence interval is important for agricultural and water resources planning and management besides particularly for a semi-arid country like Botswana where the annual rainfall is erratic with large coefficient of variation. This study which was based on long term annual rainfall data from 1961 to 2003 at 11 synoptic stations across Botswana, concluded that rainfall was increasing up to 1981, after which there has been a decline in the annual rainfall pattern. An overall downward trend shows that the decrease in the rainfall quantities in the recent years, i.e. after 1981, has been very high. Regional analysis showed that all the stations behaved homogeneously and followed a generalised extreme value (GEV) distribution. Rainfall quantiles computed with the total data also showed a decreasing pattern when compared with quantiles estimated using the data series up to 1981. It was found that for specific amounts of rainfall the recurrence interval increased considerably when the total period data were analysed. Overall it can be said that rainfall quantiles with a 10-year recurrence interval will decrease by 2–17%, while rainfall quantiles with a 50-year recurrence interval will decrease by 0–14%.

Forecasting future response behaviour of a semi-arid catchment in terms of runoff coefficient being trivial, an attempt has been made to apply an artificial neural network (ANN) model to forecast the runoff coefficients (ROC) for the rapidly urbanizing Notwane catchment system in Botswana. Runoff coefficients computed from 1978 to 2000, by the water balance technique have been used to develop the optimal network architecture with appropriate choice of the size of input vectors, number of hidden layers and number of neurons in the hidden layers, training algorithms and transfer functions for the network. The developed network has then been used to simulate the runoff coefficients for the above period. Based on its performance in terms of reproducibility of the water balance runoff coefficients, the network was used to forecast the runoff coefficients up to 2020 and it was found that the increase in runoff coefficients is about 1% per year. Based on the weights given by the network to the input variables, it was found that while about 48% contribution came from climatic factors, 52% came from the land use/land cover.