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Road fatalities remain a major public health concern as over 1.3 million people across the world die in road accidents annually, and another 20-50 million sustain injuries. Malawi, with vehicle ownership about 437,416, has not been an exception to this, with reported fatality rates of 35 crash deaths per 100,000 population, possibly due to limited understanding of factors that contribute to such high road fatality rates. This may have resulted in implementing inappropriate and ineffective mitigation measures. Thus, the overall objective of this study was to investigate the availability, including their functionality and usage of seatbelts in motor vehicles, which could possibly be one of the major factors for increase in road fatalities. To establish the rates of availability, ncluding their functionality and usage of seatbelts in motor vehicles, primary data through survey for a sample of 1,200 vehicles were investigated. The independent ariables of data were cross tabulated with the dependent variables to establish these rates. Chi-square test was also performed to establish the significance of associations between the cross tabulated variables. The analysis found that Malawi’s drivers and assengers poorly comply with seatbelt laws. The overall seatbelt usage rate was 35.8%. In this regard, where over 90% of motor vehicles in Malawi have functional seatbelts, poor enforcement of existing seatbelt laws and awareness on the dangers of not using eatbelts could account for the low usage.

Influences of El Nino southern oscillation (ENSO) on weather systems have increased the frequency and amplitude of extreme events over the last century. This even continues to exacerbate the already warming earth, with 2014–2016 which coincided with the strongest El Nino years observed as the warmest period in recent past. This study presents an approach of characterizing droughts at various timescales and establishes teleconnections between ENSO and drought severity in Botswana. The study uses Standardized Precipitation Evaporation Index (SPEI) at timescales of 1, 3, 6, 12 and 24 month to characterize droughts and Pearson’s correlations to study the teleconnections between SPEIs and ENSO. Results from the study reveal that extreme droughts are a rare occurrence in Botswana though it is more prone to moderate droughts at 12 month SPEI with a probability of 19% in the north. The highest severe drought probability was 7% recorded in the east. Linear trends indicate increasing dryness of around 0.8% per decade. These results have demonstrated that warm sea surface temperatures combined with negative Southern Oscillation Index correspond to persistent negative SPEI values and thus are likely to result in dry conditions. Significant correlations were observed in the mid austral summer in December and January. Due to this relationship, the drought early warning systems could use ENSO as one of the instruments for predicting drought over the study area and hence in its management.

Pedestrian behaviour is one of the major contributors to road fatalities. The negative binomial regression model was found to better agree with road fatality data, and this study used this model to assess the influence of pedestrian behaviour factors on road fatalities in Malawi. The data used in this analysis were crash reports of pedestrian behaviour factors and observed fatalities for the period 2000–2015 obtained from the national database, except for the 2013 data, which were disregarded because they appeared to be incomplete. Whereas pedestrian behaviour factors of walking on roads, crossing outside pedestrian crossings, and other negligent and careless behaviours were found to be positively correlated with road deaths, indicating that road-related fatalities increased with increasing input data, factors of being under the influence of alcohol and crossing at pedestrian crossings demonstrated negligible influence. The study also found that there was a 1% increase in the number of crash deaths for every additional fatal crash involving pedestrians walking on roads. Moreover, an additional 0.5% increase in the number of fatalities was recorded for every fatal crash involving a pedestrian behaviour factor of crossing outside the pedestrian crossing or other negligent behaviour. An increase of 0.3% in the number of the fatalities was seen for every extra fatal crash caused by crossing carelessly or factors other than pedestrian behaviour. Despite coefficient values being small in all variables, which is a major limitation of this study, enforcement can prioritise those variables that increase road-related fatalities or even couple them with other risk factors such as speed.

Land use/land cover (LULC) changes have been observed in the Gaborone dam catchment since the 1980s. A comprehensive analysis of future LULC changes is therefore necessary for the purposes of future land use and water resource planning and management. Recent advances in geospatial modelling techniques and the availability of remotely sensed data have become central to the monitoring and assessment of both past and future environmental changes. This study employed the cellular automata and Markov chain (CA-Markov) model combinations to simulate future LULC changes in the Gaborone dam catchment. Classified Landsat images from 1984, 1995, 2005 and 2015 were used to simulate the likely LULCs in 2015 and 2035. Model validation compared the simulated and observed LULCs of 2015 and showed a high level of agreement with Kappa variation estimates of Kno (0.82), Kloc (0.82) and Kstandard (0.76). Simulation results indicated a projected increase of 26.09%, 65.65% and 55.78% in cropland, built-up and bare land categories between 2015 and 2035, respectively. Reductions of 16.03%, 28.76% and 21.89% in areal coverage are expected for shrubland, tree savanna and water body categories, respectively. An increase in built-up and cropland areas is anticipated in order to meet the population’s demand for residential, industry and food production, which should be taken into consideration in future plans for the sustainability of the catchment. In addition, this may lead to water quality and quantity (both surface and groundwater) deterioration in the catchment. Moreover, water body reductions may contribute to water shortages and exacerbate droughts in an already water-stressed catchment. The loss of vegetal cover and an increase in built-up areas may result in increased runoff incidents, leading to flash floods. The output of the study provides useful information for land use planners and water resource managers to make better decisions in improving future land use policies and formulating catchment management strategies within the framework of sustainable land use planning and water resource management.

Semiarid areas exhibit high climate variability whose frequency has increased in the recent past. This variability ultimately influences the spatial and temporal characteristics of droughts which in turn require a reliable drought index that can adequately characterize drought both in time and space. This study applied two drought indices viz.: Standardized precipitation index (SPI) and Standardized precipitation and evapotranspiration index (SPEI) at timescales of 1-, 3-, 6-, 12-, 18- and 24-months for a period of 1960–2016. The monotonic changes in drought severity were studied using Mann-Kendall Z-statistic and Sen's slope estimator. Both drought indices were able to detect the historical drought events of 1961/62–1965/66, 1980/81–1986/87, 1991/92, 2001/02–2005/06, 2009/10–2012/13 and 2014/15–2015/16. These events mainly coincided with El Nino years, hence the influence of El Nino southern oscillation (ENSO) on drought occurrence could not be ruled out. The drought evolutions were characterized by low frequency and longer durations at timescales of 12-, 18-, and 24-months. It was also observed that SPI overestimated drought severity during dry winter months while SPEI showed higher spatial coverage of drought vulnerability compared to SPI. Spatial and temporal trends of droughts showed significant wetting trends during the period 1960–1979 while during the entire period it was mostly drying tendencies after 1979/80. Pandamatenga and Shakawe showed higher vulnerability towards drying conditions during the period of analysis. The association between SPI and SPEI was also investigated using Spearman's rank correlation. Results from this analysis indicate that SPI accounts for >50% of the variations in SPEI and the association was strongest (96%) at 12-months timescale. The influence of temperature on drought evolutions was demonstrated at Francistown with SPI recording positive trends and SPEI negative ones. This study concludes that with the ongoing global warming, SPEI proves more robust in characterizing droughts in semiarid areas. It is hoped that findings from this study will augment ongoing efforts of mitigating negative impacts of increased incidences of climate variability especially in areas of agriculture and water resource management.

Dynamics of droughts have been an associated feature of climate variability particularly in semiarid regions which impact on the response of hydrological systems. This study attempts to determine drought timescale that is suitable for monitoring the effects of drought on hydrological systems which can then be used to assess the long term persistence or reversion and forecasts of the dynamics. Based on this, climatological and hydrological drought indices characterized by Standardized precipitation evapotranspiration index (SPEI) and Standardized flow index (SFI) respectively have been determined using monthly rainfall, temperature and flow data from two major river systems. The association between climatological and hydrological droughts in Botswana has been investigated using these river systems namely: Okavango that is predominantly a storage type and Limpopo which is non-storage for a period of 1975–2014. Dynamics of climatological and hydrological droughts are showing trends towards drying conditions at both river systems. It was also observed that hydrological droughts lag climatological droughts by 7 months in Limpopo and 6 months in Okavango river systems respectively. Analyses of the association between climatic and flow indices indicate that the degree of association becomes stronger with increasing timescale at the Okavango river system. However in the Limpopo river system, it was observed that high timescales of 18- and 24-months were not useful in drought monitoring. 15-months timescale was identified to best monitor drought dynamics at both locations. Therefore SPEIs and SFIs computed at 15-months timescale have been used to assess the variability and long term persistence in drought dynamics through rescaled range analysis (R/S). H-coefficients of 0.06 and 0.08 resulted for Limpopo and Okavango respectively. These H-coefficients being significantly less than 0.5 is an indication of high variability and suggests a change in dynamics from the existing conditions in these river systems. To forecast possible changes, the nonlinear autoregressive with exogenous input (NARX) artificial neural network model has been used. Results from this model agree with those of the R/S and projects generally dry conditions for the next 40 months. Results from this study are helpful not only in choosing a proper timescale but also in evaluating the futuristic drought dynamics necessary for water resources planning and management.

Booster chlorination reduces the risks associated with conventional disinfection such as high chlorine residuals near water treatment plants and low chlorine residuals at remote parts of water distribution systems (WDSs). Network operational interventions have a significant influence on water age and chlorine decay in WDSs. In this study, an integrated booster chlorination method is developed to obtain optimal designs that reduce the risks associated with conventional disinfection in WDSs. The method integrates booster chlorination with network operational interventions to reduce water age and improve chlorine residuals in WDSs. A multi-objective booster optimisation problem is formulated based on risks associated with chlorine disinfection and solved using a non-dominated sorting genetic algorithm (NSGA-II) and the EPANET hydraulic and water quality solver. The proposed methodology was tested in the Phakalane WDS in Botswana. The integrated booster disinfection method significantly improves chlorine residuals in a WDSs with lower mass and cost of chlorine than conventional disinfection. Furthermore, the study indicates that integrated booster chlorination designs are influenced by changes in network conditions such as water demand and chlorine decay coefficients. Therefore, periodic monitoring of these parameters is required to ensure that the acceptable performance of chlorine boosters in WDSs is maintained.

Uncertainties in rainfall have increased in the recent past exacerbating climate risks which are projected to be higher in semiarid environments. This study investigates the associated features of rainfall such as rain onset, cessation, length of the rain season (LRS), and dry spell frequency (DSF) as part of climate risk management in Botswana. Their trends were analysed using Mann-Kendall test statistic and Sen’s Slope estimator. The rainfall-evapotranspiration relationships were used in formulating the rain onset and cessation criteria. To understand some of the complexities arising from such uncertainties, artificial neural network (ANN) is used to predict onset and cessation of rain. Results reveal higher coefficients of variation in onset dates as compared to cessation of rain. Pandamatenga experiences the earliest onset on 28th of November while Tsabong the latest on 14th of January. Likewise, earliest cessation is observed at Tshane on 22nd of February and the latest on 30th of March at Shakawe. The shortest LRS of 45 days is registered at Tsabong whereas the northern locations show LRS greater than 100 days. Stations across the country experience strong negative correlation between onset and LRS of − 0.9. DSF shows increasing trends in 50% of the stations but only significant at Mahalapye, Pandamatenga, and Shakawe. Combining the LRS criteria and DSF, Kasane, Pandamatenga, and Shakawe were identified to be suitable for rainfed agriculture in Botswana especially for short to medium maturing cereal varieties. Predictions of onset and cessation indicate the possibility of delayed onset by 2–5 weeks in the next 5 years. Information generated from this study could help Botswana in climate risk management in the context of rainfed farming.

Climate variability and change is expected to affect agricultural productivity among other sectors. Studying the influence of this variability on crop production is one measure of generating climate change resilience strategies. In this study, the influence of climate variability on crop yield is investigated by determining the degree of association between climatic indices and crop yields of maize and sorghum using spearman’s rank correlation. The climatic indices used in this study are aridity index (AI), standardised precipitation evapotranspiration index (SPEI) at timescales of 1, 3, 6 and 12 months and southern oscillation index (SOI) representing El Nino southern oscillation (ENSO) influence on local climate. Local rainfall characteristics are expressed through length of the rainy season (LRS). Results reveal that ENSO influence is the most dominant across Botswana accounting for 85% and 78% variations in maize and sorghum yields respectively. Whereas AI and SPEI accounts for 70% and 65% variations in maize and sorghum respectively, LRS accounts for only 50% and 62% respectively. To facilitate agricultural planning, crop yield projections have been made using artificial neural network (ANN) models. The ANN projections indicate a likelihood of maize and sorghum yields declining by 51% and 70% respectively in the next 5 years. The high association between ENSO and crop yields in Botswana could further facilitate yield projections. Information generated from this study is useful in agricultural planning and hence strengthens farmers’ strategies in mitigating impacts of climate variability and change in semiarid areas.

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.

Skillful and high-resolution datasets are critical if climate change is to be fully assimilated into water resources management and planning strategies. This is, however, a challenge for data-poor parts of the globe like Africa where adaptive capacity is also a challenge. Global datasets are also often not sufficient, as they come in coarse resolutions that are limited. Global reanalyses continue to help alleviate this challenge when they are downscaled to useful resolution, especially at the basin scale. This chapter provides an overview of dynamical downscaling of global reanalyses to useful resolution. The usefulness of dynamical downscaling through providing relatively high resolution and skillful datasets is also highlighted as very important for climate impact studies, especially with regard to data-sparse regions of Africa.

Gaborone city water distribution system (GCWDS) is rapidly expanding and has been faced with the major problems of high water losses due to leakage, water shortages due to drought and inadequate chlorine residuals at remote areas of the network. This study investigated the probable causes of chlorine decay, due to pipe wall conditions and distribution system water quality in the GCWDS. An experimental approach, which applied a pipe-loop network model to estimate biofilm growth and chlorine reaction rate constants, was used to analyse pipe wall chlorine decay. Also, effects of key water quality parameters on chlorine decay were analysed. The water quality parameters considered were: natural organic matter (measured by total organic carbon, TOC; dissolved organic carbon, DOC; and ultraviolet absorbance at wavelength 254, UVA-254, as surrogates), inorganic compounds (iron and manganese) and heterotrophic plate count (HPC). Samples were collected from selected locations in the GCWDS for analysis of water quality parameters. The results of biofilm growth and chlorine reaction rate constants revealed that chlorine decay was higher in pipe walls than in the bulk of water in the GCWDS. The analysis of key water quality parameters revealed the presence of TOC, DOC and significant levels of organics (measured by UVA-254), which suggests that organic compounds contributed to chlorine decay in the GCWDS. However, low amounts of iron and manganese (< 0.3 mg/L) indicated that inorganic compounds may have had insignificant contributions to chlorine decay. The knowledge gained on chlorine decay would be useful for improving water treatment and network operating conditions so that appropriate chlorine residuals are maintained to protect the network from the risks of poor water quality that may occur due to the aforementioned problems. Keywords: water distribution system, chlorine decay factors, bulk chlorine decay, pipe wall chlorine decay, water quality

Wind speed modelling has been key to many environmental and engineering applications, particularly in environmentally friendly wind power generation to meet energy demands. Efficient assessment of wind speed at different recurrence intervals requires the choice of a suitable statistical distribution and an unbiased method of parameter estimation. This study suggests the use of a four parameter Kappa distribution, with its parameters estimated using the method of L-moments, to model Botswana’s monthly maximum wind speed data at six meteorological weather stations. These synoptic weather stations are Gaborone, Sir Seretse Khama Airport, Tsabong, Tshane, Gantsi and Maun which are broadly spread across the country’s economic activity centres. Reliable wind speed quantiles have been obtained for the selected stations and have been found to fall within the interval 13.80 to 21.69 m s-1. Mean maximum wind speeds have been found to range between 12.65 and 14.97 m s-1, with standard deviations ranging between 1.58 and 2.44 m s-1. These results can reliably be used by environmentalists and technologists working in the energy sector in Botswana.

Future global warming may result in extreme precipitation events leading to crop, environment and infrastructure damage. Rainfall is a major input for the livelihood of peasant farmers in the Aswa catchment where the future rainfall variability, onset and cessation are also likely to be affected. The Aswa catchment has limited rainfall data, therefore, use of secondary datasets from Tropical Rainfall Measuring Mission (TRMM) is considered in this study, based on the close correlation of the recorded and TRMM rainfall. The latter was used to calibrate the statistical downscaling model for downscaling of two general circulation models to simulate future changes in rainfall. These data were analyzed for trends, wet and dry conditions/variability, onset and cessations of rain using the Mann&ndash, Kendall test, Standardized Precipitation Index (SPI) and the cumulative percentage mean rainfall method, respectively. Results show future rainfall is likely to increase, accompanied by increasing variability reaching as high as 118.5%. The frequency of SPI values above 2 (extreme wetness) is to increase above current level during mid and end of the century. The highest rainfall variability is expected especially during the onset and cessation months, which are generally expected to come earlier and later, by up to four and five weeks, respectively. The reliability worsens from the midterm (2036&ndash, 2065) to long term (2066&ndash, 2099). These likely changes in rainfall quantities, variability, onset and cessation months are some of the key rainfall dynamics that have implications for future arable agriculture, environment and water resource availability and planning over the Aswa catchment, as is increasingly the case elsewhere.

Arid and semi-arid environments have been identified with locations prone to impacts of climate variability and change. Investigating long-term trends is one way of tracing climate change impacts. This study investigates variability through annual and seasonal meteorological time series. Possible inhomogeneities and years of intervention are analysed using four absolute homogeneity tests. Trends in the climatic variables were determined using Mann–Kendall and Sen’s Slope estimator statistics. Association of El Nino Southern Oscillation (ENSO) with local climate is also investigated through multivariate analysis. Results from the study show that rainfall time series are fully homogeneous with 78.6 and 50% of the stations for maximum and minimum temperature, respectively, showing homogeneity. Trends also indicate a general decrease of 5.8, 7.4 and 18.1% in annual, summer and winter rainfall, respectively. Warming trends are observed in annual and winter temperature at 0.3 and 1.5% for maximum temperature and 1.7 and 6.5% for minimum temperature, respectively. Rainfall reported a positive correlation with Southern Oscillation Index (SOI) and at the same time negative association with Sea Surface Temperatures (SSTs). Strong relationships between SSTs and maximum temperature are observed during the El Nino and La Nina years. These study findings could facilitate planning and management of agricultural and water resources in Botswana.

Rainfall is a major requirement for many water resources applications, including food production and security. Understanding the main drivers of rainfall and its variability in semi-arid areas is a key to unlocking the complex rainfall processes influencing the translation of rainfall into runoff. In recent studies, temperature and humidity were found to be among rainfall predictors in Botswana and South African catchments when using complex rainfall models based on the generalized linear models (GLMs). In this study, we explore the use of other less complex models such as artificial neural networks (ANNs), and Multiplicative Autoregressive Integrated Moving Average (MARIMA) (a) to further investigate the association between rainfall and large-scale rainfall predictors in Botswana, and (b) to forecast these predictors to simulate rainfall at shorter future time scales (October–December) for policy applications. The results indicate that ANN yields better estimates of forecasted temperatures and ra...

In recent years, the scientific community has been urged to undertake research that can immediately have impact on development issues, including national policies, strategies, and people's livelihoods, among others. While this is a fair call from decision makers, it should also be realized that science by nature is about innovation, discovery and knowledge generation. In this context, there is need for a balance between long term scientific investigations and short term scientific applications. With regard to the former, researchers spend years investigating (or need data of sufficient record length) to provide sound and reliable solutions to a problem at hand while in the latter, it is possible to reach a solution with few selected analyses. In all cases, it is advisable that researchers, where possible should link their studies to topical development issues in their case studies. In this paper, we use a hydrometeorological project in the Notwane catchment, Botswana, to show the importance of linking research to development agenda for mutual benefit of researchers and policy makers. The results indicate that some key development issues are being addressed by the Project and the scope exists to improve the impact of the project.

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.

Pluviographic data at 15 min interval from 6 stations in Pulau Penang of Peninsular Malaysia were used to compute rainfall erosivity factor (R) for the revised universal soil loss equation (RUSLE). Three different modelling procedures were applied for the estimation of monthly rainfall erosivity (EI30) values. While storm rainfall (P) and duration (D) data were used in the first approach, the second approach used monthly rainfall for days with rainfall ≥ 10 mm (rain10) and monthly number of days with rainfall ≥ 10 (days10). The third approach however used the Fournier index as the independent variable. Based on the root mean squared error (RMSE) and the percentage error (PE) criteria, models developed using the Fournier index approach was adjudged the best with an average PE value of 0.92 and an average RMSE value of 164.6. Further, this approach was extended to the development of a regional model. Using data from additional sixteen stations and the Fournier index based regional model, EI30 values were computed for each month. ArcView GIS was used to generate monthly maps of EI30 values and also annual rainfall erosivity (R). The rainfall erosivity factor (R) in the region was estimated to vary from 9000 to 14,000 MJ mm ha− 1 h− 1 year− 1.

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%.

Water is one of the most important elements essential to attain food and health security, as well as economic development of any country, particularly for a semi-arid country like Botswana. Effective utilisation of this valuable but scarce resource has necessitated emphasis on demand management as opposed to supply management, such that sustainable use of the resource to meet the increasing demand can be achieved. This paper examines the various strategies made/proposed in the country to manage the growing demand for water. The strategies encompass the use of tariffs, water reuse/recycling and water restrictions. Other attempts encourage water conservation through rainwater harvesting and implementation of technological innovations with exploration of non-conventional sources.

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.

This study analysed water quality data collected from the river Ganges in India from 1981 to 1990 for forecasting using stochastic models. Initially the box and whisker plots and Kendall's tau test were used to identify the trends during the study period. For detecting the possible intervention in the data the time series plots and cusum charts were used. The three approaches of stochastic modelling which account for the effect of seasonality in different ways, i.e. multiplicative autoregressive integrated moving average (ARIMA) model, deseasonalised model and Thomas–Fiering model were used to model the observed pattern in water quality. The multiplicative ARIMA model having both nonseasonal and seasonal components were, in general, identified as appropriate models. In the deseasonalised modelling approach, the lower order ARIMA models were found appropriate for the stochastic component. The set of Thomas–Fiering models were formed for each month for all water quality parameters. These models were then used to forecast the future values. The error estimates of forecasts from the three approaches were compared to identify the most suitable approach for the reliable forecast. The deseasonalised modelling approach was recommended for forecasting of water quality parameters of a river.

A graphical test is presented to check if recorded annual maximum flood data for a group of gauging stations in a region belong to a common parent distribution (P). The test compares the observed at site L-coefficient of variation (Lcv) with its sampling distribution. The latter is obtained by generating synthetic sequences from an assumed parent distribution (P). A group of sites is deemed to be homogeneous if the observed Lcv, treated as an order statistic, lies within its sampling distribution. The proposed test has been applied to annual maximum flood data from Tanzania to delineate the country into 12 homogeneous regions.

In attempting to model the random behaviour of the Indian summer monsoon, the possibility of using a generalized four-parameter Kappa distribution representing a family of distributions has been explored. An L-Moment procedure developed by Hosking (1994) IBM J. Res. De6., 38(3), 251‐258, has been used for estimation of reliable rainfall quantiles. Using these estimates, isopluvial maps have been developed for some commonly used recurrence intervals (viz. 20, 50, 100, 200, 500 and 1000 years) which can be used by operational hydro-meteorologists. Copyright © 1999 Royal Meteorological Society.

With the objective of investigating the hydrological homogeneity of India's hydrometeorological subzone 3-a and identification of a suitable frequency distribution for it, a regional flood frequency analysis has been carried out using the index flood procedure and the L-moments. Based on analysis of flood data at 12 gauged sites, the Mahi-Sabarmati basin is shown to be hydrologically homogeneous and follows the generalized normal distribution. Regional curve developed based on the analysis has been recommended for carrying out flood frequency analysis at both gauged and ungauged sites in this region.

Traditional techniques for design flood estimation use historical rainfall-runoff data and unit hydrographs derived from them. Such procedures are questioned for their reliability due to the climatic and physical changes in the watershed and their application to ungauged areas. To overcome such difficulties, the use of physically based rainfall-runoff estimation methods such as the geomorphological instantaneous unit hydrograph (GIUH) have evolved. In this study, the GIUH is derived from watershed geomorphological characteristics and is then related to the parameters of the Nash instantaneous unit hydrograph (IUH) model for deriving its complete shape. The model parameters of the GIUH and the Nash IUH model are derived using two different approaches. In the first approach (referred to as GIUH-I) the rainfall intensity during each time interval is allowed to vary, whereas in the second approach (referred to as GIUH-II) rainfall intensity is averaged over the entire storm period. This methodology has been a...

Rapid population growth, accompanied with increase in affluence, continues to put the semi-arid environments’ limited water resources under pressure. This study has attempted to compare population projections for two cities of Botswana (Gaborone and Francistown) using various methods including the Logistic Curve, and Artificial Neural Networks (ANNs). For both the cities, the Geometric Increase, Logistic Curve and ANNs performed better. The ANNs (-0.03 % deviation from observed) was the best for Gaborone City followed by the Geometric Increase (+2.97 % deviation from observed) and Logistic Curve (-3.40 deviation from observed). For the City of Francistown, the Logistic Curve outperformed the Geometric Increase and the ANNs with deviations of 0.03 %, -0.27 %, and +11.6 5 respectively. The findings indicate that the Cohort Component method for population projections should be supplemented with the superior methods of Geometric Increase, Logistic Curve and ANNs. As census data increase with time, ANNs might prove to be the best approach. With improved population projections, determining likely future water demands against available water resources could be improved and this can help feed into water resources planning strategies.

Low flow studies for the Notwane River at Gaborone dam has been undertaken using daily flow records between 1979 and 1999 to determine the magnitude of annual maximum deficit volumes and deficit durations at a threshold level equivalent to 75 % dependable flow. Statistical modeling of these annual maximum values, separately, using a PWM/L-moment procedure, showed that a GEV distribution best describes these series. Among others, it has been estimated that deficit volumes of 3000 and 4700 m3 can be experienced on an average every 2 and 5 years respectively. Analysis even show that these deficit volumes are likely to be spread over a period of 318 and 390 days respectively, meaning that while deficit duration at T=2 years may spread over the whole year, deficit duration at T=5 years may spread into the next year thus posing challenges to the supply position over two subsequent years and hence requiring development of sound strategy in water supply. Keywords : Low flows, Percentile flow, L- Moments, Probability Weighted Moments

This paper attempts to identify a possible common statistical distribution, to model the annual maximum floods observed at various rivers and streams of Botswana using goodness of fit indices based on K-S Statistics and L-Moment Ratios. Results from the two approaches, suggest that Log-Normal distribution adequately describes the annual maximum flood series obtained from various gauged sites across the country and can possibly be used as a common distribution for estimation of flood quantiles at specified risks which can be used in design of various water resourcesand hydraulic structures across streams and rivers of Botswana. Based on the homogeneity criteria, two standardised curves have been developed for two catchment systems which can be used for the above purpose while dealing with ungauged basins in these systems. Keywords : flood estimation, K-S Statistics, L-Moment Ratios, hydraulic structures, ungauged basins Botswana Journal of Technology Vol. 14(2) 2005: 1-8