The forecast of rainfall and temperature is a difficult task due to their variability in time and space and also the inability to access all the parameters influencing rainfall of a region or locality. Their forecast is of relevance to agriculture and watershed management, which significantly contribute to the economy. Rainfall prediction requires mathematical modelling and simulation because of its extremely irregular and complex nature. Autoregressive integrated moving average (ARIMA) model was used to analyse annual rainfall and maximum temperature over Tordzie watershed and the forecast. Autocorrelation function (ACF) and partial autocorrelation function (PACF) were used to identify the models by aid of visual inspection. Stationarity tests were conducted using the augmented Dickey–Fuller (ADF), Mann–Kendall (MK) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests respectively. The chosen models were evaluated and validated using the Akaike information criterion corrected (AICC) and also Schwartz Bayesian criteria (SBC). The diagnostic analysis of the models comprised of the independence, normality, homoscedascity, P–P and Q–Q plots of the residuals respectively. The best ARIMA model for rainfall for Kpetoe and Tordzinu were (3, 0, 3) and (3, 1, 3) with AICC values of 190.07 and 178.23. That of maximum temperature for Kpetoe and Tordzinu were (3, 1, 3) and (3, 1, 3) and the corresponding AICC values of 23.81 and 36.10. The models efficiency was checked using sum of square error (SSE), mean square error (MSE), mean absolute percent error (MAPE) and root mean square error (RMSE) respectively. The results of the various analysis indicated that the models were adequate and can aid future water planning projections.

Land use/ land cover (LULC) change has been identified as the main driving force of global change. The study investigated LULC change in Tordzie watershed in Ghana and predicted the future development. The supervised classification procedure was applied to Landsat images of 1987, 2003, and 2017. The cellular automata–Markov model embedded in IDRISI 17 software was employed to model LULC for the years 2030 and 2050. The trend of LULC change was exploited from 1987 to 2003, from 2003 to 2017, and projected to 2030 and 2050. Settlement and crop land, respectively, increased from 2.68% to 16.46% in 1987 to 3.65% and 53.47% in 2003 and finally to 20.61% and 58.52% in 2017. Vegetation cover declined from 23.2% in 1987 to 13.9% in 2003 and finally to 11.3% in 2017. The annual rate of change was determined. In 2030 and 2050, the dominant land use type will be crop land (56%). However, it decreased between 2017 and 2030 by −1.73%. The findings of the study are very relevant to land and water resource planners, policy formulators and implementers, and environmental and climate change advocates. Sustainable land use policy and its implementation are recommended.

Rainwater harvesting is the art and science of capturing rain for human use. This study analysed rainfall data for the Regional Hospital's catchment area to ascertain potential harvestable rainfall. Assessment of the buildings and their respective roof areas were also done to determine the possible roof catchment areas. The potential rainwater that could be harvested from the different blocks within the hospital range between 3,306.18 -9,943.45m³. The potential total average rainwater that could be harvested from the different blocks within the hospital catchment area of 51,939.11m2 is 53,524.29 m³ per annum. According to data collected from the GWCL, the monthly and yearly demand of water by the hospital are 3,146 m3 and 337,752m3 respectively, which is lower than the total potential rainwater that could be harvested per annum of 53, 524.29m3 . This implies that there would be an excess water of 15,772.29m³ . Theoretically, it means that harvested and stored rainwater could meet the water needs of the hospital. Keywords: Rainwater harvesting (RWH); Water storage; Organizational water usage