Rainwater harvesting (RWH) plays an important role in increasing water security for individuals and governments. The demand for tools to enable technical and economic analysis of RWH systems has led to a substantial body of research in the recent past. This paper focuses on the economic aspects of domestic RWH in urban and peri-urban environments. In this regard, key issues are identified and discussed including quality and quantity of harvested water, the water demand profile, the scale of installation, interest rates, the period of analysis, real estate value, and the water-energy-food nexus. Kenya and Australia are used as reference points having different economies and opposing RWH policies. It has been found that the previous studies on financial aspects of RWH systems often had conflicting results. Most of the economic analyses have ignored the full benefits that a RWH system can offer. In view of the varying and conflicting results, there is a need to standardize the methods of economic analysis of RWH systems.

The Mkurumudzi River originates in the Shimba hills and runs through Kwale County on the Kenyan Coast. Study on this river has been informed by the many economic activities that the river supports, which include sugarcane plantations, mining, tourism and subsistence farming. The main objective of this study was to use the soil moisture accounting (SMA) model specified in the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) settings for the continuous modeling of stream flow in the Mkurumudzi catchment. Data from past years were compared with observed stream flow data in order to evaluate whether the model can be used for further prediction. The calibration was performed using data from 1988 to 1991 and validation for the period from 1992 to 1995 at a daily time step. The model performance was evaluated based on computed statistical parameters and visual checking of plotted hydrographs. For the calibration period of the continuous modeling, the performance of the model was very good, with a coefficient of determination R² = 0.80, Nash-Sutcliffe Efficiency NSE = 0.80, index of agreement d = 0.94, and a Root Mean Squared Error (RMSE)/observations’ standard deviation ratio—RSR = 0.46. Similarly, the continuous model performance for the validation period was good, with R² = 0.67, NSE = 0.65, RSR = 0.62 and d = 0.88. Based on these performance results, the SMA model in the HEC-HMS was found to give a satisfactory prediction of stream flow in the Mkurumudzi Catchment. The sensitivity analysis of the model parameters was performed, and the different parameters were ranked according to their sensitivity in terms of percent change in simulated runoff volume, peaks, Nash-Efficiency, seven-day low flow and base flow index. Sensitivity analysis helped to understand the relationships between the key model parameters and the variables.

This study examined the trends in annual rainfall and temperature extremes over the Vea catchment for the period 1985–2016, using quality-controlled stations and a high resolution (5 km) Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. The CHIRPS gridded precipitation data’s ability in reproducing the climatology of the catchment was evaluated. The extreme rainfall and temperature indices were computed using a RClimdex package by considering seventeen (17) climate change indices from the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI). Trend detection and quantification in the rainfall (frequency and intensity) and temperature extreme indices were analyzed using the non-parametric Mann–Kendall (MK) test and Sen’s slope estimator. The results show a very high seasonal correlation coefficient (r = 0.99), Nash–Sutcliff efficiency (0.98) and percentage bias (4.4% and −8.1%) between the stations and the gridded data. An investigation of dry and wet years using Standardized Anomaly Index shows 45.5% frequency of drier than normal periods compared to 54.5% wetter than normal periods in the catchment with 1999 and 2003 been extremely wet years while the year 1990 and 2013 were extremely dry. The intensity and magnitude of extreme rainfall indices show a decreasing trend for more than 78% of the rainfall locations while positive trends were observed in the frequency of extreme rainfall indices (R10mm, R20mm, and CDD) with the exception of consecutive wet days (CWD) that shows a decreasing trend. A general warming trend over the catchment was observed through the increase in the annual number of warm days (TX90p), warm nights (TN90p) and warm spells (WSDI). The spatial distribution analysis shows a high frequency and intensity of extremes rainfall indices in the south of the catchment compared to the middle and northern of part of the catchment, while temperature extremes were uniformly distributed over the catchment.

Study RegionThe Upper catchments of the Nzoia River basin in western Kenya.Study FocusThe potential streamflow responses to climate change in the upper Nzoia River basin are studied. The Soil and Water Assessment Tool (SWAT) was forced with monthly temperature and precipitation change scenarios for the periods 2011–2040 (2020s), 2041–2070 (2050s) and 2071–2100 (2080s). Data from 10 climate models and three greenhouse gases emission scenarios was downscaled using the delta change method and used in the SWAT model. Streamflow data for the periods 1986–1998 and 1973–1985 was used for model calibration and validation respectively.New Hydrological Insights for the RegionComparison between the simulated baseline and future streamflow shows that in the Koitobos and Kimilili watersheds, August to December streamflow is likely to be highly altered. In the Kuywa watershed, March to June flows is likely to change considerably due to climate change. Major streamflow changes are likely in March to June and August to November in the Rongai watershed. Projected changes differed between the four watersheds despite their proximity, indicating different sensitivities to climate change and uncertainty about the potential hydrological impacts of climate change in the area