Water demand increases as population increases leading to overexploitation of water resource. Consequently, there is need for improved water resources management complemented with rain water harvesting within the catchments. This study sought to assess land suitability for surface runoff harvesting using geospatial techniques. Land use/land cover maps of the area were derived from Landsat image. Land use and soils data were used in generating curve number map of the catchment. Lineaments greatly affect the storage depending on whether runoff is for surface storage or ground water recharge purposes. As a result, ArcGIS was used in delineating the lineaments from Digital Elevation Model (DEM) of the catchment. Further, using weighted overlay the catchment was grouped into categories of restricted, not suitable, moderately suitable, suitable, or highly suitable. The study found that forest, agriculture, and built-up areas occupied about 39.42%, 36.32%, and 1.35% of catchment area, respectively. A large part of catchment was found to have curve number range of 82-89. About 50% of the catchment was found to fall within suitable and highly suitable categories. This implied that a great potential exists for rain water harvesting within the catchment.

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Lakes and reservoirs play important roles as freshwater sources for domestic, industrial, agricultural, fisheries and recreational purposes. However, for the lakes to be sustainably exploited, there is need to understand their bathymetric characteristics by conducting bathymetric surveys. This aids in generating information that can guide lakes stakeholders and managers in establishing the volume of available water. It is recommended, therefore, that bathymetric surveys be conducted at ten‐year intervals. Such continuous bathymetric information is lacking in many lakes, especially in developing countries. One example is Lake Naivasha in Kenya, which is largely exploited for various socio‐economic purposes. Despite its importance, its most recent published bathymetric data were collected in 1991. The goal of the present study, therefore, was to conduct a bathymetric survey of Lake Naivasha and its satellite Lake Oloiden, using an Acoustic Profiling System (APS) to generate Depth–Area–Volume relationships for the lakes. The survey results indicate the in the year 2016 mean depth, volume and surface area of the lake were 4.68 m, 722 × 106 m3 and 154.17 × 106 m2, respectively. Because of limited information from the 1991 survey, the 2016 survey results were comparable with those of 1983. The difference in the lakes mean, and maximum depth for the 1983 and 2016 survey was less by 0.23 and 2 m, respectively. This could be an indicator the lake is being affected by anthropogenic activities or environmental changes. The established Depth–Area–Volume relationships are crucial since they provide invaluable information to lake and water resources managers for making informed decisions regarding management of the lake's water resources.

Sediment cores hold large information on the history of human interactions with lakes and surrounding environments. Hence, this study investigated the geochronological and heavy metals characteristics of sediment from Lake Naivasha, Kenya. Geochronological characteristics were established from sediment cores, using 210 Pb and 137 Cs. On the other hand, heavy metals; Al, As, Cr, Cu, Fe, Mn, Ni, Pb, and Zn were analyzed using Inductively Coupled Plasma -Optical Emission Spectrometer (ICP -OES). Their probable sources were predicted using Pearson's correlation and Principal Component Analysis (PCA) while heavy metal contamination levels were assessed using geoaccumulation Index (Igeo), Contamination Factor (CF) and Enrichment Factor (EF) pollution indices. Results showed that the cores were about 140 years old with an estimated average mass sedimentation rate of 0.32 g/cm 2 /yr. Vertical fluctuations of heavy metal contamination were observed along the sediment cores with high values recorded near the surface. Further, PCA and pollution indices showed that Al, Cr, Cu, Ni, and Pb were from natural sources while, Zn, Mn, Fe, and As, (in order of contamination levels), were both from natural and anthropogenic sources. Therefore, this study showed the geochronological trends of sedimentation and impacts of human activities on Lake Naivasha.