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.

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.

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.

Drought events across the world are increasingly becoming a critical problem owing to its negative effects on water resources. There is need to understand on-site drought characteristics for the purpose of planning mitigation measures. In this paper, meteorological drought episodes on spatial, temporal and trend domains were detected using Standardized Precipitation Index (SPI) and Effective Drought Index (EDI) in the upper Tana River basin. 41 years monthly precipitation data from eight meteorological stations were used in the study. The SPI and EDI were used for reconstruction of the drought events and used to characterize the spatial, temporal and trend distribution of drought occurrence. Drought frequency was estimated as the ratio of a defined severity to its total number of events. The change in drought events was detected using a non-parametric man-Kendall trend test. The main drought conditions detected by SPI and EDI are severe drought, moderate drought, near normal, moderate wet, very wet and extremely wet conditions. From the results the average drought frequency between 1970 and 2010 for the south-eastern and north-western areas ranged from 12.16 to 14.93 and 3.82 to 6.63 percent respectively. The Mann-Kendall trend test show that drought trend increased in the south-eastern parts of the basin at 90% and 95% significant levels. However, there was no significant trend that was detected in the North-western areas. This is an indication that the south-eastern parts are more drought-prone areas compared to the North-western areas of the upper Tana River basin. Both the SPI and the EDI were effective in de-How to cite this paper: Wambua, R.M., Mutua, B.M. and Raude, J.M. (2018) Detection of Spatial, Temporal and Trend of Meteorological Drought Using Standardized Precipitation Index (SPI) and Effective Drought Index (EDI) in the Upper Tana River Basin, Kenya. Open Journal of Modern Hydrology, 8, 83-100. 84Open Journal of Modern Hydrology tecting the on-set of drought, description of the temporal variability, severity and spatial extent across the basin. It is recommended that the findings be adopted for decision making for drought-early warning systems in the river basin.The Standard Precipitation Index (SPI) was developed by [4] to quantify the R. M. Wambua et al.

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.