Water flow and sedimentation processes have been significantly erratic at the Chókwè Irrigation Scheme (CIS) and have affected its hydraulic performance. Given its expansion there is need to understand these processes taking place on-site and along the channels of the scheme. CIS being the biggest project of its kind in Mozambique requires proper management of water flow and sedimentation processes. Therefore, the effect of water flow, sediment transport and deposition parameters on the performance of the CIS is needed. In order to determine the effect of spatial and temporal water flow and sediment distribution trends along the irrigation canals, there is need to establish a correlation between these parameters. Determining the influence of water flow velocity on sediment settling rate at different depths along the canal reaches is important in managing the CIS. In addition, a developed decision-support tool to predict sediment deposition is required. For this reason, it is therefore crucial to carry out a timely assessment of water flow and sedimentation processes in CIS in a review concept. From the current review, some gaps that exist for more focused research on Chókwè Irrigation Scheme have been identified. In this regard therefore, there is need to develop an effective support tool for managing water flow and sediment deposition along the canal reaches with a view to increasing crop production in CIS.

Analysis of spatial and temporal drought variability in the upper Tana River basin using Palmer Drought Severity Index (PDSI) was conducted. The drought is critical for formulation of mitigation measures in the river basin. A monthly temporal and 90-m spatial resolution was applied. This was achieved within ArcGIS environment. Climatic data for 1970 to 2010 was used for computation of the PDSI while the missing data sets were filled using Artificial Neural Networks (ANNs). The results of PDSI for dry and wet seasons at meteorological stations indicate that the time series plots for the PDSI values for dry season are generally lower than those for the wet seasons. The PDSI values for meteorological stations located at the lower elevation of the basin are lower than those located at higher elevation. On the other hand, spatially distributed drought severity based on PDSI show that the ranges of maximum and minimum drought severity values in 1970 are -0.868 to -0.804 and -0.675 to -0.610 respectively. These values of drought severity occur respectively in the north-western and south-eastern areas of the basin. PDSI values increased from the range -0.675 to -0.610 in 1970 and from -1.087 to 0.957 in 2010 for the north-eastern areas of the upper basin. The south eastern areas of the basin are more prone to drought risks than north-western parts. Use of the PDSI reflects the spatial heterogeneity and temporal variability of drought across the basin. The drought assessment offer technical approach for comprehensive understanding of drought for effective drought-induced disaster mitigation and its management, with a view to reducing adverse effects on livelihoods. Key words: Palmer Drought Severity Index (PDSI), drought severity, upper Tana River basin, monthly resolution, drought-induced disaster.

In the present study, temporal and spatial drought-risk events are characterized using a Modified Surface Water Supply Index (SWSI) for upper Tana River basin, located in tropics, Kenya. Forty years (1970-2010) hydro-meteorological data for low, lower middle, middle and high elevations at eight hydro-metric stations was used to characterize spatial and temporal distribution of drought. The spatial drought distribution is obtained via interpolation of hydro-metric stations data using Kriging interpolation technique embedded ArcGIS 10.1 was used. The drought trends were analyzed using a non-parametric Mann-Kendall trend test. The results show that southeastern parts of the basin are the most drought-prone areas at different drought severities and frequencies. Drought severity classes ranged from 1.69-2.22 to 3.39-4.17 and 2.68-3.21 to 4.37-4.96 in 1970 and 2010 respectively across the river basin. The southwestern parts of the basin exhibited highest drought frequency ranging from 10.61 to 13.16 while the northwestern areas gave frequency values ranging from 3.74 to 6.29. The findings of the study are applicable in Early Drought Warning Systems, prioritized water resources planning and management.

Appropriate water quality and quantity are critical problem greatly affecting significant proportion of the population particularly those living in semi-arid and arid areas. Rainfall characteristics, streamflow and reservoir levels are some of the key hydro-meteorological variables within a catchment in the determination of sufficiency of water quantity. Lake Naivasha reservoir level continues to be hit by anthropogenic stressors such as loss of wetlands, water abstraction and eutrophication. Also there has been a lot of fluctuation in flow discharge of Malewa River over the years where the tributaries (Turasha and Karati streams) usually dries up during dry season. This paper presents the results of a research to assess the time series trends of rainfall, streamflow and lake basin levels data in the catchment from 1980-2018 using Mann-Kendall trend test to assess water quantity status. Monthly stream flow, rainfall and reservoir level data in the catchment were obtained from Water Resources Management Authority (WRMA) and Kenya Meteorological Department (KMD) for the years 1980-2018. Non-parametric Mann-Kendall rank statistics test was used to examine the trend components of hydro-meteorological variables. The P-values were used to test whether the null hypotheses were statistically significant (α<0.05) at a 5% level of significance. Rainfall values had an increasing trend from 2011 onwards but not statistically significant while annual streamflow values had no trend as P-value showed weak evidences to rejected the null hypothesis. Reservoir level had an upward trend in years 1981, 2001, 2003, 2012 and 2018 while downward trends in 1980, 1982, 1984, 1986, 1987, 2000, 2004, 2008 and 2014. From 2010 onwards, in monthly data, there have been upward water levels in the lake as shown by the trend. These show that the hydro-meteorological indicators in the catchment are independently trending, therefore some other factors such as land cover changes or climate changes were adversely affecting the hydrological cycle. These findings are helpful for planning and management in water resource systems such as forecasting water distribution and mitigate flood and drought.