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.

Hydrological drought in upper Tana River basin adversely affects water resources. In this study, a hydrological drought was forecasted using a Surface Water Supply Index (SWSI), a Streamflow Drought Index (SDI) and an Artificial Neural Networks (ANNs). The best SWSI involved combinations of rainfall and the index values integrated into ANNs. The best forecasts with SDI entailed composite functions of rainfall, stream flow and SDI. Different ANN models for both SWSI and SDI with lead times of 1 to 24 months were tested at hydrometric stations. Results show that the forecasting ability of all the networks decreased with the increase in lead-time. The best ANNs with specific architecture performed differently based on forecasting lead-time. SWSI drought forecasts were better than those of the SDI for all lead-times. The SWSI and SDI depicted R values of 0.752 and 0.732 for station 4AB05 for one-month lead-time. The findings are useful as an effective hydrological-drought early warning for viable mitigation and preparedness approaches to minimize the negative effects of drought.

Due to increased impact of drought on water availability at different scales there is need to understand droughts especially in upper Tana River basin which is a critical and largest water system in Kenya. There is need to correlate trends of drought as influenced by the climate variability of the present times. Drought frequency, duration and intensity in the basin have been increasing. The influencing hydro-meteorological parameters and their interaction are necessary in developing measures for mitigating impacts of droughts. It is important to have a timely review of drought definitions and fundamental concepts of droughts, classification of droughts, types of drought indices, historical droughts and artificial neural networks with special focus of Kenyan a basin. Out of the review, this paper draws conclusions where gaps for more focused research especially for a typical river basin in Kenya exist. By developing effective drought forecasting tool for on-set detection and drought classification and drought forecasting, information on decision making on matters of drought preparedness and mitigation programmes will be available for proper water resources management.

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.

Aims: To model tomato water productivity under deficit sub – surface drip irrigation and grass mulch densities using Aquacrop model. Study Design: The study was factorial experimental with twelve treatments. Place and Duration of Study: Tatton Agriculture Park, Egerton University, Nakuru, Kenya between January to May 2019. Methodology: Tomato (Lycopersicon esculentum mill) crop (Tylka F1) was used to determine the effect of deficit irrigation and mulching on its productivity. Aquacrop model was calibrated to simulate the tomato yield, biomass and water productivity. Aquacrop model was used to estimate the tomato water requirements, water productivity, yield and biomass under deficit irrigation and mulching. The study was carried out on 36 experimental plots measuring 2 by 2 m with the total area under study being 144 m2. Results: The results showed a good correlation between the actual and simulated water productivity as determined by the Nash and Sutcliffe efficiency (NSE) of 0.00, Root Mean Square Error (RMSE) (%) of 0.04 and Coefficient of determination (R2) of 0.72. Conclusion: The study calibrated Aquacrop model for simulating tomato crop water productivity in Njoro Sub County and showed that the model is a good estimator of tomato water productivity.