Browsing by Author "Wambua, Raphael M."

Now showing 1 - 5 of 5
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    Publication
    Development of a non-linear integrated drought index (NDI) for managing drought and water resources forecasting in the upper Tana river basin-Kenya
    (2020) Wambua, Raphael M.
    This article uses the non-linear integrated drought index (NDI) for managing drought and water resources forecasting in a tropical river basin. The NDI was formulated using principal component analysis (PCA). The NDI used hydro-meteorological data and forecasted using recursive multi-step neural networks. In this article, drought forecasting and projection is adopted for planning ahead for mitigation and for the adaptation of adverse effects of droughts and food insecurity in the river basin. Results that forecasting ability of NDI model using ANNs decreased with increase in lead time. The formulated NDI as a tool for projecting into the future.
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    Publication
    Hydrological Drought Forecasting Using Modified Surface Water Supply Index (SWSI) and Streamflow Drought Index (SDI) in Conjunction with Artificial Neural Networks (ANNs)
    (IGI Global, 2019) Wambua, Raphael M.
    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.
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    Publication
    Modeling climate variability influence on river regime in upper Njoro catchment, Kenya
    (Science Publishing Group, 2020-10-13) Amisi, Edwin O.; Kundu, Peter M.; Wambua, Raphael M.
    To establish the effect of climate variability on annual discharge in Upper Njoro Catchment, hybrid models were developed by coupling Soil and Water Assessment Tool and Artificial Neural Networks. Daily surface runoff, lateral flow, and groundwater flow were first simulated with SWAT for the period (1978-1987) using climate variables from Egerton University weather station and LULC of 1978. The daily hydrologic variables simulated without calibration and validation of SWAT and observed discharge data were then used for ANN training, which led to the creation of discharge generation hybrid models for the dry, wet and wetter seasons. SWAT_ANN models generated discharges were compared with observed data and the performance rating were achieved at R2 (0.94, 0.91, 0.92) and NSE (0.89, 0.87, 0.87) for DJFM, AMJJ, and ASON seasons respectively. SUFI-2 algorithm in SWAT-CUP was run separately to compare the performance of SWAT with that of SWAT_ANN. SWAT-CUP sensitivity analysis revealed satisfactory values of both the p-factor (0.61) and the r-factor (0.69). Calibration and validation of monthly streamflow were realized at R2 (0.86 and 0.78) and NSE (0.83 and 0.74). The results showed that coupling SWAT and ANN improved flow prediction. Further, the potential of the SWAT_ANN modeling approach to separate the influence of climate variability on river regime from the effect of LULC was evaluated by comparing trends in the differences between observed and SWAT_ANN simulated monthly streamflow with trends of the quantified LULC changes. The findings provided sufficient evidence that the SWAT_ANN modeling approach was reliable and could also be applied to detect changes in LULC.
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    Publication
    Soil and water conservation review in Rombo and Mondabogho watersheds in Kajiado and Taita Taveta counties, Kenya
    (2022-12-12) Wambua, Raphael M.; Kosgei, Alice
    Land degradation adversely affect agroecosystems and agricultural productivity, resulting in food insecuries. It also plays a role in accelerating climate change effects. There has been a critical on-site and off-site land degradation structures in selected Arid and Semiarid Lands (ASALs) of Kenya. Such ASAL areas include Rombo and Mondambogho watersheds in Kajiado and Taita Taveta Counties respectively, and this has subsequently resulted in a decline in land productivity. The interaction of continuous land cultivation, over-grazing and influence of climate change without appropriate soil and water conservation (SWC) has accelerated the problem. This paper presents a critical review of land degradation alongside soil and water conservation in the two watersheds. The objective of this research was to evaluate the status of SWC practices in the two watersheds. It was found that the two watersheds experience both on-site and off-site soil erosion due to poor agricultural land use practices. As such viable research and solutions needed for sustainable soil and water conservation are highlighted. It is recommended that mechanized SWC techniques should be employed for the two case studies for increased land productivity, food and nutritional security in these areas.
  • Publication
    Publication
    Stochastic Drought Forecasting Exploration for Water Resources Management in the Upper Tana River Basin, Kenya
    (Engineering Science Reference (an imprint of IGI Global), 2015-01) Wambua, Raphael M.; Mutua, Benedict M.; Raude, James M.
    This chapter presents the trend of drought as a stochastic natural disaster influenced by the climate variability for the upper Tana River basin in Kenya. Drought frequency, duration and intensity in the upper Tana River basin have been increasing over the years. To develop measures for mitigating impacts of drought, the influencing hydro-meteorological parameters and their interaction are necessary. Drought definitions, fundamental concepts of droughts, classification of droughts, types of drought indices, historical droughts and application of artificial neural networks in analyzing impacts of drought on water resources with special focus on a Kenyan river basin is presented. Gaps for more focused research are identified. Although drought forecasting is very vital in managing key sectors such as water, agriculture and hydro-power generation, drought forecasting techniques in Kenya are limited. There is need therefore to develop an effective drought forecasting tool for on-set detection, classification and drought forecasting. The forecasting is necessary for decision making on matters of drought preparedness and proper water resources planning and management