Assessment of Groundwater Potential and Prediction of the Potential Trend up to 2042 Using GIS-Based Model and Remote Sensing Techniques for Kiambu County

Groundwater is one of the important necessary renewable resources of the world. It forms part of the natural water cycle that is present in the underground strata with the principal sources being precipitation and streamflow. Traditionally, information on the potential occurrence of groundwater was obtained using techniques such as drilling, geophysical, geological, hydro-geological and geo-electrical which are time-consuming, costly and lacked full coverage. This study shows that remote sensing and GIS techniques can be utilized to map groundwater potential using a GIS-based model, the Modified DRASTIC Model, which incorporates factors that influence groundwater occurrence. These factors are the surface attributes that infer groundwater potentials and they include geology, soil texture, land use, lithology, landforms, slope steepness, lineaments and drainage systems. A prediction of the groundwater prediction was done by utilizing the MOLUSCE tool, a plugin in Qgis that utilizes ANN, multicriteria evaluation, weights of evidence and LRs algorithms in predicting land changes. The kappa value for prediction was 0.83. The results showed areas in the Southwest region had low to very low potential and the central region had high to very high potential for all the years and there were little changes between the years. The prediction showed that by 2042, the eastern region of Kiambu County will have a decline in groundwater potential.

Assessment of Ground Water Dynamics and Potential Zones in Urban Areas: A Case Study of Voi Town, Kenya

Water plays a role in sustaining all the biotic elements. Unfortunately, in the recent times with persistent climate change impacts, parts of the world are facing cases of inadequate water causing stress and increased vulnerability among the people. This is the case with urban areas across the globe as their populations keep increasing with little to no attention paid to urban planning that allows sustainable management of resources amidst rapid development. Urban areas are surrounded by high yielding aquifers that have better water services from groundwater. However, the urban sprawl phenomena have limited attempts in assessing ground water potential in urban areas contributing to urban water scarcity. Therefore, the study aims to look at the problem of urban water scarcity, by analyzing the levels and distribution of groundwater in Voi town using remote sensing and GIS techniques, in order to suggest suitable sites for underground water exploration in regard to the overall urban water supply. From the analysis, the results showed that the area majorly has low to potential zones of groundwater. High potential areas were very few and were mostly on the western side of the area. Very low potential zones were seen on the east and north side of the area.

Geo-Hazard Susceptibility Assessment and Its Impacts on Livelihoods in Kerio Valley, Kenya

Geohazards are a recurrent issue in the Kerio River catchment of Kenya, which usually results in life and property loss. This research focuses on mapping geo-hazard risk zones of the region. The risk zones were developed from a combination of land use land cover maps, agroecological zones maps and soil erosion maps using the Analytical Hierarchy Process method of multi-criteria analysis. The final results depict the geohazard risk maps which show the susceptibility of different areas in the catchment (classified as risk zones) to hazards. The zones range from no risk zones to very high-risk zones. The results showed that the lowlands are most susceptible to hazards as they were classified as high-risk zones. These risk zone areas have impacts on the socio-economic development hence negatively impacting livelihoods in the area.

Morphometric and Change Detection Analysis for Prioritization of Sub Basin Conservation, Case Study of Taita Hills

The study is aimed at analyzing the risk of Taita Hills region of harmful runoff and soil erosion by employing morphometric analysis and change detection in a GIS environment to prioritize the Taita Hills in Taita Taveta County. The objective of the study was to characterize and give hierarchy in which the region should be conserved. The methodology adopted hydrological modeling, morphometric computation, Weighted Sum Analysis (WSA) and change detection. Hydrological modeling was vital in delineating the sub-watersheds and stream network. Morphometric computation and WSA was applicable in coming up with parameters and weighting the parameters for each sub-watershed’s prioritization. Change detection is related to how human activity is important for conservation as the effect of land forms and dimensions are compounded. Twenty-one fourth order streamed sub-watersheds were generated and prioritized using morphometry and change detection. Every sub-watershed is given a hierarchy based on the calculated compound parameter from the WSA equation developed and shows the risk of runoff and soil erosion. The morphometric prioritization shows 47% of the watersheds are in the high and very highly susceptible areas and there are two sub-watersheds with the highest land cover change. As well six sub-watersheds are risky with both land cover change and morphometry.

GIS-Based Multi-Criteria Evaluation to Identify Areas for Soil and Water Conservation in Lower Lake Bogoria Landscapes, Baringo County, Kenya

This study was meant to ensure that there is proper and efficient conservation of soil and water using geospatial tools to enable us identify priority areas to carry out conservation. Over the past years, various fields of study have established how critical it is to conserve these natural resources in the ecosystem and to ensure sustainability in not only green livelihoods but also to enhance living conditions of the life on earth. The aim of this research was to generate high priority sites for establishing soil and water conservation techniques in the Lower Bogoria Landscapes in Baringo, Kenya using GIS-based multicriteria decision analysis. Various criteria were analyzed to generate the final conservation priority sites, such as land use land cover, rainfall runoff, soil erosion and slope. The criteria were assigned weights using the AHP technique and overlayed using the weighted overlay tools to produce the final outputs. Land use land cover maps were generated using supervised maximum likelihood technique, rainfall run-off maps were generated using the SCS-CN method and soil erosion maps were generated using RUSLE model. The final soil and water conservation maps showed that high and moderate priority areas requiring the establishment of techniques and mechanisms to control soil erosion and conserve water increased from 1990 to 2020. In 2020, more than 50% of the total study area was classified as moderate to high priority for water and soil conservation. Soil and water conservation structures such as water pans, percolation tanks, farm ponds and stop dams should be constructed in such areas.

Modeling the Spatial Distribution of Soil Heavy Metals Using Random Forest Model—A Case Study of Nairobi and Thirirka Rivers’ Confluence

Modeling the spatial distribution of soil heavy metals is important in determining the safety of contaminated soils for agricultural use. This study utilized 60 topsoil samples (0 - 30 cm), multispectral images (Sentinel-2), spectral indices, and ancillary data to model the spatial distribution of heavy metals in the soils along the Nairobi River. The model was generated using the Random Forest package in R. Using R2 to assess the prediction accuracy, the Random Forest model generated satisfactory results for all the elements. It also ranked the variables in order of their importance in the overall prediction. Spectral indices were the most important variables within the rankings. From the predicted topsoil maps, there were high concentrations of Cadmium on the easterly end of the river. Cadmium is an impurity in detergents, and this section is in close proximity to the Nairobi water sewerage plant, which could be a direct source of Cadmium. Some farms had Zinc levels which were above the World Health Organization recommended limit. The Random Forest model performed satisfactorily. However, the predictions can be improved further if the spatial resolutions of the various variables are increased and through the addition of more predictor variables.

Ecological Niche Modeling of Zebra Species within Laikipia County, Kenya

Wildlife conservation is essential, especially for countries like Kenya which rely on tourism as a major earner of foreign exchange. Conservation of species with minimal ecological information such as Grevy’s zebra, though a challenge, is critical to enable the future survival of such species. Grevy’s and Plains zebra have been classified as endangered and near-threatened by International Union for Conservation of Nature and Natural Resources (IUCN) respectively, with Grevy’s zebra found mostly in Northern Kenya and Ethiopia. This has been due to habitat degradation from livestock grazing, local hunting and development of resorts. Six prediction variables i.e. rainfall, temperature, land use, population, NDVI and cattle occurrence were used in Maxent algorithm to produce a habitat prediction map for both species. Both prediction maps had an AUC > 0.75, which is adequate for conservation planning. Niche similarity based on Warren’s I index (I = 0.78) indicates that both zebra species are identical based on their occupied niche environments, suggesting that similar conversation strategies can be adopted for both species.