Soils and water are major resources that drive a country’s economy, and therefore should be conserved and utilized sustainably. However, in Kenya, these two resources are facing huge depletion and degradation due to anthropogenic factors and climate change. Bamboo species, especially on large plantations, can significantly alter ecological, hydrological, and biogeochemical processes in the long term. This study aimed to evaluate the effects of different species of bamboo and tree plantations on important soil–water processes like infiltration, bulk density, runoff, and soil loss in Kenya. The research was conducted at two sites (Gede in Arabuko Sokoke forest and at Baolala, in Kilifi County) managed by the Kenya Forestry Research Institute (KEFRI). The Arabuko Sokoke forest has a hot–humid coastal climate, while Baolala is a hot semi-arid area with little precipitation. The study involved measurement of soil–water infiltration rates using infiltrometers, installing runoff plots to quantify surface runoff and sediment loss, and analyzing soil properties like bulk density for growing periods for different bamboo and tree species. At the Gede forest site, the 30-year-old Thyrsostachys siamensis and Bambusa bambos plantations recorded the highest infiltration rates. Mature bamboo plantations of T. siamensis and B. bambos recorded higher infiltration rates compared to mature plantations of E. camaldulensis and G. arborea. It was observed that the bamboo plantations manifested lower soil bulk density compared to bare land, which recorded the highest bulk density. At Boalala, infiltration rates were significantly higher in the bamboo species compared to grassland and bare land. The painted bamboo (B. vulgaris vittata) had a slightly higher water infiltration rate compared to B. vulgaris. Runoff and erosion patterns reinforced the benefits of more mature bamboo plantations as well. There was a significant correlation between amount of runoff and collected soil loss through erosion. The data showed reductions in surface runoff volumes and sediment loss as the bamboo plantations aged compared to younger species. Therefore, by enhancing infiltration and reducing runoff and erosion, well-managed bamboo plantations can protect valuable soil resources, improve water recharge, and support sustainable land use over the long term. In conclusion, this study showed the strong potential of bamboo as a soil and water conservation tool in Kenya.

Understanding the seasonal regulation of transpiration and stomatal conductance is critical for evaluating plant water-use strategies in response to environmental variability. This study assessed these physiological traits in three dominant savanna tree species (Piliostigma thonningii (Schumach.) Milne-Redh., Combretum molle R.Br. ex G.Don, and Balanites aegyptiaca (L.) Delile) in Ruma National Park, Kenya. Measurements were taken during wet and dry seasons under varying canopy light conditions (light-exposed vs. shaded leaves) and soil moisture regimes. A randomized design with four treatments and three replicates was employed. Results showed significantly higher transpiration and stomatal conductance during wet seasons, especially in sunlit leaves (p < 0.05). P. thonningii exhibited the highest rates of transpiration (9 mmol m⁻² s⁻¹) and stomatal conductance (~2.2 mmol m⁻² s⁻¹) in light conditions, while B. aegyptiaca maintained consistently low values, reflecting a drought-tolerant strategy. C. molle demonstrated intermediate responses, suggesting a balance between water conservation and resource use. Despite seasonal trends, low R² values indicated that internal physiological regulation outweighed the influence of external climatic drivers. These findings reveal species-specific water-use strategies and highlight the ecological significance of leaf-level responses to light and moisture availability in tropical savannas. The study provides valuable insights for forest management and climate-resilient restoration planning in water-limited ecosystems.

In regions lacking sufficient data, remote sensing (RS) offers a reliable alternative for precipitation estimation, enabling more effective drought management. This study comprehensively evaluates four commonly used RS datasets—Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS), Tropical Applications of Meteorology using Satellite data (TAMSAT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) against ground-based data—with respect to their performance in detecting precipitation and drought patterns in the Great Ruaha River Basin (GRRB), Tanzania (1983–2020). Statistical metrics including the Pearson correlation coefficient (r), mean error (ME), root mean square error (RMSE), and bias were employed to assess the performance at daily, monthly, seasonal (wet/dry), and annual timescales. Most of the RS products exhibited lower correlations (r < 0.5) at daily timestep and low RMSE, bias, and ME. Monthly performance improved substantially (r > 0.8 at most stations) particularly during the wet season (r = 0.52–0.82) while annual and dry-season performance declined (r < 0.5 and r < 0.3, respectively). Performance under RMSE, bias, and ME declined at higher timescales, particularly during the wet season and annually. CHIRPS, MSWEP, and PERSIANN generally overestimated precipitation while TAMSAT consistently underestimated it. Spatially, CHIRPS and MSWEP reproduced coherent basin-scale patterns of drought persistence, with longer dry-spells concentrated in the northern, central, and western parts of the basin and shorter dry-spells in the eastern and southern regions. Trend analysis further revealed that most products captured consistent large-scale changes in dry-spell characteristics, although localized drought events were more variably detected. CHIRPS and MSWEP showed superior performance especially in capturing monthly precipitation patterns and major drought events in the basin. Most products struggled to detect extreme dry conditions with the exception of CHIRPS and MSWEP at certain stations and periods. Based on these findings, CHIRPS and MSWEP are recommended for drought monitoring and water resource planning in the GRRB. Their appropriate use can help water managers make informed decisions, promote sustainable resource use, and strengthen resilience to extreme weather events.

Drought is a pervasive and intensifying climate hazard with profound implications for food security, water availability, and socioeconomic stability, particularly in sub-Saharan Africa. In Kenya, where over 80% of the landmass comprises arid and semi-arid lands (ASALs), recurrent droughts have become a critical threat to agricultural productivity and climate resilience. This study presents a comprehensive spatiotemporal analysis of seasonal drought dynamics in Kenya for June–July–August–September (JJAS) from 2000 to 2024, leveraging remote sensing-based drought indices and geospatial analysis for climate risk planning. Using the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Soil Moisture Anomaly (SMA), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) anomaly, a Combined Drought Indicator (CDI) was developed to assess drought severity, persistence, and impact across Kenya’s four climatological seasons. Data were processed using Google Earth Engine and visualized through GIS platforms to produce high-resolution drought maps disaggregated by county and land-use class. The results revealed a marked intensification of drought conditions, with Alert and Warning classifications expanding significantly in ASALs, particularly in Garissa, Kitui, Marsabit, and Tana River. The drought persistence analysis revealed chronic exposure in drought conditions in northeastern and southeastern counties, while cropland exposure increased by over 100% while rangeland vulnerability rose nearly 56-fold. Population exposure to drought also rose sharply, underscoring the socioeconomic risks associated with climate-induced water stress. The study provides an operational framework for integrating remote sensing into early warning systems and policy planning, aligning with global climate adaptation goals and national resilience strategies. The findings advocate for proactive, data-driven drought management and localized adaptation interventions in Kenya’s most vulnerable regions.

In regions lacking sufficient data, remote sensing (RS) offers a reliable alternative for precipitation estimation, enabling more effective drought management. This study comprehensively evaluates four commonly used RS datasets-Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS), Tropical Applications of Meteorology using Satellite data (TAMSAT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) against ground-based data-with respect to their performance in detecting precipitation and drought patterns in the Great Ruaha River Basin (GRRB), Tanzania (1983-2020). Statistical metrics including the Pearson correlation coefficient (r), Mean Error (ME), Root Mean Square Error (RMSE), and Bias were employed to assess the performance at daily, monthly, seasonal (wet/dry), and annual timescales. Most of the RS products exhibited lower correlations (r 0.8 at most stations) particularly during wet season (r = 0.52-0.82) while annual and dry-season performance declined (r < 0.5 and r < 0.3, respectively). Performance under RMSE, Bias, and ME declined at higher timescales, particularly during wet season and annually. CHIRPS, MSWEP, and PERSIANN generally overestimated precipitation while TAMSAT consistently underestimated it. CHIRPS and MSWEP showed superior performance especially in capturing monthly precipitation patterns and major drought events in the basin. Most products struggled to detect extreme dry conditions with exception of CHIRPS and MSWEP at certain stations and periods. Based on these findings, CHIRPS and MSWEP are recommended for drought monitoring and water resource planning in the GRRB. Their appropriate use can help water managers make informed decisions, promote sustainable resource use and strengthen resilience to extreme weather events.

Many wastewater treatment plants do not incorporate advanced nutrient removal technologies, resulting in only partial removal of nitrogen and phosphorus during primary and secondary treatment. This poses risks to receiving water bodies. This study evaluates the effectiveness of a pilot institutional wastewater treatment plant (WWTP) at JOOUST and its impact on a receiving stream. Such decentralized, pond-based systems are common in Kenyan institutions and serve a critical role in managing sewage in areas lacking centralized municipal infrastructure. Key water quality parameters including nutrient concentrations (Total Nitrogen (TN) and Total Phosphorus (TP)), Microbial abundance, pH, dissolved oxygen (DO), conductivity and temperature were measured at various points: treatment ponds, upstream (S1), midstream (S2) and downstream (S3). Data were statistically analyzed using SPSS and Excel, with significance tested via one-way ANOVA at P < 0.05. Results showed nutrient removal efficiencies of 90.39% for TN and 67.11% for TP, indicating the system's effectiveness. Colony-forming units (CFUs) decreased along the treatment sequence, showing reduced microbial contamination. Downstream reductions in total suspended solids (TSS) and total dissolved solids (TDS) suggested dilution and settling. However, downstream TP of 1483.3 µg/L remained above recommended standards, indicating ongoing pollution. While DO and pH remained within the regulatory standards downstream, persistent nutrient levels highlight the need for advanced treatment and regular monitoring to protect aquatic ecosystems and ensure safe discharge.