In drylands, characterised by water scarcity and frequent meteorological droughts, knowledge of soil moisture dynamics and its drivers (evapotranspiration, soil physical properties and the timing and sequencing of precipitation events) is fundamental to understanding changes in water availability to plants and human society, especially under a nonstationary climate. Given the episodic and stochastic nature of rainfall in drylands and the limited availability of data in these regions, we sought to explore what effects the temporal resolution of precipitation data has on soil moisture and how soil moisture distributions might evolve under different scenarios of climate change. Such information is critical for anticipating the impact of a changing climate on dryland communities across the globe, especially those that depend on rainfed agriculture and groundwater wells for drinking water for humans and livestock. A major challenge to understanding soil moisture in response to climate is the availability of precipitation datasets for dryland regions across the globe. Gridded precipitation data may only be available for daily or weekly time periods, even though rainstorms in drylands often occur on much shorter time scales, but it is currently unknown how this timescale mismatch might affect our understanding of soil moisture. Numerical modelling enables retrodiction or prediction of how climate translates into dynamically evolving moisture within the soil profile. It can be used to explore how climate data at different temporal resolutions affect these soil moisture dynamics, as well as to explore the influence of shifts in rainfall characteristics (e.g., storm intensity) under potential scenarios of climate change. This study uses Hydrus 1-D, to investigate the dynamics of soil moisture over a period of decades in response to the same underlying rainfall data resolved at hourly, daily, and weekly resolutions, as well as to step changes in rainfall delivery, which is expected under a warming atmosphere. We parameterised the model using rainfall, evaporative demand, and soils data from the semi-arid Walnut Gulch Experimental Watershed (WGEW) in SE Arizona, but we present the results as a generalized study of how rainfall resolution and shifts in rainfall intensity may affect dryland soil moisture at different depths. Our results indicate that hourly or better rainfall resolution captures the dynamics of soil moisture in drylands, and that critical information on soil water content, moisture availability to vegetation, actual evapotranspiration, and deep percolation of infiltrated water is lost when soil moisture modelling is driven by rainfall data at coarser temporal resolutions (daily, weekly). We further show that modest changes in rainfall intensity dramatically shift soil water content and the overall water balance. These findings are relevant to the prediction of soil moisture for crop yield forecasts, for adaptation to climate-related risks, and for anticipating the challenges of water scarcity and food insecurity in dryland communities around the globe, where available datasets are of low spatial and temporal resolution.

Nairobi National Park (NNP), a rare urban wildlife sanctuary bordering Kenya’s capital, is experiencing accelerating habitat destruction due to urban expansion, infrastructure development, and shifting land tenure. This study applies a two-decade satellite-based spatial analysis (2005–2025) to assess vegetation disturbance and ecological thresholds across the park. Using monthly MODIS NDVI data and the BFAST framework, the present study detected abrupt structural changes in vegetation dynamics that traditional linear based trend-based vegetation indices analysis methods failed to capture. The study further compares inflection points of commonly used vegetation indices such as kNDVI, NIRV, and LAI with breakpoint markers to show time lag before change signals are recorded when the indices are used alone. The seasonal-trend model used the split sample technique where part of the data was used for training, with break detection applied to the other end of the timeseries data. Results show that nearly one-third of the park’s vegetation pixels (about 30%) experienced sudden changes in condition between 2005 and 2025. The year 2020 marked the most active period, with 201 pixels showing abrupt shifts and the highest rate of vegetation greening at 76.1%. In contrast, 2018 and 2023 recorded the most intense vegetation decline, with browning rates of 92.7% and 97.2%, respectively. These fluctuations reflect alternating cycles of ecological stress and recovery, with annual change intensity ranging from −0.107 in 2014 (severe decline) to +0.047 in 2016 (moderate recovery). The BFAST method consistently detected short-term vegetation shocks that were missed by standard statistical tools like Mann-Kendall tests and linear regression. In many cases, BFAST identified structural breakpoints up to 3 weeks before visible changes appeared in conventional vegetation indices. This early detection capability highlights BFAST’s value as a diagnostic tool for monitoring rapid ecological shifts and informing early action conservation responses.

The rapid urbanization and population growth in Kenya highlight the need to revive the National Addressing System (NAS). This study aims to examine address systems across the globe, focusing on countries known for their efficient addressing infrastructures, such as the USA, UK, Japan, Switzerland, and Germany, and compare them to the currently used system in Kenya. Additionally, we critique the format proposed by NAS and use this knowledge to build conceptual models of a responsive NAS. Beyond conducting a simple survey of best practices, our objective is to extract the underlying principles and intricacies that lead to the success of these models. Drawing on this extensive global knowledge, our research aims to propose a detailed conceptual framework specifically designed to address the unique attributes and challenges that characterize Kenya’s dynamic addressing landscape. By combining global insights with local needs, this paper advocates for a NAS that goes beyond traditional boundaries, aligning with international standards while promoting adaptability and resilience in response to the ever-changing urban fabric of Kenya.

Sister city relationships promise to serve as strategic mechanisms for fostering international cooperation and urban development, providing cities with avenues for economic growth, cultural enrichment, and global connectivity. This study examines the impact of sister city partnerships on Eldoret, a newly designated city in Kenya, assessing how these collaborations contribute to its modernization and global positioning while preserving its unique strengths. We analyze key thematic domains to evaluate the role of international networks in shaping Eldoret’s urban trajectory. Eldoret’s sister city agreements with Bad Vilbel, Indianapolis, Minneapolis, and Ithaca have enabled dynamic exchanges that reinforce the city’s strong athletic identity, agricultural significance, and manufacturing sector. Through comparative analysis, we investigate how these partnerships have supported cultural diplomacy, economic expansion, and institutional development in both Eldoret and its counterparts. Using GDP trends as an empirical marker, our findings indicate that post-partnership years account for approximately 37% of the variation in Eldoret’s GDP (R² = 0.37), with a positive slope estimate of USD 1.64 billion. We posit that integrating economic data with insights from established partnership models provides a robust framework for understanding how sister city connections influence urban development. By examining Eldoret’s unique positioning and engagement strategies, this study contributes to a broader discussion on the role of international collaborations in shaping the modernization pathways of new African cities.

Soil moisture is an important hydrological state variable governing water balance partitioning, agricultural productivity, and ecosystem functioning. Despite its importance, particularly in the arid and semi‑arid lands (ASALs), no comprehensive synthesis has assessed how soil moisture is observed, modeled, and applied across Kenya’s diverse hydro climatic regions, with approximately 89% of its land being ASAL. This systematic review evaluates the state of soil moisture science in Kenya by analyzing peer‑reviewed literature published between 2000 and 2026. Following the PRISMA guidelines, we screened 847 records from Web of Science, Scopus, and Google Scholar, with 63 studies meeting the inclusion criteria. Results show research activity on soil moisture has increased markedly since 2015, dominated by remote sensing approaches (54%), followed by in‑situ measurements (29%) and modeling studies (17%). Spatially, studies are concentrated in the central highlands of Kenya and the Mount Kenya region (41%), while ASAL regions, where soil moisture plays a critical role in drought risk and water security, remain underrepresented (18%). Thematically, agricultural applications predominate (52%). despite their prevalence, relatively few agricultural studies demonstrated how soil moisture information is integrated into operational decision-making or advisory systems, indicating a gap between research outputs and practical implementation, with fewer studies addressing drought monitoring (21%), ecosystem dynamics (15%), or hydrological processes in urban systems (4%). Key gaps include limited ground‑truth networks, weak integration of soil moisture into operational early warning systems, sparse coverage of pastoral and urban landscapes, and minimal attention to subsurface and vadose‑zone dynamics. We propose a research agenda emphasizing expansion of in‑situ monitoring networks, development of Kenya‑specific soil moisture products, improved coupling with climate services, and greater focus on water‑limited systems. This review provides the first national‑scale synthesis of soil moisture science in Kenya and establishes a foundation for advancing hydrological research and applications in the region.

In drylands, characterised by water scarcity and frequent meteorological droughts, knowledge of soil moisture dynamics and its drivers (evapotranspiration, soil physical properties and the timing and sequencing of precipitation events) is fundamental to understanding changes in water availability to plants and human society, especially under a nonstationary climate. Given the episodic and stochastic nature of rainfall in drylands and the limited availability of data in these regions, we sought to explore what effects the temporal resolution of precipitation data has on soil moisture and how soil moisture distributions might evolve under different scenarios of climate change. Such information is critical for anticipating the impact of a changing climate on dryland communities across the globe, especially those that depend on rainfed agriculture and groundwater wells for drinking water for humans and livestock. A major challenge to understanding soil moisture in response to climate is the availability of precipitation datasets for dryland regions across the globe. Gridded precipitation data may only be available for daily or weekly time periods, even though rainstorms in drylands often occur on much shorter time scales, but it is currently unknown how this timescale mismatch might affect our understanding of soil moisture. Numerical modelling enables retrodiction or prediction of how climate translates into dynamically evolving moisture within the soil profile. It can be used to explore how climate data at different temporal resolutions affect these soil moisture dynamics, as well as to explore the influence of shifts in rainfall characteristics (e.g., storm intensity) under potential scenarios of climate change. This study uses Hydrus 1-D, to investigate the dynamics of soil moisture over a period of decades in response to the same underlying rainfall data resolved at hourly, daily, and weekly resolutions, as well as to step changes in rainfall delivery, which is expected under a warming atmosphere. We parameterised the model using rainfall, evaporative demand, and soils data from the semi-arid Walnut Gulch Experimental Watershed (WGEW) in SE Arizona, but we present the results as a generalized study of how rainfall resolution and shifts in rainfall intensity may affect dryland soil moisture at different depths. Our results indicate that hourly or better rainfall resolution captures the dynamics of soil moisture in drylands, and that critical information on soil water content, moisture availability to vegetation, actual evapotranspiration, and deep percolation of infiltrated water is lost when soil moisture modelling is driven by rainfall data at coarser temporal resolutions (daily, weekly). We further show that modest changes in rainfall intensity dramatically shift soil water content and the overall water balance. These findings are relevant to the prediction of soil moisture for crop yield forecasts, for adaptation to climate-related risks, and for anticipating the challenges of water scarcity and food insecurity in dryland communities around the globe, where available datasets are of low spatial and temporal resolution.