Resilience through Integrated Early Warning Systems in the Karamoja Region of Kenya and Uganda

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Drawing on 10 key informant interviews with institutional actors and five focus group discussions with indigenous community members, we analyzed community perceptions. While Kenya’s EWS is more technologically embedded and institutionally coordinated, leveraging SMS alerts, satellite data, and county-level governance, Uganda’s system remains more deeply rooted in culturally transmitted, community-led forecasting methods, though elements of formal science are also present. Despite their divergent approaches, both systems face common barriers: limited community feedback, language and literacy gaps, and insufficient recognition of indigenous indicators. Our findings support the development of a hybrid EWS model that combines scientific precision with cultural legitimacy through co-production between meteorological institutions and local knowledge holders. We recommend embedding IKS into national DRM policies, expanding multi-modal dissemination channels, and institutionalizing community feedback mechanisms. By aligning scientific and traditional knowledge systems and strengthening regional coordination, this integrated approach can build trust, improve forecast usability, and promote anticipatory action in vulnerable pastoralist communities. The study contributes to the broader discourse on decolonizing climate governance and emphasizes the importance of inclusive, localized solutions to environmental risk in Sub-Saharan Africa. Early Warning Systems (EWS) Indigenous Knowledge Systems (IKS) Climate Resilience Disaster Risk Management (DRM) Karamoja Cluster Community-Based Adaptation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 1. Introduction The Karamoja Cluster, straddling northeastern Uganda and northwestern Kenya, is one of the most disaster-prone and food-insecure regions in sub-Saharan Africa. Communities such as the Pokot in Kenya and the Karamojong in Uganda face overlapping vulnerabilities. These include climate extremes such as prolonged droughts, erratic rainfall, and flash floods, as well as socio-political stressors, including resource-based conflicts, livestock raids, locust infestations, and chronic underinvestment in infrastructure and public services (Pariyar, 2019 ; Recha et al., 2013 ). These converging stressors result in cyclical humanitarian emergencies, erosion of livelihoods, and the weakening of local coping capacities. Despite these challenges, local communities have developed intricate systems of knowledge and practice for navigating uncertainty, particularly through Indigenous Knowledge Systems (IKS). Conventional early warning systems (EWS), intended to predict and communicate risk, are often modeled on technical and institutional architectures that emphasize scientific forecasting, satellite remote sensing, and top-down dissemination strategies. While these approaches provide valuable spatial and temporal insights, their practical impact at the community level has often been limited due to access and trust issues. In the Karamoja Cluster, the uptake and usefulness of conventional EWS are constrained by a range of barriers: low literacy, limited internet or radio access, language mismatches, and, critically, a lack of cultural resonance and trust in formal warning mechanisms. These shortcomings highlight a disconnect between how risk is forecasted institutionally and how it is perceived, interpreted, and acted upon locally. By contrast, Indigenous Knowledge Systems (IKS) in both West Pokot and Karamoja represent context-specific, time-tested approaches to climate forecasting and adaptation. Rooted in generations of ecological observation, spiritual traditions, and oral transmission, IKS encompasses a range of indicators. These include changes in animal behavior, plant phenology, cloud formations, and celestial signs, as well as community-based rituals like rainmaking ceremonies and storytelling archives that document past disasters (Gumo, 2017 ; Haokip, 2023 ). These knowledge systems are not static or mythical remnants of the past; rather, they are dynamic, continually updated through lived experience, and often serve as the first and most trusted line of response for pastoralist communities facing climatic uncertainties. Despite their widespread use and credibility at the grassroots level, IKS remains largely excluded from formal disaster risk management (DRM) and EWS frameworks in both Kenya and Uganda. This exclusion is deeply entrenched in historical legacies of colonial and post-colonial governance, where dual authority systems positioned customary institutions as subordinate to centralized bureaucracies (Mamdani, 1996 ; Ntsebeza, 2005 ). Today, these fragmented governance structures continue to impede the institutional recognition of IKS and limit opportunities for knowledge co-production, especially in transboundary regions like Karamoja where coordination across national borders is already weak. Even within Kenya’s relatively decentralized governance model, which offers more latitude for county-level innovation and community engagement, the integration of IKS into formal EWS is constrained by weak institutional linkages, limited funding, and a lack of frameworks for validating and scaling local knowledge within official protocols. In Uganda, where the disaster risk management framework is more centralized, community engagement in forecasting is often informal and lacks formal channels for interface with national systems. This dichotomy reveals both challenges and opportunities: while Kenya offers a platform for localization and integration, Uganda’s centralized model can potentially standardize and scale best practices, but only if both systems are reformed through inclusive and participatory policy design that prioritizes local voices and equitable knowledge systems. Against this backdrop, there is growing momentum, both regionally and globally, toward developing hybrid EWS that synthesize the strengths of both scientific forecasting and indigenous knowledge. Studies from East Africa show that such integrated systems, combining satellite-derived indices like NDVI, TCI, and CHIRPS with phenological and social signals observed by communities, lead to more accurate drought predictions, earlier community action, and improved trust in warnings (Opiyo et al., 2015 ; Gebreyesus & Bauer, 2021 ; Zamani et al., 2023 ). However, technical integration alone is not sufficient. The legitimacy, usability, and sustainability of EWS hinge on active community participation, particularly in the design, interpretation, and dissemination of early warnings. Globally, institutions such as the IFRC, UNDRR, and IPCC have increasingly emphasized the role of Indigenous and Local Knowledge (ILK) in enhancing anticipatory action, equity, and sustainability in disaster risk governance (IPCC, 2022 ; IFRC, 2022 ). From the Arctic to the Sahel, hybrid approaches that respect and elevate IKS have been shown to improve warning accuracy, increase community engagement, and foster climate justice. Yet, operationalizing such integration requires not only methodological innovation but also political will and structural reforms, particularly in settings marked by governance fragmentation and historical marginalization. This study aims to contribute to this global and regional discourse by co-developing inclusive, context-sensitive early warning strategies with the Pokot and Karamojong communities of the Karamoja Cluster. Using participatory methods, such as focus group discussions, scenario planning, and GIS-based vulnerability mapping, the research integrates Indigenous Knowledge with scientific forecasting tools to create a hybrid EWS that is both technically robust and socially legitimate. The ultimate objective is to strengthen community agency in the design, implementation, and institutional uptake of early warning mechanisms, thereby enhancing both predictive accuracy and the cultural legitimacy of disaster response across this cross-border region. By advancing a model that foregrounds local agency and bridges Indigenous and scientific systems, this research responds to calls for climate justice, decolonial governance, and equitable disaster risk management. It proposes a shift away from externally driven crisis response toward community-led resilience building, ensuring that those most at risk from climate shocks are also those most empowered to act. 2. Study Area This study focuses on two transboundary pastoralist communities located within the broader Karamoja Cluster: the Pokot community, residing in both West Pokot and East Pokot counties in northwestern Kenya, and the Karamojong community in the Karamoja Sub-Region of northeastern Uganda. As illustrated in Fig. 1 , the Pokot and Karamojong communities occupy contiguous territories along the Kenya–Uganda border, characterized by strong socio-cultural linkages and frequent cross-border interactions. The region is characterized by semi-arid conditions, complex topography, and a shared exposure to climate extremes and disaster risks, making it a critical area for examining transboundary pastoral dynamics and resilience strategies. 2.1. Geographical Context West Pokot County, located in northwestern Kenya, borders Uganda to the west, Turkana County to the north, Elgeyo-Marakwet to the east, and Trans Nzoia to the south. The county spans approximately 9,169.4 square kilometers and is divided into four main sub-counties: Pokot North (Kacheliba), Pokot South (Chepareria), Pokot Central (Sigor), and Pokot West (Kapenguria). The terrain is diverse, ranging from lowland semi-arid areas to highland zones with fertile volcanic soils. Karamoja, on the other hand, is located in northeastern Uganda and comprises seven districts: Kotido, Kaabong, Abim, Moroto, Napak, Nakapiripirit, and Amudat. The Karamojong predominantly inhabit the central and southern parts of this region, which features expansive savannah plains, rugged hills, and intermittent watercourses across an estimated 27,000 square kilometers. The region lies within Uganda’s cattle corridor and is known for its arid to semi-arid climate. 2.2. Socio-Economic Dynamics Both communities depend heavily on pastoralism and agro-pastoralism as their primary economic activities. The Pokot maintain a mixed economy, engaging in livestock rearing (especially cattle, goats, and sheep) and subsistence farming. In highland zones, crop cultivation, particularly maize, beans, and vegetables, supplements household income. However, limited market access, weak infrastructure, and seasonal mobility hinder economic diversification. Similarly, the Karamojong depend primarily on cattle herding for both livelihood and social identity. Although sedentary farming has increased—especially among women and youth—food insecurity remains widespread. According to FAO ( 2022 ), over 60% of households in Karamoja experience chronic hunger due to poor harvests, droughts, and insecurity. Both communities face high poverty levels, low literacy rates, and significant gender disparities in access to land and credit. Women, in particular, shoulder disproportionate climate stresses, often with limited decision-making power or adaptive options (Muhereza et al., 2021 ). 2.3. Climate and Meteorological Patterns The Pokot and Karamojong regions fall within the Arid and Semi-Arid Lands (ASALs) of East Africa, characterized by bimodal rainfall patterns, erratic precipitation, and prolonged dry seasons. In West Pokot, annual rainfall ranges between 400 mm in lowland areas and up to 1,500 mm in the highlands. Temperatures range from 15°C to 32°C, with the hottest periods occurring between January and March. In Karamoja, average rainfall is approximately 500–800 mm per year, falling primarily between April and October. However, climate variability has increased, resulting in frequent droughts, flash floods, and prolonged dry spells that disrupt pastoral mobility and agricultural planning (ICPAC, 2022). Rising temperatures and erratic rainfall have also intensified environmental degradation, reduced pasture availability, and contributed to resource-based conflicts. 2.4. Early Warning Systems (EWS) Both communities operate parallel EWS structures, a combination of indigenous knowledge systems (IKS) and formal scientific mechanisms, though integration between the two remains limited. 2.4.1. Indigenous (Traditional) Early Warning Indicators (system) in Pokot and Karamoja Communities Indigenous communities across the globe possess intricate systems of early warning indicators that have evolved through centuries of lived experience and environmental interaction. These Indigenous Knowledge Systems (IKS) are rooted in cultural traditions, ecological observation, and collective memory, and have historically played a vital role in enabling communities to anticipate and respond to environmental hazards, particularly droughts and climate-related shocks (Sillitoe, 1998 ; Nakashima et al., 2000 ; Mutasa, 2015 ). IKS encompasses a wide array of practices, including agriculture, water management, food security, and healthcare, and remains foundational to the survival and resilience of many rural societies. In sub-Saharan Africa, including the Pokot of Kenya and the Karamojong of Uganda, these knowledge systems continue to serve as critical tools for disaster preparedness, albeit increasingly marginalized by the dominant emphasis on Western scientific paradigms. The rise of satellite-based meteorological forecasting, technological innovation, and formal disaster risk management institutions has often sidelined indigenous practices, leading to the erosion of their usage and intergenerational transmission (Leal Filho et al., 2022 ; Sitas et al., 2016 ). Yet, the urgency of climate change and the limited reach of formal Early Warning Systems (EWS) in remote pastoralist regions underscore the need for a more integrative approach that values and incorporates indigenous perspectives. Both the Pokot and Karamojong communities possess rich IKS frameworks for environmental forecasting, relying on a mix of meteorological, biological, astrological, and supernatural indicators. These locally derived signals help guide seasonal decision-making, inform agricultural cycles, and prompt early action to mitigate climate-induced risks. Meteorological indicators (M.I) are widely used. Among the Pokot, wind direction, particularly from west to east, is seen as a harbinger of rain, as is the formation of dark rain clouds. In Karamoja, lightning in specific locations is interpreted as the onset of rainfall, while strong winds during the dry season foretell prolonged drought. Morning dew accompanied by mist and unusually high nighttime temperatures are also seen as precursors of rain across both communities (Haokip, 2023 ). Biological indicators (B.I) offer another layer of predictive insight. Animal behavior, such as increased restlessness in male goats, playful calves, or cows urinating while lying down, is interpreted as a signal of approaching rain. In the Pokot community, specific plant phenological changes, including the shedding of leaves or premature flowering, also signify seasonal transitions. Additionally, Pokot experts engage in goat intestine reading, a specialized practice through which the community interprets intestinal patterns to predict droughts, disease outbreaks, or intercommunal conflict (Gumo, 2017 ). Astrological observations (A.I), particularly of the sun, moon, and stars, are traditionally used to infer the timing of seasonal changes. While this knowledge is widely shared, its application may vary between clans and is often the domain of elders or spiritual custodians. Supernatural indicators (S.I) play a prominent role, particularly in decision-making processes that affect community-wide behavior. Seers interpret dreams as omens of events to come, and in Pokot culture, shoe reading, deciphering the alignment of traditional sandals, is employed to forecast blessings or misfortunes. These indicators are not interpreted in isolation. Instead, they undergo community-based deliberation and validation through a stepwise process, as illustrated in Fig. 2 , which maps the indigenous forecasting cycle from environmental observation to collective decision-making and adaptive response. Despite their proven relevance, these systems remain underrepresented in formal policy frameworks and disaster risk reduction programs. Recognizing and integrating IKS with scientific tools not only enhances predictive accuracy but also improves cultural legitimacy, community ownership, and the sustainability of disaster response systems (Kelman et al., 2012 ; Ford et al., 2016 ). 2.4.2. Modern Early Warning Systems Formal Early Warning Systems (EWS) in both West Pokot (Kenya) and Karamoja (Uganda) are central components of their respective national Disaster Risk Reduction (DRR) frameworks, and are primarily supported by national meteorological agencies, namely, the Kenya Meteorological Department (KMD) and the Uganda National Meteorological Authority (UNMA). These agencies are mandated to collect, analyze, and disseminate weather and climate-related data, and serve as the technical backbones of their national EWS. Regionally, these efforts are bolstered by the IGAD Climate Prediction and Applications Centre (ICPAC), which provides cross-border climate forecasting, early warning bulletins, and capacity support through tools such as the Greater Horn of Africa Climate Outlook Forum (GHACOF). The Kenyan EWS functions within the National Disaster Risk Management Policy (2018) and is coordinated by the National Disaster Operations Centre (NDOC) under the Ministry of Interior and Coordination of National Government. KMD provides seasonal and short-term forecasts, disseminating them through county governments, radio stations, SMS alerts, and extension officers, who act as key intermediaries between national agencies and local communities, particularly in arid and semi-arid lands (ASALs) like West Pokot. In Uganda, the EWS is embedded within the National Policy for Disaster Preparedness and Management (2010) and is overseen by the Office of the Prime Minister’s Department of Disaster Preparedness and Refugees (DDPR). UNMA leads climate and weather forecasting efforts, working in tandem with district-level Disaster Management Committees (DMCs), local government units, NGOs, and radio broadcasters to ensure timely dissemination of alerts and advisories across Karamoja. The Uganda Red Cross Society and community-based organizations also play a supporting role in last-mile communication, preparedness activities, and response coordination. The technological backbone of these systems includes the use of satellite-based data (e.g., MODIS, Landsat) and drought-monitoring tools such as the Vegetation Health Index (VHI), Standardized Precipitation Index (SPI), and the Normalized Difference Vegetation Index (NDVI). These indices help identify anomalies in vegetation health, rainfall distribution, and surface temperature, enabling more accurate forecasting of droughts, floods, and other climate-related hazards. Forecasts and alerts are communicated through multiple platforms: seasonal bulletins, community radio, mobile SMS platforms (e.g., Kenya’s Weather SMS service), and face-to-face engagement by agricultural extension workers, who are often trusted members of the community. In West Pokot, county disaster management teams further contextualize this information to local conditions, while in Karamoja, traditional leaders and community-based volunteers assist in translation and dissemination (Fig. 3 ). However, despite the scientific sophistication of these systems, they face critical limitations that reduce their overall impact. These include limited coverage in remote and pastoralist regions, language and literacy barriers, and the exclusion of Indigenous Knowledge Systems (IKS). The disconnect between top-down scientific forecasting and local socio-cultural realities often erodes community trust and diminishes the uptake of early warning information. This misalignment between formal systems and grassroots needs highlights the importance of integrating indigenous and community-based mechanisms into official DRR strategies (Titz et al., 2018 ; Leal Filho et al., 2022 ; ICPAC, 2021). 3. Methodology 3.1. Data collection Field data were collected from key stakeholders involved in both indigenous and modern Early Warning Systems (EWS) across West Pokot (Kenya) and Karamoja (Uganda). The study aimed to document and compare standard operating procedures, practices, limitations, and perceived impacts of both EWS types. To achieve this, the study consulted the following stakeholders: Traditional knowledge holders (e.g., elders, seers, pastoralist leaders), National meteorological authorities and technical experts (e.g., Kenya Meteorological Department – KMD; Uganda National Meteorological Authority – UNMA; National Drought Management Authority – NDMA), Local disaster risk management committees, NGOs and CBOs engaged in resilience and early warning dissemination, Agricultural and climate extension agents. A qualitative approach was adopted, employing a combination of semi-structured interviews, focus group discussions (FGDs), and direct field observations. Each of the ten semi-structured interviews lasted approximately 30 minutes, allowing for rich, open-ended responses while maintaining focused dialogue. Separate interview guides were developed for two main stakeholder categories, as shown in Table 1 below: Table 1 Interview Focus Area and Stakeholder category Interview Focus Area Stakeholder Category Indigenous Stakeholders Modern/Institutional Stakeholders 1 Local indicators used to predict environmental changes (e.g., animal behavior, celestial signs, rituals) Structure and protocols of formal EWS operations (SOPs) 2 Methods of warning dissemination and community mobilization Tools and platforms used (e.g., satellite data, radio, SMS) 3 Traditional decision-making structures and response mechanisms Dissemination mechanisms and reach to vulnerable communities 4 Challenges to maintaining and transmitting Indigenous Knowledge Systems (IKS) Collaboration or integration with local knowledge systems 5 Interaction with modern forecasting sources Barriers to effectiveness, including last-mile delivery and trust issues The interview tools were designed to ensure thematic alignment across groups while tailoring the language and focus on each stakeholder’s domain of expertise. Responses were audio-recorded (with consent), transcribed, and coded for thematic analysis, enabling triangulation between modern and traditional systems. Tables 2 and 3 below list key informants targeted for interviews. The interviews in Table 2 targeted institutional members associated in the modern early warning system in Kenya. Meanwhile, Table 3 includes Indigenous people with knowledge of the early warning systems in both Kenya and Uganda. Table 2 Key Informants from Formal Institutions Involved in Modern Early Warning Systems KI Institutions Country 1 Disaster Risk Reduction (DRR) Kenya 2 Disaster Management Kenya 3 National Drought Management Authority (NDMA) Kenya 4 Kenya Red Cross Society (KRCS) Kenya 5 Meteorological Services Kenya Table 3 Key Informants from Indigenous Communities with Knowledge of Traditional Early Warning Systems KI Indigenous communities Country 1 FGD Indigenous 1 West Pokot, Kenya 2 FGD Indigenous 2 Kanyerus, West Pokot, Kenya 3 FGD Indigenous 3 Amudat, Uganda 4 FGD Indigenous 4 Muino-location, West Pokot, Kenya 5 FGD Indigenous 5 Amudat, Uganda A comprehensive desk review of policy documents and operational guidelines was also conducted to map institutional arrangements and governance structures relevant to Early Warning Systems (EWS) and disaster risk reduction (DRR) in both Kenya and Uganda. This included national-level frameworks such as Kenya’s National Disaster Risk Management Policy (2018), Uganda’s National Policy for Disaster Preparedness and Management (2010), as well as strategic documents from meteorological and disaster agencies, including Standard Operating Procedures (SOPs) from the Kenya Meteorological Department (KMD), the Uganda National Meteorological Authority (UNMA), and the National Drought Management Authority (NDMA) in Kenya. At the regional level, documents produced by the IGAD Climate Prediction and Applications Centre (ICPAC), such as the Greater Horn of Africa Climate Outlook Forum (GHACOF) communiqués, protocols on regional EWS coordination, and cross-border resilience strategies, were also examined to understand how transboundary risks are managed and how indigenous systems are (or are not) incorporated into formal DRR governance structures. 3.2. Data Analysis The data analysis process adopted a multi-tiered qualitative strategy designed to systematically explore, compare, and interpret the operations and effectiveness of both Indigenous and modern Early Warning Systems (EWS) across West Pokot (Kenya) and Karamoja (Uganda). A combination of thematic content analysis, comparative coding, and policy mapping was employed to ensure analytical rigor and to identify both convergences and divergences in early warning practices across stakeholder groups. Step 1: Transcription and Data Organization All semi-structured interviews and focus group discussions (FGDs) were audio-recorded with participant consent and subsequently transcribed verbatim. Field notes from direct observations and participatory engagement activities were also digitized and integrated into the data corpus. Transcripts were uploaded into NVivo 12, enabling structured thematic coding and retrieval of patterns. Step 2: Thematic Coding A deductive-inductive approach to thematic analysis was adopted. A predefined coding framework was developed based on the interview guide (e.g., prediction indicators, dissemination practices, operational protocols, perceived barriers, integration potential), and additional emergent themes were identified during the coding process. Thematic codes were grouped into five core domains, as shown in Table 4 below: Table 4 Thematic Coding Framework: Domains and Associated Codes for Early Warning Systems Analysis Theme Potential Codes Indicator systems Wind direction, animal behavior, cloud patterns, dew/mist, goat intestine reading, satellite data use Dissemination and communication pathways Community gatherings, oral messages, SMS alerts, radio announcements, use of extension agents Institutional coordination and governance Role of KMD/UNMA, involvement of DRM committees, cross-border coordination, SOP clarity Perceived trust, accuracy, and relevance Trust in elders vs. meteorological officers, perceived accuracy of traditional signs vs. forecasts Barriers to effectiveness and integration potential Language barriers, low literacy, exclusion of indigenous knowledge, lack of feedback mechanisms Step 3: Comparative Analysis The coded data were systematically compared across the two stakeholder categories: Indigenous and Modern/Institutional actors. A cross-case matrix was developed to explore similarities, contradictions, and areas of potential synergy between systems. This comparison facilitated an understanding of how early warning signals are interpreted, operationalized, and received at different levels of the EWS ecosystem. Step 4: Policy and Institutional Mapping Using content analysis, national and regional DRR policy documents (e.g., Kenya’s National Disaster Risk Management Policy, Uganda’s Disaster Preparedness Policy, and ICPAC protocols) were analyzed to identify formal mandates, coordination hierarchies, and the extent to which community-based or Indigenous knowledge is acknowledged and integrated. This mapping helped contextualize field-level practices within broader institutional architectures. 4. Result 4.1. Synthesizing Stakeholder Perspectives on Early Warning Systems in Kenya and Uganda Key informant interviews in both Kenya and Uganda revealed that indigenous forecasting methods such as animal behavior observation, cloud formations, dew patterns, and goat intestine reading remain deeply embedded in local agricultural and disaster preparedness practices. These techniques are regarded as time-tested, intuitive, and specific to the local environment. A pastoral elder in Kenya noted, “When the birds change direction and the clouds sit low in the morning, we know rain is coming.” Likewise, a Ugandan farmer emphasized, “We still look to the sky and the movement of ants before planting, it has never failed us.” These narratives reflect a strong emotional and cultural attachment to traditional ecological knowledge, which continues to serve as a trusted source of environmental interpretation. However, these practices are not without criticism. Younger respondents and technical experts voiced concern about the lack of standardization and reproducibility of such indicators. For instance, interpretations can vary between communities, and there are no mechanisms for systematic validation. This echoes the findings of Roncoli et al. ( 2002 ), who observed that while traditional forecasts foster local ownership, their integration into scientific frameworks is often hindered by epistemological gaps. Formal early warning systems (EWS), provided by agencies such as the Kenya Meteorological Department (KMD) and Uganda National Meteorological Authority (UNMA), are becoming more visible. They are disseminated through SMS, radio, and extension officers. However, community responses reveal varied levels of trust and reliance. As shown in Figure 4 , pastoralists followed by farmers report greater reliance on formal institutions such as KMD, UNMA, and local government, in contrast to elders who continue to trust traditional forecasting. This suggests a gradual shift from traditional to institutional sources of climate information, though the integration between the two remains limited. Community perceptions underscore this tension. In Kenya, one participant observed, “ We get messages on the phone, but sometimes they come late or are too general for our village. ” In Uganda, concerns focused more on access and comprehension: “ Not all people have phones, and some don’t understand the language used in warnings ,” a respondent noted. These reflections point to a disconnect between technological systems and local realities. Although formal systems offer scientific rigor, particularly through satellite and meteorological modeling, their effectiveness is often constrained by issues of localization, timing, and accessibility (Fig. 5 ). This mirrors findings by Tall et al. ( 2014 ), who argue that EWS uptake across Sub-Saharan Africa is frequently hindered by top-down communication and insufficient contextualization. In light of these challenges, stakeholders increasingly call for a hybrid approach that combines traditional and scientific forecasting. As one disaster risk management (DRM) officer emphasized, “ If we can combine the wisdom of the elders with satellite maps, we can prepare better. ” This perspective is echoed by Orlove et al. ( 2010 ), who highlight that co-production of knowledge systems not only improves predictive accuracy but also builds trust, enhances legitimacy, and increases community uptake of early warning messages. Integrating Indigenous Knowledge Systems (IKS) with formal forecasting thus emerges as a critical step toward building inclusive and actionable early warning mechanisms. 4.2. Thematic Coding outcomes This section presents the outcomes of thematic coding based on key informant interviews (KIIs) and focus group discussions conducted in Kenya and Uganda. A comparative radar chart (Fig. 6 ) visualizes the frequency and intensity of five core domains within Early Warning Systems (EWS): Indicator Systems, Dissemination & Communication, Institutional Coordination, Perceived Trust & Relevance, and Barriers to Integration. Figure 6 highlights notable national contrasts: Kenya registers higher emphasis on Institutional Coordination and Dissemination, suggesting a more structured and technologically embedded EWS landscape. Uganda, in contrast, demonstrates stronger emphasis on Indicator Systems and Trust, likely reflecting its continued reliance on indigenous knowledge systems and community-based practices. These findings help surface both systemic strengths and opportunities for capacity-building in each country. Moving into the specific domains, Fig. 7 presents a comparison of indicator systems used by communities. In Kenya, traditional indicators such as animal behavior and cloud patterns are frequently referenced, reflecting a reliance on local ecological knowledge. Uganda’s respondents highlighted greater trust in satellite data, suggesting more formal integration of scientific forecasting tools. This divergence may be influenced by differing institutional outreach and historical exposure to modern meteorological systems. Figure 8 explores the dissemination and communication pathways employed. Kenyan respondents reported widespread use of SMS alerts and agricultural extension agents, pointing to a semi-digitized dissemination network. In Uganda, community gatherings and radio broadcasts remain dominant, underlining the continued importance of interpersonal and oral communication. These differences signal the need for country-specific strategies to enhance EWS reach and usability. Figure 9 assesses Institutional Coordination and Governance. Kenya outperformed in areas such as the operational role of the Kenya Meteorological Department (KMD) and the activity of Disaster Risk Management (DRM) committees, suggesting relatively clearer standard operating procedures (SOPs) and stronger institutional presence. Uganda, while showing engagement at the local level, lacked coordinated mechanisms across ministries and borders, highlighting the potential for structural reform and investment. Figure 10 reflects respondents' perceptions of trust, accuracy, and relevance of EWS. Ugandan communities displayed higher trust in elders and traditional signs, whereas Kenyan stakeholders favored the accuracy of forecasts issued by meteorological officers. These epistemological differences are key to understanding the varying degrees of EWS adoption, and underscore the importance of integrating traditional knowledge with scientific systems to build community confidence. Figure 11 delves into barriers to EWS effectiveness and integration. Both countries face common challenges such as low literacy and language barriers, which hinder message comprehension and action. Kenya flagged the exclusion of indigenous knowledge as a key issue, suggesting tension between local practices and formal systems. Uganda, on the other hand, cited lack of feedback mechanisms as a primary barrier, pointing to limited opportunities for two-way communication and participatory feedback loops. This comparative analysis illustrates not only the diversity of early warning system (EWS) approaches between Kenya and Uganda but also underscores the opportunities for mutual learning and hybrid innovation. By comparing how each country employs both indigenous and scientific forecasting tools, we can identify common strengths, gaps, and contextual nuances that shape community-level disaster risk reduction strategies. Figure 12 below summarizes the weighted scores (on a scale of 1–10) derived from Key Informant Interviews (KIIs) conducted in both countries. These scores reflect the perceived importance, usage, and effectiveness of different early warning components across five thematic domains: Indicator Systems, Dissemination & Communication, Institutional Coordination & Governance, Perceived Trust & Relevance, and Barriers to Integration. These comparative scores show that Kenya leans more heavily on formal structures, SMS technologies, and scientific data integration (e.g., satellite use), while Uganda demonstrates deeper cultural attachment to traditional signs, oral communication, and inclusive governance via community gatherings. Both countries face shared barriers such as language and feedback gaps, but in differing degrees. 4.3. Integrating EWS for Community Resilience To enhance climate resilience and disaster preparedness in vulnerable regions like West Pokot (Kenya) and Karamoja (Uganda), there is a pressing need to develop hybrid Early Warning Systems (EWS) that blend scientific forecasting with Indigenous Knowledge Systems (IKS). These integrated approaches offer the potential to bridge the trust gap between formal institutions and communities, improving the accuracy, cultural legitimacy, and community uptake of warnings. This integration must go beyond superficial recognition of traditional practices. It requires the co-production of knowledge through active collaboration between local elders, youth representatives, traditional seers, and technical institutions like the Kenya Meteorological Department (KMD) and Uganda National Meteorological Authority (UNMA). Such partnerships ensure that early warnings are both technically robust and socially anchored. Drawing from both the weighted scores in Table 3 and qualitative insights from Key Informant Interviews (KIIs), this section compares Kenya’s and Uganda’s five-stage EWS frameworks (see Fig. 13 and Fig. 14 ), highlighting how each country combines indigenous and scientific systems to strengthen resilience. 4.3.1. Step 1: Observation Phase Dissemination methods vary notably. Kenya favors digital tools, SMS alerts and extension workers (both scoring 9.4), alongside community meetings and radio. This reflects stronger infrastructure and higher digital penetration. Uganda, in contrast, emphasizes oral messages (score: 8), radio announcements (8), and community gatherings (7.1), with moderate use of SMS alerts (score: 6.9). While both rely on national meteorological agencies, Uganda’s approach is more accessible in digitally underserved areas. Kenya’s system is more formalized; Uganda’s is more culturally embedded. 4.3.2. Step 2: Interpretation and Validation Both countries act upon warnings, but the drivers differ. In Kenya, actions such as food stockpiling, agricultural adjustments, and livestock movement are data-informed and institutionally guided. Uganda mirrors these actions but grounds them more in collective experience and tradition, especially in rural zones. Kenya’s model favors structured government alignment, while Uganda’s decisions stem from consensus and local adaptation strategies. 4.3.3. Step 3: Knowledge Dissemination The final stage reflects how systems learn and evolve. Kenya operates through formal DRM structures that incorporate feedback to refine forecasts and dissemination. Community insights contribute to improved messaging and timeliness. In Uganda, adaptation is grassroots-led, relying on continued observation by local elders and informal feedback loops that gradually influence institutional practice. Kenya reflects a top-down adaptive model, whereas Uganda demonstrates a bottom-up learning structure. 4.3.4. Step 4: Decision-Making and Response Both countries convert warnings into tangible actions, but the decision-making pathways vary. In Kenya, actions such as livestock relocation, agricultural planning, and household stockpiling are closely tied to government advisories and data-informed planning. Uganda, while implementing similar actions, more often relies on communal experience and local customs to guide choices. Thus, Kenya’s responses are shaped by formal science and institutional alignment, while Uganda’s are embedded in community consensus and local adaptation strategies. Both systems are functionally effective, but rooted in different knowledge sources. 4.3.5. Step 5: Monitoring, Feedback, and Adaptation The final stage reflects how systems learn and evolve. Kenya operates through formal DRM structures that incorporate feedback to refine forecasts and dissemination. Community insights contribute to improved messaging and timeliness. In Uganda, adaptation is grassroots-led, relying on continued observation by local elders and informal feedback loops that gradually influence institutional practice. Kenya reflects a top-down adaptive model, whereas Uganda demonstrates a bottom-up learning structure. This comparative analysis reveals complementary strengths in both systems. Kenya offers scientific rigor, institutional coordination, and tech-based dissemination. Uganda contributes cultural legitimacy, cross-border inclusiveness, and deep-rooted oral traditions. Both countries face barriers, language, literacy, and integration gaps, but also possess shared practices such as co-validation, participatory engagement, and adaptive learning. A hybrid regional strategy could effectively combine Kenya’s precision and infrastructure with Uganda’s grassroots engagement and cultural embedding. Such a model would create a more inclusive, trusted, and context-specific early warning system, one that is critical for managing climate-related risks and fostering long-term resilience in Sub-Saharan Africa. 5. Discussion This study presents compelling evidence for the urgent need to reform and hybridize Early Warning Systems (EWS) in Sub-Saharan Africa through the integration of Indigenous Knowledge Systems (IKS) and scientific forecasting. Drawing on comparative fieldwork from Kenya and Uganda, the findings emphasize that while both countries have made strides in building EWS frameworks, systemic gaps remain, particularly in terms of localization, equity, and trust. Bridging these gaps requires deliberate, policy-driven efforts that elevate community voices and anchor climate risk management in culturally relevant knowledge systems (Mercer et al., 2009 ; Orlove et al., 2010 ). From a policy standpoint, Kenya exemplifies a structured and technologically advanced model, with strong institutional roles played by the Kenya Meteorological Department (KMD) and disaster risk management (DRM) committees. However, this centralization often sidelines traditional knowledge, despite its enduring relevance in guiding community preparedness (Roncoli et al., 2002 ). In Uganda, by contrast, Indigenous systems remain deeply embedded in community life and continue to shape environmental interpretation and response. Yet national institutions, such as the Uganda National Meteorological Authority (UNMA), still face limitations in integrating local insights into formal protocols and planning mechanisms (Tall et al., 2014 ). To reconcile the structural and knowledge-system differences between traditional and scientific EWS, policymakers must move beyond symbolic acknowledgments of Indigenous knowledge and commit to its meaningful integration through co-produced policies and protocols. This involves embedding traditional forecasting indicators, such as animal behavior, dew patterns, and celestial observations, within national climate data frameworks (Kalanda-Joshua et al., 2011 ; Graw & Mosello, 2021 ). Inclusive decision-making should also be mandated at subnational levels, ensuring that elders, women, youth, and local practitioners are systematically involved in interpreting risks, planning responses, and communicating alerts (UNDRR, 2022 ). Capacity-building initiatives must support both traditional knowledge holders and scientific experts to collaborate effectively in validating forecasts, disseminating information, and fostering mutual learning. Such reforms will not only enhance the technical robustness of EWS but also increase their cultural legitimacy and community acceptance (Leclerc et al., 2013 ). At the community level, this study underscores the critical role of trust in determining the uptake and effectiveness of early warnings. In many rural contexts, particularly in Uganda, community gatherings, oral messages, and the authority of elders continue to outperform digital messaging like SMS and radio alerts (Rufat et al., 2015 ). This calls for more participatory communication strategies that respect oral traditions and engage trusted local messengers. Concrete actions might include establishing hybrid EWS committees composed of both meteorological officers and traditional seers to ensure that warnings are co-produced and contextually grounded (Mercer et al., 2009 ). Designing two-way feedback systems would allow local communities to report environmental changes and verify alert accuracy, while engaging youth as knowledge bridges could support intergenerational exchange and digital inclusion (Speranza et al., 2010 ). Furthermore, Uganda’s example of cross-border coordination in Karamoja should be formalized and scaled to other regions to better manage shared ecological threats and migratory movements (Twigg, 2015 ). Ultimately, the findings advocate for a hybrid, decentralized, and culturally grounded EWS model. By institutionalizing both scientific and Indigenous knowledge, such a system can foster deeper community ownership, improve the relevance and clarity of warnings, and increase the likelihood of early action, critical for reducing disaster impacts (IFRC, 2022 ). Policy frameworks must therefore view EWS not merely as technological infrastructure but as social systems, where inclusion, trust, and shared learning are as crucial as digital innovation (Leach et al., 2018 ). In doing so, they unlock the full potential of EWS as a foundation for resilient, climate-smart development across East Africa. 6. Policy Recommendations To strengthen Early Warning Systems (EWS) and build climate resilience across Kenya, Uganda, and broader Sub-Saharan Africa, this study proposes a series of policy interventions rooted in inclusivity, community engagement, and institutional innovation. 6.1. Institutionalize the Integration of Indigenous Knowledge in National EWS Frameworks First, to institutionalize the integration of Indigenous Knowledge Systems (IKS) within national Early Warning System (EWS) frameworks, governments should establish formal protocols within meteorological agencies, such as goat intestine reading, dew and cloud observations, or dream interpretation rituals, which are already in use in parts of Kenya and Uganda, that recognize and incorporate community-based forecasting indicators. These indicators may include observations of animal behavior, dew patterns, and cloud formations, which have historically informed local risk perception and decision-making. Complementing this, training programs should be developed to equip both technical personnel and traditional elders with tools for shared interpretation and collaborative analysis. This co-learning approach would foster mutual respect, bridge knowledge divides, and strengthen the credibility of hybrid forecasts. Additionally, Indigenous knowledge holders should be formally included as stakeholders in disaster risk management (DRM) committees at the county or district level, ensuring their insights are embedded in local planning and decision-making processes. Such institutional recognition would not only validate traditional expertise but also enhance the social legitimacy and uptake of EWS messages across diverse communities. 6.2. Expand and Diversify Dissemination Channels Second, to expand and diversify dissemination channels for Early Warning Systems (EWS), it is essential to establish multi-modal communication strategies that combine SMS alerts, radio broadcasts, oral announcements, and outreach through extension workers. This blended approach ensures that warnings are accessible to marginalized groups, particularly those with limited digital access or literacy. To further enhance understanding, early warning messages should be translated into local languages and enriched with culturally resonant metaphors or symbols, making the information more relatable and actionable. Moreover, leveraging trusted community institutions, such as religious leaders, respected elders, and youth groups, as intermediaries can significantly boost message credibility and uptake. These actors often serve as informal information gatekeepers and can bridge the gap between formal systems and local populations, ensuring that vital alerts are both received and acted upon in a timely manner. 6.3. Strengthen Feedback Mechanisms and Adaptive Learning Third, effective EWS must be adaptive and responsive, supported by robust feedback mechanisms. Strengthening feedback mechanisms and adaptive learning within Early Warning Systems (EWS) requires creating channels that facilitate two-way communication between communities and technical institutions. This can be achieved by establishing platforms such as toll-free SMS lines, radio call-in shows, or community dialogue forums, where individuals can verify information, ask questions, or offer observations related to early warnings. Institutionalizing post-disaster reviews that bring together scientific experts and local residents is also crucial for assessing the timeliness, accuracy, and real-world impact of alerts. These participatory evaluations not only enhance accountability but also help identify gaps and areas for improvement. Importantly, feedback gathered from these interactions should be systematically analyzed and used to refine alert thresholds, language, and risk categories, ensuring that EWS remain responsive, context-specific, and capable of evolving in line with changing climatic and community dynamics. 6.4. Promote Cross-Border and Regional EWS Harmonization Fourth, given the transboundary nature of many climate risks, there is a pressing need to promote regional harmonization of EWS across East Africa. Promoting cross-border and regional harmonization of Early Warning Systems (EWS) is essential for managing shared environmental risks that transcend national boundaries. To achieve this, regional knowledge exchanges between meteorological and disaster risk management (DRM) agencies within the East African Community (EAC) should be institutionalized, allowing for alignment in data formats, warning triggers, and response protocols. Establishing transboundary EWS working groups in vulnerable regions such as Karamoja and West Pokot, where climatic shocks, livestock movements, and livelihoods are interlinked across borders, would facilitate joint planning and action. Additionally, coordinated regional simulation exercises and forecasting models should be developed to account for cross-border dynamics like seasonal migration and flash flooding. These efforts would not only strengthen institutional collaboration but also improve early action capabilities in managing complex, transnational climate risks across East Africa. 6.5. Empower Local Actors and Youth as EWS Agents Fifth, the role of local actors, particularly youth, must be strengthened. Empowering local actors and youth as agents of Early Warning Systems (EWS) is critical to enhancing community-based preparedness and ensuring that warnings are acted upon promptly and effectively. This requires targeted investment in training and funding for local Disaster Risk Management (DRM) committees to build their capacity in interpreting and responding to both Indigenous and scientific forecasts. Youth, in particular, should be engaged as “knowledge brokers” who can bridge generational and technological divides, translating forecasts into accessible formats, using digital tools to disseminate warnings, and learning from the experiential knowledge of elders. To sustain participation and incentivize timely reporting, localized support mechanisms, such as mobile data credits, stipends, or recognition programs, should be introduced for community reporters. These actions would not only decentralize EWS but also enhance trust, responsiveness, and intergenerational collaboration at the grassroots level (Fig. 15 ). 6.6. Embed EWS Integration into National Climate and DRR Strategies Finally, integration of hybrid EWS approaches must be embedded within national climate and disaster risk reduction (DRR) strategies. Embedding Early Warning System (EWS) integration into national climate and disaster risk reduction (DRR) strategies is essential for institutional sustainability and long-term impact. Reforms should align with existing national frameworks such as Kenya’s National Climate Change Action Plan (NCCAP) and Uganda’s National Policy for Disaster Preparedness and Management to ensure coherence and policy legitimacy. Dedicated budget allocations at the county and district levels are necessary to support the development and implementation of community-based early warning activities and the evolution of hybrid systems that merge scientific forecasting with Indigenous knowledge. Furthermore, incorporating EWS integration as a measurable performance indicator within national and subnational climate adaptation finance proposals can help attract donor funding, galvanize political commitment, and scale successful models across regions. Such mainstreaming ensures that early warning is not treated as a standalone intervention but embedded within the broader architecture of climate resilience and risk governance (Fig. 16 ). 7. Conclusion This study contributes to the growing discourse on hybrid knowledge systems by offering a comparative analysis of Early Warning Systems (EWS) in the Karamoja Cluster, a transboundary region straddling Kenya and Uganda. Through qualitative inquiry including interviews, focus group discussions, and policy mapping we examined how Indigenous Knowledge Systems (IKS) and scientific forecasting coexist, interact, and diverge within pastoralist risk management frameworks. Our findings demonstrate that while both Kenya and Uganda have developed operational EWS structures, they reflect fundamentally different knowledge systems and governance models. Kenya’s system is characterized by technological advancement, decentralized governance, and strong institutional mandates. However, it largely excludes traditional knowledge, limiting local ownership and trust. Conversely, Uganda’s EWS is deeply embedded in oral traditions and community rituals, with high social legitimacy, yet remains structurally fragmented and weakly institutionalized. This divergence reveals the critical need for a hybrid EWS model that leverages the complementary strengths of each approach: the spatial and temporal accuracy of scientific tools and the contextual specificity, cultural legitimacy, and trust inherent in IKS. By combining meteorological data with indigenous forecasting practices such as phenological observation, divinatory rituals, and communal storytelling early warning systems can become not only more accurate, but also more actionable and widely accepted. The study also exposes systemic barriers to integration, including asymmetrical knowledge hierarchies, lack of feedback loops, language and literacy constraints, and inadequate cross-border coordination. These challenges inhibit anticipatory action, undermine community trust, and limit the scalability of existing systems. Addressing these barriers requires institutional reform, capacity-building, and participatory governance mechanisms that elevate IKS from peripheral consultation to core system design. Future research should explore pilot testing of hybrid systems and their long-term scalability across other ASAL regions. It affirms that EWS are not merely technical instruments but socio-political infrastructures shaped by power, knowledge, and legitimacy. The inclusion of IKS is not a cultural token, but a knowledge imperative for effective, community-owned early warning systems. Policy implications include the need for formal protocols that institutionalize indigenous forecasting, cross-border harmonization under regional bodies such as IGAD, and targeted investment in local actors particularly youth and women as agents of early warning dissemination and knowledge translation. In sum, advancing climate resilience in pastoralist regions demands early warning systems that are not only scientifically robust but socially rooted. Hybrid systems, grounded in trust, participation, and plural knowledge, represent a transformative pathway toward inclusive and adaptive climate governance in Sub-Saharan Africa. Declarations 8. Patents Supplementary Materials Not applicable Ethics Accordance: The study protocol was reviewed and approved by the EPFL Human Research Ethics Committee (HREC) (Approval No: 003-2023, 26 January 2023) and the Kenya National Commission for Science, Technology & Innovation (NACOSTI) (License No: NACOSTI/P/23/23521), in accordance with the provisions of the Science, Technology and Innovation Act, 2013, and international ethical standards governing research involving human participants. This research was conducted in full accordance with relevant international ethical guidelines and regulations, including the Declaration of Helsinki and the Belmont Report, which emphasize respect for persons, beneficence, and justice. All participants provided informed consent, and data confidentiality was maintained throughout the study. Clinical trial number Not applicable Consent to Participate declaration : All participants involved in the semi-structured interviews and focus group discussions provided informed consent prior to taking part in the study. The purpose of the research, procedures, voluntary nature of participation, and confidentiality safeguards were clearly explained to all participants in their preferred language. Participants were informed that they could withdraw at any time without consequence. Written (or verbal, where literacy constraints applied) consent was obtained in accordance with institutional ethical guidelines. No data was collected from individuals under the age of 16. Consent to Publish declaration : Not applicable Conflicts of Interest: The authors declare no conflict of interest. Funding: Collaborative Research on Science and Society (CROSS) Programme 2023, EPFL. Author Contribution Conceptualization, J.C., J.L.T, and J-C.M.B.; methodology, J-C.M.B.; software, J-C.M.B., M.H. and S.A.I.A.; validation, J.C. and O.G.; formal analysis, J-C.M.B.; investigation, J.L.T.; resources, J.C. and O.G..; data curation, M.H. and S.A.I.A.; writing—original draft preparation, J-C.M.B.; writing—review and editing, J.C., M.H., S.A.I.A. and O.G.; visualization, J.-C.M.B., M.H.; supervision, J.C. and O.G.; project administration, J.C., O.G. and J-C.M.B.; funding acquisition, J.C. and O.G. All authors have read and agreed to the published version of the manuscript. Data Availability The datasets generated and/or analyzed during the current study are not publicly available due to institutional data privacy policies but are available from the corresponding author on reasonable request. References Pariyar D. Drought vulnerability and adaptation strategies in East Africa: A review of pastoralist resilience. Afr J Environ Sci Technol. 2019;13(8):297–308. 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09:39:20","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":36933,"visible":true,"origin":"","legend":"\u003cp\u003ePerceived Trust, Accuracy, and Relevance in Kenya and Uganda.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-7981470/v1/3b740de7afd8320ea61ffa65.png"},{"id":99308590,"identity":"99a9d85c-dfe4-458e-b841-1e5a70063306","added_by":"auto","created_at":"2025-12-31 16:08:48","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":51507,"visible":true,"origin":"","legend":"\u003cp\u003eBarriers to Effectiveness and Integration Potential: Kenya vs. Uganda.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-7981470/v1/57f4d945bbbf20f54300003f.png"},{"id":98862054,"identity":"2e37dff3-2e9e-4c1d-ae62-f1a014a9ecd2","added_by":"auto","created_at":"2025-12-23 09:39:20","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":23621,"visible":true,"origin":"","legend":"\u003cp\u003eComparative Scores from KIIs – Kenya vs. Uganda.\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-7981470/v1/f745a34dd7524c58dfbcb065.png"},{"id":99308627,"identity":"ea92768f-02c8-445e-84d2-73d5033182e4","added_by":"auto","created_at":"2025-12-31 16:08:51","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":25907,"visible":true,"origin":"","legend":"\u003cp\u003eFive-Step Framework of Kenyan Integrated Early Warning Systems.\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-7981470/v1/0b0c66ac4296ce3677b4fe93.png"},{"id":98862060,"identity":"7164330c-abfe-4b97-9726-c22c77bbb296","added_by":"auto","created_at":"2025-12-23 09:39:20","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":25608,"visible":true,"origin":"","legend":"\u003cp\u003eFive-Step Framework of Ugandan Integrated Early Warning Systems.\u003c/p\u003e","description":"","filename":"image14.png","url":"https://assets-eu.researchsquare.com/files/rs-7981470/v1/d54be63b10de01a97198cf34.png"},{"id":98862062,"identity":"a314db5f-a443-41e0-9030-c12f83d1cb0b","added_by":"auto","created_at":"2025-12-23 09:39:20","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":55766,"visible":true,"origin":"","legend":"\u003cp\u003eCommunity Engagement vs. System Effectiveness.\u003c/p\u003e","description":"","filename":"image15.png","url":"https://assets-eu.researchsquare.com/files/rs-7981470/v1/460f0d5c3741bdc7f239e123.png"},{"id":99308587,"identity":"1dbe1e92-d4c0-4e86-8a0b-2ad2f99d4dde","added_by":"auto","created_at":"2025-12-31 16:08:48","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":499606,"visible":true,"origin":"","legend":"\u003cp\u003eMultidimensional Analysis of EWS Performance Factors.\u003c/p\u003e","description":"","filename":"image16.png","url":"https://assets-eu.researchsquare.com/files/rs-7981470/v1/e82c1fdbb927fb66388e7635.png"},{"id":107927683,"identity":"3b8783fa-4cc7-4936-bcf3-4c0e350e41af","added_by":"auto","created_at":"2026-04-27 16:01:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1930089,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7981470/v1/a177022d-966d-45af-9f0c-e543bd25f8a7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Resilience through Integrated Early Warning Systems in the Karamoja Region of Kenya and Uganda","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Karamoja Cluster, straddling northeastern Uganda and northwestern Kenya, is one of the most disaster-prone and food-insecure regions in sub-Saharan Africa. Communities such as the Pokot in Kenya and the Karamojong in Uganda face overlapping vulnerabilities. These include climate extremes such as prolonged droughts, erratic rainfall, and flash floods, as well as socio-political stressors, including resource-based conflicts, livestock raids, locust infestations, and chronic underinvestment in infrastructure and public services (Pariyar, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Recha et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These converging stressors result in cyclical humanitarian emergencies, erosion of livelihoods, and the weakening of local coping capacities. Despite these challenges, local communities have developed intricate systems of knowledge and practice for navigating uncertainty, particularly through Indigenous Knowledge Systems (IKS).\u003c/p\u003e \u003cp\u003eConventional early warning systems (EWS), intended to predict and communicate risk, are often modeled on technical and institutional architectures that emphasize scientific forecasting, satellite remote sensing, and top-down dissemination strategies. While these approaches provide valuable spatial and temporal insights, their practical impact at the community level has often been limited due to access and trust issues. In the Karamoja Cluster, the uptake and usefulness of conventional EWS are constrained by a range of barriers: low literacy, limited internet or radio access, language mismatches, and, critically, a lack of cultural resonance and trust in formal warning mechanisms. These shortcomings highlight a disconnect between how risk is forecasted institutionally and how it is perceived, interpreted, and acted upon locally.\u003c/p\u003e \u003cp\u003eBy contrast, Indigenous Knowledge Systems (IKS) in both West Pokot and Karamoja represent context-specific, time-tested approaches to climate forecasting and adaptation. Rooted in generations of ecological observation, spiritual traditions, and oral transmission, IKS encompasses a range of indicators. These include changes in animal behavior, plant phenology, cloud formations, and celestial signs, as well as community-based rituals like rainmaking ceremonies and storytelling archives that document past disasters (Gumo, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Haokip, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These knowledge systems are not static or mythical remnants of the past; rather, they are dynamic, continually updated through lived experience, and often serve as the first and most trusted line of response for pastoralist communities facing climatic uncertainties.\u003c/p\u003e \u003cp\u003eDespite their widespread use and credibility at the grassroots level, IKS remains largely excluded from formal disaster risk management (DRM) and EWS frameworks in both Kenya and Uganda. This exclusion is deeply entrenched in historical legacies of colonial and post-colonial governance, where dual authority systems positioned customary institutions as subordinate to centralized bureaucracies (Mamdani, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Ntsebeza, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Today, these fragmented governance structures continue to impede the institutional recognition of IKS and limit opportunities for knowledge co-production, especially in transboundary regions like Karamoja where coordination across national borders is already weak.\u003c/p\u003e \u003cp\u003eEven within Kenya\u0026rsquo;s relatively decentralized governance model, which offers more latitude for county-level innovation and community engagement, the integration of IKS into formal EWS is constrained by weak institutional linkages, limited funding, and a lack of frameworks for validating and scaling local knowledge within official protocols. In Uganda, where the disaster risk management framework is more centralized, community engagement in forecasting is often informal and lacks formal channels for interface with national systems. This dichotomy reveals both challenges and opportunities: while Kenya offers a platform for localization and integration, Uganda\u0026rsquo;s centralized model can potentially standardize and scale best practices, but only if both systems are reformed through inclusive and participatory policy design that prioritizes local voices and equitable knowledge systems.\u003c/p\u003e \u003cp\u003eAgainst this backdrop, there is growing momentum, both regionally and globally, toward developing hybrid EWS that synthesize the strengths of both scientific forecasting and indigenous knowledge. Studies from East Africa show that such integrated systems, combining satellite-derived indices like NDVI, TCI, and CHIRPS with phenological and social signals observed by communities, lead to more accurate drought predictions, earlier community action, and improved trust in warnings (Opiyo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gebreyesus \u0026amp; Bauer, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zamani et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, technical integration alone is not sufficient. The legitimacy, usability, and sustainability of EWS hinge on active community participation, particularly in the design, interpretation, and dissemination of early warnings.\u003c/p\u003e \u003cp\u003eGlobally, institutions such as the IFRC, UNDRR, and IPCC have increasingly emphasized the role of Indigenous and Local Knowledge (ILK) in enhancing anticipatory action, equity, and sustainability in disaster risk governance (IPCC, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; IFRC, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). From the Arctic to the Sahel, hybrid approaches that respect and elevate IKS have been shown to improve warning accuracy, increase community engagement, and foster climate justice. Yet, operationalizing such integration requires not only methodological innovation but also political will and structural reforms, particularly in settings marked by governance fragmentation and historical marginalization.\u003c/p\u003e \u003cp\u003eThis study aims to contribute to this global and regional discourse by co-developing inclusive, context-sensitive early warning strategies with the Pokot and Karamojong communities of the Karamoja Cluster. Using participatory methods, such as focus group discussions, scenario planning, and GIS-based vulnerability mapping, the research integrates Indigenous Knowledge with scientific forecasting tools to create a hybrid EWS that is both technically robust and socially legitimate. The ultimate objective is to strengthen community agency in the design, implementation, and institutional uptake of early warning mechanisms, thereby enhancing both predictive accuracy and the cultural legitimacy of disaster response across this cross-border region.\u003c/p\u003e \u003cp\u003eBy advancing a model that foregrounds local agency and bridges Indigenous and scientific systems, this research responds to calls for climate justice, decolonial governance, and equitable disaster risk management. It proposes a shift away from externally driven crisis response toward community-led resilience building, ensuring that those most at risk from climate shocks are also those most empowered to act.\u003c/p\u003e"},{"header":"2. Study Area","content":"\u003cp\u003eThis study focuses on two transboundary pastoralist communities located within the broader Karamoja Cluster: the Pokot community, residing in both West Pokot and East Pokot counties in northwestern Kenya, and the Karamojong community in the Karamoja Sub-Region of northeastern Uganda. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the Pokot and Karamojong communities occupy contiguous territories along the Kenya\u0026ndash;Uganda border, characterized by strong socio-cultural linkages and frequent cross-border interactions. The region is characterized by semi-arid conditions, complex topography, and a shared exposure to climate extremes and disaster risks, making it a critical area for examining transboundary pastoral dynamics and resilience strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Geographical Context\u003c/h2\u003e \u003cp\u003eWest Pokot County, located in northwestern Kenya, borders Uganda to the west, Turkana County to the north, Elgeyo-Marakwet to the east, and Trans Nzoia to the south. The county spans approximately 9,169.4 square kilometers and is divided into four main sub-counties: Pokot North (Kacheliba), Pokot South (Chepareria), Pokot Central (Sigor), and Pokot West (Kapenguria). The terrain is diverse, ranging from lowland semi-arid areas to highland zones with fertile volcanic soils.\u003c/p\u003e \u003cp\u003eKaramoja, on the other hand, is located in northeastern Uganda and comprises seven districts: Kotido, Kaabong, Abim, Moroto, Napak, Nakapiripirit, and Amudat. The Karamojong predominantly inhabit the central and southern parts of this region, which features expansive savannah plains, rugged hills, and intermittent watercourses across an estimated 27,000 square kilometers. The region lies within Uganda\u0026rsquo;s cattle corridor and is known for its arid to semi-arid climate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Socio-Economic Dynamics\u003c/h2\u003e \u003cp\u003eBoth communities depend heavily on pastoralism and agro-pastoralism as their primary economic activities. The Pokot maintain a mixed economy, engaging in livestock rearing (especially cattle, goats, and sheep) and subsistence farming. In highland zones, crop cultivation, particularly maize, beans, and vegetables, supplements household income. However, limited market access, weak infrastructure, and seasonal mobility hinder economic diversification.\u003c/p\u003e \u003cp\u003eSimilarly, the Karamojong depend primarily on cattle herding for both livelihood and social identity. Although sedentary farming has increased\u0026mdash;especially among women and youth\u0026mdash;food insecurity remains widespread. According to FAO (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), over 60% of households in Karamoja experience chronic hunger due to poor harvests, droughts, and insecurity.\u003c/p\u003e \u003cp\u003eBoth communities face high poverty levels, low literacy rates, and significant gender disparities in access to land and credit. Women, in particular, shoulder disproportionate climate stresses, often with limited decision-making power or adaptive options (Muhereza et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Climate and Meteorological Patterns\u003c/h2\u003e \u003cp\u003eThe Pokot and Karamojong regions fall within the Arid and Semi-Arid Lands (ASALs) of East Africa, characterized by bimodal rainfall patterns, erratic precipitation, and prolonged dry seasons. In West Pokot, annual rainfall ranges between 400 mm in lowland areas and up to 1,500 mm in the highlands. Temperatures range from 15\u0026deg;C to 32\u0026deg;C, with the hottest periods occurring between January and March.\u003c/p\u003e \u003cp\u003eIn Karamoja, average rainfall is approximately 500\u0026ndash;800 mm per year, falling primarily between April and October. However, climate variability has increased, resulting in frequent droughts, flash floods, and prolonged dry spells that disrupt pastoral mobility and agricultural planning (ICPAC, 2022). Rising temperatures and erratic rainfall have also intensified environmental degradation, reduced pasture availability, and contributed to resource-based conflicts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Early Warning Systems (EWS)\u003c/h2\u003e \u003cp\u003eBoth communities operate parallel EWS structures, a combination of indigenous knowledge systems (IKS) and formal scientific mechanisms, though integration between the two remains limited.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Indigenous (Traditional) Early Warning Indicators (system) in Pokot and Karamoja Communities\u003c/h2\u003e \u003cp\u003eIndigenous communities across the globe possess intricate systems of early warning indicators that have evolved through centuries of lived experience and environmental interaction. These Indigenous Knowledge Systems (IKS) are rooted in cultural traditions, ecological observation, and collective memory, and have historically played a vital role in enabling communities to anticipate and respond to environmental hazards, particularly droughts and climate-related shocks (Sillitoe, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Nakashima et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Mutasa, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). IKS encompasses a wide array of practices, including agriculture, water management, food security, and healthcare, and remains foundational to the survival and resilience of many rural societies.\u003c/p\u003e \u003cp\u003eIn sub-Saharan Africa, including the Pokot of Kenya and the Karamojong of Uganda, these knowledge systems continue to serve as critical tools for disaster preparedness, albeit increasingly marginalized by the dominant emphasis on Western scientific paradigms. The rise of satellite-based meteorological forecasting, technological innovation, and formal disaster risk management institutions has often sidelined indigenous practices, leading to the erosion of their usage and intergenerational transmission (Leal Filho et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sitas et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Yet, the urgency of climate change and the limited reach of formal Early Warning Systems (EWS) in remote pastoralist regions underscore the need for a more integrative approach that values and incorporates indigenous perspectives.\u003c/p\u003e \u003cp\u003eBoth the Pokot and Karamojong communities possess rich IKS frameworks for environmental forecasting, relying on a mix of meteorological, biological, astrological, and supernatural indicators. These locally derived signals help guide seasonal decision-making, inform agricultural cycles, and prompt early action to mitigate climate-induced risks.\u003c/p\u003e \u003cp\u003eMeteorological indicators (M.I) are widely used. Among the Pokot, wind direction, particularly from west to east, is seen as a harbinger of rain, as is the formation of dark rain clouds. In Karamoja, lightning in specific locations is interpreted as the onset of rainfall, while strong winds during the dry season foretell prolonged drought. Morning dew accompanied by mist and unusually high nighttime temperatures are also seen as precursors of rain across both communities (Haokip, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBiological indicators (B.I) offer another layer of predictive insight. Animal behavior, such as increased restlessness in male goats, playful calves, or cows urinating while lying down, is interpreted as a signal of approaching rain. In the Pokot community, specific plant phenological changes, including the shedding of leaves or premature flowering, also signify seasonal transitions. Additionally, Pokot experts engage in goat intestine reading, a specialized practice through which the community interprets intestinal patterns to predict droughts, disease outbreaks, or intercommunal conflict (Gumo, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAstrological observations (A.I), particularly of the sun, moon, and stars, are traditionally used to infer the timing of seasonal changes. While this knowledge is widely shared, its application may vary between clans and is often the domain of elders or spiritual custodians.\u003c/p\u003e \u003cp\u003eSupernatural indicators (S.I) play a prominent role, particularly in decision-making processes that affect community-wide behavior. Seers interpret dreams as omens of events to come, and in Pokot culture, shoe reading, deciphering the alignment of traditional sandals, is employed to forecast blessings or misfortunes.\u003c/p\u003e \u003cp\u003eThese indicators are not interpreted in isolation. Instead, they undergo community-based deliberation and validation through a stepwise process, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which maps the indigenous forecasting cycle from environmental observation to collective decision-making and adaptive response.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDespite their proven relevance, these systems remain underrepresented in formal policy frameworks and disaster risk reduction programs. Recognizing and integrating IKS with scientific tools not only enhances predictive accuracy but also improves cultural legitimacy, community ownership, and the sustainability of disaster response systems (Kelman et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ford et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Modern Early Warning Systems\u003c/h2\u003e \u003cp\u003eFormal Early Warning Systems (EWS) in both West Pokot (Kenya) and Karamoja (Uganda) are central components of their respective national Disaster Risk Reduction (DRR) frameworks, and are primarily supported by national meteorological agencies, namely, the Kenya Meteorological Department (KMD) and the Uganda National Meteorological Authority (UNMA). These agencies are mandated to collect, analyze, and disseminate weather and climate-related data, and serve as the technical backbones of their national EWS. Regionally, these efforts are bolstered by the IGAD Climate Prediction and Applications Centre (ICPAC), which provides cross-border climate forecasting, early warning bulletins, and capacity support through tools such as the Greater Horn of Africa Climate Outlook Forum (GHACOF).\u003c/p\u003e \u003cp\u003eThe Kenyan EWS functions within the National Disaster Risk Management Policy (2018) and is coordinated by the National Disaster Operations Centre (NDOC) under the Ministry of Interior and Coordination of National Government. KMD provides seasonal and short-term forecasts, disseminating them through county governments, radio stations, SMS alerts, and extension officers, who act as key intermediaries between national agencies and local communities, particularly in arid and semi-arid lands (ASALs) like West Pokot.\u003c/p\u003e \u003cp\u003eIn Uganda, the EWS is embedded within the National Policy for Disaster Preparedness and Management (2010) and is overseen by the Office of the Prime Minister\u0026rsquo;s Department of Disaster Preparedness and Refugees (DDPR). UNMA leads climate and weather forecasting efforts, working in tandem with district-level Disaster Management Committees (DMCs), local government units, NGOs, and radio broadcasters to ensure timely dissemination of alerts and advisories across Karamoja. The Uganda Red Cross Society and community-based organizations also play a supporting role in last-mile communication, preparedness activities, and response coordination.\u003c/p\u003e \u003cp\u003eThe technological backbone of these systems includes the use of satellite-based data (e.g., MODIS, Landsat) and drought-monitoring tools such as the Vegetation Health Index (VHI), Standardized Precipitation Index (SPI), and the Normalized Difference Vegetation Index (NDVI). These indices help identify anomalies in vegetation health, rainfall distribution, and surface temperature, enabling more accurate forecasting of droughts, floods, and other climate-related hazards.\u003c/p\u003e \u003cp\u003eForecasts and alerts are communicated through multiple platforms: seasonal bulletins, community radio, mobile SMS platforms (e.g., Kenya\u0026rsquo;s Weather SMS service), and face-to-face engagement by agricultural extension workers, who are often trusted members of the community. In West Pokot, county disaster management teams further contextualize this information to local conditions, while in Karamoja, traditional leaders and community-based volunteers assist in translation and dissemination (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, despite the scientific sophistication of these systems, they face critical limitations that reduce their overall impact. These include limited coverage in remote and pastoralist regions, language and literacy barriers, and the exclusion of Indigenous Knowledge Systems (IKS). The disconnect between top-down scientific forecasting and local socio-cultural realities often erodes community trust and diminishes the uptake of early warning information. This misalignment between formal systems and grassroots needs highlights the importance of integrating indigenous and community-based mechanisms into official DRR strategies (Titz et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Leal Filho et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; ICPAC, 2021).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Data collection\u003c/h2\u003e \u003cp\u003eField data were collected from key stakeholders involved in both indigenous and modern Early Warning Systems (EWS) across West Pokot (Kenya) and Karamoja (Uganda). The study aimed to document and compare standard operating procedures, practices, limitations, and perceived impacts of both EWS types. To achieve this, the study consulted the following stakeholders:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTraditional knowledge holders (e.g., elders, seers, pastoralist leaders),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNational meteorological authorities and technical experts (e.g., Kenya Meteorological Department \u0026ndash; KMD; Uganda National Meteorological Authority \u0026ndash; UNMA; National Drought Management Authority \u0026ndash; NDMA),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLocal disaster risk management committees,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNGOs and CBOs engaged in resilience and early warning dissemination,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAgricultural and climate extension agents.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eA qualitative approach was adopted, employing a combination of semi-structured interviews, focus group discussions (FGDs), and direct field observations. Each of the ten semi-structured interviews lasted approximately 30 minutes, allowing for rich, open-ended responses while maintaining focused dialogue. Separate interview guides were developed for two main stakeholder categories, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInterview Focus Area and Stakeholder category\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInterview Focus Area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eStakeholder Category\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndigenous Stakeholders\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModern/Institutional Stakeholders\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocal indicators used to predict environmental changes (e.g., animal behavior, celestial signs, rituals)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStructure and protocols of formal EWS operations (SOPs)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethods of warning dissemination and community mobilization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTools and platforms used (e.g., satellite data, radio, SMS)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraditional decision-making structures and response mechanisms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDissemination mechanisms and reach to vulnerable communities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChallenges to maintaining and transmitting Indigenous Knowledge Systems (IKS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCollaboration or integration with local knowledge systems\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteraction with modern forecasting sources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBarriers to effectiveness, including last-mile delivery and trust issues\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe interview tools were designed to ensure thematic alignment across groups while tailoring the language and focus on each stakeholder\u0026rsquo;s domain of expertise. Responses were audio-recorded (with consent), transcribed, and coded for thematic analysis, enabling triangulation between modern and traditional systems. Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e below list key informants targeted for interviews. The interviews in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e targeted institutional members associated in the modern early warning system in Kenya. Meanwhile, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e includes Indigenous people with knowledge of the early warning systems in both Kenya and Uganda.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey Informants from Formal Institutions Involved in Modern Early Warning Systems\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitutions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisaster Risk Reduction (DRR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisaster Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNational Drought Management Authority (NDMA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKenya Red Cross Society (KRCS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeteorological Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKey Informants from Indigenous Communities with Knowledge of Traditional Early Warning Systems\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndigenous communities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGD Indigenous 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWest Pokot, Kenya\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGD Indigenous 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKanyerus, West Pokot, Kenya\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGD Indigenous 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmudat, Uganda\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGD Indigenous 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMuino-location, West Pokot, Kenya\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGD Indigenous 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmudat, Uganda\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e A comprehensive desk review of policy documents and operational guidelines was also conducted to map institutional arrangements and governance structures relevant to Early Warning Systems (EWS) and disaster risk reduction (DRR) in both Kenya and Uganda. This included national-level frameworks such as Kenya\u0026rsquo;s National Disaster Risk Management Policy (2018), Uganda\u0026rsquo;s National Policy for Disaster Preparedness and Management (2010), as well as strategic documents from meteorological and disaster agencies, including Standard Operating Procedures (SOPs) from the Kenya Meteorological Department (KMD), the Uganda National Meteorological Authority (UNMA), and the National Drought Management Authority (NDMA) in Kenya. At the regional level, documents produced by the IGAD Climate Prediction and Applications Centre (ICPAC), such as the Greater Horn of Africa Climate Outlook Forum (GHACOF) communiqu\u0026eacute;s, protocols on regional EWS coordination, and cross-border resilience strategies, were also examined to understand how transboundary risks are managed and how indigenous systems are (or are not) incorporated into formal DRR governance structures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Data Analysis\u003c/h2\u003e \u003cp\u003eThe data analysis process adopted a multi-tiered qualitative strategy designed to systematically explore, compare, and interpret the operations and effectiveness of both Indigenous and modern Early Warning Systems (EWS) across West Pokot (Kenya) and Karamoja (Uganda). A combination of thematic content analysis, comparative coding, and policy mapping was employed to ensure analytical rigor and to identify both convergences and divergences in early warning practices across stakeholder groups.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 1: Transcription and Data Organization\u003c/b\u003e \u003c/p\u003e \u003cp\u003e All semi-structured interviews and focus group discussions (FGDs) were audio-recorded with participant consent and subsequently transcribed verbatim. Field notes from direct observations and participatory engagement activities were also digitized and integrated into the data corpus. Transcripts were uploaded into NVivo 12, enabling structured thematic coding and retrieval of patterns.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 2: Thematic Coding\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA deductive-inductive approach to thematic analysis was adopted. A predefined coding framework was developed based on the interview guide (e.g., prediction indicators, dissemination practices, operational protocols, perceived barriers, integration potential), and additional emergent themes were identified during the coding process. Thematic codes were grouped into five core domains, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e below:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThematic Coding Framework: Domains and Associated Codes for Early Warning Systems Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePotential Codes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWind direction, animal behavior, cloud patterns, dew/mist, goat intestine reading, satellite data use\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissemination and communication pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommunity gatherings, oral messages, SMS alerts, radio announcements, use of extension agents\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional coordination and governance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRole of KMD/UNMA, involvement of DRM committees, cross-border coordination, SOP clarity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived trust, accuracy, and relevance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrust in elders vs. meteorological officers, perceived accuracy of traditional signs vs. forecasts\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarriers to effectiveness and integration potential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLanguage barriers, low literacy, exclusion of indigenous knowledge, lack of feedback mechanisms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 3: Comparative Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe coded data were systematically compared across the two stakeholder categories: Indigenous and Modern/Institutional actors. A cross-case matrix was developed to explore similarities, contradictions, and areas of potential synergy between systems. This comparison facilitated an understanding of how early warning signals are interpreted, operationalized, and received at different levels of the EWS ecosystem.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 4: Policy and Institutional Mapping\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUsing content analysis, national and regional DRR policy documents (e.g., Kenya\u0026rsquo;s National Disaster Risk Management Policy, Uganda\u0026rsquo;s Disaster Preparedness Policy, and ICPAC protocols) were analyzed to identify formal mandates, coordination hierarchies, and the extent to which community-based or Indigenous knowledge is acknowledged and integrated. This mapping helped contextualize field-level practices within broader institutional architectures.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Result","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Synthesizing Stakeholder Perspectives on Early Warning Systems in Kenya and Uganda\u003c/h2\u003e \u003cp\u003eKey informant interviews in both Kenya and Uganda revealed that indigenous forecasting methods such as animal behavior observation, cloud formations, dew patterns, and goat intestine reading remain deeply embedded in local agricultural and disaster preparedness practices. These techniques are regarded as time-tested, intuitive, and specific to the local environment. A pastoral elder in Kenya noted, \u003cem\u003e\u0026ldquo;When the birds change direction and the clouds sit low in the morning, we know rain is coming.\u0026rdquo;\u003c/em\u003e Likewise, a Ugandan farmer emphasized, \u003cem\u003e\u0026ldquo;We still look to the sky and the movement of ants before planting, it has never failed us.\u0026rdquo;\u003c/em\u003e These narratives reflect a strong emotional and cultural attachment to traditional ecological knowledge, which continues to serve as a trusted source of environmental interpretation.\u003c/p\u003e \u003cp\u003eHowever, these practices are not without criticism. Younger respondents and technical experts voiced concern about the lack of standardization and reproducibility of such indicators. For instance, interpretations can vary between communities, and there are no mechanisms for systematic validation. This echoes the findings of Roncoli et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), who observed that while traditional forecasts foster local ownership, their integration into scientific frameworks is often hindered by epistemological gaps.\u003c/p\u003e \u003cp\u003eFormal early warning systems (EWS), provided by agencies such as the Kenya Meteorological Department (KMD) and Uganda National Meteorological Authority (UNMA), are becoming more visible. They are disseminated through SMS, radio, and extension officers. However, community responses reveal varied levels of trust and reliance. As shown in\u003cb\u003eFigure 4\u003c/b\u003e, pastoralists followed by farmers report greater reliance on formal institutions such as KMD, UNMA, and local government, in contrast to elders who continue to trust traditional forecasting. This suggests a gradual shift from traditional to institutional sources of climate information, though the integration between the two remains limited.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCommunity perceptions underscore this tension. In Kenya, one participant observed, \u0026ldquo;\u003cem\u003eWe get messages on the phone, but sometimes they come late or are too general for our village.\u003c/em\u003e\u0026rdquo; In Uganda, concerns focused more on access and comprehension: \u0026ldquo;\u003cem\u003eNot all people have phones, and some don\u0026rsquo;t understand the language used in warnings\u003c/em\u003e,\u0026rdquo; a respondent noted. These reflections point to a disconnect between technological systems and local realities.\u003c/p\u003e \u003cp\u003eAlthough formal systems offer scientific rigor, particularly through satellite and meteorological modeling, their effectiveness is often constrained by issues of localization, timing, and accessibility (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This mirrors findings by Tall et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), who argue that EWS uptake across Sub-Saharan Africa is frequently hindered by top-down communication and insufficient contextualization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn light of these challenges, stakeholders increasingly call for a hybrid approach that combines traditional and scientific forecasting. As one disaster risk management (DRM) officer emphasized, \u0026ldquo;\u003cem\u003eIf we can combine the wisdom of the elders with satellite maps, we can prepare better.\u003c/em\u003e\u0026rdquo; This perspective is echoed by Orlove et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), who highlight that co-production of knowledge systems not only improves predictive accuracy but also builds trust, enhances legitimacy, and increases community uptake of early warning messages. Integrating Indigenous Knowledge Systems (IKS) with formal forecasting thus emerges as a critical step toward building inclusive and actionable early warning mechanisms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Thematic Coding outcomes\u003c/h2\u003e \u003cp\u003eThis section presents the outcomes of thematic coding based on key informant interviews (KIIs) and focus group discussions conducted in Kenya and Uganda. A comparative radar chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) visualizes the frequency and intensity of five core domains within Early Warning Systems (EWS): Indicator Systems, Dissemination \u0026amp; Communication, Institutional Coordination, Perceived Trust \u0026amp; Relevance, and Barriers to Integration.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e highlights notable national contrasts: Kenya registers higher emphasis on Institutional Coordination and Dissemination, suggesting a more structured and technologically embedded EWS landscape. Uganda, in contrast, demonstrates stronger emphasis on Indicator Systems and Trust, likely reflecting its continued reliance on indigenous knowledge systems and community-based practices. These findings help surface both systemic strengths and opportunities for capacity-building in each country.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoving into the specific domains, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents a comparison of indicator systems used by communities. In Kenya, traditional indicators such as animal behavior and cloud patterns are frequently referenced, reflecting a reliance on local ecological knowledge. Uganda\u0026rsquo;s respondents highlighted greater trust in satellite data, suggesting more formal integration of scientific forecasting tools. This divergence may be influenced by differing institutional outreach and historical exposure to modern meteorological systems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e explores the dissemination and communication pathways employed. Kenyan respondents reported widespread use of SMS alerts and agricultural extension agents, pointing to a semi-digitized dissemination network. In Uganda, community gatherings and radio broadcasts remain dominant, underlining the continued importance of interpersonal and oral communication. These differences signal the need for country-specific strategies to enhance EWS reach and usability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e assesses Institutional Coordination and Governance. Kenya outperformed in areas such as the operational role of the Kenya Meteorological Department (KMD) and the activity of Disaster Risk Management (DRM) committees, suggesting relatively clearer standard operating procedures (SOPs) and stronger institutional presence. Uganda, while showing engagement at the local level, lacked coordinated mechanisms across ministries and borders, highlighting the potential for structural reform and investment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e reflects respondents' perceptions of trust, accuracy, and relevance of EWS. Ugandan communities displayed higher trust in elders and traditional signs, whereas Kenyan stakeholders favored the accuracy of forecasts issued by meteorological officers. These epistemological differences are key to understanding the varying degrees of EWS adoption, and underscore the importance of integrating traditional knowledge with scientific systems to build community confidence.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e delves into barriers to EWS effectiveness and integration. Both countries face common challenges such as low literacy and language barriers, which hinder message comprehension and action. Kenya flagged the exclusion of indigenous knowledge as a key issue, suggesting tension between local practices and formal systems. Uganda, on the other hand, cited lack of feedback mechanisms as a primary barrier, pointing to limited opportunities for two-way communication and participatory feedback loops.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis comparative analysis illustrates not only the diversity of early warning system (EWS) approaches between Kenya and Uganda but also underscores the opportunities for mutual learning and hybrid innovation. By comparing how each country employs both indigenous and scientific forecasting tools, we can identify common strengths, gaps, and contextual nuances that shape community-level disaster risk reduction strategies.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e below summarizes the weighted scores (on a scale of 1\u0026ndash;10) derived from Key Informant Interviews (KIIs) conducted in both countries. These scores reflect the perceived importance, usage, and effectiveness of different early warning components across five thematic domains: Indicator Systems, Dissemination \u0026amp; Communication, Institutional Coordination \u0026amp; Governance, Perceived Trust \u0026amp; Relevance, and Barriers to Integration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese comparative scores show that Kenya leans more heavily on formal structures, SMS technologies, and scientific data integration (e.g., satellite use), while Uganda demonstrates deeper cultural attachment to traditional signs, oral communication, and inclusive governance via community gatherings. Both countries face shared barriers such as language and feedback gaps, but in differing degrees.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Integrating EWS for Community Resilience\u003c/h2\u003e \u003cp\u003eTo enhance climate resilience and disaster preparedness in vulnerable regions like West Pokot (Kenya) and Karamoja (Uganda), there is a pressing need to develop hybrid Early Warning Systems (EWS) that blend scientific forecasting with Indigenous Knowledge Systems (IKS). These integrated approaches offer the potential to bridge the trust gap between formal institutions and communities, improving the accuracy, cultural legitimacy, and community uptake of warnings.\u003c/p\u003e \u003cp\u003eThis integration must go beyond superficial recognition of traditional practices. It requires the co-production of knowledge through active collaboration between local elders, youth representatives, traditional seers, and technical institutions like the Kenya Meteorological Department (KMD) and Uganda National Meteorological Authority (UNMA). Such partnerships ensure that early warnings are both technically robust and socially anchored.\u003c/p\u003e \u003cp\u003eDrawing from both the weighted scores in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and qualitative insights from Key Informant Interviews (KIIs), this section compares Kenya\u0026rsquo;s and Uganda\u0026rsquo;s five-stage EWS frameworks (see Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e), highlighting how each country combines indigenous and scientific systems to strengthen resilience.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1. Step 1: Observation Phase\u003c/h2\u003e \u003cp\u003eDissemination methods vary notably. Kenya favors digital tools, SMS alerts and extension workers (both scoring 9.4), alongside community meetings and radio. This reflects stronger infrastructure and higher digital penetration. Uganda, in contrast, emphasizes oral messages (score: 8), radio announcements (8), and community gatherings (7.1), with moderate use of SMS alerts (score: 6.9). While both rely on national meteorological agencies, Uganda\u0026rsquo;s approach is more accessible in digitally underserved areas. Kenya\u0026rsquo;s system is more formalized; Uganda\u0026rsquo;s is more culturally embedded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2. Step 2: Interpretation and Validation\u003c/h2\u003e \u003cp\u003eBoth countries act upon warnings, but the drivers differ. In Kenya, actions such as food stockpiling, agricultural adjustments, and livestock movement are data-informed and institutionally guided. Uganda mirrors these actions but grounds them more in collective experience and tradition, especially in rural zones. Kenya\u0026rsquo;s model favors structured government alignment, while Uganda\u0026rsquo;s decisions stem from consensus and local adaptation strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3. Step 3: Knowledge Dissemination\u003c/h2\u003e \u003cp\u003eThe final stage reflects how systems learn and evolve. Kenya operates through formal DRM structures that incorporate feedback to refine forecasts and dissemination. Community insights contribute to improved messaging and timeliness. In Uganda, adaptation is grassroots-led, relying on continued observation by local elders and informal feedback loops that gradually influence institutional practice. Kenya reflects a top-down adaptive model, whereas Uganda demonstrates a bottom-up learning structure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.3.4. Step 4: Decision-Making and Response\u003c/h2\u003e \u003cp\u003eBoth countries convert warnings into tangible actions, but the decision-making pathways vary. In Kenya, actions such as livestock relocation, agricultural planning, and household stockpiling are closely tied to government advisories and data-informed planning. Uganda, while implementing similar actions, more often relies on communal experience and local customs to guide choices. Thus, Kenya\u0026rsquo;s responses are shaped by formal science and institutional alignment, while Uganda\u0026rsquo;s are embedded in community consensus and local adaptation strategies. Both systems are functionally effective, but rooted in different knowledge sources.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.3.5. Step 5: Monitoring, Feedback, and Adaptation\u003c/h2\u003e \u003cp\u003eThe final stage reflects how systems learn and evolve. Kenya operates through formal DRM structures that incorporate feedback to refine forecasts and dissemination. Community insights contribute to improved messaging and timeliness. In Uganda, adaptation is grassroots-led, relying on continued observation by local elders and informal feedback loops that gradually influence institutional practice. Kenya reflects a top-down adaptive model, whereas Uganda demonstrates a bottom-up learning structure.\u003c/p\u003e \u003cp\u003eThis comparative analysis reveals complementary strengths in both systems. Kenya offers scientific rigor, institutional coordination, and tech-based dissemination. Uganda contributes cultural legitimacy, cross-border inclusiveness, and deep-rooted oral traditions. Both countries face barriers, language, literacy, and integration gaps, but also possess shared practices such as co-validation, participatory engagement, and adaptive learning.\u003c/p\u003e \u003cp\u003eA hybrid regional strategy could effectively combine Kenya\u0026rsquo;s precision and infrastructure with Uganda\u0026rsquo;s grassroots engagement and cultural embedding. Such a model would create a more inclusive, trusted, and context-specific early warning system, one that is critical for managing climate-related risks and fostering long-term resilience in Sub-Saharan Africa.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study presents compelling evidence for the urgent need to reform and hybridize Early Warning Systems (EWS) in Sub-Saharan Africa through the integration of Indigenous Knowledge Systems (IKS) and scientific forecasting. Drawing on comparative fieldwork from Kenya and Uganda, the findings emphasize that while both countries have made strides in building EWS frameworks, systemic gaps remain, particularly in terms of localization, equity, and trust. Bridging these gaps requires deliberate, policy-driven efforts that elevate community voices and anchor climate risk management in culturally relevant knowledge systems (Mercer et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Orlove et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a policy standpoint, Kenya exemplifies a structured and technologically advanced model, with strong institutional roles played by the Kenya Meteorological Department (KMD) and disaster risk management (DRM) committees. However, this centralization often sidelines traditional knowledge, despite its enduring relevance in guiding community preparedness (Roncoli et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In Uganda, by contrast, Indigenous systems remain deeply embedded in community life and continue to shape environmental interpretation and response. Yet national institutions, such as the Uganda National Meteorological Authority (UNMA), still face limitations in integrating local insights into formal protocols and planning mechanisms (Tall et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo reconcile the structural and knowledge-system differences between traditional and scientific EWS, policymakers must move beyond symbolic acknowledgments of Indigenous knowledge and commit to its meaningful integration through co-produced policies and protocols. This involves embedding traditional forecasting indicators, such as animal behavior, dew patterns, and celestial observations, within national climate data frameworks (Kalanda-Joshua et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Graw \u0026amp; Mosello, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Inclusive decision-making should also be mandated at subnational levels, ensuring that elders, women, youth, and local practitioners are systematically involved in interpreting risks, planning responses, and communicating alerts (UNDRR, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Capacity-building initiatives must support both traditional knowledge holders and scientific experts to collaborate effectively in validating forecasts, disseminating information, and fostering mutual learning. Such reforms will not only enhance the technical robustness of EWS but also increase their cultural legitimacy and community acceptance (Leclerc et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the community level, this study underscores the critical role of trust in determining the uptake and effectiveness of early warnings. In many rural contexts, particularly in Uganda, community gatherings, oral messages, and the authority of elders continue to outperform digital messaging like SMS and radio alerts (Rufat et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This calls for more participatory communication strategies that respect oral traditions and engage trusted local messengers. Concrete actions might include establishing hybrid EWS committees composed of both meteorological officers and traditional seers to ensure that warnings are co-produced and contextually grounded (Mercer et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Designing two-way feedback systems would allow local communities to report environmental changes and verify alert accuracy, while engaging youth as knowledge bridges could support intergenerational exchange and digital inclusion (Speranza et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Furthermore, Uganda\u0026rsquo;s example of cross-border coordination in Karamoja should be formalized and scaled to other regions to better manage shared ecological threats and migratory movements (Twigg, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUltimately, the findings advocate for a hybrid, decentralized, and culturally grounded EWS model. By institutionalizing both scientific and Indigenous knowledge, such a system can foster deeper community ownership, improve the relevance and clarity of warnings, and increase the likelihood of early action, critical for reducing disaster impacts (IFRC, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Policy frameworks must therefore view EWS not merely as technological infrastructure but as social systems, where inclusion, trust, and shared learning are as crucial as digital innovation (Leach et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In doing so, they unlock the full potential of EWS as a foundation for resilient, climate-smart development across East Africa.\u003c/p\u003e"},{"header":"6. Policy Recommendations","content":"\u003cp\u003eTo strengthen Early Warning Systems (EWS) and build climate resilience across Kenya, Uganda, and broader Sub-Saharan Africa, this study proposes a series of policy interventions rooted in inclusivity, community engagement, and institutional innovation.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e6.1. Institutionalize the Integration of Indigenous Knowledge in National EWS Frameworks\u003c/h2\u003e \u003cp\u003eFirst, to institutionalize the integration of Indigenous Knowledge Systems (IKS) within national Early Warning System (EWS) frameworks, governments should establish formal protocols within meteorological agencies, such as goat intestine reading, dew and cloud observations, or dream interpretation rituals, which are already in use in parts of Kenya and Uganda, that recognize and incorporate community-based forecasting indicators. These indicators may include observations of animal behavior, dew patterns, and cloud formations, which have historically informed local risk perception and decision-making. Complementing this, training programs should be developed to equip both technical personnel and traditional elders with tools for shared interpretation and collaborative analysis. This co-learning approach would foster mutual respect, bridge knowledge divides, and strengthen the credibility of hybrid forecasts. Additionally, Indigenous knowledge holders should be formally included as stakeholders in disaster risk management (DRM) committees at the county or district level, ensuring their insights are embedded in local planning and decision-making processes. Such institutional recognition would not only validate traditional expertise but also enhance the social legitimacy and uptake of EWS messages across diverse communities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e6.2. Expand and Diversify Dissemination Channels\u003c/h2\u003e \u003cp\u003eSecond, to expand and diversify dissemination channels for Early Warning Systems (EWS), it is essential to establish multi-modal communication strategies that combine SMS alerts, radio broadcasts, oral announcements, and outreach through extension workers. This blended approach ensures that warnings are accessible to marginalized groups, particularly those with limited digital access or literacy. To further enhance understanding, early warning messages should be translated into local languages and enriched with culturally resonant metaphors or symbols, making the information more relatable and actionable. Moreover, leveraging trusted community institutions, such as religious leaders, respected elders, and youth groups, as intermediaries can significantly boost message credibility and uptake. These actors often serve as informal information gatekeepers and can bridge the gap between formal systems and local populations, ensuring that vital alerts are both received and acted upon in a timely manner.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e6.3. Strengthen Feedback Mechanisms and Adaptive Learning\u003c/h2\u003e \u003cp\u003eThird, effective EWS must be adaptive and responsive, supported by robust feedback mechanisms. Strengthening feedback mechanisms and adaptive learning within Early Warning Systems (EWS) requires creating channels that facilitate two-way communication between communities and technical institutions. This can be achieved by establishing platforms such as toll-free SMS lines, radio call-in shows, or community dialogue forums, where individuals can verify information, ask questions, or offer observations related to early warnings. Institutionalizing post-disaster reviews that bring together scientific experts and local residents is also crucial for assessing the timeliness, accuracy, and real-world impact of alerts. These participatory evaluations not only enhance accountability but also help identify gaps and areas for improvement. Importantly, feedback gathered from these interactions should be systematically analyzed and used to refine alert thresholds, language, and risk categories, ensuring that EWS remain responsive, context-specific, and capable of evolving in line with changing climatic and community dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e6.4. Promote Cross-Border and Regional EWS Harmonization\u003c/h2\u003e \u003cp\u003eFourth, given the transboundary nature of many climate risks, there is a pressing need to promote regional harmonization of EWS across East Africa. Promoting cross-border and regional harmonization of Early Warning Systems (EWS) is essential for managing shared environmental risks that transcend national boundaries. To achieve this, regional knowledge exchanges between meteorological and disaster risk management (DRM) agencies within the East African Community (EAC) should be institutionalized, allowing for alignment in data formats, warning triggers, and response protocols. Establishing transboundary EWS working groups in vulnerable regions such as Karamoja and West Pokot, where climatic shocks, livestock movements, and livelihoods are interlinked across borders, would facilitate joint planning and action. Additionally, coordinated regional simulation exercises and forecasting models should be developed to account for cross-border dynamics like seasonal migration and flash flooding. These efforts would not only strengthen institutional collaboration but also improve early action capabilities in managing complex, transnational climate risks across East Africa.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e6.5. Empower Local Actors and Youth as EWS Agents\u003c/h2\u003e \u003cp\u003eFifth, the role of local actors, particularly youth, must be strengthened. Empowering local actors and youth as agents of Early Warning Systems (EWS) is critical to enhancing community-based preparedness and ensuring that warnings are acted upon promptly and effectively. This requires targeted investment in training and funding for local Disaster Risk Management (DRM) committees to build their capacity in interpreting and responding to both Indigenous and scientific forecasts. Youth, in particular, should be engaged as \u0026ldquo;knowledge brokers\u0026rdquo; who can bridge generational and technological divides, translating forecasts into accessible formats, using digital tools to disseminate warnings, and learning from the experiential knowledge of elders. To sustain participation and incentivize timely reporting, localized support mechanisms, such as mobile data credits, stipends, or recognition programs, should be introduced for community reporters. These actions would not only decentralize EWS but also enhance trust, responsiveness, and intergenerational collaboration at the grassroots level (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e6.6. Embed EWS Integration into National Climate and DRR Strategies\u003c/h2\u003e \u003cp\u003eFinally, integration of hybrid EWS approaches must be embedded within national climate and disaster risk reduction (DRR) strategies. Embedding Early Warning System (EWS) integration into national climate and disaster risk reduction (DRR) strategies is essential for institutional sustainability and long-term impact. Reforms should align with existing national frameworks such as Kenya\u0026rsquo;s National Climate Change Action Plan (NCCAP) and Uganda\u0026rsquo;s National Policy for Disaster Preparedness and Management to ensure coherence and policy legitimacy. Dedicated budget allocations at the county and district levels are necessary to support the development and implementation of community-based early warning activities and the evolution of hybrid systems that merge scientific forecasting with Indigenous knowledge. Furthermore, incorporating EWS integration as a measurable performance indicator within national and subnational climate adaptation finance proposals can help attract donor funding, galvanize political commitment, and scale successful models across regions. Such mainstreaming ensures that early warning is not treated as a standalone intervention but embedded within the broader architecture of climate resilience and risk governance (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study contributes to the growing discourse on hybrid knowledge systems by offering a comparative analysis of Early Warning Systems (EWS) in the Karamoja Cluster, a transboundary region straddling Kenya and Uganda. Through qualitative inquiry including interviews, focus group discussions, and policy mapping we examined how Indigenous Knowledge Systems (IKS) and scientific forecasting coexist, interact, and diverge within pastoralist risk management frameworks.\u003c/p\u003e \u003cp\u003eOur findings demonstrate that while both Kenya and Uganda have developed operational EWS structures, they reflect fundamentally different knowledge systems and governance models. Kenya\u0026rsquo;s system is characterized by technological advancement, decentralized governance, and strong institutional mandates. However, it largely excludes traditional knowledge, limiting local ownership and trust. Conversely, Uganda\u0026rsquo;s EWS is deeply embedded in oral traditions and community rituals, with high social legitimacy, yet remains structurally fragmented and weakly institutionalized.\u003c/p\u003e \u003cp\u003eThis divergence reveals the critical need for a hybrid EWS model that leverages the complementary strengths of each approach: the spatial and temporal accuracy of scientific tools and the contextual specificity, cultural legitimacy, and trust inherent in IKS. By combining meteorological data with indigenous forecasting practices such as phenological observation, divinatory rituals, and communal storytelling early warning systems can become not only more accurate, but also more actionable and widely accepted.\u003c/p\u003e \u003cp\u003eThe study also exposes systemic barriers to integration, including asymmetrical knowledge hierarchies, lack of feedback loops, language and literacy constraints, and inadequate cross-border coordination. These challenges inhibit anticipatory action, undermine community trust, and limit the scalability of existing systems. Addressing these barriers requires institutional reform, capacity-building, and participatory governance mechanisms that elevate IKS from peripheral consultation to core system design.\u003c/p\u003e \u003cp\u003eFuture research should explore pilot testing of hybrid systems and their long-term scalability across other ASAL regions. It affirms that EWS are not merely technical instruments but socio-political infrastructures shaped by power, knowledge, and legitimacy. The inclusion of IKS is not a cultural token, but a knowledge imperative for effective, community-owned early warning systems.\u003c/p\u003e \u003cp\u003ePolicy implications include the need for formal protocols that institutionalize indigenous forecasting, cross-border harmonization under regional bodies such as IGAD, and targeted investment in local actors particularly youth and women as agents of early warning dissemination and knowledge translation.\u003c/p\u003e \u003cp\u003eIn sum, advancing climate resilience in pastoralist regions demands early warning systems that are not only scientifically robust but socially rooted. Hybrid systems, grounded in trust, participation, and plural knowledge, represent a transformative pathway toward inclusive and adaptive climate governance in Sub-Saharan Africa.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e8. Patents\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e \u003ch2\u003eSupplementary Materials\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics Accordance:\u003c/h2\u003e \u003cp\u003eThe study protocol was reviewed and approved by the EPFL Human Research Ethics Committee (HREC) (Approval No: 003-2023, 26 January 2023) and the Kenya National Commission for Science, Technology \u0026amp; Innovation (NACOSTI) (License No: NACOSTI/P/23/23521), in accordance with the provisions of the Science, Technology and Innovation Act, 2013, and international ethical standards governing research involving human participants. This research was conducted in full accordance with relevant international ethical guidelines and regulations, including the Declaration of Helsinki and the Belmont Report, which emphasize respect for persons, beneficence, and justice. All participants provided informed consent, and data confidentiality was maintained throughout the study.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003eConsent to Participate declaration\u003c/b\u003e: All participants involved in the semi-structured interviews and focus group discussions provided informed consent prior to taking part in the study. The purpose of the research, procedures, voluntary nature of participation, and confidentiality safeguards were clearly explained to all participants in their preferred language. Participants were informed that they could withdraw at any time without consequence. Written (or verbal, where literacy constraints applied) consent was obtained in accordance with institutional ethical guidelines. No data was collected from individuals under the age of 16.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eConsent to Publish declaration\u003c/b\u003e: Not applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eCollaborative Research on Science and Society (CROSS) Programme 2023, EPFL.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, J.C., J.L.T, and J-C.M.B.; methodology, J-C.M.B.; software, J-C.M.B., M.H. and S.A.I.A.; validation, J.C. and O.G.; formal analysis, J-C.M.B.; investigation, J.L.T.; resources, J.C. and O.G..; data curation, M.H. and S.A.I.A.; writing\u0026mdash;original draft preparation, J-C.M.B.; writing\u0026mdash;review and editing, J.C., M.H., S.A.I.A. and O.G.; visualization, J.-C.M.B., M.H.; supervision, J.C. and O.G.; project administration, J.C., O.G. and J-C.M.B.; funding acquisition, J.C. and O.G. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to institutional data privacy policies but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePariyar D. Drought vulnerability and adaptation strategies in East Africa: A review of pastoralist resilience. Afr J Environ Sci Technol. 2019;13(8):297\u0026ndash;308.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRecha CW, Makokha GL, Traore PS, Shisanya CA. Determinants of rainfall variability in semi-arid Kenya and the potential for forecasting agricultural drought. Climate Res. 2013;57(2):127\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGumo S. 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Social Science \u0026amp; Medicine; 2018. pp. 112\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Early Warning Systems (EWS), Indigenous Knowledge Systems (IKS), Climate Resilience, Disaster Risk Management (DRM), Karamoja Cluster, Community-Based Adaptation","lastPublishedDoi":"10.21203/rs.3.rs-7981470/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7981470/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a comparative analysis of Early Warning Systems (EWS) in Kenya and Uganda, with a focus on integrating Indigenous Knowledge Systems (IKS) and scientific forecasting to enhance climate resilience in the Karamoja cross-border region. Drawing on 10 key informant interviews with institutional actors and five focus group discussions with indigenous community members, we analyzed community perceptions. While Kenya\u0026rsquo;s EWS is more technologically embedded and institutionally coordinated, leveraging SMS alerts, satellite data, and county-level governance, Uganda\u0026rsquo;s system remains more deeply rooted in culturally transmitted, community-led forecasting methods, though elements of formal science are also present. Despite their divergent approaches, both systems face common barriers: limited community feedback, language and literacy gaps, and insufficient recognition of indigenous indicators. Our findings support the development of a hybrid EWS model that combines scientific precision with cultural legitimacy through co-production between meteorological institutions and local knowledge holders. We recommend embedding IKS into national DRM policies, expanding multi-modal dissemination channels, and institutionalizing community feedback mechanisms. By aligning scientific and traditional knowledge systems and strengthening regional coordination, this integrated approach can build trust, improve forecast usability, and promote anticipatory action in vulnerable pastoralist communities. The study contributes to the broader discourse on decolonizing climate governance and emphasizes the importance of inclusive, localized solutions to environmental risk in Sub-Saharan Africa.\u003c/p\u003e","manuscriptTitle":"Resilience through Integrated Early Warning Systems in the Karamoja Region of Kenya and Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-23 09:39:14","doi":"10.21203/rs.3.rs-7981470/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-28T16:48:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-23T15:19:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-05T16:20:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127582528164161306509848908921924232572","date":"2025-12-29T14:12:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324451218038117043963643039711229685518","date":"2025-12-19T08:23:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-17T05:50:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-02T04:55:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-01T11:54:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-12-01T08:30:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9d27b34c-3451-43f2-9e9e-7cd9084e6fac","owner":[],"postedDate":"December 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:00:48+00:00","versionOfRecord":{"articleIdentity":"rs-7981470","link":"https://doi.org/10.1007/s43621-026-03271-0","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2026-04-21 15:57:33","publishedOnDateReadable":"April 21st, 2026"},"versionCreatedAt":"2025-12-23 09:39:14","video":"","vorDoi":"10.1007/s43621-026-03271-0","vorDoiUrl":"https://doi.org/10.1007/s43621-026-03271-0","workflowStages":[]},"version":"v1","identity":"rs-7981470","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7981470","identity":"rs-7981470","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}