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Schematic representing drought management structures in Kenya and the connections between county and national level structures
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Drought and food security crises heighten risks to lives and livelihoods in East Africa. In recent years, a shift towards acting in advance of such events has gained momentum, notably among the humanitarian and development community. This shift is premised on tools that link climate forecasts with pre-agreed actions and funding, known as Forecast-b...
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Context 1
... coordinates structures at national and county levels for the 23 ASAL counties. Whilst there is considerable interaction across the national and county levels of governance (see Figure 2), and we outline both structures in this section, our paper focuses primarily on the county level system with the case study of Kitui county. ...
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... the county level, the key structure for drought management is the County Steering Group (CSG) (Figure 2), co-ordinated by NDMA involving the NDMA County staff, NGOs and key ministries such as agriculture and livestock, as well as a county Kenya Meteorological Department representative. Through the CSGs, NDMA operates a system of drought early warning, based on a two-pronged approach: ...
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... county level, KMD operates a decentralized climate service with CDMSs communicating localized meteorological services through accessible channels (Barrett et al., 2020b). In many counties, the CDMS sits on the CSG (see Section 3.1) and presents the national forecasts and a range of 'tailored', downscaled county climate services. ...
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... this research, we facilitated co-production of forecasts between NDMA and KMD across multiple levels. At the top level, NDMA leadership identified the potential for the existing drought classification system (Figure 2) to become forecast oriented with a new 'Early Alert' phase that could be triggered by forecasts of the monitoring indicators currently used (see Figure 3). At the county level, by working with the CSG we co-produced new prototype forecast products that match the drought biophysical monitoring metrics used in phase classification, specifically VCI and SPI. ...
Context 5
... coordinates structures at national and county levels for the 23 ASAL counties. Whilst there is considerable interaction across the national and county levels of governance (see Figure 2), and we outline both structures in this section, our paper focuses primarily on the county level system with the case study of Kitui county. ...
Context 6
... the county level, the key structure for drought management is the County Steering Group (CSG) (Figure 2), co-ordinated by NDMA involving the NDMA County staff, NGOs and key ministries such as agriculture and livestock, as well as a county Kenya Meteorological Department representative. Through the CSGs, NDMA operates a system of drought early warning, based on a two-pronged approach: ...
Context 7
... county level, KMD operates a decentralized climate service with CDMSs communicating localized meteorological services through accessible channels (Barrett et al., 2020b). In many counties, the CDMS sits on the CSG (see Section 3.1) and presents the national forecasts and a range of 'tailored', downscaled county climate services. ...
Context 8
... this research, we facilitated co-production of forecasts between NDMA and KMD across multiple levels. At the top level, NDMA leadership identified the potential for the existing drought classification system (Figure 2) to become forecast oriented with a new 'Early Alert' phase that could be triggered by forecasts of the monitoring indicators currently used (see Figure 3). At the county level, by working with the CSG we co-produced new prototype forecast products that match the drought biophysical monitoring metrics used in phase classification, specifically VCI and SPI. ...
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Citations
... project suggests that a narrative explanation of the underlying drivers, for example, the state of the MJO and associated teleconnections, can usefully augment subseasonal forecasts and add confidence in stakeholder interpretation (White et al., 2022). Our results here reinforce the call for enhanced forecasting capability in EA Meteorological services, continued access to subseasonal forecasting information from global modelling centres, and for enhanced and continued co-production between meteorological services and stakeholders to make most effective use of forecast skill in EA, in line with the growing body of evidence in the region (Gudoshava et al., 2022b;Hirons et al., 2021;Muita et al., 2021;Mwangi et al., 2022). Such activities can ensure advantage is taken from the opportunity afforded by relatively high climate predictability in EA across subseasonal to seasonal timescales. ...
Over the East Africa region forecasts of the onset of the rainy seasons have the potential to support decision‐making, especially in the largely rain‐fed agricultural sector. However, the understanding of key features of onset remains limited. Here, we analyse the variability of onset and associated drivers at interannual and subseasonal timescales, using several onset definitions. Results show that the onset date is especially variable from year to year in some of the high‐potential agricultural areas (standard deviation >20 days), which has implications for agricultural risk management. The choice of onset definition metric matters; agronomic definitions have limited applicability at the regional scale and are also highly sensitive to the spatial scale of analysis and to the choice of rainfall data. Onset information provided at coarse scales should be used with caution for decision‐making at the local scale; the “hit rate” of coarse‐scale tercile onset information at the local scale is less than 40% on average. To varying degrees, onset is related to total seasonal rainfall and thus to dominant interannual drivers of rainfall, including the Indian Ocean Dipole and ENSO modes in October–December and the western Pacific “V‐gradient” pattern in March–May. However, by analysing the dominant proportion of onset variance unrelated to total rainfall during the climatological season we show a substantial influence of subseasonal drivers, notably the Madden–Julian Oscillation. As such, there is an opportunity for rainfall onset information to be provided across seasonal and subseasonal timescales. Our work reinforces the need for enhanced co‐production of such onset information with stakeholders, especially regarding the choice of metric, alignment of forecasts with livelihood calendars, interpretation of the credibility of information content for local‐level decision‐making, as well as appropriate strategies for staggered risk management interventions informed by forecasts over “seamless” lead times.
... The heading of the early warning bulletins provides information on the drought phase classification, according to the following levels: "normal", "alert", "alarm", "emergency" and "recovery". This classification is based on biophysical variables, such as SPI and VCI, and socio-economic indicators of food security (Mwangi et al., 2022). Only the bulletins mentioning the phases "alert", "alarm" or "emergency" were considered for this analysis. ...
The relation between drought severity and drought impacts is complex and relatively unexplored in the African continent. This study assesses the relation between reported drought impacts, drought indices, water scarcity and aridity across several counties in Kenya. The monthly bulletins of the National Drought Management Authority in Kenya provided drought impact data. A random forest (RF) model was used to explore which set of drought indices (standardized precipitation index, standardized precipitation evapotranspiration index, standardized soil moisture index and standardized streamflow index) best explains drought impacts on pasture, livestock deaths, milk production, crop losses, food insecurity, trekking distance for water and malnutrition. The findings of this study suggest a relation between drought severity and the frequency of drought impacts, whereby the latter also showed a positive relation with aridity. A relation between water scarcity and aridity was not found. The RF model revealed that every region, aggregated by aridity, had their own set of predictors for every impact category. Longer timescales (≥ 12 months) and the standardized streamflow index were strongly represented in the list of predictors, indicating the importance of hydrological drought to predict drought impact occurrences. This study highlights the potential of linking drought indices with text-based impact reports while acknowledging that the findings strongly depend on the availability of drought impact data. Moreover, it emphasizes the importance of considering spatial differences in aridity, water scarcity and socio-economic conditions within a region when exploring the relationships between drought impacts and indices.
... • A practice approach reinforces the importance of understanding how climate information fits within people's working procedures and the application of specific methodologies. Others have previously stressed the importance of connecting weather and climate information to existing procedures and decision-making processes (Mwangi et al, 2022;Patterson, 2018;Patt & Gwata, 2002;Pagano et al. 2001; Gupta & van der Grijp, 2010). However, a practice approach further emphasises the importance of understanding how social practices woven around specific procedures and processes occurring in the workplace mediate the usability of climate information. ...
Providing usable climate information to city planners and decision makers is considered a pre-requisite to develop robust urban adaptation strategies. However, despite efforts to increase the use of climate information, its integration in decision processes is still low. This article argues that a key aspect hindering the uptake of climate information is the lack of understanding about the social dimensions underpinning its use. The article contributes to bridge this gap by explaining the use of climate information as social practice.Drawing on three case studies of municipal administrations, the article shows how the use of climate information unfolds within a network of practices that are intricately woven into the workflow of each respective municipality. Findings suggest that the use of climate information transpires in four social dimensions: a legitimacy dimension, which recognises that using climate information requires deliberate legitimation action; a dependency dimension, which stresses that the use of climate information depends on customary-working practices; a consequential dimension, which suggests that using climate information provokes changes to working practices; and a processual dimension, which reveals that the use climate information co-evolves with working practices as two mutually constitutive phenomena. The article concludes by suggesting that a practice approach helps us to shed light on how the use of climate information transpires inherently tied to a specific decision context.
... Our research also demonstrates that there are several cross-cutting issues that would enhance flood risk management in Kenya. Notably, there is ample evidence presented here and elsewhere (Carter et al., 2019;Mwangi et al., 2021) that co-production is a most effective approach to the design and operation of the FldEWS and the associated preparedness actions is most effective. The involvement of a wide range of stakeholders ensures that forecast information is decision-relevant and actionable and is necessary in developing Impact based Forecasting (IBF) to conform to WMO guidelines (WMO, 2015). ...
Kenya and the wider East African region suffer from significant flood risk, as illustrated by major losses of lives, livelihoods and assets in the most recent years. This is likely to increase in future as exposure rises and rainfall intensifies under climate change. Accordingly, flood risk management is a priority action area in Kenya's national climate change adaptation planning. Here, we outline the opportunities and challenges to improve end‐to‐end flood early warning systems, considering the scientific, technical and institutional/governance dimensions. We demonstrate improvements in rainfall forecasts, river flow, inundation and baseline flood risk information. Notably, East Africa is a ‘sweetspot’ for rainfall predictability at sub‐seasonal to seasonal timescales for extending forecast lead times beyond a few days and for ensemble flood forecasting. Further, we demonstrate coupled ensemble flow forecasting, new flood inundation simulation, vulnerability and exposure data to support Impact based Forecasting (IbF). We illustrate these advances in the case of fluvial and urban flooding and reflect on the potential for improved flood preparedness action. However, we note that, unlike for drought, there remains no national flood risk management framework in Kenya and there is need to enhance institutional capacities and arrangements to take full advantage of these scientific advances.
... • A practice approach reinforces the importance of understanding how climate information fits within people's working procedures and the application of specific methodologies. Others have previously stressed the importance of connecting weather and climate information to existing procedures and decision-making processes (Mwangi et al, 2022;Patterson, 2018;Patt & Gwata, 2002;Pagano et al. 2001; Gupta & van der Grijp, 2010). However, a practice approach further emphasises the importance of understanding how social practices woven around specific procedures and processes occurring in the workplace mediate the usability of climate information. ...
... The need for longer-lead seasonal forecasts has been noted as advantageous for user applications where forecast input is necessary at fixed points in the decision chain of user applications (Mwangi et al., 2021). Results presented here suggest levels of long-lead skill for the GHA ON season, using the hybrid approach discussed, may be sufficiently high to accommodate earlier issue of seasonal rainfall predictions. ...
We evaluate the skill of predictions of the East African short‐rains (October–December) season, of up to 5 months lead, based on a two‐stage dynamical/statistical hybrid approach. The statistical component is a well‐established, zero‐month lead, method based on observed ocean‐scale patterns of July–September sea surface temperature. A strong advantage of using this method is that its skill is high and also very robustly measured – a correlation of 0.77 between forecast and observed area‐mean rainfall is achieved over the 26 years of real‐time forecasts. Here, we extend the lead time by driving the statistical component with predicted July–September sea surface temperatures available from a number of dynamical model seasonal prediction systems. There is correlation skill near 0.5 for models initialized in May and positive skill from forecasts initialized as early as the previous October.
... Climate forecasts could inform the food prognosis; however, the forecasts are not available at the time when assessments are conducted. The Kenya Meteorological Department issues the long rains forecast towards Mid-February while the short rains forecast is issued in September, which are times when the SRA and LRA have been concluded (Audia et al., 2021;Mwangi et al., 2021). ...
The ‘silent revolution’ of numerical weather prediction (NWP) has led to significant social benefits and billions of dollars in economic benefits to mid-latitude countries, however the level of benefit in sub-Saharan Africa has been very limited, despite the potential to save lives, improve livelihoods, protect property and infrastructure and boost economies. Ongoing climate change in Africa, and the associated projected intensification of weather impacts in coming decades, makes the realisation of effective and more reliable weather forecasts and climate services even more urgent. It is widely recognised that to achieve this potential, investment is required in strengthening decision makers’ understanding of weather predictions and confidence in interpreting and appropriately applying forecasts, alongside transparent communication of the levels of skill and probability or certainty in forecast products. However, on all time scales of prediction, it is generally unrecognised that many forecasts that produce user-relevant metrics have such low skill that they are only marginally valuable to stakeholders, creating significant practical and ethical barriers to increasing uptake and generating benefits. Here, we present substantial evidence that even a modest investment in science for weather information and forecast techniques, to provide new technology and tools for Africa, can significantly increase the skill of user-relevant forecast products on all time scales. This will be a necessary enabler for building trust in and uptake of decision-relevant forecasts with the potential to deliver significant social and economic benefits. We present here an argument that incremental improvements in the skill of weather forecasting across all timescales in the African tropics, alongside strengthening communication and understanding of these forecasts, is fundamental to saving lives and enhancing livelihoods. Investing in the capacity and capability of National Meteorological Services and research institutions is essential to ensure lifesaving and life-enhancing services continue to be developed with and designed to serve the populations of sub-Saharan countries.
The threats of climate change have become fundamental for the humanitarian sector. 305 million people—or every 26th person worldwide—will need humanitarian aid in 2025 comprising a funding requirement of USD 47.4 billion. Inserting climate change-related forecast information to compute sound economic decisions is a cutting-edge consideration for global humanitarian financing institutions, such as the United Nations and the European Union, to cope with the era of climate losses and damages. Thus, we asked an interdisciplinary question: How useful is climate change-related modelling for economic decision-making in humanitarian aid resource allocation? We ran an exploratory literature review on this specific question by taking a snapshot of 41 studies on the Web of Science, assessed to which extent the utility of the modelling for economic decision-making was examined, and ranked them based on their usefulness. The review indicates that there should be more efforts to improve the forecasting ability and the transformation of information from climate modelling fluidly to economic decision-making in the humanitarian sector to be actionable for effective resource allocation. We assessed that more than half (23/41) of our dataset had limited discussion on the utility or mostly challenges of further use for utility documented, the two least valuable ranks. By extension, similar allocation issues will exist in development and climate policy, where we adapt and build resilience before assistance is needed. To curb the problem, research on integrating the different communities is proposed.
This article explores the recent history of early warning systems in Kenya, determining key features of the entangled political, technical and conceptual processes that prefigure contemporary drought management there. In doing so, it draws out wider implications regarding drought and anticipatory action across Africa's drylands, considering the friction between the dynamics of disaster risk management that structure formal early warning systems and those that shape pastoralist engagements with the volatile and uncertain worlds they inhabit. Surveying recent literature on pastoralism's unique relationship with uncertainty, and associated forms of networked, relational resilience, it reflects on some of the inherent limitations of current approaches to "local knowledge" in the humanitarian sphere. In doing so, it emphasises the need for new, creative approaches to early warning and anticipatory action, which are not merely established via the external synthesis of data but are rather oriented around local pastoralist drought preparation and mitigation strategies and comprise enough flexibility to adapt to a fast-shifting terrain of challenges and possibilities.
Drought poses a continual threat to both lives and livelihoods in the Global South. Although the impact on food security from drought could be reduced through early release of funds, the humanitarian sector typically reacts to crises rather than anticipates them. A significant challenge lies in devising a drought monitoring and forecasting system that can function across environmentally and economically diverse regions. This is particularly evident in Pakistan, which encompasses environments ranging from fertile riverbeds to arid deserts. This paper details the development, implementation, and operation of an anticipatory drought Disaster Risk Financing (DRF) programme for the provinces of Punjab, Sindh, and Baluchistan in Pakistan. Key to the DRF development are a new yield model for the primary crop in the target season (winter wheat), and a novel forecasting system for four seasonal drought indicators - namely winter wheat yield, precipitation, normalised difference vegetation index (NDVI) and vegetation health index (VHI). Formal evaluations demonstrate that the forecasts are skillful up to 2 months in advance of the end of the season – enabling anticipatory release of funds. The work presented here is applicable beyond Pakistan. Indeed, the model and the methodologies are sufficiently broad and adaptable to be utilised in arid and semi-arid regions across the Global South.
