doi:10.1111/disa.12108

Dealing with uncertainty: integrating local and scientific knowledge of the climate and weather Dominic Kniveton, Emma Visman, Arame Tall, Mariane Diop, Richard Ewbank, Ezekiel Njoroge, and Lucy Pearson1

While climate science has made great progress in the projection of weather and climate information, its uptake by local communities remains largely elusive. This paper describes two innovative approaches that strengthen understanding between the providers and users of weather and climate information and support-appropriate application: (1) knowledge timelines, which compare different sources and levels of certainty in community and scientific weather and climate information; and (2) participatory downscaling, which supports users to translate national and regional information into a range of outcomes at the local level. Results from piloting these approaches among flood-prone communities in Senegal and drought-prone farmers in Kenya highlight the importance of co-producing ‘user-useful’ climate information. Recognising that disaster risk management actions draw on a wide range of knowledge sources, climate information that can effectively support community-based decision-making needs to be integrated with local knowledge systems and based on an appreciation of the inherent uncertainty of weather and climate information.

Introduction The Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) concluded that by 2080 the impacts of climate change are likely to cause 1.1–3.2 billion people to experience water scarcity and 200–600 million to suffer from hunger, and result in 2–7 million more people per year being subjected to coastal flooding, depending on the mitigation and adaptation measures implemented in the coming years (Solomon et al., 2007). The IPCC also noted that adaptation efforts to cope with the impacts of climate change share common goals and determinants with those of sustainable development, including the need to increase access to resources (including information and technology); increase equity in the distribution of resources and stocks of human and social capital; increase access to risk-sharing mechanisms; and build the abilities of decision-support mechanisms to cope with uncertainty (Yohe et al., 2007). In this respect, the challenges of climate change in Africa could be addressed by more effective engagement with climate variability through better integrating climate science and user communities (including in earlywarning and disaster management systems), improving the dissemination and communication of information, and developing seasonal and intra-seasonal information (Washington et al., 2006).   Hazard early-warning information based on atmospheric variability spans a range of temporal and spatial scales, from warning of imminent extreme local weather events Disasters, 2014, 39(S1): S35−S53. © 2014 The Author(s). Disasters © Overseas Development Institute, 2014 Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford, OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA

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through to seasonal forecasting, and finally towards decadal and beyond predictions. In an African context, considerable past effort has been focused on seasonal climate forecasting, which involves making forecasts of the weather conditions averaged over a season (typically two months) with lead times of an average of between one and six months (Stockdale et al., 2010). The chaotic nature of atmospheric processes means that many forecasts of weather made on a particular day have nearly no skill 2 beyond about one week. However, in many parts of the world the seasonal climate is influenced most substantially by conditions at the earth’s surface, notably sea surface temperature (SST) and to a lesser extent land cover conditions like soil moisture and snow cover. Both SST and land cover conditions vary and evolve quite slowly over timescales of between weeks and many months, and therefore influence the atmosphere for an extended period. Observing and predicting anomalies in these slowly varying components of the climate system provide the physical basis for predicting the seasonal climate. The main source of predictability for much of the tropics comes from the widespread quasi-global impact of Pacific El Nino Southern Oscillation (ENSO) events, which evolve over a period of about a year. Many regions of western, eastern and southern Africa are strongly influenced by ENSO events and SST anomalies in other ocean basins.   In the late 1990s the World Meteorological Organisation, national meteorological and hydrological services, and regional and international institutions established regional climate outlook forums (RCOFs) to develop and deliver climate services to a variety of users in government agencies, non-governmental organisations, civil society organisations, and private and public utilities. Primarily focused on seasonal rainfall forecasts, RCOFs aim to bring together experts from a climatologically homogeneous region with experts from international prediction centres to provide consensus-based climate forecasts and information to a range of users. This process was designed to ensure consistency in the development, access to, and interpretation of climate information, and facilitate interaction with sectoral users, extension agencies and policymakers. However, despite considerable scientific progress, according to the World Climate Research Programme’s position paper on seasonal prediction (Kirtman and Pirani, 2007), which reviewed the status of prediction quality and value, relatively little uptake of seasonal prediction information has occurred during the period that RCOFs have been operating (Millner and Washington, 2011). Various reasons for this have been proposed, including problems in the dissemination of forecast information; the difficulty for decision-makers to interpret probabilistic forecast information; cognitive biases in decision-making under conditions of uncertainty; and various aspects of the political, institutional and cultural contexts that affect users’ ability to make use of forecasts in decision-making (Millner and Washington, 2011). There is, therefore, a critical need to bridge the gap between producers and users of climate information.   One of the most cited pieces of work related to the uptake of weather and climate forecasts by directly affected communities has been that by Patt and Gwata (2002). Using a case study of subsistence farmers in Zimbabwe, they highlighted six key

Dealing with uncertainty: integrating local and scientific knowledge of the climate and weather

constraints that prevent the effective application of forecasts: credibility, legitimacy, scale, cognition, institutional practices and norms, and the complex nature of choices and decision-making. According to Patt and Gwata (2002), credibility can be influenced by the perceived unreliability of forecasts. This is a particular concern when forecasts are communicated in deterministic rather than probabilistic form.   While a number of authors have suggested that many lay people have trouble correctly understanding probabilistic forecasts (e.g. Glantz, Betsill and Crandall, 1997; Nicholls, 1999), recent research has focused on different visualisation approaches to effectively communicate probabilistic and ensemble-based information (Spiegelhalter, Pearson and Short, 2011; Stephens, Edwards and Demeritt, 2012). From a scientific and user perspective, the conveyance of the inherent uncertainty in weather and climate information is necessary to maintain the credibility of the information and source of knowledge, while it could also be argued that understanding how to use uncertain information builds resilience in a community. Patt and Gwata (2002) also present legitimacy as an issue when attempting to make weather and climate information useful. Accordingly, they note that when users question the political agenda of the communicators, they choose to ignore any information originating from those they do not trust. In their study, Patt and Gwata (2002) note that some respondents recognised that actions recommended for the benefit of the group may not be best for individual decision-makers. For example, in some cases the aggregated goals of individual farmers (e.g. those focused on producing enough food for their families while minimising risk) may clash with those of national policymakers (including maximising expected yields). The scale of forecasts is also noted as a common constraint of forecast uptake. This arises when the weather or climate information available is an average covering a wide geographical area and the impacts of the atmospheric phenomena at the local level are unclear. Cognition of the forecast information is also important to ensuring that the information is not applied incorrectly.   The literature on risk communication has increasingly turned to the importance of participatory approaches to improve user understanding of climate and weather information (Patt and Gwata, 2002; Renn, 1998). It has also been recognised that the context in which the information is delivered and used can present particular challenges to the uptake of early warnings. For example, linkages between the timing of the delivery of information and farmers’ practices (such as when seeds are purchased) will affect how much the forecast can be utilised. If these timelines are not flexible, then there are limits on how much scope users have to apply forecasts. Farmers also tend to make a large variety of decisions surrounding their crops based on many different factors, with the choices that they face often being too complex for simple decision trees. Additionally, it may be the case that a forecast is perceived not to contain enough new information to alter existing complex decisions. Further, as noted above, individual farmers’ decisions may be focused on survival rather than maximising expected yields.   Recent work by Tall (2012) has attempted to explore the contexts in which climaterisk-based decisions are made using role-playing games involving a group of local

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community representatives, climate scientists, and agricultural, hydrological and other sectorally relevant experts. Perhaps the most comprehensive study of the uptake of climate information in decision-making was that conducted by the International Research Institute in the ‘Climate and Society’ series (IRI, 2011). This study reviewed 17 case studies of the use of climate information to manage risks and improve livelihoods. Its primary conclusion was that interaction and dialogue in partnerships between climate scientists and decision-makers was key to the successful uptake of forecast information. By definition, involving local communities in this process of dialogue requires the integration of local knowledge with that of the scientific community.   Local knowledge encompasses the knowledge and practices that are acquired by local people over a period of time through the accumulation of experiences over generations, society–nature relationships, and community practices and institutions. In the context of disaster preparedness, local knowledge encompasses local environmental or agricultural indicators, technical knowledge, and sociocultural and historical information. Local knowledge provides a range of benefits that the science community cannot. It stems from local values and the use of traditional social structures, is understood by communities both physically and spiritually, is often supported by respected community elders, and is more recognisable to communities who are sometimes distrustful of modern technology (Denkens, 2007). The utilisation of local knowledge and practices can therefore address the challenges of credibility, legitimacy, scale, cognition, familiar institutional practices and complex decision-making currently experienced in the risk communication of scientific information, as detailed by Patt and Gwata (2002). In addition, while some argue that personal memories of past events are irrelevant in a drastically changing environment, Tscakert and Dietrich (2010) found that people who subsist in hazard-prone areas of Africa tend to have good recollection of past thresholds that have driven the community into poverty traps and that these experiences can be drawn on to identify potential future thresholds.   However, ‘indigenous’ knowledge has often in the past been seen as inefficient, inferior or even an obstacle to development. More often than not local knowledge is hidden and dismissed by the tendency of scientific knowledge to deny its importance (Agrawal, 1995). In addition, many developing countries adopt policies that directly or indirectly provide incentives to abandon traditional livelihoods. These may undermine the ability of these local knowledge-based systems to respond to hazards. However, the emerging consensus is that local knowledge and practices need to be integrated with those from the science community for forecasts to be more accurate, accepted and acted on (Cronin et al., 2004; Howell, 2003; Mercer et al., 2007; Rajasekaran, 1993; UNISDR, 2008). Reflecting this, the importance of local knowledge and coping strategies has begun to enter international and national policies. For example, the Hyogo Framework for Action priority 3 emphasises the importance of encouraging the use of traditional knowledge.3   Going beyond the recognition of the positive contributions of indigenous knowledge, Agrawal (1995) questions the divide between knowledge types, concluding that the classification of knowledge into indigenous and scientific is arbitrary and bound to fail. He notes that the classification aims to separate and fix in time and space

Dealing with uncertainty: integrating local and scientific knowledge of the climate and weather

systems that can never be separated or fixed due to their intertwined histories. Exchange and learning between the two forms of knowledge bases have taken place for centuries and so the perspective that they are ‘untouched’ (Agrawal, 1995, p. 422) by each other is inaccurate. Scientific knowledge production streams have been influenced by history and culture, and indigenous knowledge has also been shaped over time by scientific findings. Nightingale (2010) notes that geographers—her focus is on those in the feminist field—are increasingly mixing epistemologically diverse methods in their research, but that they rarely place equal emphasis on these methods and often do not use them together to achieve a single outcome or to answer the same set of research questions at the same scale. She suggests that there is a misplaced insistence that quantitative, scientific data provides the base and context, while other knowledge and methods are used to explore more nuanced questions. Challenging this, she proposes thoroughly mixing methods from different epistemological traditions so as to produce triangulated results particularly sensitive to gender, power and context, as well as alternative knowledge sources.   Few studies exist on exactly how integration between local and scientific knowledge sources should take place to inform and strengthen resilience building, including in community, district, national, regional and international decision-making processes. However, work is beginning to emerge. Most significantly, Denkens (2007) has produced extensive research on what kind of local knowledge is available in developing countries and how it could be useful for disaster risk reduction. Mercer et al. (2010) have created a framework for integration in the context of disaster preparedness involving a loose step-by-step process and the identification of influencing factors. A number of examples of integration in the context of disaster preparedness have been documented. For example, Victoria (2008) describes the combination of indigenous and scientific knowledge in the Dagupan City, Philippines, flood-warning system, which incorporates local flood markers and traditional warning instruments. Integration has also been observed in the communication of early warnings on Tikopia Island, Solomon Islands. Here dissemination has been achieved through integrating Radio Australia’s transmission of cyclone warning (scientific method) with local runners taking the message out to the wider community in the local language (indigenous method) (McAdoo, Baumwoll and Moore, 2008). While rare, these examples demonstrate the added benefits of combining local and scientific knowledge types and dissemination channels. In this paper we build on these examples to explore the use of two participatory approaches that attempt to combine local and scientific knowledge to help build a common understanding of uncertainties in weather and climate forecasts so that local communities can take enhanced—and thus resilient— agricultural decisions. In the next section we outline the development of the approaches, while in the following section we describe their use in two case study regions in Senegal and Kenya. The subsequent section discusses the impact of the use of these techniques on agricultural decision-making in the case study regions, while the conclusion deals with the implications of the work reported here. It should be noted that this paper does not attempt to address community perceptions of climate trends, but looks at weather and seasonal climate forecasts only.

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Methodologies: the development of knowledgetimelines and participatory-downscaling approaches Background to the project in which the approaches were developed Tscakert and Dietrich (2010) suggest that the existing ‘methodological toolbox’ is not sufficient to facilitate and sustain the anticipatory learning required for future uncertain disaster and climate-change contexts. They note that while some progress has been made in the Western world to develop new learning approaches, in developing countries, and particularly in Africa, access to information, networks and learning tools to help build resilience is limited. Furthermore, they suggest that a multifaceted and iterative way of learning about uncertainties is required to support investment in effective preparedness measures in advance of potential future shocks. Accordingly, it is acknowledged that in order for seasonal and weather forecasts to be understood and effectively applied, and for knowledge from both scientific and local communities to be combined to alleviate some of the common barriers to risk communication, the way that learning takes place can no longer be one directional and stagnant (Tscakert and Dietrich, 2010).   Many authors have identified the need to create spaces for two-way dialogue as a crucial component for achieving anticipatory learning (e.g. Kesby, 2005; Thomas and Twyman, 2005). Kesby (2005) perceives these spaces as arenas where people can assess their own understanding and its boundaries, consider alterations in behaviour, and improve communication. However, Tscakert and Dietrich (2010) note that such arenas for transformative and anticipatory learning rarely exist and even when they do, they are not sufficiently structured to facilitate the required reflection, creativity and innovation, as advocated by Pelling and High (2005) in their discussion on the need for ‘shadow systems’, i.e. informal spaces outside but connected to formal institutions where experimentation is allowed to happen. Situated in such thinking, the methodologies discussed in this paper are based on the observation that many past risk communication practices have prevented the application of information that has the ability to build disaster resilience in communities that are most affected. The approaches developed here stem from the recognition that knowledge from scientific and local communities is valuable, that the two types of knowledge can complement each other in an integrated way, and that spaces for this integration and learning need to be created that enable questioning and experimentation. Key to the dialogue process in such efforts at integration is the understanding of the inherent uncertainty in the information and knowledge of climate and weather. Indeed, the work is premised on the assumption that acceptance and understanding of the inherent uncertainties in climate information serve as a basis for the uptake of forecast information and the making of appropriate decisions according to the priorities of the decision-maker.   The Humanitarian Futures Programme (HFP) has been working to create a learning space to combine scientific and local knowledge through a variety of methods. The HFP efforts include an extended exchange between climate scientists and users of climate information in the humanitarian, disaster risk reduction and development

Dealing with uncertainty: integrating local and scientific knowledge of the climate and weather

sectors, together with decision-makers in communities in flood- and drought-prone zones, that has attempted to identify and develop a variety of interdisciplinary dialogue approaches. Initiated in early 2009, the two-way exchanges were based on the idea that only through sustained and collaborative partnership will humanitarian and development policymakers and community decision-makers learn to ask the right questions of emerging climate science. Conversely, it assumes that this partnership will help climate scientists better understand the climate information requirements of communities at risk and the humanitarian and development decision-makers who seek to support them.  In 2011 the Climate and Development Knowledge Network funded two pilot demonstration studies, one in Senegal and the other in Kenya, each extending over two rainy seasons. The specific objectives of these demonstration case studies were to: • demonstrate how climate science can effectively inform a range of humanitarian, disaster risk reduction and development planning processes; • contextualise emerging understanding of climate science together with other drivers of future human vulnerability so as to gauge where intervention informed by climate science might be useful; strengthen humanitarian and development organisations’ ability to access, under• stand and appropriately apply relevant climate information; and • improve climate scientists’ understanding of the climate information needs of humanitarian and development policymakers and the partners and communities with whom they work.   Activities in each demonstration study were timed around the rainy seasons to provide access to, strengthen understanding of, and support the appropriate application of information before the onset of rains and on a continuous basis over the course of the rains, with community-based evaluation and technical review following each season. The participatory processes employed enabled the approaches described here to be continuously improved to address additional needs that were identified and maximise potential opportunities. Uncertainty in weather and climate information Predictions of future atmospheric conditions are inherently uncertain, due in part to the chaotic nature of the atmosphere. Weather and climate projections are also uncertain, due in part to inadequacies in climate and weather forecasting models and, in the case of the future climate, the unknown future atmospheric concentrations of greenhouse gases and aerosols. Over time the relative contribution of each of these uncertainties to the total uncertainty in predictions/projections changes, such that for short-term forecasts the major uncertainty arises from the chaotic nature of the atmosphere and model inadequacies, while for longer-term projections the major uncertainties emanate from the range of possible greenhouse gas emissions and model inadequacies.

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  Importantly for humanitarian and development agencies, an added source of decisionmaker uncertainty arises from the difference in the scales at which climate information is generally provided (such as national and regional, and seasonal or annual) and that at which it is used (such as local and subseasonal). Thus, one result of these scale changes from the decision-maker’s perspective is that the state of the atmosphere cannot be relied on to be the same at the location and time of use when compared with the prediction information supplied. A number of scientific techniques exist to translate climate information at one scale to smaller ones and are collectively known as downscaling. Techniques based on, for example, regression relationships derived with past observed data are known as statistical downscaling, while the use of high resolution climate models is known as dynamical downscaling. There are, however, both limitations to the applications of these techniques and also uncertainties in them if and when they are employed. Statistical downscaling is, for example, limited by observed data availability, while in some cases dynamical downscaling is hindered by model inadequacies (see Jones et al., 2004, chap. 4 for further information).   In addition to these scientifically based uncertainties, humanitarian and development users of climate and weather information are often presented with data that does not always fulfil their needs. For example, current seasonal rainfall forecasts concentrate on rainfall totals rather than information of equal or superior importance for crop production and food security, such as dates of the start, end and length of the wet season. This difference results from the problems experienced by forecast models in accurately simulating the atmosphere at daily time scales over seasonal time scales. Knowledge-timelines and participatory-downscaling approaches Rather than trying to improve on inherently uncertain scientific forecasts, in this paper we present two techniques that use local knowledge to understand and downscale scientifically based climate and weather information in time, space and information type to a range of outcomes and risks that can be acted on appropriately by local communities. In doing so, it is hoped that this process will extend the ownership of uncertainty to the wider community of users. In turn, it is hoped that a sharing of ownership of uncertainty will lead to more collaborative knowledge production and the development of emergent solutions to climate- and weather-related livelihood stresses and shocks.   The following set of exercises are premised on the notion that knowledge of the weather and climate originates from a number of sources, including locally based or cultural interpretations of natural phenomena, as well as scientific analysis and everyday experience. In essence, one could argue that in communities dependent on the weather and climate, everybody is a forecaster.

Knowledge timelines The aim of this technique was to explore the different weather and climate knowledge types that people use to make decisions, to understand their similarities and differences, and to triangulate concepts of uncertainty and confidence in these different knowledge types. The approach developed adopted the following steps:

Dealing with uncertainty: integrating local and scientific knowledge of the climate and weather

1. Encourage participants to remember a past climate event using non-climate events to prompt their memory. 2. Ask about the different information that people had on the climate/weather event before it occurred. 3. Describe the scientific information available for this event. 4. Describe the uncertainty and confidence in this scientific information as a function of forecast time and space. 5. Ask participants to describe the confidence they have in the information they use and their uncertainties regarding it. 6. Ask for information about the basis of this assessment. 7. Compare and contrast the features of each knowledge type.

Participatory downscaling The aim of this exercise was to help build a dynamically evolving local capacity to translate national and regional climate and weather information into a range of outcomes at local spatial and higher temporal scales. The technique is based around a simplified event history analysis using a group of policymakers and decision-makers split into groups of three or four, and the following steps: • Starting with a time series of observed atmospheric data, a sample of years is selected that represents different atmospheric related events (e.g. floods, droughts). • For each event one or two non-climate culturally, politically or economically important events are selected to provide a mental trigger to participants regarding the year of the event being referred to. • For each year, starting with the most recent, and without revealing the flood or rain conditions that year, each group is asked to discuss whether the location in which they were in that year experienced a wet, dry or average rainy season and whether the communities where they lived experienced the atmospheric-related hazard of interest. • The national and regional picture of the rains and atmospheric-related hazard are then revealed to the group participants and the range of experiences then collated for years that were similar in terms of rainfall at the national and regional level. • A discussion is then held with all participants about the decision-making implications of this range of outcomes at the local scale for the same national event.

Case studies Senegal case study The first case study outlined in this paper concerns flooding events in Senegal, West Africa, from 2005 to 2010. With an annual gross domestic product per capita of approximately USD 1,000, Senegal is a poor country where over three-quarters of the population work in the agricultural sector. With livelihoods and an economy

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highly vulnerable to climate shocks, the country has witnessed an increase in flood disasters since the mid-1990s, destroying harvests, depriving agriculture-dependent families of income, and driving people to migrate to equally flood-prone informal settlements in urban centres (Tall, 2012).   Examples of recent floods include those in 2005, when 200,000 people were affected by floods throughout the country; in 2008, when flash floods in the suburbs of Dakar impacted 2,882 households; in 2009, when 360,000 people were directly affected by flooding in the periurban areas of Dakar and 125,000 were affected elsewhere in the country; and in 2010, when 3,000 households were displaced in flash floods in various Dakar suburbs (Tall, 2012).   Against this background of recent flood activity, the Senegalese Red Cross, together with organisations partnering in the HFP-coordinated exchange, initiated a pilot project to set up a network of information providers, Red Cross volunteers, community leaders and extension service providers to disseminate seasonal, three-day, oneday and three-hour meteorology forecasts issued by the national meteorological service to vulnerable communities to allow disaster mitigation action to be taken directly by those vulnerable to floods. As part of this process, representatives from communities in Kaffrine and Kounghel were gathered in a workshop in July 2011 to develop the methodology for this pilot project, and the knowledge-timelines and participatorydownscaling approaches were trialled during this forum.   The knowledge-timelines approach made evident that Senegalese communities employ a range of indicators to predict the weather. These include the prophecies of the saltigués (forecasters), the appearance of the Maia star in the Seven Sisters (Pleiades) constellation, certain bird songs, memories of the previous year, the darkening of skies, the nesting behaviour of birds and the flowering of certain local trees. Just like climate scientists, members of the community recognised that their local information was sometimes correct and sometimes wrong, and the degree of accuracy varied for different locations. Community decision-makers also mentioned that they currently had minimal access to scientific sources of climate information.   In the case of floods, people indicated that in the previous year’s floods the darkening of the sky and the appearance of a star in the Seven Sisters were the most robust indicators. A partnering climate scientist then introduced and reviewed the levels of certainty in the various climate products currently available, including longterm climate projections and seasonal, weekly, and daily forecasts. This process served to introduce the idea of the presence of uncertainty in all types of climate knowledge.   A sample of visual prompts was shown to help participants remember particular years for the participatory-downscaling exercise. For example, for 2008 there was an image linked to the the 37th Francophone Scrabble World Championships, which were held in Senegal; for 2007, of President Wade being named the winner of the presidential elections held in February of that year; and for 2006, an image recalling Senegal’s defeat in its African Cup of Nations quarter-final football match. Table 1 gives the results of the downscaling process for the Senegal case study.

Dealing with uncertainty: integrating local and scientific knowledge of the climate and weather

Table 1. Rain recollection, rain gauge estimations, and observations of flood and rainfall* for Kaffrine and Kounghel Year

Location

Group recollections of above-average, average or below-average rains (in numbers of people in each group)

Rainfall anomaly (%)

National observation of floods

2010

Kaffrine

All above-average

110

Kounghel

All above-average

78

Flash flooding in and around Dakar

Kaffrine

4 above-average, 1 average

-9

Kounghel

3 average, 1 above-average

32

Kaffrine

4 above-average, 1 average

13

Kounghel

3 above-average, 1 average

8

Kaffrine

4 above-average, 1 average

2

Kounghel

5 above-average

-7

Kaffrine

3 above-average, 2 average

29

No flooding noted

Kounghel

4 average

-15

 

Kaffrine

3 above-average, 2 average

71

Kounghel

3 average, 1 above-average

35

Widespread flooding throughout country

2009

2008

2007

2006

2005

Flash flooding across country

Flash flooding in and around Dakar No flooding noted

Note: * Rainfall is represented as a percentage anomaly from the 1971–2000 climatological mean for each region. Information from the Senegal National Agency for Civil Agency and Meteorology. Source: authors.

Kenya case study In the second case study the exercises were carried out in a parallel workshop coordinated by Christian Aid and the Christian Community Services for Mount Kenya East in the Mbeere district in eastern Kenya. Participants in the workshop included local farmers, agricultural and livestock extension workers, and humanitarian and development actors from partner institutions.   In the 2013 UNDP Human Development Report Kenya is ranked 145th with an annual income averaging USD 1,541. Many communities living in the arid and semiarid areas of Kenya are vulnerable to climate stresses and shocks, in part due to their dependence on rain-fed agriculture and livestock production. This occurs in a context in which 75 per cent of Kenyans are employed in the agricultural sector. At the time of the exercise Mbeere district had not received adequate rainfall in the previous four seasons and had experienced a succession of poor harvests and associated high levels of livestock mortality. HFP-coordinated efforts were integrated with a Christian Aid-funded Strengthening Agricultural Livelihoods Innovations Project that aims to enable access to and support the appropriate utilisation of climate knowledge and market intelligence by small-scale farmer groups.   The knowledge-timelines exercise revealed that farmers use a variety of local sources of climate information in the Mbeere district. Community indicators include

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Table 2. Rainfall recollections and national observations of floods/droughts for Mt Kenya South Year

Group recollections of good, average or poor rains, droughts and floods (in numbers of groups)

Other recollections

National observation of droughts and floods

2009/2010

4 poor rains, 1 average

Short, intense rains; worse drought than previous year

Countrywide drought

2008/2009

3 poor rains, 2 good rains

Broken wet season

Countrywide drought

2007/2008

2 poor rains, 1 average rains, 2 couldn’t remember

2006/2007

2 poor rains, 3 average rains

2004/2005

2 poor rains, 1 average rains, 2 good rains

1997/1998

1 poor rains, 4 floods

Broken wet season Countrywide drought No break in the rains with a poor harvest apart from early root crops

Countrywide floods

Source: authors.

the behaviour of dragon flies (the proximity of the onset of rain being indicated by the dragon flies’ landing), the shooting of strawberry plants and the migration of bees. As with the Senegal case study, participants expressed a range of confidence in the local measures, with the behaviour of dragon flies being particularly trusted as an indicator of the imminent arrival of the rains. It was particularly noted that the local indicators of the rains occurred relative to the start of the rains, e.g. two or three weeks before rain onset, whereas scientific knowledge tends to be delivered at set dates in the calendar.   The participatory-downscaling exercise was then run with events identified by local partners as being of relevance to the Mbeere community. Among these was a local killing in 2010 that received extensive media coverage, a major cultural event in 2009 and a controversy over the treatment of inmates in a Meru jail in 2004. Table 2 gives the results of the downscaling process.

Impact of employing approaches that bring together indigenous and scientific sources of weather and climate An evaluation of the impact of weather and climate forecasts in an African agricultural context is notable for its reliance on modelled estimates of the potential value of seasonal total forecasts. For example, in a study to support maize-planting and fertiliser management decisions at two semi-arid locations in southern Kenya it was found that General Circulation Model (GCM) predictions could potentially increase average gross margins by 24 per cent and 9 per cent, respectively (Hansen et al., 2009).

Dealing with uncertainty: integrating local and scientific knowledge of the climate and weather

Further, perfect foreknowledge of daily weather for the growing season would be worth an estimated 15–30 per cent of the average gross value of production and 24–69 per cent of average gross margin, depending on location and whether household labour is included in cost calculations. The differences in the values gained between GCM-based forecasts and perfect foreknowledge give some indication of the magnitude of the potential economic benefits associated with the use of improving seasonal forecasting in agriculture alone. However, it should also be recognised that these estimates were made using crop simulation models, which notoriously produce significantly higher and (temporally varying) different yields to the actual yields achieved by most farmers. A similar study in West Africa using a bioeconomic model of a smallholder farm in Senegal revealed that existing statistical and dynamical modelbased forecasts could potentially increase farmers’ income by 9–13 per cent (Sultan et al., 2010). Probably the most cited example of value being attributable to seasonal forecasts is that described by Patt and Gwata (2002), which focused on a two-year period in rural Zimbabwe. The results of this study purport to show that the use of forecasts was associated with an increase in harvest of 9.4 per cent compared to a typical range of harvests, although it should be recognised that this relied on farmers’ self-reporting of harvests.   The exchange framework in which the knowledge-timelines and participatorydownscaling approaches were delivered encompassed a range of activities to support increased access to, understanding of, and appropriate application of weather and climate information. These included providing timely weather and seasonal climate forecasts, engaging the community in developing appropriate channels for communication, and promoting local ownership of climate information through the provision of community-managed rain gauges. Thus, valuing the impact of the two approaches by themselves is impossible. However the following impacts—to which the two approaches contributed—can be discerned:

(i) Increased understanding of and trust in weather and climate information among both the participating community and humanitarian and development partners Access to and confidence in weather and climate information among both communities at risk and humanitarian and development partners participating in each demonstration study were low at the beginning of the process. The methodologies employed raised awareness of the reasons for climate science uncertainty among both these user groups and demonstrated to climate scientists the vital importance of better addressing the weather- and climate-information needs of communities at risk.   Farmers’ groups participating in the approaches confirmed having made decisions about cropping practices that were based on both the information received in the scientifically based forecasts and their own local knowledge. Prior to the exchange activities in which the approaches were embedded, participating farmers in Kenya considered that the forecasts issued by the national meteorological services were not relevant to their locality. Participating farmers groups in Mbeere relied more heavily on scientific forecasts once these had been explained and they had a chance to assess

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their reliability over two seasons. By the end of the second season it was clear that farmers’ confidence in scientific forecasts was increasing.   In both demonstration studies the employment of innovative participatory methodologies has supported the creation of ‘demanding customers’. Community members participating in the demonstration study in Senegal, for example, contacted Red Cross information relays to ask where the forecast was when it was not delivered on time. Similarly, farmers participating in the demonstration study in Kenya have been willing to invest in the costs incurred in receiving SMS forecasts.

(ii) Tangible benefits in terms of lives saved and increased agricultural yields Flood-risk communities participating in the exchange in Senegal employed 24-hour forecasts of heavy rains to make timely decisions on when it was safe to farm and travel, and whether it was necessary to take action to protect their lives, children’s safety, and livelihood assets, including both livestock and equipment. Information on the start and quality of seasonal rains received from the Senegalese National Agency for Civil Aviation and Meteorology, coupled with observations from the community-managed rain gauges, supported decisions on when to plant and the variety of seeds to use.   Farmers’ groups participating in the Kenyan demonstration study were able to make a wide range of planting decisions depending on the type of forecast received: seven-day forecasts informed a range of agricultural activities, including the application of fertiliser, weeding and harvesting, while seasonal forecasts supported decisions on the selection of crop and seed type. In Kenya’s ‘short rains’ of October, November and December 2011, for example, based on a seasonal forecast projecting an increased probability of an early start to the rains and arising in part from the capacities developed through these exchange methodologies, participating farmers sought to use early-maturing crop varieties and/or deployed agricultural techniques that could withstand the early cessation of the rains. In many areas farmers only grew maize with conservation agricultural techniques, such as the nine-seed hole, to conserve moisture. Particular attention was also given to moisture-conserving land management such as contour bunds and ways of channelling rainfall into cultivated areas.   While obtaining a full and statistically sound understanding of the attributable yield impact is a significant challenge, the review of the second season (the 2012 long rains) asked farmers how they assessed the decisions they made differently as a result of obtaining and understanding scientifically based weather and climate information. As shown in Table 3, preliminary results suggest that farmers attributed significant yield improvements to their ability to change key agricultural decisions based on improved access to and understanding of seasonal and short-term forecasts. Overall, most farmers attributed an increase in yield of greater than 5 per cent, and two-thirds attributed a greater than 15 per cent increase in crop output to the use of the forecast for the 2012 long rains.   Exchange activities have highlighted that better use of weather and climate information requires that the users receive the information in probabilistic rather than

Dealing with uncertainty: integrating local and scientific knowledge of the climate and weather

Table 3. Farmers’ attribution of yield increase to climate information-enhanced agricultural decision-making, August 2012 Effect of forecast on crop output

Negative effect

No effect

Small increase, 50%

No. of farmers

2

0

1

14

24

12

Source: Ewbank (2012).

deterministic formats. Issuing forecasts in the form of deterministic information, albeit as recommendations with qualifications, seems to set the forecast up for failure if a lower probability event happens. Yet it should also be recognised that a reliance on probabilistic information requires farmers to be adequately trained in the interpretation of seasonal forecasts and how to navigate the uncertainty they contain.   Exchange activities have found that those living in multi-risk environments are used to taking decisions in situations of uncertainty and highlighted the value of developing approaches that can strengthen users’ understanding of the current levels of certainty in relevant areas of climate science. Participating farmers’ groups in Mbeere, for example, eagerly took to probabilistic information once it had been explained in straightforward terms using practical examples.

Discussion and conclusion Approaches that support improved interaction between climate scientists and decisionmakers, as well as increasing understanding and respect of each other’s sources of weather and climate knowledge, can be seen to support successful uptake of forecast information (IRI, 2011; McAdoo, Baumwoll and Moore, 2008; Victoria, 2008). Involving local communities in this process necessarily requires the integration of local knowledge with that from the scientific community. The integration of local knowledge and practices can address key risks affecting communication issues, including those of credibility, legitimacy, scale, cognition, institutional practices and norms, and the complex nature of choices and decision-making (Patt and Gwata, 2002). This process of integration also offers the opportunity to triangulate and validate national and regional forecasts with local observations.   While disaster risk reduction frameworks have recognised the need to integrate local and scientific sources of information (UNISDR, 2005, sec. 3(i)(a), 2008), methodologies to support concrete integration at all levels of disaster risk decision-making, from community to international, remain relatively rare (Denkens, 2007; Mercer et al., 2010).   While knowledge can be categorised in a number of ways depending on whether it is local or generalised; informal or formal; novice or expert; tacit, implicit or explicit; and traditional or scientific (Raymond et al., 2010), such divides are arbitrary and do not represent historical interlinkages (Agrawal, 1995). Different knowledge

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types vary in terms of how the creators of knowledge perceive the nature of that knowledge, what counts as evidence and which forms of knowledge are valid (Firestone, 1987; Kuhn, 1977). In both the knowledge-timelines and participatory-downscaling approaches there is no attempt to form or conform to a hierarchy of knowledge. Rather, the techniques described attempt to draw on similarities between knowledge types to understand the limits of current information in forecasting the weather and climate. In doing so, these techniques act as a basis for the production of ‘user-inspired’ and ‘user-useful’ management approaches (Raymond et al., 2010). These management approaches represent a movement away from policy and user interventions being informed purely by reductionism, which is prominent in many of the pure sciences, to a post-normal science encompassing different forms of knowledge and rationality that link social and ecological systems (Berkes, 2004; Folke et al., 2005; Nowotny, Scott and Gibbons, 2001; Scones, 1999).   In this paper we have presented two tools that attempt to help communities and decision-makers utilise scientific information by understanding some of the uncertainty that is inherent in the climate information they receive. In the first, communities are asked to compare their understanding of local knowledge of weather and climate with knowledge from the scientific community. In doing so, it was found that both scientific and local knowledge types share the characteristics of being accurate sometimes and inaccurate at other times, varying across geographical areas. From this basis of understanding of scientific information, the research embarked on the participatory-downscaling process.   Clearly the employment of this approach revealed some disagreement between the different ‘measurements’ of rainfall, drought and flood conditions as estimated by scientific observations, on the one hand, and by the group recollections, on the other, as well as by these two sources. Uncertainties are present in all observation systems in relation to recollections of droughts, floods and rains. This was due to confusion over which years were being referred to, rainfall measurements being represented by sparse rain gauges in each region, and the flood and drought observations being biased towards conditions in the country capitals at some distance from the case study locations.   However, there is also good reason to believe that there are real differences in the measurements caused by rainfall being highly variable in space and time (Diop and Grimes, 2003), such that rainfall conditions in one part of a region are quite likely to be different to those experienced in other parts. This is particularly apparent in Senegal, due to the convective nature of rain systems linked to intense rainfall and flooding, while the Mbeere district in Kenya has very different topographies and distinct micro-climates with varying levels of irrigation, such that even a large-scale drought is expected to have different manifestations spatially. Future research should be directed to triangulating local recollections of weather and climate with modelled downscaled information, the encouragement of the documentation of local weather and climate conditions, and the investigation of the implications of the uncertainty in weather and climate information for the decisions taken by local communities.

Dealing with uncertainty: integrating local and scientific knowledge of the climate and weather

Acknowledgements Our thanks go to those who participated in the community-based workshops in Kaolak and Kaffrine in Senegal and Mbeere in Kenya where these methodologies were developed and to those who reviewed this article, especially Dr Mike Harrison. The research was supported by the HFP.

Correspondence Emma Visman, Department of Geography, King’s College London, 138–142 The Strand, Strand Bridge House, London WC2 R1HH, United Kingdom. Telephone: +44 (0)1303840309; mobile: +44 (0)7905620794. E-mail: [email protected]

Endnotes 1

Dominic Kniveton works at the School of Global Studies, University of Sussex; Emma Visman and Lucy Pearson are members of the Humanitarian Futures Programme, King’s College London; Arame Tall is a member of the Consultative Group on International Agricultural Research Program on Climate Change, Agriculture and Food Security; Mariane Diop works for the Senegal National Agency for Civil Aviation and Meteorology; Richard Ewbank is a member of Christian Aid; and Ezekiel Njoroge works for the Kenya Meteorological Department. 2 A statistical evaluation of the accuracy or predictability of a forecast. 3 Reference to the incorporation of traditional and indigenous knowledge is included in UNISDR (2005, sec. 3(i)(a), p. 9).

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Dealing with uncertainty: integrating local and scientific knowledge of the climate and weather.

While climate science has made great progress in the projection of weather and climate information, its uptake by local communities remains largely el...
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