Tigithi: Micro-Level Preparedness and Response to the 2015–16 El Niño Early Warning

Abstract

The 2015–16 El Nino weather was highly anticipated in Kenya. This anticipation arose, in part, from the experiences with previous El Nino events such as the 1997–98 El Nino rains that resulted in widespread losses of lives and destruction of property worth millions of dollars. This study, undertaken in Tigithi Location of Laikipia County in Kenya sought to explore the extent to which communities were prepared to manage and respond to the 2015–16 El Nino weather. Specifically, the study sought to answer three interrelated research questions namely: 1) did rural agro-pastoral communities in Laikipia find advisories by forecasters helpful?; 2) how did communities prepare and respond to the advisories; and 3) to what extent were the preparedness and response actions helpful in managing the impacts of El Nino-influenced weather and other day to day development challenges that perpetuate their vulnerability to such hazards? The study employed a quantitative approach involving structured interviews with 238 households. The study shows that although there were several negative outcomes which include impassible roads and destruction of short term crops, the 2015–16 El Nino event in this region was rather, mainly beneficial. Improved food and livestock production and enhanced access to water marked a turning point to a prolonged drought season that had characterized this region resulting in widespread food insecurity. Yet, the extent to which the residents were able to take advantage of the enhanced rains was limited by low economic capacities thereby causing them to invest only in low cost, short-term investments that were helpful but not for long-term risk reduction. The Kenya Meteorological Department (KMD) played an instrumental role in producing El Niño weather-related early warning information. The early warning information by KMD got clearer with time, in the backdrop of an apparent struggle to balance this information with other divergent sources and with politics of uncertainty. The El Niño early warning lead time was also significantly short, with a majority of respondents stating that they only first received the early warning messages one month before the onset of the rains. These findings underscore the importance of reliable, context specific and timely El Nino weather early warning. They also bring to the fore the critical role of long-term development efforts that support localized disaster preparedness and risk reduction initiatives.

Full text

Kenya—Local
Tigithi: Micro-Level Preparedness and Response
to the 2015–16 El Niño Early Warning
Jackson Wachira and Lydia Cumiskey
Abstract The 2015–16 El Nino weather was highly anticipated in Kenya. This
anticipation arose, in part, from the experiences with previous El Nino events such as
the 1997–98 El Nino rains that resulted in widespread losses of lives and destruction
of property worth millions of dollars. This study, undertaken in Tigithi Location
of Laikipia County in Kenya sought to explore the extent to which communities
were prepared to manage and respond to the 2015–16 El Nino weather. Specifically,
the study sought to answer three interrelated research questions namely: 1) did rural
agro-pastoral communities in Laikipia find advisories by forecasters helpful?; 2) how
did communities prepare and respond to the advisories; and 3) to what extent were
the preparedness and response actions helpful in managing the impacts of El Nino-
influenced weather and other day to day development challenges that perpetuate
their vulnerability to such hazards? The study employed a quantitative approach
involving structured interviews with 238 households. The study shows that although
there were several negative outcomes which include impassible roads and destruction
of short term crops, the 2015–16 El Nino event in this region was rather, mainly
beneficial. Improved food and livestock production and enhanced access to water
marked a turning point to a prolonged drought season that had characterized this
region resulting in widespread food insecurity. Yet, the extent to which the residents
were able to take advantage of the enhanced rains was limited by low economic
This Kenya study used a quantitative approach involving interviews with household heads in order
to capture a broad range of insights into local-level preparedness and response to El Niño. The
study area was selected for this research because it reported above normal Short Rains for October,
November, and December (OND). The research team was familiar with both the area and the local
language.
The original version of this book was revised: Abstract has been updated with correct lines. The
correction to this book can be found at
https://doi.org/10.1007/978-3-030-86503-0_21
J. Wachira (B
)·L. Cumiskey
Water Youth Network, Department of Earth and Climate Sciences, University of Nairobi, Nairobi,
Kenya
e-mail: j.wachira@students.uonbi.ac.ke;j.wachira@outlook.com
L. Cumiskey
e-mail: l.cumiskey@wateryouthnetwork.org
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022,
corrected publication 2022
M. H. Glantz (ed.), El Niño Ready Nations and Disaster Risk Reduction,
Disaster Studies and Management, https://doi.org/10.1007/978-3-030- 86503-0_14
249

250 J. Wachira and L. Cumiskey
capacities thereby causing them to invest only in low cost, short-term investments
that were helpful but not for long-term risk reduction. The Kenya Meteorological
Department (KMD) played an instrumental role in producing El Niño weather-related
early warning information. The early warning information by KMD got clearer with
time, in the backdrop of an apparent struggle to balance this information with other
divergent sources and with politics of uncertainty. The El Niño early warning lead
time was also significantly short, with a majority of respondents stating that they only
first received the early warning messages one month before the onset of the rains.
These findings underscore the importance of reliable, context specific and timely El
Nino weather early warning. They also bring to the fore the critical role of long-term
development efforts that support localized disaster preparedness and risk reduction
initiatives.
Keywords El Niño ·La Niña ·Early warning ·Preparedness ·Response ·
Laikipia ·Kenya
1 Introduction
Kenya’s weather is significantly influenced by the El Niño Southern Oscillation
(ENSO), as is the case with many other regions of the world (Stige et al. 2006,
Kovats et al. 1999, Siderius et al. 2018, Gannon et al. 2018, Rembold et al. 2015). El
Niño teleconnections (during warm phases of ENSO) in Kenya tend to be associated
with above-average precipitation during the October-December (OND) short rains
(Karanja and Mutua 2000). Conversely, La Niña teleconnections (during cold phases
of ENSO) tend to be associated with prolonged drought in many parts of East Africa.
El Niño teleconnections, however, vary, and it is now widely accepted that no one El
Niño is exactly like another (Gannon et al. 2018; Siderius et al. 2018; Rembold et al.
2015; Karanja and Mutua 2000). The huge impact of ENSO variations on Africa’s
weather-dependent crop production regimes has been well described. Stige et al.
(2006), for example, approximate that ENSO variations on maize production—a
major food crop for many countries across Africa—corresponds to a crop that could
feed 20 million people per annum.
ENSOs are generally predictable and advances in prediction models have
enhanced this predictability, a development that has increased expectations of govern-
ments and humanitarian organizations to effectively mitigate and respond to adverse
impacts associated with ENSO events (Stige et al. 2006; Tozier de la Poterie et al.
2018). With predictions indicating that the 2015–16 El Niño would be one of the
largest in recent history, such expectations were apparent as governments across many
regions were alerted to prepare for the worst—excessive rains, drought, flooding,
and other weather-related impacts. The El Niño was predicted as early as 2014 and
by May 2015 many national and international meteorological agencies had agreed
that the event would be one of the strongest on the historical record (Bremmer 2015;

Kenya—Local 251
L’Heureux 2017). Preparedness advisories detailing a grim picture of possibly devas-
tating outcomes dominated the messaging that was transmitted by print, broadcast,
and social media across the globe. The International Business Times, for instance,
wrote: “A potentially record-breaking El Niño brewing in the tropical Pacific Ocean
will soon hit the eastern shores of sub-Saharan Africa, even as African leaders say
they aren’t sure how to prepare” (qutd. in Winsor 2015). This bleak reporting was no
doubt informed by the 1997–98 El Niño that resulted in widespread deaths, disruption
of lives, and destruction of property and livelihood sources for millions of people
in Kenya specifically (Karanja et al. 2001a) and across the world more generally
(Buizer et al. 2000).
Indeed, the 2015–16 El Niño did result in a considerable increase in the global
hydrological cycle and precipitation amounts (Gannon et al. 2018); however, it did
not materialize as anticipated in Kenya, as only a fraction of the country experienced
very wet to extremely wet conditions that were broadly similar to—or even drier
than—non-El Niño years (Siderius 2018: 9). Yet the 2015–16 El Niño is now an
important point of reference for ENSO researchers, as it was one of the strongest
since the 1997–1998 event (Tozier de la Poterie et al. 2018). Although many studies
on the socioeconomic and hydrological impacts of ENSO events in Africa exist (see,
for instance, Siderius et al. 2018; Stige et al. 2006, Glantz 2001a), generalization
of results across regions remains a challenge due to the uniqueness of each event.
Moreover, in Kenya the impacts of the 2015–16 El Niño were largely localized
(Gannon et al. 2018), necessitating studies on micro-level experiences particularly
in rural areas where El Niño public forecasts have mostly been quite limited (Glantz
2001a).
This chapter is based on field data from research conducted in February 2016 at
Tigithi Location, Laikipia County (Fig. 1), Kenya and commissioned by the Water
Fig. 1 Laikipia County in
Kenya (Karell Africa 2020)

252 J. Wachira and L. Cumiskey
Youth Network. 1. Considering the highly anticipated impacts of the 2015–16 El
Niño in Kenya, this study sought to collect scientifically relevant information on
local-level preparedness and response. Specifically, the study sought to answer the
following three questions: (1) Did rural, agropastoral communities in Laikipia, Kenya
find forecast advisories helpful (2) Did communities prepare and respond to the
advisories? and (3) Were the preparedness and response actions helpful to residents
in managing the impacts of the El Niño-influenced weather as well as other day-to-
day development challenges that tend to perpetuate their vulnerability to such natural
hazards? In addition, the study sought to engage young Disaster Risk Reduction
scientists (i.e., students and volunteers) in the collection and synthesis of event data
and to share Kenyan El Niño experiences with other countries as part of the “El Niño
Ready Nations” initiative (https://elninoreadynations.com/).
The next section of this chapter undertakes a brief review of the conceptual litera-
ture that provides the basis for an analysis of respondents’ preparedness and response
to risk early warnings. Overall, this review demonstrates the challenges to developing
and communicating warnings effectively, but it also discusses the tremendous bene-
fits available when proactive measures are taken in response to extreme weather
and climate forecasts. The study methods are then introduced, followed by a brief
description of the study area and the broader context within which it is situated.
Finally, main findings are discussed, and key conclusions and recommendations are
elaborated.
2 Enabling Effective Preparedness and Response to Early
Warning
Effectively preparing for and responding to El Niño forecasts and early warnings
presents similar challenges for achieving end-to-end, people-centered, or impact-
based Early Warning Systems (EWSs) for other climate and weather-related hazards
(Agrawala et al. 2001;WMO2015,2018). Warning source, lead time, communica-
tion channels, and message content are all factors that have been found to influence
user response to warnings (Mileti 1995; Lindell and Perry 2003; Patt and Gwata
2002). If warnings are received and understood, a broad range of preventative, mitiga-
tive, or adaptive actions can be taken. Such actions are, however, dependent on the
vulnerability and capacity of those who are at risk (Wisner et al. 2004).
The reputation and track record of the “warning source,” including the extent to
which observed phenomena match initial forecasts, will influence users’ trust and
belief in a warning (Mileti 1995; Lindell and Perry 2003; Patt and Gwata 2002).
1N.B. The Water Youth Network was created soon after the 6th World Water Forum in 2012 in
Marseille, France, when a group of young people from all over the world met to share their solutions
to address water-related issues. The group realized that together they could positively influence the
water sector. The Water Youth Network is now an inclusive connector in the water sector, with a
vibrant community of active young people across disciplines. This work was supported by WYN
as a pilot research project conducted by the Disaster Risk Reduction (DRR) team.

Kenya—Local 253
Furthermore, scientific forecasts can also conflict with local traditional forecasts,
making it difficult for some users to build trust in warnings (Patt and Gwata 2002;
Masinde 2015). Additionally, the transparency of forecasts and the accountability of
forecasters is crucial, which means that forecasting agencies need to publicly admit
any limitations or weaknesses that have gone into decision-making processes (Parker
et al. 2007; Baudoin et al. 2017).
The lead time an early warning provides users is crucial for effective response. The
shorter the lead time, the narrower the range of actions users can undertake. Short lead
times also makes it more difficult for weak governance systems to respond compre-
hensively—especially in support of the most vulnerable communities (Agrawala
et al. 2001). To be sure, the longer the lead time, the more uncertain a forecast is,
the more risk users have to consider when making decisions. In addition, the timing
of forecast dissemination does not always align with the timing that local decision
makers need to mitigate impacts, as with deciding on what seed varieties are to be
planted (Patt and Gwata 2002).
Warnings can be disseminated through a range of channels to reach end users and
play an important role in shaping user response (Mileti 1995). Such channels include
technical ones like mobile services (e.g. SMS, cell broadcasting, and interactive
voice response; see Cumiskey et al. 2015) and media like local and national radio,
television, and print (Glantz 2001a,b), but they can also include personal channels
like word-of-mouth communications form respected individuals or local religious
leaders (Eisenman et al. 2007). Warning messages can also be considered more
credible if they are consistent and verifiable across different communication channels
(Mileti 1995; Shah et al. 2012). Coordinating consistent warning dissemination is,
however, particularly challenging where there are poor institutional linkages between
organizations from national- to local-levels, as between meteorologists, food security
planners, and agricultural officers (Patt and Gwata 2002; Baudoin et al. 2017).
Less generic and more tailored warning message content is advocated because
it can provide more specific information on expected impacts and response actions
(Twigger-Ross et al. 2009; Coughlan de Perez et al. 2015;WMO2015,2018). On
the other hand, whether or not to include information on uncertainty and probability
in warning messages is a debated issue among researchers: Although some research
demonstrates that forecast users such as farmers struggle to interpret probabilistic
information over deterministic forecasts (Glantz et al. 1997;Parkeretal.2009), others
argue that users do have the ability to understand probabilistic information but need
focused and continued training to understand the limitations of such information
(Patt and Gwata 2002). For example, in southern Africa many people interpreted the
1998 El Niño seasonal forecast as a deterministic prediction of drought, not having
understood the forecast’s probabilistic nature (Dilley 2000).
For seasonal forecasts, a range of preparedness and response actions can be taken
by users such as farmers or humanitarian responders at different timescales to prepare
for alterations in weather or climate conditions. These actions can result in signifi-
cant economic and social benefits but are typically underutilized (Meinke and Stone
2005; Coughlan de Perez et al. 2015). Such actions include, for example, tailoring
crop rotations, making planting and harvesting decisions, adjusting storage regimes,

254 J. Wachira and L. Cumiskey
streamlining transport and export logistics, distributing water purification tablets or
mosquito nets, fortifying vulnerable structures, and providing free or low-cost cash
transfers (Meinke and Stone 2005; Coughlan de Perez et al. 2015; Nobre et al. 2019;
Agrawala et al. 2001).
Overall, if forecasts are used effectively, they can reap large benefits for end users.
An example of this for seasonal forecasts was demonstrated in one relatively small
sugar milling region in Australia where the value of the climate forecasting system
exceeded AUD$1.9 million for the studied season in 1998 (Antony et al. 2002). A
case study in Bangladesh for five-day forecasts demonstrated an average savings of
USD$472 per household, with the agriculture, livestock, and fishery sectors gaining
the most benefits (Cumiskey et al. 2015). The actions taken included early harvesting,
delaying seedbed preparation, netting fishponds, heightening dams around ponds,
and moving cattle.
Multiple interacting societal factors influence exposure and vulnerability to
extreme climate and weather events, however, thus affecting response capacity within
any specific population. For example, users can only take response actions they can
afford (Agrawala et al. 2001; Patt and Gwata 2002; Coughlan de Perez et al. 2015).
As such, what underlying support systems are in place will strongly influence the
effectiveness of responses to short-term and seasonal forecasts.
3 Data Collection Methods
This study took a quantitative approach that involved interviews with household
heads so as to capture a broad range of insights into local-level preparedness and
response to the 2015–16 El Niño. The study area selected for this research was Tigithi
Location in Laikipia County, Kenya. This area was selected because it reported above
normal short rains for October-December (OND), and the research team was familiar
with both the area and the local language. Using the ‘Cochrane formula’ (Israel 1992:
3), a representative sample size of 227 households was selected at 95% confidence
interval and ±6.5% precision;
n0=z2pq
e2
nois the sample size, z is the selected critical value of desired confidence level,
p is the estimated proportion of the attribute that is present in the population, q is
1-p and e is the desired level of precision (ibid) (Wachira and Cumiskey, 2017).
The sample size was eventually increased by 13 households to account for non-
responses and to allow for quality control. A total of 238 questionnaires were used
for results analysis. A random sampling method was used to select households to
be interviewed, targeting heads of household. Where the household head was not
available, the head’s spouse was asked to participate in the study. Data was collected

Kenya—Local 255
by a team of university students under the supervision of an experienced researcher.
Completed questionnaires were gathered and checked for accuracy. Data was then
coded and analyzed using Microsoft Excel.
4 Study Area and Broader Context
Tigithi is one of 55 Locations in Laikipia County and is an extensive social-political
and administrative region in Kenya, measuring 9,462 km2between latitudes 0°18
South and 0°51 North and between longitudes 36°11 and 37°24East (Government
of Kenya 2018). As with its topography, the County’s socioeconomic and political
systems are quite heterogeneous, which is perhaps attributable to the enormous trans-
formations that have characterized the region over the past century. Before the onset
of colonization and establishment of a settler economy, Laikipia region was inhabited
by the Maasai who used the expansive semi-arid highlands for nomadic pastoralism
(Hughes 2002). Early European settlers were, however, excited by the agricultural
production potential of the region, which led to the relocation of the Maasai to the
less favorable southern reserves through a 1914 agreement that is widely thought
to have favored white settlers’ imperial interests at the expense of Maasai interests
(Hughes 2002,2006).
Upon independence in 1963, a ‘willing seller-willing buyer’ approach to land
redistribution saw some settlers return to Europe after selling their land to the
Kenyan government, while others remained. Land acquired by the government at
this time was to be used to settle landless “natives,” though much of it ended being
given to the community from which the president came, the Kikuyus (Kanyinga
2009). Other lands were purchased by companies that were controlled by an
emerging class of political-economic elites aligned to the post-independence Kenyan
government (Gravesen 2020).
Thus, although Laikipia County is today seen as cosmopolitan, settled by up
to 23 distinct communities (Government of Kenya 2018), rural settlements and
land are dominated by three distinct populations. A brief history highlighting the
differences between the three main populations in the area is important to explicate
because it affects the broader socioeconomic organization of the study site. The first
group includes original British settlers and later British government beneficiaries
who even today maintain control over most of the productive land in the region
(Letai 2011, Evans and Adams 2016). They tend to be commercial farmers and own
about 40% of the total land of Laikipia (Letai 2011, Evans and Adams 2016). The
second distinct group is the Kikuyu people, who, as noted above, benefited from
post-independence government interventions and elite cum politician-driven land
acquisitions that resettled them into the semi-arid Laikipia due to increasing popu-
lation pressures in their native central highlands. The last group is the Maasai, the
original inhabitants of the regions, including those who never made the move to the
southern rangelands as well as those who returned upon Kenyan independence after
having had been relocated out of the region decades earlier (Hughes 2006).

256 J. Wachira and L. Cumiskey
The implication of this history is that land in Laikipia, which is the main source
of livelihood among rural inhabitants and effectively their “first line of defence
against a disaster” (Cannon 2008: 3), is highly contested. The Maasai and other
related pastoral communities such as the Samburu, who insist on de facto ownership
of the semi-arid highlands, claim original rights to land access and use. With this
claim, conflicts have arisen that have typically been characterized by subtle, resource-
based insecurity but have also occasionally spilled over into violent confrontations
between pastoral communities and agropastoral and large-scale commercial farmers
(Gravesen 2020, Letai 2011, Evans and Adams 2016; Galaty 2016; Lengoiboni et al.
2000).
Tigithi Location exists within this complex land-tenure framework. It is located
about 20 km southwest of Nanyuki, the county seat that is inhabited primarily by
members of the Kikuyu community who settled there in the 1970s. Annual rainfall
in the Location is typically between 450 and 750 mm, and treed grassland is the main
vegetation type. On the settled plots, crop cultivation and grazing are the primary
land-use forms. Maize, beans, and potatoes are the predominant crops; however,
agricultural production is constrained by unreliable and poorly distributed rainfall,
which is characterized by periodic intra- and off-season dry spells.
For the period prior to this study, the area had experienced an extended dry season
spanning five years and resulting in crop losses, livestock death and wasting, and a
severe disruption to food security. According to a local NGO Caritas Nyeri, 40–66%
of the population had required some sort of food relief in most years between 2005
and 2011 (Wachira 2013: 3). Conflicts over scarce natural resources such as water
and pasture are common across the wider region (Gichuki 2002), and poverty rates
are strikingly high, with Human Poverty Index in the area exceeding 57% against a
Kenyan national HPI of just over 29% (Government of Kenya 2018).
Most residents of the area have a secondary education and undertake subsis-
tence crop farming for a living. Reliance on subsistence crops and livestock farming
is, however, highly constrained by occasional droughts and limitations related to
resource-based conflicts, which might explain the high rate of poverty in the area
(see Table 1).
Tabl e 1 General socioeconomic characteristics of Tigithi Location (Survey data). General char-
acteristics of survey respondents in Tigithi Location and their economic activities (Wachira and
Cumiskey 2017)
Percentage of
population
Median age Percentage of
respondents
with basic
education
Percentage of
respondents with
secondary
education
Percentage of
respondents
engaged in
subsistence
farming
Male 44 58 38 39 61
Female 56 48.5 43 35 65
Average 53.25 40 37 63

Kenya—Local 257
5 Findings and Discussion
5.1 2015–16 El Niño Predictions in Kenya
According to World Food Programme forecast models (WFP 2015), the 2015–16 El
Niño had been forming for some time before its official declaration in March 2015,
when it was forecasted to be one of the strongest in record history (Fig. 2) and to
likely remain locked in for over a year. Although it was able to identify El Niño-like
sea temperatures in May 2015, the Kenya Meteorological Department (KMD) did not
issue an official El Niño Advisory to the public until August 2015 (Tozier de la Poterie
et al. 2017). The literature indicates that the early caution by KMD was informed
by a need to maximize the accuracy of prediction based on lessons learnt from other
El Niño predictions, like from the 2014 event that did not materialize (Tozier de la
Poterie et al. 2017) or from KMD’s early warning for the 1997–98 event which “was
not taken seriously and hence no mitigation/emergency response measures were put
in place” (Karanja et al. 2001b) Thus, like other previous events, the 2015–16 El
Niño demonstrated what uncertainties remain surrounding the phenomenon, how
the credibility of forecasts affect response, and why awareness of the nature of its
potential impacts can lead to different outcomes.
Indeed, even as KMD warned communities to prepare for El Niño rains, some
politicians actively deprecated these advisories as lies. One Member of Parliament
(MP) went so far as to threaten to sue KMD “should it fail to rain El-Niño as they
say.” This MP also noted: “It has been three years now the meteorological department
has been lying to us that there will be heavy rains, yet farmers had been borrowing
loans to prepare their lands, buying inputs and additional livestock” (The Standard
Newspaper 2015a). In this statement, the MP could be referring to the ‘failed’ 2014
forecast for the El Niño that began to form but ended up petering out. Importantly,
he is also genuinely highlighting the dilemma facing many citizens in Kenya who
need to make difficult, forecast-based choices. The consequences of such choices can
mean the difference between benefitting by having invested to reduce risk before an
event or suffering by having invested what meagre resources one has to mitigate the
impacts of an event that, like the one in 2014, eventually fails to form as forecasted.
The latter scenario is, it should be understood, a lose-lose for everyone, forecasters
and farmers alike.
Fig. 2 Inter-tropical Pacific Sea surface temperature anomalies from 1982 to 2015. The red line
indicates the El Niño threshold (WFP 2015)

258 J. Wachira and L. Cumiskey
KMD’s monthly advisories indicate everything from definitions to global impacts.
They also include detailed explanations of the current status of an El Niño’s evolution
as well as typical impacts of an event on Kenyan agriculture, food security and
fisheries, disaster management, health, transport and public safety, water resources
management, and energy generation. Early advisories for the 2015–16 event stated
a 90% chance that El Niño rains would continue into early 2016 (The Standard
Newspaper 2015b); however, a consistent message from KMD indicated that its
intensity was not expected to be as extreme as in 1997. Thus, KMD advisories sought
to moderate the rather alarmist misrepresentation of media coverage of the anticipated
El Niño rains. Out of 47 counties in the country, 23 were deemed as high-risk and
were predicted to experience severe rains throughout the period (Fig. 3).
Some Countries affected by El
Niño impacts:
Nairobi Migori
Narok Turkana
Kwale Busia
Garissa Siaya
Mandera Baringo
Wajir Elgeyo
Samburu Marakwet
Isiolo Mombasa
Meru Taita Taveta
Makueni Lamu
Murang’s Tana River
Nakuru
Fig. 3 Map of counties predicted to be at high risk to El Niño effects (KSFFG 2016)
Although El Niño researchers have emphasized the need to consider the potentially
beneficial impacts of El Niño teleconnections (e.g. Glantz 2001b), this research has
identified a need to more strongly focus on preparing for potential negative impacts,
especially with regard to flooding, the damage from which tends to outweigh any
beneficial impacts.
5.2 Micro-Level Understanding and Perception of the El
Niño Phenomenon
Findings show that over half of the respondents (54%) (Fig. 4) understood El Niño as
simply bringing heavy rains, while 28% perceived it as bringing heavy and destructive

Kenya—Local 259
54%
28%
6%
15%
3% 0.80%
0%
10%
20%
30%
40%
50%
60%
Heavy rains Heavy
destrucƟve
rains
Heavy
Beneficial
Rains
Both heavy
beneficial and
destrucƟve
rains
A weather
phenomenon
emanaƟng
from warming
of the Pacific
Ocean
No idea
% of respondents
Respondents understanding of El Niño
Fig. 4 Survey respondents’ understandings of El Niño (Authors)
rains. Of the total, 15% of respondents perceived the rains as both heavy destructive
and beneficial. Overall, 97% of the respondents associated El Niño rains with heav-
iness. Intriguingly, only 3% of respondents associated El Niño as a Pacific Ocean
phenomenon that led to atypical rainfall patterns in Kenya.
Various factors play a role in shaping a population’s understanding of risk to
hazards like El Niño. Wachinger et al. (2013), in their research on floods, droughts,
earthquakes, volcanic eruptions, wildfires, and landslides, found that personal expe-
rience with a natural hazard, trust—or lack of trust—in authorities, and knowledge of
expert opinions have the most substantial impact on risk perception. In our research,
97% of respondents had recollections about past events, with 91% having had specific
recollections of the 1997–98 El Niño rains that had adversely impacted many parts of
Kenya. Additionally, most respondents (69%) attributed their perceptions and under-
standings to their own past experiences with El Niño events. Far fewer respondents
attributed their perceptions and understandings to the media (24%) or to storytelling
(4%). Interestingly, the impacts of formal and informal training on the respondents’
perceptions were also found to be negligible, which points to a major gap in people’s
responses to El Niño. To be sure, a wealth of literature already exists that suggests
that continued training and engagement is needed to bridge the gap and help users
better understand forecasts (see, for instance, Glantz 2001a;b) (parts of this are from
Wachira and Cumiskey 2017).

260 J. Wachira and L. Cumiskey
5.3 Micro-Level Impacts of the El Niño Rains, 2015
This study specifically sought to understand if and how 2015–16 El Niño weather
impacted the Tigithi Location. The results show that 80% of respondents perceived
the El Niño rains as having impacted the area. Impacts were both positive and
negative, though the positive ones tended to dominate (see Fig. 5).
0
10
20
30
40
50
60
70
80
90
Crop destrucon
impassible roads
destrucon of
infrustructure
disease outbreak
loss of livestock
Increased food producon
Increased livestock
producon
Agroforestly
Access to water
% of respondents cing impact
Negave and Posive Impacts of 2015-2016 El Niño Rains
Fig. 5 Negative (Red) and positive (Blue) impacts of El Niño Rains at Tigithi location (Wachira
and Cumiskey 2017)
5.3.1 Primary Positive Impacts
a. Increased Food Production
Surveys indicated that 79% of respondents reported an increase in food production
due to increased rainfall. Clearly, the El Niño rains of 2015 marked a critical food
security turning point for Tigithi Location residents, as a considerable number of
them had been relying on food relief because of the prolonged drought. A report
from Caritas Nyeri, a local NGO indicated that on average 51% of the Location’s
population had been receiving food aid in the years between 2005 and 2011 (Wachira
2013). This need for aid is directly attributable to the poor, erratic rains in the area
that often result in extensive crop failure.
b. Increased Livestock Production
Overall, 74% of respondents reported an increase in livestock production. This
increase was attributed to greater pasture availability. The area had seen five years of

Kenya—Local 261
drought before the 2015 El Niño rains, which directly led to livestock malnutrition,
low milk and beef production, and a general increase in the cost of stock rearing,
further eroding people’s purchasing power. The El Niño rains were, therefore, seen
as beneficial, with livestock production having received a boost through an increase
in adequate pasturage.
c. Improved Access to Water
Access to water tends to be a major challenge in Laikipia County—only 30% of the
population has access to potable water and residents have to walk an average of 5 km
for clean water (Government of Kenya 2018). In Tigithi, residents mostly rely on
rivers and community water supply projects that are continually under pressure due
to a combination of factors such as population increases and weak water management
structures. In this study, 26% of respondents felt that the 2015–16 rains offered a
short-term reprieve from these normal difficulties, as many residents were able to
take advantage of the situation by, for example, harvesting rainwater.
5.3.2 Primary Negative Impacts
a. Impassable Roads/Destruction of Infrastructure
Overall, 53% of respondents reported that the El Niño rains resulted in either impass-
able roads (33%) or other infrastructure destruction (20%). These impacts limited
residents’ abilities to attend to their daily chores or transport their farm produce,
erecting a significant barrier to income generation for a community heavily depen-
dent on small-scale farming. Still, the affected communities attempted to cope with
these challenges by controlling infrastructure erosion as much as possible using
branches and laying gravel.
b. Crop Destruction
Another significant negative impact of the El Niño rains in the study area, as cited by
41% of respondents, was the destruction of crops. Crop destruction was especially
pronounced with the loss of short-term crops like beans and potatoes that were
submerged under the flood waters. Such destruction was experienced despite the
mitigative efforts of the community, which dug furrows to drain excess water and
replanted where crops had been destroyed.
5.4 Effectiveness of El Niño Early Warning Dissemination
at the Local Level
5.4.1 Source of Warning
Study results indicate that 84% of respondents had received early warning informa-
tion on likely El Niño weather patterns. Of those, 88% of respondents credited KMD

262 J. Wachira and L. Cumiskey
Fig. 6 Perceived sources of
El Niño early warning
messages (Authors)
88%
0%
4%
4% 4%
Kenya Met Department
church
NGO
Barazas
with the early warning messages. As illustrated in Fig. 6, the remaining 12% believed
that the early warning information had come from the church, from barazas (public
meeting places), or from NGOs.
Notably, the County Department for Environment did not feature at all in any
respondents’ understanding of where early warning information had originated. This
exclusion might have resulted because county government departments had at the
time of the event been newly established, and many were still finding their footing
within their respective communities. Furthermore, due to their lack of standing among
community members, county departments may also not have as yet effectively distin-
guished themselves from the national government departments of which people had
long known and mostly trusted for forecasting information.
The implication here is that KMD played a pivotal role in disseminating early
warning information. Critical analysis of KMD early warning messages shows a
cautious approach, one that struggled to deal not only with the uncertainty of the
impending event but also with the important (but often overlooked) need for consis-
tent messaging. In one of KMD’s press releases, for example, acting Director James
G. Kongoti wrote:
The Kenya Meteorological Department (KMD) has noted with concern the content of infor-
mation related to El Niño conditions that has been disseminated to the general public in the
last few weeks. This is being done without reference to KMD, the service that is authorized
to provide guidance on the current status and development of El Niño conditions and its
potential impacts on the rainfall patterns in Kenya. The information is not only confusing
Kenyans but also creating anxiety and panic. It is therefore imperative that technical advice
be sought from KMD prior to such information being disseminated to the general public
(Kenya Meteorological Department nd).
In this message, Director Kongoti affirms KMD’s de jure responsibility for
predicting and releasing public advisories about the country’s weather conditions.
He notably asserts KMD’s mandated role in a context in which other actors such as
the media had been taking a de facto role, one that had possibly been confusing to
the general public.
As identified above, the fact is that KMD had strategically decided not to issue El
Niño precipitation warnings early in its advisories in order to maximize the chance
that they would provide accurate information to the general public. An unintended
outcome of this strategic delay may have been that other actors had exploited the

Kenya—Local 263
ensuing information gap, which then necessitated the reassertion by Director Kongoti
of KMD’s exclusive mandate. Further research beyond the scope of this chapter is
needed to verify this proposition. The following paragraph is updated from Wachira
and Cumiskey’s report (2017).
Not until the onset of the OND rains did KMD’s messaging become clearer in its
issuance of repeated early warnings predicting that the impact of the El Niño rains of
2015–16 would not be as devastating as those of the 1997 event. Although the Kenya
Red Cross had also taken a strong leadership role in monitoring the evolution of the
El Niño rains, no evidence exists that it or other NGOs played a significant role in
disseminating early warning messages. This outcome is perhaps because monitoring
by the Kenya Red Cross was largely situational, focused on large-scale impacts that
would inform its responses to any impending disaster situations. Worth highlighting,
therefore, is that the Kenya Red Cross Society also relied on KMD’s daily weather
forecast in its planning and reporting.
5.4.2 Warning Lead Time
A very small percentage of respondents (1%) said that they first received early
warning messages in 2014, but early warning information was available incremen-
tally in the months preceding the 2015 El Niño. That said, nearly half of all respon-
dents (47%) said that they first received early warning messages in September 2015,
which means that the lead time for most of the inhabitants of the study area was
one month prior to the onset of the OND short rains (Wachira and Cumiskey 2017)
(Fig. 7).
The major role of a prediction system such as the one operated by KMD is to
increase the lead times of early warnings to enable more timely and effective interven-
tions. A short lead time adversely impacts the effectiveness and quality of prepared-
ness and response, as decisions are made and resources are allocated in haste, if at all,
051015
0
50
100
150
200
250
0
5
10
15
20
25
30
35
40
45
50
In 2014
February
April
June
August
October
December
rainfal in mm
% of respondents
me
% of Respondents
Receiving Early Warning
OND Rainfal average (mm)
Fig. 7 Percentage of respondents receiving El Niño early warning messages over time (Authors)

264 J. Wachira and L. Cumiskey
due to limited actionable timeframes. This result was the case in Tigithi, where struc-
tural poverty is endemic and insufficient infrastructure is marked. Here, the short lead
time resulted in insufficient short-term preparedness investments. Indeed, the study
results show a positive relationship between the nature and the cost of investments,
with a majority of respondents having had invested in the least costly interventions
such as cropping. While these investments may have been somewhat helpful in the
short-term, they were no less problematic in that they could not provide a sufficient
buffer to adequately protect the area’s at-risk populations from the adverse impacts
of either the 2015–16 event or of future events yet to form.
5.4.3 Early Warning Communication Channels
Survey results identified local FM radio stations as the most common channels for
early warning information dissemination, as indicated by 69% of respondents (Table
2).
Because FM radio stations often broadcast in the local language, they are often
easy for people to understand. This relatability is further promoted by the shared
local identity, as the broadcasters and listeners typically come from the same ‘tribal’
regions.
There is evidence, however, that in past disaster crises both positive and negative
solidarity existed between local FM stations and their listeners. For instance, during
the Kenya 2007–08 post-election violence, some FM stations sought either to protect
or to incite their followers through coded communications. Like this, some respon-
dents indicated that a number of FM stations chose to broadcast their own distinct El
Niño early warning messages. Broadcasting of this kind can, from one point of view,
be seen as a positive contribution to risk reduction through awareness and prepared-
ness. Taken from a different viewpoint, however, such broadcasting can readily been
seen to lead to misunderstandings among the general public, as such self-generated
media messages are often overly simplistic (or simply erroneous) and can even be
indulgently populist in nature. Indeed, upwards of 70% of respondents in this study
indicated that they expected El Niño rains to be “far above normal” in 2015–16,
an expectation that proved to be not only misinformed but also wholly inconsistent
with KMD’s official early warning messaging. This inconsistency speaks to the need
Tabl e 2 Popular early
warning dissemination
channels (Wachira and
Cumiskey 2017)
Channel Popularity (%)
Local FM radio station 69
National radio station 18
NGOs 3
Newspapers 0.4
Chief’s Baraza 0.8
Other 1

Kenya—Local 265
Tabl e 3 Early warning messaging (Wachira and Cumiskey 2017)
Content Rationale
Avoid sheltering under trees during rains Sheltering under a tree during stormy rain
increases lightning strike risks
Avoid using phones and other electronic
devices during rains
Using electronic devices during rains increases
lightning strike risk
Flood control methods, e.g. raising houses,
digging trenches
Raised houses reduce flooding risk
Water harvesting To reserve water for future use
Relocation from flood-prone areas To avoid the risk of floods and landslides
Observe hygiene To prevent water-borne diseases
for a collaborative approach to effective early warning messaging, an approach that
is most beneficial to a local ommunity when its seamlessness safeguards quality
preparedness and its streamlining ensures appropriate response actions.
5.4.4 Early Warning Message Content
Although warning messaging did include advisory actions (as highlighted in Table
3), nearly half of respondents (47%) said that advisories for people to relocate from
areas prone to natural hazards (such as floods and landslides) dominated the contents
of early warning messages. This advisory was incongruous with known facts about
the study area, however—neither had it been pre-identified as high-risk for land- or
mudslides nor did it have a history of such hazards. What this incongruity demon-
strates is the difficulty of disseminating contextualized early warning information
in a heterogenous society. Thus, the 2015–16 early warning messaging may not
have been contextualized sufficiently enough to effectively highlight what localized
impacts should have been expected or to truly benefit those who were reading or
listening to them.
5.5 Community Response and Preparedness Actions
Survey results, as illustrated in Fig. 8, showed that only 46% of respondents success-
fully undertook investments in readiness for the 2015–16 El Niño rains. These actions
were largely undertaken at the household level and tended consist only of short-term
tactics meant to mitigate—or take advantage of—the effects of the enhanced OND
rains. A positive relationship exists between the nature of investment and cost, with
a majority of people having invested in lowest cost actions. Most respondents cited
financial constraints as the main barrier leading to their having invested more in
low-cost efforts than in those more robust efforts that could have helped the entire

266 J. Wachira and L. Cumiskey
9% 31% 4% 2%
Furrowing/trenching PlanƟng/ploughing
Water
harvesƟng/desilƟng
of pans
Agro-forestly
Average cost ($) 533 39 145 42
0
100
200
300
400
500
600
Fig. 8 Community response and preparedness actions (Wachira and Cumiskey 2017)
community mitigate the negative impacts of the El Niño rains. Even so, such invest-
ments may not have provided sufficient protection from the adverse impacts of similar
hazards in the future for the most at-risk populations. Requiring further research, this
supposition implies that government bodies and other agencies need to play a more
pivotal role in boosting community preparedness activities if risk reduction efforts
are truly to make strides in safeguarding entire populations (Wachira and Cumiskey
2017).
6 Conclusions and Recommendations
This study established that El Niño weather conditions in Tigithi Location, Laikipia
County, Kenya were, on the whole, rather beneficial to the residents of the area during
the 2015–16 event. In fact, the increased precipitation during the OND rains of 2015
marked a turning point to the dry weather that had characterized the area for more
than five years and that had resulted in poor agricultural and livestock production,
adversely impacting household food security. The extent to which the residents were
able to take advantage of the enhanced rains was, however, limited by low financial
capacity that forced them to make only short-term investments that were helpful but
that could not provide for a long-term reduction of risk from foreseeable hazards in
the area.
KMD fulfilled its mandated role in producing and broadcasting El Niño weather-
related early warning information. KMD’s early warning messaging tended to be
cautious, implying the forecasters’ struggles with the politics of uncertainty. As the

Kenya—Local 267
onset of the OND rains neared, however, messaging became clearer, with repeated
warnings emphasizing how the impacts of the El Niño rains of 2015–16 were not
expected to be as severe as those of the destructive 1997 event. Nationally, though,
KMD’s messaging conflicted with other sources of early warning messaging, such as
those generated by media outlets and politicians, neither of which can be said to have
made adequate invests in either obtaining or communicating credible information.
These alternative messages were broadly disseminated through local radio stations,
communities’ preferred mode of communication, leading to some level of confusion
about the event’s expected impacts.
El Niño early warning lead time was insufficiently short, with a majority of
respondents claiming that they first received advisory messaging in September 2015,
only a month before the start of the OND rains. In an area characterized with high
poverty levels, the impact of such a short lead time resulted in only cheap, short-
term preparedness investments. Indeed, the study results show a positive relationship
between the nature of investment and cost, with a majority investing in the lowest
cost interventions such as planting. While these investments may have been helpful
in the short run, they may nonetheless prove problematic in that they will not provide
a sufficient buffer to protect most at-risk populations from the adverse impacts of
similar hazards in the future (Wachira and Cumiskey 2017). This study recommends
the following actions in preparation for future El Niño events:
1. Close collaboration between KMD and other departments and actors to avoid
conflicting early warning messages;
2. Investment in early dissemination of warning messages to provide sufficient
lead times for at-risk communities to invest in preparedness measures;
3. Emphasis on both potential positive and negative impacts of El Niño to enable
at-risk communities to invest in a broad range of preventive, mitigative. and
adaptive strategies that can have long-term benefits; and
4. Further research to understand the interplay between the producers of early
warning messaging and the channels that disseminate that messaging.
Acknowledgements The authors would like to thank the Water Youth Network (WYN) for
commissioning and funding this study as a pilot research project within the Disaster Risk Reduction
team. In addition, we acknowledge the field research team comprising of Jackson Mwihuri, Margaret
Wanjiku, Gidraf Burii, Harrison Kisio, Millicent Kamau and Lewise Wainaina who together with
the many residents of Tigithi Location made the actualization of this research possible.
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