1. Introduction
Several studies have classified Africa as the most vulnerable continent to the impacts of climate change, due to its dependence on agricultural activities as well as its poor financial, technical and institutional resilience capacities [
1,
2,
3,
4]. Local sustainable development in Africa is heavily threatened by the impacts that climate change has on livelihood activities, ecosystem services and water supply [
5]. Among African countries, Kenya is one of the most vulnerable to climate change. According to the Notre Dame Global Adaptation Initiative index, which measures the current level of vulnerability to climate disruption for 182 countries, Kenya was ranked as the 38th most vulnerable country in the world to the effect of climate change and the 23rd least ready to face future impacts in 2020 [
6]. Changes in rainfall patterns and increasing temperatures are expected to generate prolonged periods of drought and more intense floods in the country [
7], causing huge economic losses, especially for the pastoralist communities in the north of the country [
8]. Most of the rural communities in the northern regions live under conditions of poverty and extreme weather events will increasingly threaten the area [
9]. Therefore, monitoring, forecasting and early warning systems are the best strategies to mitigate negative socio-economic impacts and strengthen the resilience of the communities [
10,
11].
The present investigation is based on the principles of the One Health approach. This multidisciplinary approach aims to achieve global health, addressing the needs of the most vulnerable populations and increasing their resilience on the basis of the intimate relationship between human health, animal health and environment conditions [
12]. In this context, historical weather observations are essential, since they allow the examination of long-term climate trends and the comparison of past patterns with present and future values, in order to have a broader view of what is happening at the local scale. However, the weather observation network in arid and semi-arid lands (ASALs) has a poor spatial distribution. The majority of the land-based meteorological stations are located in the south and in the coastal areas of the country, which are the territories that attract most of the tourist flows [
13]. Furthermore, due to the scarce investment in technological renovation of the infrastructures, the national meteorological network lacks modern facilities for data analysis, which are needed to adequately represent past and present local climate trends [
14,
15]. These observation deficiencies do not allow the proper interpretation and prediction of local extreme weather events and consequently prevent the mitigation of related risks [
16].
With the progress of technologies and the increasing research effort, different sources have been produced over recent decades to fill these data gaps, such as remote sensing, climate models and reanalysis [
17]. Recent studies have tried to assess the reliability of different reanalysis datasets in Kenya and, more generally, in East Africa [
18,
19,
20,
21]. However, all of these studies approached the issue with a regional rather than a local perspective.
The present study analyzes the reliability of different maximum and minimum temperature datasets derived from temperature reanalysis to assess which is the most appropriate to represent the local climate. In North Horr Sub-County, situated in Marsabit County in northern Kenya, there is only one land-based meteorological station, which was installed in North Horr village in 2019. Due to the lack of historical observations in the sub-county, an area within a 250 km radius from North Horr has been defined and the land-based meteorological stations located inside the area have been selected in order to maximize the spatial coverage and the local representativeness. Specifically, these land-based meteorological stations are situated in Lodwar, Moyale and Marsabit. The climatic products were evaluated against the historical observations recorded by the Lodwar, Marsabit and Moyale land-based meteorological stations using a direct, point-to-pixel validation based on a statistical indices approach [
22].
As proposed by [
23], the closest grid point to each land-based meteorological station has been selected for each dataset, regardless of the position of the station inside the grid box and of the spatial resolution of the datasets. Although the resolution of the datasets is known to have an influence on the validation results [
24,
25], the use of this approach is supported by the low topographic complexity of the area [
22,
26] and by the need to assess the datasets’ performances in their original format, i.e., original resolutions. Thus, the evaluation involves the datasets’ ability, as available for the end users, to appreciate the local peculiarities, that is, how the selected product will be used for future local-scale research on the area. Five performance indicators were considered in order to assess the goodness of the models [
23,
27,
28]. In addition, Taylor diagrams were calculated in order to display the statistical agreement between the models’ data and the observations. The datasets which provided the highest scores were chosen for the calculation of the reference T
MAX and T
MIN.
The novelty of this study consists in the creation of an innovative methodology for the calculation of reference TMAX and TMIN values and temperature ranges. The reference values and temperature ranges obtained can provide a benchmark to strengthen the warning and response systems against extreme weather events. This methodology can support local populations in developing adaptation strategies and increasing their resilience against climatic anomalies.
Section 2 describes the geographic features in the study area, the dataset used, and the methodology adopted. The results are presented and discussed in
Section 3, whereas conclusions are drawn in
Section 4.
4. Conclusions
The availability of reliable historical climatic datasets is key to understanding and predicting extreme weather events and consequently reducing the vulnerability in African countries, especially in ASAL regions. Similar to many other East African countries, Kenya lacks historical land-based meteorological observations. This study identifies which maximum and minimum temperature datasets are able to fill the data gap in North Horr Sub-County. Four different climatic products are chosen and validated against historical TMAX and TMIN observations recorded by the land-based meteorological stations of Lodwar, Marsabit and Moyale. The comparison between ERA, HAD, KMD and ORH datasets highlights that ORH TMAX and TMIN datasets are able to better represent local temperatures and to successfully describe the seasonal patterns.
To strengthen the response systems against extreme weather events, temperature reference values and climatological temperature ranges were computed using ORH data for the eight main villages located in the study area (Balesa, Dukana, El-Gade, El-Hadi, Gas, Kalacha, Malabot and North Horr). Temperature reference values were calculated by averaging monthly and seasonal TMAX and TMIN data derived from the ORH reanalysis and climatological ranges were calculated with an amplitude of 2 degrees centered on the monthly temperature reference values. Monthly temperature ranges were compared against (i) the observations recorded by the Lodwar and Moyale land-based meteorological stations and then against (ii) the North Horr land-based automatic weather station observations recorded in the period of 03/2019–06/2020. Lodwar and Moyale temperature ranges contain the majority of the records (80–95%), demonstrating the ability to adapt to the local context and to represent temperatures. Differently, despite the small number of observations, temperature ranges computed for North Horr contain roughly 70% of both TMAX and TMIN values recorded from March 2019 to June 2020. The high performances obtained by the temperature ranges in the validation process confirm the adequacy of this methodology’s application at a regional level.
High-resolution temperature data are urgently needed to understand climatic trends in East African countries. This study provides a solution to the scarcity of observed data in North Horr Sub-County, by identifying monthly maximum and minimum temperature ranges. Anomalous temperature values can be detected through the comparison between current observations and the temperature ranges, strengthening the local population’s ability to cope with forthcoming extreme events.
Future research should test and validate the methodology proposed here in other locations. Moreover, the same methodology should be retested after more observations from the North Horr land-based automatic weather station are provided.