**2. Materials and Methods**

#### *2.1. Study Area*

Kitui County is a largely semi-arid to arid locality in south-eastern Kenya (Figure 1) with an intermittent river regime. The county has a population of over 1.1 million persons with a density of 37 persons per km2, an average household size of 4.3 and a total area of about 30,430 km<sup>2</sup> [20]. The county is characterized by relatively high poverty levels, with indicators of food and water insecurity highlighted in the sub-national development blueprint, the Kitui County Integrated Development Plan (2018–2022) [37]. Food poverty is estimated at about 39.4% compared to Kenya's average of 32% [37]. Approximately 50% of inhabitants do not have access to water sources within a walking distance of 5 km [37]. The erratic rainfall regime is considered a principal driver of the risk to the viability of the mixed crop agroecosystem in the face of recurrent drought conditions [11]. As in most of East Africa, small-scale mixed crop farming is the primary livelihood in Kitui County, supporting food production among other benefits [11].

Kenya receives rainfall in two seasons, a longer one in March–May (MAM) and a shorter but more reliable season in October–December (OND) [38]. Temperatures range from 14 to 34 ◦C, with January–February being the warmest months followed by MAM [39]. The ecological profile of the county includes seven agroecological zones that reflect the agricultural development potential as well as varying vegetative cover. Dominant soil groups include Dystric Regosols, Lithosols and Humic Cambisols, the Ferralo category consisting of Acrisols (ferric), Luvisols and Ferralsols, and Chromic Luvisols and Ferralsols [8].

#### *2.2. SPEI Calculation*

The SPEI was calculated using the R package SPEI version 1.7 Vicente-Serrano et al. [40] for a 30-year period (1987–2016) using all combinations of 10 monthly rainfall (P) and four monthly min/max temperature (Tmin/Tmax) products (Table 1), which yielded a total of 40 data blends. These products were chosen because they had proven reliable in the variable terrain of East Africa [27,34,35,41]. A 30-year window of analysis was chosen as all products overlapped during this period. The units of all data sources were harmonized to mm month−<sup>1</sup> and ◦C (monthly average), respectively. Monthly PET was calculated from Tmin and Tmax using the reduced data Hargreaves method in the SPEI package. Following previous studies, a 12-month accumulation was used as it yielded a smoother annual drought visualization compared to 3- and 6-month accumulations, while depicting generally similar drought patterns [27,42]. The 12-month SPEI also represented an annual hydrometeorological regime matching the semi-arid agro-ecology of the study area which often receives minimal rainfall. It also aligned with the observed inter-annual distribution of drought instances as learned from interviews in the field. The accumulated differences between rainfall and PET were normalized using the log-logistic distribution, fitted using the unbiased estimator of probability-weighted moments, as implemented in the SPEI package. In addition to the SPEI, the P and Tmin/Tmax anomaly were derived by computing the Standardized Anomaly Index (SAI) after Ali and Lebel [43] where the annual deviation of the 30-year mean is calculated and then normalized by the 30-year standard deviation.

**Figure 1.** Map of the study area, Kitui West Sub County, in Kitui County, south-eastern Kenya, with a Digital Elevation Model (DEM) overlay obtained from, https://dwtkns.com/srtm30m/, accessed on 25 February 2020.
