**1. Introduction**

Drought is a slow-onset phenomenon characterized by spatiotemporal water deficits restricting water accessibility and availability for social–ecological systems at varying temporal scales [1–5]. Characteristic persistent negative anomalies in precipitation and high temperatures leading to high evapotranspiration from soils and crops eventually have cross-sectoral effects on agriculture, food, and livelihoods, particularly in East Africa where rainfed agriculture is the economic mainstay [1,6–11]. Droughts and other environmental changes prevalent in East Africa, such as agricultural expansion and corresponding land degradation, contribute to water crises as they aggravate the competition of water demands [1]. Droughts may be categorized as: (i) meteorological (resulting from rainfall deficit) or, depending on duration and additional drivers and impacts, (ii) agricultural (exceptionally low soil moisture), (iii) hydrological (exceptionally low surface and/or subsurface water levels), and (iv) socio-economic (resulting from water supply and demand failure in relation to the previous categories) [1,4].

Droughts have severe, widespread effects on livelihoods, especially in arid and semiarid regions, contributing *inter alia* to declining crop quality and quantity and forest productivity [12,13], and deterioration of aquatic life [10]. East Africa, and especially Kenya, is emblematic of the recurring drought regions worldwide [10,14–17]. The agroecosystems of semi-arid eastern Kenya are particularly vulnerable, with an inconsistent rainfall regime

**Citation:** Borona, P.; Busch, F.; Krueger, T.; Rufin, P. Uncertainty in Drought Identification Due to Data Choices, and the Value of Triangulation. *Water* **2021**, *13*, 3611. https://doi.org/10.3390/w13243611

Academic Editors: Alban Kuriqi and Luis Garrote

Received: 1 November 2021 Accepted: 6 December 2021 Published: 16 December 2021

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and the frequency and intensity of droughts increasing [3,10,12,18]. Kitui County in southeastern Kenya is such a vulnerable semi-arid region with inconsistent rainfall and high temperatures, featuring dry spells in the growing season that impede the dominantly rainfed agriculture [10,16,19]. Water demand will likely follow the projected population increase in the area KNBS [20]; hence, monitoring and understanding of drought dynamics and the development of management interventions are ever more necessary.

Precipitation and temperature are the primary meteorological variables modulating drought duration and severity. However, the impact of prevailing data uncertainties as McMillan et al. [21] found in the identification of past droughts, particularly in data scarce regions like East Africa, has received little attention in the literature. Identification of past drought occurrence is essential to assess responses and mitigate against current and future events. The inherent interrelation of hydrological and social factors in drought occurrences, impacts, and responses has attracted a range of research fields across the natural and social sciences [2,22,23]. It seems apt, therefore, to complement the meteorological data with qualitative, ground-based information from disaster response and other sources in order to verify the drought identification based on the quantitative products. This promising approach has to date remained largely unexplored.

The Standardized Precipitation Index (SPI) and the Standardized Precipitation-Evapotranspiration Index (SPEI) are two widely used drought intensity monitoring indices. The SPI is recommended by the World Meteorological Organization (WMO) [1,15,24] and requires rainfall as the only parameter. The SPEI, an extension of the SPI, is a more recent statistical index where the water balance is represented by precipitation and potential evapotranspiration (PET) Svoboda and Fuchs [25], making it arguably more reliable for the detection and monitoring of drought [25–27]. The SPEI identifies meteorological drought at a sub-annual scale but can be a proxy for hydrological, agricultural, and socioeconomic drought [28].

SPI and SPEI have been applied to various ecosystems in East Africa. Studies have typically responded to the uneven distribution and general scarcity of station-based data over East Africa with the use of gridded data products [7,9,29–32]. For instance, Polong et al. [27] demonstrated near similarity of SPEI and SPI using the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) temperature product, merged with the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) rainfall product. Nguvava et al. [33], by contrast emphasized the value of PET for drought identification, and hence the superiority of SPEI over SPI. Bayissa et al. [34] showed the value of gridded data for drought assessment in the Ethiopian Upper Blue Nile Basin; in their case, the CHIRPS product outperformed the Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT) product, the TAMSAT African Rainfall Climatology And Time series (TARCAT) product, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN) product, and the Tropical Rainfall Measuring Mission (TRMM) product. Gebrechorkos et al. [35] also emphasized the usefulness of CHIRPS considering the uneven topography of East Africa. The authors revealed the value of precipitation and minimum and maximum temperature at monthly resolution for long-term climate variability assessment.

Naumann et al. [9] used an array of five gridded data products to compute SPI, SPEI, and soil moisture anomalies, demonstrating the uncertainty in existing products, with discrepancies particularly in mountainous areas and areas with low ground-station density. Gebrechorkos et al. [35] emphasized the need to consider temperature variation alongside rainfall and the need for higher quality data to manage data-related uncertainties in the central Kenyan highlands. Gebremeskel, Gebremedhin, Qiuhong Tang, Siao Sun, Zhongwei Huang, Xuejun Zhang, and Xingcai Liu [36] provided an account of drought impacts over East African agroecosystems and the importance of temporal assessment using gridded data, further emphasizing uncertainty and spatial variability.

Against this background, the objectives of the present study were to: (i) quantify similarities and differences between precipitation and temperature products available for the study region; (ii) propagate these similarities and differences to trend analyses and SPEI to judge the ambiguity of trends and drought identification; and (iii) explore whether ambiguities in drought identification can be resolved by triangulation with key informant information. The paper is structured as follows. Section 2 introduces data and methods. Sections 3 and 4 present and discuss the results in light of other studies in Kenya and East Africa. Section 5 concludes with a summary and recommendations for policy and practice.
