Rainfall changes perceived by farmers and captured by meteorological data: Two sides to every story

Hubertus, Lena; Groth, Juliane; Teucher, Mike; Hermans, Kathleen Article — Published Version Rainfall changes perceived by farmers and captured by meteorological data: Two sides to every story Regional Environmental Change Provided in Cooperation with: Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Halle (Saale) Suggested Citation: Hubertus, Lena; Groth, Juliane; Teucher, Mike; Hermans, Kathleen (2023) : Rainfall changes perceived by farmers and captured by meteorological data: Two sides to every story, Regional Environmental Change, ISSN 1436-378X, Springer Nature, Berlin, Vol. 23, Iss. 2, pp. 1-15, https://doi.org/10.1007/s10113-023-02064-9 , https://link.springer.com/article/10.1007/s10113-023-02064-9 This Version is available at: https://hdl.handle.net/10419/270983 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0 Vol.:(0123456789) 1 3 Regional Environmental Change (2023) 23:75 https://doi.org/10.1007/s10113-023-02064-9 ORIGINAL ARTICLE Rainfall changes perceived byfarmers andcaptured bymeteorological data: two sides toevery story LenaHubertus1,2,3 · JulianeGroth1 · MikeTeucher2 · KathleenHermans1,4 Received: 4 October 2022 / Accepted: 6 April 2023 © The Author(s) 2023 Abstract Subsistence farmers with high dependency on natural resources are exceptionally vulnerable to rainfall changes. Besides, they are in the front row when it comes to observing these changes. Studies that systematically investigate similarities and differences between measured and perceived rainfall changes are typically limited to trends in rainfall amounts, thereby disregarding changes in extreme events, rainy season durations, and timing. We address this gap by contrasting rainfall changes perceived by subsistence farmers in the Ethiopian highlands with meteorological daily rainfall data derived from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). We applied nine distinct metrics for rainfall dynamics, accounting for rainfall variability, including extreme events and changes in the onset and cessation of the two rainy seasons. Farmers perceived increasingly unreliable rainfall for both the short and the long rainy seasons, with later onset and earlier cessation, increasing rainfall intensity, and increasing occurrence of untimely rainfall and droughts. This partially disagrees with the CHIRPS data that indicate most significant rainfall changes for the short rainy season only. Since the early 1980s, this season has been experiencing decreasing rainfall amounts, with high variability between years and an increasingly uncertain – yet delayed – onset. In contrast, the long rainy season experienced little changes in rainfall. Our results point towards changing farmers' water availability and water demand as an explanation for the perceived deteriorating rainfall conditions. As farmers’ perceptions partly diverge from meteorological observations, both data sources should be used complementarily to improve our understanding of climatic change. Keywords Rainfall changes· Perceptions· Meteorological data· CHIRPS· Ethiopian highlands Introduction Climate change has severe consequences for natural resource dependent subsistence farmers. Slow onset changes with respect to precipitation pattern and temperatures, as well as increasing weather and climate extreme events have significantly affected food production and increased food insecurity (Allen etal. 2018). One of the most vulnerable and exposed regions is East Africa. Given its lacking economic, developmental, and institutional capacity, East Africa is among the regions with the most urgent need for improved adaptive capacity (IPCC 2022). Subsistence farmers in East Africa, as in many other regions in the global South, are exceptionally vulnerable to changes in climate and rainfall, but they are also uniquely positioned when it comes to observing these changes (Ayal and Leal Filho 2017). Perceptions of environmental change are considered a prerequisite for adaptation as only changes that are perceived as a risk are acted upon (Adger etal. 2009; Alessa etal. 2008). The extent to which change is perceived influences support for adaptation and alters the impacts of change (Fosu-Mensah etal. 2012; Howe etal. 2014). Consequently, misperceptions of climate risks can result in a lack of adaptation or maladaptation with potentially severe consequences for highly exposed populations (Alessa etal. 2008; Kosmowski etal. Communicated by Angus Naylor * Kathleen Hermans KHer[email protected] 1 Helmholtz Centre forEnvironmental Research GmbH (UFZ), Leipzig, Germany 2 Department ofGeoecology, Institute forGeosciences andGeography, Martin Luther University Halle-Wittenberg, Hallea/dSaale, Germany 3 Thünen Institute ofRural Studies, Braunschweig, Germany 4 Leibniz Institute ofAgricultural Development inTransition Economies (IAMO), Hallea/dSaale, Germany Regional Environmental Change (2023) 23:75 1 3 75 Page 2 of 15 2016). Especially in Sub-Saharan Africa, lack of access to information is one of the most important barriers to climate change adaptation (Juana etal. 2013; Tessema etal. 2013; Thompson etal. 2010). In recent years, the number of studies investigating similarities and differences between measured and perceived rainfall changes for regions with highly variable climate conditions, such as East Africa, has grown. Meze-Hausken (2004) was one of the first who pointed at the divergence between measured and perceived rainfall changes during the second half of the last century. The author found that farmers in the northern Ethiopian highlands perceived rainfall declines during the short rainy season, which was, however, not substantiated by meteorological data. For the same region, Ayal and Leal Filho (2017) have found that farmers perceive increasingly unreliable rainfall, with delayed onset and earlier cessation of the rainy seasons, increasing rainfall intensity and an increasing occurrence of untimely rainfall and drought frequency, which was in disagreement with meteorological rainfall data. Both studies focus on long-term trends of rainfall amounts using meteorological rainfall data aggregated for each rainy season, which is acknowledged by the authors as possible explanation for the discrepancies between measurements and perceptions. Similarly, Osbahr (2011) found no major changes in rainfall amounts, intensity of rainfall events or start of the seasons based on station data from 1963 through 2008. This was in contrast to farmers perceptions, who reported changes in monthly patterns of rainfall, delayed onset and earlier cessation of rainy seasons, decreasing rainfall amounts and, increasing intensity of rainfall. The authors conclude that farmers were generally better able to remember extreme events rather than slow climate trends and that they likely refer to agricultural production rather than climate to define “normal” or “good” years in terms of rainfall. Overall, East African farmers typically mention changes in timing of rainy seasons, including shift of onset and cessation dates, timing and magnitude of extreme events, including torrential rain and droughts rather than referring to rainfall amounts for describing experienced rainfall changes (Asfaw etal. 2018; Ayal and Leal Filho 2017; Cochrane etal. 2020; Debela etal. 2015; Habtemariam etal. 2016; Osbahr etal. 2011). Despite the importance of these indices the majority of studies that contrast perceptions with meteorological data utilizes rainfall amounts, mainly due to limited availability of reliable longitudinal daily rainfall data as well as conceptual challenges related to indices development (Asfaw etal. 2018; Esayas etal. 2019; Habtemariam etal. 2016). Studies that include dry spells (Adimassu etal. 2014), intensity of rainfall events or start of the seasons (Osbahr etal. 2011) in their analysis of meteorological data remain exceptions. Taken together, the existing research body provides valuable contributions to understanding long-term trends of rainfall amounts as both observed in meteorological data and perceived by rural farmers as well as possible discrepancies between both data sources. However, with the focus on rainfall amounts, existing research tends to neglect extreme events and, especially, changes in timing of rainfall seasons. Both can equally undermine rainfed agricultural production of subsistence farmers. This knowledge gap is particularly problematic given that variability of rainfall is expected to increase in East Africa and beyond (Dosio etal. 2021; Haile etal. 2020). Taken together, agreement and disagreement of climate change perceptions with meteorological data are not sufficiently understood, which limits the adaptive capacity of farmers to changes in rainfall pattern, including extreme events and hampers our understanding of farmers’ behavior. Our study addresses this gap. As a case study, we selected South Wollo, located in the northern Ethiopian highlands because of its high climate variability together with a high vulnerability of rural farmers to climate change. We contrast rainfall changes as perceived by subsistence farmers with longitudinal meteorological daily rainfall data derived from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) covering the past nearly four decades. Specifically, we applied nine distinct metrics for rainfall dynamics, explicitly accounting for changes in timing of rainy seasons and extreme events. Thereby, we move beyond analyzing aggregated trends in rainfall amounts and pay particular attention to temporal metrics that are key for farmers' perceptions (Cochrane etal. 2020). The results will improve our understanding of the differences between measured and perceived rainfall changes to overcome barriers to successful climate change adaptation and to increase farmer resilience. Study area South Wollo We conducted this study in six kebeles (the smallest administrative unit of Ethiopia) in the South Wollo Zone in Amhara Regional State in the northern Ethiopian highlands (Fig.1). The livelihoods of the local subsistence farmers in this region are characterized by a mix of livestock farming and rainfed agriculture (Little etal. 2006). Due to a growing population with increasingly fractionalized landholdings, fallow land is essentially non-existent and land scarcity has become a contentious issue for families and local communities (Ege 2017; Hermans and Garbe 2019; Morrissey 2013). In addition, the region is affected by severe land degradation, mainly in the form of top soil loss, gully erosion and declining soil fertility (Groth etal. 2020; Nyssen etal. 2004). Despite intense soil and water conservation efforts, soil fertility continues to decline and large areas in the Ethiopian highlands have become unsuitable for agriculture (Adimassu etal. 2017; Mekuriaw etal. 2018; Meshesha etal. 2014). Regional Environmental Change (2023) 23:75 1 3 Page 3 of 15 75 In South Wollo, rainfall is highly variable within and between years as well as across space (Alemu and Bawoke 2019; Mekonen and Berlie 2020). The rainfall regime is characterized by three distinct seasons: i) the short rainy season (belg) from February/March until May, ii) the long rainy season (kiremt) from late June to September/October, and iii) the dry season (bega) during the remaining months. It is particularly the belg season that is important for smallholders in South Wollo (Rosell 2011). In the high altitude areas of the highlands, farmers exclusively use the light belg rains as low temperatures, including frost, intense rainfall and hail during the kiremt season inhibit them from using the more abundant kiremt rains for agriculture (Groth etal. 2020; Hermans and Garbe 2019). Belg-dependent farmers are considered to be highly vulnerable to changes in rainfall conditions including extreme climate events such as droughts (Rosell and Holmer 2007). Smallholder farmers in the Ethiopian highlands were found to perceive increasingly erratic and unpredictable rainfall, with delayed onset and earlier cessation of the rainy seasons, increasing rainfall intensity and an increasing occurrence of untimely rainfall and drought frequency (Ayal and Leal Filho 2017). However, existing rainfall analyses for the northern highlands have shown slightly diverging trends. While some scholars have identified temporal changes in rainfall amounts (Alemayehu and Bewket 2017; Mekonen and Berlie 2020) others found no significant changes (Alemu and Bawoke 2019; Ayalew etal. 2012; Mengistu etal. 2014). Besides, some studies show a delayed onset of belg (Rosell 2011) and an earlier cessation of kiremt (Ayalew etal. 2012), both leading to a shortening of the growing period. Temporal variability of rainfall was found to have increased by Rosell and Holmer (2007) while others describe temporal variation as high but largely stable, with belg showing larger variability than kiremt (Abtew etal. 2009; Rosell 2011). In addition, the mountainous terrain causes spatially highly variable rainfall patterns, which heavily influence local cropping activities. Reasons for these differences between individual studies are manifold and include the (spatio-temporal) data resolution, the characteristics of the applied indices as well as the choice of observation periods (Bewket and Conway 2007). Fig. 1 Maps of South Wollo in Amhara Regional State in Ethiopia (right) and of the kebeles considered in our analysis, including elevation based on Farr etal. (2007) and administrative boundaries of South Wollo derived from OpenStreetMap contributors (left) Regional Environmental Change (2023) 23:75 1 3 75 Page 4 of 15 Data Meteorological rainfall data CHIRPS is a quasi-global (50°N-50°S) precipitation dataset which blends the Climate Hazards Group’s Precipitation Climatology (CHPclim) with satellite-based thermal infrared (TIR) precipitation estimates and gauge observations (Funk etal. 2015). In our analysis, we used daily rainfall amount data for the period 1981 through 2017 with a spatial resolution of 0.05° × 0.05°, which corresponds to approximately 5km x 5km in South Wollo. CHIRPS has been validated over East Africa (Dinku etal. 2018; Gebrechorkos etal. 2018) and Ethiopia specifically (Ayehu etal. 2018; Bayissa etal. 2017) through comparison with gauge data. CHIRPS generally performed well in the validations compared to other satellite-based rainfall products. Specifically in the Ethiopian highlands, CHIRPS showed reliable performance at different elevations during the wet seasons. Compared to other satellite products Ayehu etal. (2018) and Dinku etal. (2018) evidenced that CHIRPS showed good to very good performance concerning bias by incorporating concurrent station observations for adjustment. The better performance of the CHIRPS data as compared to other satellite products makes them appropriate for various hydrological and rainfall analysis functions in complex topographic areas, such as conducted in our study (Ayehu etal. 2018; Dinku etal. 2018). Besides, the availability of a temporally and spatially complete dataset makes CHIRPS preferable for long-term analysis and facilitates the assessment of rainfall patterns at the local level. Using CHIRPS data allows us to overcome well-known limitations of satellite-based time series of climate change for comparing them with people’s perceptions of climate change, such as limited temporal coverage, poor accuracy at higher temporal and spatial resolutions, and lacking homogeneity (Mekonnen etal. 2018). Perceptions ofrainfall changes The data on perception of changes in rainfall variability used in this study are based on 42 semi-structured interviews (SSIs) and 18 focus group discussions (FGDs) with local officials and rural farmers, conducted between November 2017 and February 2018 by Groth etal. (2020) (Table1). For the selection of the research sites, Groth etal. (2020) interviewed officials in 19 kebeles located within the four districts Legambo, Dese Zuria, Kutaber and Kalu to gain information on livelihoods, (rainfall-related) risks to these livelihoods and coping and adaptation strategies. Based on this information, the authors purposively sampled six kebeles across a gradient of agro-ecological conditions ranging from 1400 to 3600 masl (for details see Groth etal. 2020). The six kebeles are distinct with respect to their severity of land degradation, level of remoteness and the rainy seasons used for farming and consequently, adaptation and coping strategies. Given the importance of rainfed agriculture, farmers’ perceptions of rainfall changes are likely to be affected by their agricultural activities (Cochrane etal. 2020), which, in turn, are influenced by the two rainy seasons. To account for this diversity, we grouped the six kebeles according to the prevailing rainy seasons: i) belg, ii) kiremt, iii) belg + kiremt (Fig.1, Table1). In group belg + kiremt, we looked at each season separately because the respondents used both seasons for their agricultural activities but distinguished between them during interviews and focus groups. The collection of perception data was done in Amharic with the help of a local assistant. The methods used during the focus groups included livelihood risk assessments and strategy ranking. In the household interviews, socioeconomic characteristics, agricultural practices, perceived changes in rainfall and land degradation were assessed. Methods Analysis ofrainfall measurements For all grid cells located within the respective kebele we extracted the median CHIRPS value and used it as the precipitation estimate on a given day, resulting in a daily time series over the 37-year period of observation for each kebele. Following the Expert Team on Climate Change Detection and Indices (ETCCDI) recommendations for monitoring Table 1 Studied kebeles with corresponding cropping seasons, the number of focus group discussions and semi structured interviews kebele rainy season(s) used for cropping perception data belg kiremt focus groups semi structured interviews Adej x 3 8 Alansha x x 3 6 Teikake x x 3 6 Amba Gibi x 3 7 Kundi x 3 7 Tincha x 3 8 total 3 5 18 42 Regional Environmental Change (2023) 23:75 1 3 Page 5 of 15 75 Table 2 Description of the indices used for rainfall analysis with means and standard deviation by decade, separated by the kebele groups according to the rainy seasons used for agriculture 1981–1990 1991–2000 2001–2010 2011–2017 RR total seasonal rainfall in mm Belg 301.01 (80.84) 243.34 (88.12) 214.53 (67.69) 227.71 (59.14) Belg + Kiremt (B) 323.56 (52.52) 262.39 (101.58) 236.23 (66.37) 260.12 (53.35) Belg + Kiremt (K) 627.7 (174.87) 808.76 (156.81) 716.72 (85.46) 735.71 (194.64) Kiremt 627.36 (172.47) 809.99 (159.28) 743.22 (101.24) 769.2 (204.63) rd number of rainy days (> 1mm) in days Belg 22.4 (10.71) 15.3 (5.87) 14.1 (2.69) 17.29 (3.86) Belg + Kiremt (B) 20.95 (7.6) 15.2 (5.75) 15.65 (2.86) 18.07 (1.54) Belg + Kiremt (K) 35.55 (10.5) 43.6 (9.18) 42.05 (6.26) 45 (7.61) Kiremt 41 (11.8) 45.7 (8.13) 44.13 (7.58) 46.86 (9.07) ons onset of the rainy season (> 15mm over three consecutive days) in day of the year Belg 52 (13.7) 60.9 (18.77) 57.2 (12.88) 76.43 (15.31) Belg + Kiremt (B) 49.65 (15.72) 58.7 (20.73) 57.65 (15.85) 71.64 (13) Belg + Kiremt (K) 187.8 (11.13) 182 (11.9) 180.6 (10.5) 187.71 (8.57) Kiremt 187.9 (10.17) 179.87 (13.46) 179.97 (10.34) 185.86 (8.49) off cessation of the rainy season (maximum seasonal cumulative anomaly) in day of the year Belg 111.6 (32.18) 115.2 (23.73) 102.7 (27.18) 116.29 (25.1) Belg + Kiremt (B) 121.9 (22.79) 106.45 (30.83) 105.6 (21.55) 125.93 (20.92) Belg + Kiremt (K) 239.2 (23.19) 253.35 (10.92) 248.8 (6.05) 257.93 (11.71) Kiremt 235.87 (22.39) 249.47 (5.38) 247.53 (3.98) 251.48 (9.33) dur number of days between onset and cessation in days Belg 60.6 (29.79) 55.3 (32.49) 46.5 (31.48) 40.86 (29.66) Belg + Kiremt (B) 73.25 (22.43) 48.75 (36.49) 48.95 (24.86) 55.29 (23.68) Belg + Kiremt (K) 52.4 (25.67) 72.35 (8.57) 69.2 (14.09) 71.21 (12.54) Kiremt 48.97 (24.38) 70.6 (11.21) 68.57 (12.68) 66.62 (12.67) CDD maximum number of consecutive dry days in days Belg 16.8 (9.3) 13.9 (7.46) 14.11 (8.25) 16.33 (8.57) Belg + Kiremt (B) 18.9 (7.81) 19.25 (9.96) 12.95 (5.39) 17.29 (9.79) Belg + Kiremt (K) 7.67 (4.9) 9.7 (4.94) 10.1 (8.87) 8.43 (4) Kiremt 6.85 (2.09) 7.63 (4.05) 10.07 (6.25) 7.86 (4.05) Rx1day maximum 1-day precipitation in mm Belg 47.48 (22.57) 50.97 (12.89) 45.19 (20.55) 42.74 (16.03) Belg + Kiremt (B) 47.63 (10.45) 50.26 (14.71) 40.55 (7.72) 45.01 (7.01) Belg + Kiremt (K) 57.04 (11.27) 55.53 (9.55) 50.85 (10.14) 47.08 (15.19) Kiremt 45.48 (9.65) 51.1 (4.54) 53.01 (9.06) 45.29 (11.59) R95p very wet days: percentage of wet days exceeding the 95th percentile of the period of observation in % Belg 7.08 (8.72) 7.12 (6.13) 4.7 (4.5) 4.6 (4.78) Belg + Kiremt (B) 5.77 (5.85) 8.37 (8.39) 6.45 (5.35) 4.75 (2.47) Belg + Kiremt (K) 8.44 (7.04) 5.78 (3.21) 4.17 (2.25) 3.56 (4.74) Kiremt 5.11 (4.12) 7.09 (3.08) 5.12 (2.57) 3.09 (3.82) SDII simple daily intensity index: total seasonal precipitation divided by the number of wet days (> 1mm) in mm/day Belg 15.96 (7.34) 16.16 (3.88) 15.02 (3.07) 13.5 (3.34) Belg + Kiremt (B) 17.14 (4.94) 19.41 (8.24) 15.8 (3.62) 14.73 (3.25) Belg + Kiremt (K) 18.68 (5.1) 18.72 (1.37) 17.19 (1.65) 16.27 (3.35) Kiremt 16.07 (4.59) 17.75 (1.3) 17.06 (1.72) 16.42 (3.08) Regional Environmental Change (2023) 23:75 1 3 75 Page 6 of 15 rainfall change and extremes (Frich etal. 2002; Zhang etal. 2011) we analyzed rainfall trends and rainfall variability by means of nine indices (Table2). We calculated the indices for each kebele and aggregated them for the three kebele groups. In our study, we applied two definitions for a rainy season to account for the variety of rainfall dynamics and enable their calculations. First, a rainy season is defined as the time between the onset and cessation of the season according to the CHIRPS data. We used this data-driven definition to determine duration of the rainy seasons (dur) and the length of the longest dry spell of the season (CDD). Our second definition of a rainy season is based on local knowledge from the study region (Legese etal. 2018; Rosell 2011; Rosell and Holmer 2007). Accordingly, we define belg as the period from 1 February to 31 May, and kiremt as the period from 1 June to 31 October. We used this second definition to capture all rainfall events within these two periods, including days of scarce rainfall for which the calculation of onset and cessation based on the first definition was challenging due to extremely scarce rainfall in a given season. We applied this calendar-based definition to indices concerning rainfall amount, intensity and extreme events (RR, rd, Rx1day, R95p, SDII). To determine rainy season onset, we used a threshold of at least 15mm rainfall over three consecutive days. Hence, if over a period of 72h at least 15mm rainfall were accumulated the rainy season has started. This threshold was identified by Rosell (2011) based on traditional local knowledge of farmers from South Wollo and serves as a proxy for soil moisture, which is key for agricultural decision-making (Lala etal. 2020; MacLeod 2018). Considering that we aim to compare farmers’ perceptions and meteorological rainfall data, using such a threshold-based onset definition that accounts for local agricultural practices of subsistence farmers is an appropriate way to derive a meaningful onset date. To determine the cessation of the rainy seasons we used the method described by Liebmann and Marengo (2001), which has been successfully applied for onset and cessation determination in Africa (Liebmann etal. 2012). Since farmers are unlikely to plant outside the given time frame when rainfall usually occurs, we used the February through May period for belg and June through October period for kiremt to calculate cessation dates (Lala etal. 2020; MacLeod 2018). The assumption of the cessation determination is, that precipitation during a given rainy season exceeds its long-term average, in our case the 37year average (Dunning etal. 2016; Liebmann and Marengo 2001). Hence, the cessation day is defined as the day of the year when the daily cumulative rainfall anomaly is at its absolute maximum, as following that day, rainfall is less than the 37year average (Liebmann etal. 2012). Further, we follow the recommendations of the ETCCDI to enhance comparability of climate change studies and included the following indices in our analyses of rainfall changes: total rainfall amount (RR), number of rainy days (rd), maximum 1-day precipitation (Rx1day), percentage of very wet days (R95p) and the simple daily intensity index (SDII) (Table2). Analyzing extreme events is particularly relevant in the context of our study since they have been found to shape perceptions significantly (Debela etal. 2015). To assess change in temporal rainfall variability, we aggregated the daily results to four periods (1981–1990, 1991–2000, 2001–2010 and 2011–2017), for each we calculated the mean value ( x ), standard deviation ( 𝜎 ) and coefficient of variation (CV). CV is calculated as the division of the standard deviation by the mean value. Since CV requires ratio scaled data, it was not calculated for rainy season onset and cessation. Additionally, we performed the non-parametric Mann–Kendall (MK) trend test (Kendall 1975; Mann 1945) and calculated Sen’s Slope estimator (Sen 1968) to detect trends and magnitude of potential rainfall changes. Before performing trend analysis, we inspected the data for possible autocorrelations, none of which were found. The rainfall analysis was implemented in R (R Core Team 2020). All calculations for the MK trend test were performed using the Kendall package (McLeod 2011). Kendall’s rank correlation coefficient (tau) was calculated to assess the direction of trends and a two-sided p-value was used to test for statistical significance. We calculated Sen’s slope estimator at the 95% confidence interval (CI) using the trend package (Pohlert 2020) to assess the average change per year. Analysis ofperception data During the semi-structured household interviews, Groth etal. (2020) asked farmers about observed changes in rainfall, their impacts and the strategies to deal with these changes. Additionally, during the FGDs farmers were asked to outline livelihood related trends within the last 20years, where rainfall was a relevant issue. In the context of risk and strategy ranking, farmers also commented on rainfall related questions. To ensure consistency and to facilitate the comparison between perceived and measured rainfall data, we developed a framework similar to Simelton etal. (2013) to organize and contrast the two different data sources (Fig.2). As a first step, we assessed whether respondents had perceived changes in rainfall or not. The responses were then categorized according to the rainy season they used for their agricultural activities. In the next step, we categorized the interview data according to rainfall changes as perceived by the farmers. This included dry spells, extreme events such as torrential rainfall and timing of the rainy seasons. In the last step, we assessed how the rainfall had changed according to the respondents in terms of rainfall amount, its Regional Environmental Change (2023) 23:75 1 3 Page 7 of 15 75 intensity, the frequency of changes occurring and whether the rain has become more or less variable over the years. We considered information potentially affecting agricultural production, such as land degradation, frost or weed infestation and adaptation strategies to contextualize the perception data. The interview analysis was performed in MAXQDA (VERBI Software 2019). Results Group belg Results of the CHIRPS analysis for group belg show that rainfall amounts during belg have decreased significantly between 1981 and 2017 with Sen’s slope estimator indicating an average annual decrease of 2.7mm (Table3). Variability of rainfall amounts peaked in the 1990s (CV 0.36) and has continually fallen in the subsequent decades. This high rainfall variability results from drought occurrences during belg seasons in this decade. Additionally, belg is starting significantly later: while belg used to start on the 52nd day of the year (21Feb) in the 1980s, the onset moved to the 76th day of the year (17-Mar) in the 2010s (Fig.3). Timing of rainfall has consistently been highly variable with standard deviations between 13 and 19days for rainy season onset and 24 to 27days for cessation. While rainy season duration has shortened (although not significantly at the 95% CI), standard deviations for rainfall duration have remained stable resulting in increasing CV throughout the period of observation. As compared to the 1980s, the occurrence of Fig. 2 Categorization of perceptions of rainfall changes from qualitative data, including example quotes from subsistence farmers (Groth etal. 2020). Modified from Simelton etal. (2013) Table 3 Results of the Mann Kendall trend test and Sen’s Slope estimator, significant trends at 95% CI are bold ons off dur CDD RR rd Rx1day R95p SDII Group belg tau 0.2736 -0.0136 -0.2121 -0.065 -0.2462 -0.1249 -0.027 -0.0592 -0.036 p 0.0185 0.9166 0.0688 0.5983 0.033 0.2934 0.824 0.6349 0.7636 Sens slope 0.6883 -0.0623 -0.7454 -0.0625 -2.6589 -0.0801 -0.0741 0 -0.0264 Group belg + kiremt (B) tau 0.2805 0.003 -0.1687 -0.0903 -0.2222 -0.064 -0.1081 0.0046 -0.1141 p 0.0155 0.9896 0.1464 0.4594 0.0545 0.5911 0.3531 0.9791 0.3266 Sens slope 0.5526 0 -0.767 -0.0952 -2.349 -0.0359 -0.1647 0 -0.0675 Group belg + kiremt (K) tau -0.0182 0.2071 0.1873 0.0258 0.2012 0.2384 -0.2222 -0.2926 -0.2162 p 0.8855 0.075 0.1074 0.8378 0.0819 0.041 0.0545 0.0115 0.0614 Sens slope -0.0109 0.2857 0.3964 0 4.4125 0.2981 -0.3576 -0.1429 -0.0829 Group kiremt tau -0.0935 0.2155 0.2078 0.066 0.2643 0.0995 0.0871 -0.0918 -0.042 p 0.4247 0.0632 0.0731 0.585 0.0221 0.395 0.456 0.4325 0.724 Sens slope -0.0976 0.2546 0.4452 0.0303 6.1692 0.135 0.1243 -0.0423 -0.0106 Regional Environmental Change (2023) 23:75 1 3 75 Page 8 of 15 extreme events (R95p) was less variable in the 1990s but have become more variable in recent decades (Fig.S5). Farmer’s perceptions of changes in rainfall largely correspond with these measured changes. Respondents are mainly concerned about the timing of the rain, in particular its onset: it is perceived as being delayed and increasingly unpredictable. Respondents view the uncertainty of belg as the biggest risk to their livelihood and are particularly concerned about it. According to the farmers, the increasingly later onset of belg delays the growing period resulting in the crops being immature by the time the strong kiremt rains start. At the high-altitude locations with steep slopes, kiremt rains can lead to crop loss through soil erosion or hail, which urges farmers to harvest their crops before kiremt starts. The increased variability of belg duration according to the CHIRPS analysis coincides with the increasing uncertainty of belg as perceived by farmers. “When belg is late we lose our harvest. In 2008 we lost food and fodder. I sold sheep and goats to sustain the family. I worked in a government program and after that the government filled the six months gap.” – local farmer from Adej. The respondent refers to the Ethiopian calendar when mentioning the year 2008, which corresponds with 2015 in the Gregorian calendar Group belg + kiremt Similar to the belg group, the onset of belg in this kebele group has moved significantly later in the year (Table3): while belg rains started on average on Feb 19 in the 1980s, it is on Mar 13 in the 2010s (Fig.3). The 1990s were an exceptionally dry decade for belg resulting in more variable rainfall, hence, higher CV values. All belg indices in group belg + kiremt reached their highest CVs and standard deviations in the 1990s and variability has declined since then with the exception of CDD who’s variability has increased again in the 2010s (Fig.S5). Overall, belg in group belg + kiremt is starting later and has become less variable since the 1990s according to the CHIRPS data, which is in contrast to the development in the belg group. Kiremt rainfall has experienced a significant increase in the number of rainy days (rd) with Sen’s slope estimator indicating an average annual increase of 0.3days (Table3). Hence, the rainy season got longer. Extreme rainfall events and intensity indices during kiremt all decreased between 1981 and 2017. However, only for very wet days (R95p), the decrease is significant at the 95% CI while decrease in maximum daily precipitation (Rx1day) and intensity (SDII) are not significant (Table3). Rainfall amount (RR) and the number of rainy days (rd) were becoming less variable between the 1980s and 2000s, but recently increased in variability. Similarly, the variability of the duration of kiremt was at CV = 49% in the 1980s and between CV = 12–20% in the following decades (Fig.S5). Except for the 1980s, the timing of kiremt can be described as largely stable (Fig.3). Extreme events and intensity indices were more variable in the 1980s than in the following decades but experienced increasing variability again in the 2010s (Fig.S5). Fig. 3 Onset and cessation per rainy season by decade. Note: the squares present the onset and cessation; the horizontal bars indicate the standard deviation Regional Environmental Change (2023) 23:75 1 3 Page 15 of 15 75 Mekuriaw A, Heinimann A, Zeleke G, Hurni H (2018) Factors influencing the adoption of physical soil and water conservation practices in the Ethiopian highlands. Int Soil Water Conserv Res 6:23–30. https:// doi. org/ 10. 1016/j. iswcr. 2017. 12. 006 Mengistu D, Bewket W, Lal R (2014) Recent spatiotemporal temperature and rainfall variability and trends over the Upper Blue Nile River Basin, Ethiopia. Int J Climatol 34:2278–2292. https:// doi. org/ 10. 1002/ joc. 3837 Meshesha DT, Tsunekawa A, Tsubo M, Ali SA, Haregeweyn N (2014) Land-use change and its socio-environmental impact in Eastern Ethiopia’s highland. Reg Environ Change 14:757–768. https:// doi. org/ 10. 1007/ s101130130535-2 Meze-Hausken E (2004) Contrasting climate variability and meteorological drought with perceived drought and climate change in northern Ethiopia. Clim Res. 27:19–31. https:// doi. org/ 10. 3354/ cr027 019 Morrissey JW (2013) Understanding the relationship between environmental change and migration: The development of an effects framework based on the case of northern Ethiopia. Glob Environ Change 23:1501–1510. https:// doi. org/ 10. 1016/j. gloen vcha. 2013. 07. 021 Niles MT, Mueller ND (2016) Farmer perceptions of climate change: Associations with observed temperature and precipitation trends, irrigation, and climate beliefs. Glob Environ Change 39:133–142. https:// doi. org/ 10. 1016/j. gloen vcha. 2016. 05. 002 Nyssen J, Poesen J, Moeyersons J, Deckers J, Haile M, Lang A (2004) Human impact on the environment in the Ethiopian and Eritrean highlands—a state of the art. Earth-Sci Rev 64:273–320. https:// doi. org/ 10. 1016/ S00128252(03) 00078-3 Olsson L, Barbosa H, Bhadwal S, Cowie A, Delusca K, Flores-Renteria D, Hermans K, Jobbagy E, Kurz W, Li D, Sonwa DJ, Stringer L (2019) Land degradation. In: Climate change and land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems [Shukla PR, Skea J, Calvo Buendia E, Masson-Delmotte V, Pörtner H-O, Roberts DC, Zhai P, Slade R, Connors S, van Diemen R, Ferrat M, Haughey E, Luz S, Neogi S, Pathak M, Petzold J, Portugal Pereira J, Vyas P, Huntley E, Kissick K, Belkacemi M, Malley J (eds.)]. In Press Osbahr H, Dorward P, Stern R, Cooper S (2011) Supporting agricultural innovation in Uganda to respond to climate risk: linking climate change and variability with farmer perceptions. Ex Agric. 47:293–316. https:// doi. org/ 10. 1017/ S0014 47971 00007 85 Piontek F, Müller C, Pugh TAM, Clark DB, Deryng D, Elliott J, Colón González FJ, Flörke M, Folberth C, Franssen W, Frieler K, Friend AD, Gosling SN, Hemming D, Khabarov N, Kim H, Lomas MR, Masaki Y, Mengel M etal (2014) Multisectoral climate impact hotspots in a warming world. Proc Natl Acad Sci U S A 111:3233–3238. https:// doi. org/ 10. 1073/ pnas. 12224 71110 Pohlert T (2020) Package ‘trend’. Non-parametric trend tests and change point detection. https:// cran.rproje ct. org/ web/ packa ges/ trend/ trend. pdf. R Core Team (2020) R. A language for statistical computing. R Foundation for Statistical Computing, Vienna https:// www.rproje ct. org/. Rosell S (2011) Regional perspective on rainfall change and variability in the central highlands of Ethiopia, 1978–2007. Appl Geogr 31:329–338. https:// doi. org/ 10. 1016/j. apgeog. 2010. 07. 005 Rosell S, Holmer B (2007) Rainfall change and its implications for Belg harvest in South Wollo, Ethiopia. Geografiska Annaler: Series A, Physical Geography 89:287–299. https:// doi. org/ 10. 1111/j. 14680459. 2007. 00327.x Sen FK (1968) Estimates of the regression coefficient based on Kendall’s Tau. J Am Stat Assoc 63:1379–1389. https:// doi. org/ 10. 1080/ 01621 459. 1968. 10480 934 Simelton E, Quinn CH, Batisani N, Dougill AJ, Dyer JC, Fraser ED, Mkwambisi D, Sallu S, Stringer LC (2013) Is rainfall really changing? Farmers’ perceptions, meteorological data, and policy implications. Clim Dev 5:123–138. https:// doi. org/ 10. 1080/ 17565 529. 2012. 751893 Tessema YA, Aweke CS, Endris GS (2013) Understanding the process of adaptation to climate change by small-holder farmers: the case of East Hararghe Zone, Ethiopia. Agric Econ 1:13. https:// doi. or g/ 10. 1186/ 21937532-113 Thompson HE, Berrang-Ford L, Ford JD (2010) climate change and food security in Sub-Saharan Africa: a systematic literature review. Sustainability 2:2719–2733. https:// doi. org/ 10. 3390/ su208 2719 VERBI Software (2019) MAXQDA. VERBI Software, Berlin https:// www. maxqda. com/ Zhang X, Alexander L, Hegerl GC, Jones P, Tank AK, Peterson TC, Trewin B, Zwiers FW (2011) Indices for monitoring changes in extremes based on daily temperature and precipitation data. WIREs Clim Change 2:851–870. https:// doi. org/ 10. 1002/ w cc. 147 Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.