Climate Change Trend Analysis and Future Projection in Guguf Watershed, Northern Ethiopia

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The main objective of this study was to analyse the climate trend and future projection in Guguf watershed of Southern Tigray, Ethiopia. 32 years (1987–2018) Meteorological data were collected from Ethiopia National Meteorological Agency (NMA). Download canESM2 (Canadian Second Generation Earth System Model) which was freely available at the Canadian climate change scenario group website. The Mann-Kendal trend test was used to test for the presence of trends using XLSTAT. SDSM 4.2.9 decision support tool was used to downscale large scale predictors and project future climate change. The period from 1987–2018 were considered as a base period whereas the period from 2019–2100 were considered as future periods. Historically, slight decrease in rainfall, and an overall increase in the mean annual minimum and maximum temperatures in the study area for the last 32 years. The highest increment of maximum temperature recorded in October month up to + 2.7°C in RCP8.5 scenarios. The precipitation increases up to a maximum of 49% (2073–2100) for RCP4.5 scenario and 66% (2073–2100) for RCP4.5 scenario in the Belg. Precipitation decreases in Kiremt (Jun–September) season by 8% (2019–2045) and 23% (2073–2100) for RCP4.5 scenarios. Future work needs to consider studying the effects of different climate change adaptation strategie. canESM2 Climate change GCM Guguf watershed Raya Valley SDSM Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Background The Intergovernmental panel on Climate change (IPCC) defined as Climate change is the weather characteristic condition such as precipitation, air temperature, humidity, wind, sunshine, cloud cover and atmospheric pressure at a specific location determined over a long period of at least 30 years. Any changes in these phenomenon for several years is known as Climate change [ 1 ]. Using watersheds as organizing units for planning and implementation of natural resources management is an approach being followed worldwide. It is because large regions can be divided along topographic lines that transcend administrative boundaries and the status and trends analysis can be done on the basis of entire natural systems in concert with social conditions. The communities within the watersheds can also better track and understand the impacts of their management activities on the larger system [ 2 ]. Studies at Bilate watershed in the Ethiopian Rift valley basin suggested that climate change could affect by decreasing of water resources based on the scenario development [ 3 ] [ 4 ]. Similarly, in Geba river basin shows the implication of reduced water availability and impacts on agricultural production [ 5 ]. Both agree that adoption of water storage practice is very important. Moreover, research conducted on Central Rift Valley Basin shows the positive change of precipitation in future[ 6 ]. Generally rapid population growth and the expansion of farming and pastoralism under a drier, warmer climate regime could dramatically increase the number of at-risk people in Ethiopia during the next 20 years. Many areas of Ethiopia will maintain moist climate conditions, and agricultural development in these areas could help offset rainfall declines and reduced production in other areas [ 7 ]. Research conducted to overcome uncertainties multi model average from LARS-WG and SDSM downscaling techniques has been used in the Upper Blue Nile River Basin. From that the positive change of precipitation in future can be a good opportunity for the farmers who are engaged in rain fed agriculture to maximize their agricultural production and to change their livelihoods [ 8 ]. It is obvious that climate change is threatening every corner of the world. Understanding the cause and the driving forces for climate change and its effect on the water resources and agricultural production could help to develop strategies for adaptability and mitigation to tackle the problem while considering the threshold resilience limit of a certain agro-climatology. In general, the aim of this study is to analyse the Climate Change Trend and Future Projection in Guguf watershed of Southern Tigray, Ethiopia. Specifically, the objectives of this study were (1) to analyse climate change trends for the last 32 years in the study area (2) project and predict climate change scenarios MATERIALS AND METHODS Description of the Study area Location Guguf watershed is one of the several watersheds found in the South Tigray Zone that contributes to Denakil basin. Guguf is located between 12 0 48 ' 50.507 '' N to 12 0 47 '' 42.576 '' N latitudes and 39 0 30 ' 36.705 '' E to 39 0 36 ' 47.214 '' E longitude (Fig. 1 ). It covers 5861.7ha total area. Climate According to the Traditional Ethiopian Agro-Ecological Zones classification, the study area is found in dry Woina-Dega agro-ecological zone, which is characterized by 759.4mm 1987–2018) annual rainfall and 1814-3444masl. Based on altitude of the area, the watershed can be categorized under Woina-Dega and Dega. The annual mean minimum and maximum air temperature of the area is 10.2 o C and 22.3 o C, respectively. Topography The watershed marked topographic variation (Fig. 3 ). The slope class generated from 30×30 m DEM by downloaded from USGS Earth explorer website and classify based on FAO slope classes. The dominant slope class are 15–30% and 3–8% those two slope classes covers 28% and 26% of the total area respectively. 8–15% which covers 25%, > 30% covers 18% which is majorly the Northern, North Eastern, Central and South Eastern escarpments of the watershed area. And the remain 3% is included in 0–3% slope class based on FAO slope classification for soil and water conservation[ 9 ]. Soil According to FAO (Food and Agriculture Organization) soil classification, the dominant soil type in Guguf watershed is Eutric cambisol (59.9%) which covers most parts of the watershed. Leptosols (30.1%) covers in the Southeast and Western part of watershed. Eutric regosoils (8%) covers Northern part of the watershed, Chromic Vertisols (2.1%) covers the outlet part of the watershed (Fig. 4 ). Table 1 soil type of Guguf watershed No. Soil type Area in ha Area in % 1 Chromic vertisols 121.7 2.1 2 Eutric cambisols 3509.8 59.9 3 4 Eutric regosols Leptosols 466.2 1764.1 8.0 30.1 Total 5861.7 100 Methodology Data types and source Relevant and appropriate data are very essential prior to simulation of any model in order to achieve the objective of the research. Therefore, the primary task of this study was getting applicable information specific to the model based on the core objective of this research. Meteorological data Weather data were used for statistical downscaling model (SDSM). Based on these objectives, meteorological data were collected from Ethiopia National Meteorological Agency (NMA). The long-term records of daily weather data for 32 years (1987–2018) were obtained for Maichew Meteorological stations located in the study watershed. Global Climate data Climate change scenario was obtained from GCM the World Climate Research Program’s (WCRP’s) Coupled Model Inter-Comparison Project phase 5 (CMIP5) multi-model dataset [ 10 ]. For this study, download canESM2 (Canadian Second Generation Earth System Model) which was freely available at the Canadian climate change scenario group website http://ccdsdscc.ec.gc.ca/?page=pred-canesm2 . Data preparation Climate change scenario and General Circulation Model (GCM) model setup For this study, download GCM models canESM2 (Canadian Second Generation Earth System Model) which was freely available at the Canadian climate change scenario group website. And downscaled to the site level using Statistical Downscaling Model (SDSM). The model results were available for RCP 4.5 (medium to low emission) and RCP 8.5 (high to medium emissions) scenarios and the result were used to produce the future scenario. This model was applied in this study because of the following four reasons. Firstly, the GCM model is widely applied in many climate change impact studies. The results of canESM2 can be easily downscaled using SDSM [ 11 ] and the model provides daily predictor variables which can be used for Statistical Downscaling Model. Secondly, it provides large scale daily predictor variables which could be used for statistical downscaling model (SDSM) [ 10 ]. Thirdly, a single run was downloaded for each scenario, and data were extracted for the pixel containing the observation station. Lastly, the model has given 20 ensemble model result when statically downscaled and correctly used RCP scenario that have high climate mitigation policy [ 12 ] and RCPs offer a better understanding in terms of the concertation of future greenhouse gases for running climate models than previous scenarios [ 13 ]. Assessing precipitation and temperature trends The Mann-Kendall trend test was used to test for the presence of trends in the precipitation and temperature data. The null hypothesis being that there is no trend in the time series whilst the alternative hypothesis was that there is a trend in the time series. The Mann-Kendall trend tests were performed in XLSTAT. The tests were carried out at 5% level of significance. The major variables for test interpretation were the p-value and the Sen’s slope. The MK test statistic (S) is calculated as follows: S= \({\sum }_{i=1}^{n-1}{\sum }_{i=i+1}^{n}sign (Xj-Xj\) 1 Where; Sign= \(\left(\begin{array}{c}\{ifX>0\\ 0 ifX=0\\ -1 if X 0, then future observations in the time series tend to be larger than those that appear earlier in the time series and it is an indicator of an increasing trend, whereas the reverse is true if S < 0 and this indicates a decreasing trend. Downscaling techniques using Statistical Downscaling Model (SDSM) For this study SDSM 4.2.9 decision support tool for the assessment of regional climate change impacts developed by [ 10 ] was used to downscale large scale predictors and it was freely downloaded from http://www.sdsm.org.uk . SDSM develops statistical relationships, based on multiple linear regression techniques, between large scale (predictors) and local (predictand) that is precipitation and maximum and minimum temperature. Screening the downscaled predictor variables The choice of appropriate downscaling predictor variables was under taken through the screen variable option of SDSM. using correlation analysis, Partial correlation analysis, and scatter plot partial correlation analysis. It was done for selected predictors to see the level of correlation with each other. These statistics identify the amount of explanatory power of the predictor to explain the predictand [ 10 ]. Finally, the scatter plot was carried out in order to identify the nature of the association (linear, nonlinear, etc.), whether or not data transformation(s) may be needed, and the importance of outliers. Table 2 List of twenty six Predictor variables derived from canESM2 for the study area Predictor Variable Description Predictor Variable Description Ncepmslpgl.dat Mean sea level pressure Ncepp5zhgl.dat 500hpa divergence Ncepp1_fgl.dat Surface air flow strength Ncepp_fgl.dat 850 hpa air flow strength Ncepp1_ugl.dat Surface zonal velocity Ncepp_ugl.dat 850 hpa zonal velocity Ncepp1_vgl.dat Surface Meridians velocity Ncepp_vgl.dat 850 hpa meridians velocity Ncepp1_zgl.dat Surface velocity Ncepp_zgl.dat 850 hpa vortices Ncepp1thgl.dat Surface wind direction Ncepp850gl.dat 850 hpa geo potential height Ncepp1zhgl.dat Surface divergence Ncepp8thgl.dat 850 hpa wind direction Ncepp5_fgl.dat 500hpa air flow strength Ncepp8zhgl.dat 850 hpa divergence Ncepp5_ugl.dat 500 hpa zonal velocity Ncepprcpgl.dat Relative humidity at 500 hpa Ncepp5_vgl.dat 500 hpa meridians velocity Nceps500gl.dat Specific humidity at 500hpa Ncepp5_zgl.dat 500 hpa vortices Nceps850gl.dat Specific humidity at 850 hpa Ncepp500gl.dat 500hpa geo potential height Ncepshumgl.dat Surface specific humidity Ncepp5thgl.dat 500hpa wind direction Nceptempgl.dat Mean temperature at 2m SDSM model has two processes called conditional and unconditional processes to be specified before the analysis takes place. In case of daily temperature where the predictand-predictor process is not regulated by intermediate process unconditional process was used, whereas for daily precipitation where the amounts depend on the occurrence of wet-day, the conditional process was chosen [ 14 ]. Significance value is used to test the significance of predictor–predictand correlations and it was set as the default of probability level at 0.05(p < 0.05). Several analyses were made by selecting a maximum of 6 out of 26 predictor variables at a time till best predictor-predictand correlations were found even though up to 12 predictors are possible to select at a time [ 10 ]. Calibration and validation of SDSM The model was calibrated for precipitation and maximum and minimum temperature (predictand variables) along with a set of predictor variables, and computed the parameters of multiple regression equations with an optimization algorithm. Daily data were used for model calibration for data representing the current climate condition of the period 19 years (1987–2005). Maichew Meteorological station was used for calibrating the predictor variables. For monthly models, different model parameters were used for each month [ 10 ]. Validation was done based on thirty years simulation for years 2006–2018. Validation of the model was performed using the results of weather generator and independent observed data that were used for calibration. Finally, scenarios were generated for precipitation and maximum and minimum temperature predictand variables, both for base and future period, using canESM2 GCM model output for the two emission scenarios (RCP4.5 and RCP8.5 emission scenarios). Scenarios generation For this study, the model CanESM2 and scenario RCP 4.5 and RCP 8.5 were the two GCM output files used for the scenario generation. The mean of the twenty ensembles for the specified period was produced for maximum and minimum temperature and precipitation. The period from 1987–2018 were considered as a base period whereas the period from 2019–2100 were considered as future periods. The future periods were divided into three time horizons from 2030s (2019–2045), 2050s (2046–2072), and 2080s (2073–2100) and analyses were made for each time horizons. The structural skeleton of this study (input/output relationships) is shown in Fig. 5 . The Figure shows the schematic representations of the steps to be followed in this research. RESULTS AND DISCUSSIONS Data Quality Control and Adjustment for Model Input Filling of missing data Data collected from Meteorological stations might not be 100% complete and accurate. The missing data for the precipitation and temperature time series was filled using SPSS and the gaps were less than 1%. Consistency analysis The consistencies of the data set of the given stations were checked using double mass-curve method with reference to their neighbourhood stations. The double mass curve was plotted by using the annual cumulative total rainfall of the base station as ordinate and the average annual cumulative total of neighbouring stations at abscissa. Assessing precipitation and temperature trends The Mann-Kendall trend test was used to test for the presence of trends in the precipitation and temperature data. The null hypothesis being that there is no trend in the time series whilst the alternative hypothesis was that there is a trend in the time series. The Mann-Kendall trend tests were performed in XLSTAT. The tests were carried out at 5% level of significance. The major variables for test interpretation were the p-value and the Sen’s slope. Table 3 Mann-Kendall trend test / Two-tailed test (Yearly) and Theil-Sen slope test of Precipitation and Temperature trends Parameters Precipitation Maximum temperature Minimum temperature Kendall's tau -0.214 0.641 0.315 S -106 318.000 156.000 Var(S) 3802.667 3802.667 3802.667 p-value 0.089 0.0001 0.012 Alpha 0.05 0.05 0.05 Slope -4.685 0.048 0.016 Theil-Sen slope [ 15 ], also known as “Kendall’s slope” or “non-parametric linear regression slope”, is an alternative to the standard linear regression slope. It is popular in earth sciences (Meteorology and Climatology) for measuring temperatures and precipitations over time. As shown in the following table (Table 3 ), the slope shows the negative value. This showed that the amount of precipitation was decreasing with time. Maximum and minimum temperature shows significantly an increasing trend for the last 32 years (1987–2018). However, the p-values were greater than the level of significance (0.05) which reveals that though the precipitation amounts were getting low, the differences were not statistically significant between the consecutive years. Similarly, [ 16 ] assessed the evidence of climate variability in the Northern part of Ethiopia indicated a slight decrease in rainfall, and an overall increase in the mean annual minimum and maximum temperatures. Moreover, [ 17 ] showed that mean annual precipitation shows a decreasing trend for the last 3 decades in Maichew station. Model calibration and Validation The SDSM calibration was done for the period of 19 years (1987–2005) at a monthly model type in order to see the monthly temporal variations. The results showed that, the simulated precipitation, maximum and minimum temperature had good agreements with the observed results. Table 4 Calibrated, precipitation, maximum and minimum temperature at Maichew station Predictands R 2 Calibration Validation Precipitation 0.97 0.97 Maximum temperature 0.99 0.85 Minimum temperature 0.99 0.98 Validation was done using an independent observed data for the period of thirty year from 2006 to 2018. Here also twenty ensembles (runs) of daily values were generated and the averages of these ensembles were taken for the comparison. The correlation coefficients that were found during the calibration are also maintained during the validation as shown in Table 4 . A good agreement was also found between the observed and simulated precipitation (R 2 = 0.97 and 0.97 for calibration and validation) though it is a conditional process. And also there was a good agreement for minimum and maximum temperature were R 2 = 0.99 and 0.99 respectively during calibration. Scenario generation The climate scenario for the future period was developed from statistical downscaling using the GCM predictor variables for the two emission scenarios for 82 years based on the mean of 20 ensembles. And the analysis was done based on 27 years period from 2019–2045, 2046–2072 and 2073–2100. The IPCC recommends 1961–1990 as a climatological base period in impact assessment. Hence, for this research the period from 1987–2018 was taken as a base period with in which the comparison was made. The observed climatological data collected from Ethiopian Meteorological stations contain more consistent time series records from the period 1987 and onwards than the period before 1987 and this is why the period from 1987–2018 were taken as a base period. Maximum Temperature The mean monthly, seasonal and annual change in maximum temperature for the future period (2016–2100) for both RCP4.5 and RCP8.5 scenarios are shown in Fig. 8 (a, b). As it can be seen from the Figure, the overall results (2019–2100) for annual mean maximum temperature showed an increasing trend for both scenarios (RCP 4.5 and RCP 8.5). Seasonally, Maximum temperature shows an increasing trend ranging between + 0.4°C and + 0.6°C in 2030s (2019–2045) and 2080s (2073–2100) respectively for RCP4.5 scenario. For RCP8.5 scenario, Maximum temperature shows an increasing trend in Bega season (October -January) ranging between + 1.0°C to + 2.0°C for 2030s (2019–2045) and 2080s (2073–2100) respectively. The mean monthly increment of maximum temperature ranges between + 1.7°C (2019–2045), + 2.0°C (2046–2072) and + 2.2°C (2073–2100) for RCP 4.5 scenarios as compared to the base period. While for RCP8.5 scenario, it ranges between + 1.3°C (2019–2045), + 1.9°C (2046–2072) and + 2.7°C (2073–2100). The highest increment of maximum temperature recorded in October month up to + 2.7°C in RCP8.5 scenario. The increment was not worth for both scenarios based on [ 18 ] in which the globally averaged surface air temperature was projected to warm 1.4°C to 5.8°C by 2100. Minimum temperature The downscaled minimum temperature shows an increasing trend in all annual future period for both RCP4.5 and RCP8.5 scenarios (Fig. 9 ). With respect to monthly minimum temperature, the downscaled minimum temperature scenario indicates that there might be an increasing trend (2019–2100) on the months of January, February, May, Jun, August, September and October for RCP4.5 scenario. But July (2019–2100), November (2019–2100), December (2019–2100) months shows a decreasing trend for RCP4.5 scenario. In the RCP8.5 scenario the projected minimum temperature shows an increasing trend in January, February, Jun, August-October months from 2019–2100. But, May, November and December months the minimum temperature shows a decreasing trend from 2019–2100. The highest increment of minimum temperature was projected on RCP8.5 scenario in the month of August + 0.8°C, + 1.4°C, and + 1.7°C for the 2030s, 2050s and 2080s periods respectively. The overall result of minimum temperature showed an increasing trend which is in line with previous studies conducted on Bilate watershed [ 3 ] and Tikur wuha watershed [ 6 ] of the Central Rift Valley of Ethiopia. Precipitation As it can be seen from Fig. 10 (a, b) the overall results (2019–2100) for annual percentage change in precipitation shows an increasing trend for both scenarios (RCP4.5 and RCP8.5). The increment of annual percentage change of precipitation ranges from + 1% (2019–2045) to + 11% (2073–2100) for RCP4.5 scenario. For RCP8.5 scenario the increment ranges between + 1% (2019–2045) and + 14% (2073–2100) which indicat similar result with [ 18 ] that rainfall increase or decrease (up to ± 20%) in 2100. The results of this study was thus in line with the previous researches done on Tana basin, upper Blue Nile basin, Central Rift Valley basin [ 6 , 19 , 20 ]. [ 21 ] assessed regional impacts and vulnerabilities to climate change in four regions including Africa and the results showed that annual mean rainfall would increase in East Africa due to climate change. Additionally, [ 22 ] conducted research around Adama district, most CMIP5 GCMs project an increase in rainfall at all locations (Nazereth, Wonji and Melkasa). On monthly basis, the percentage change in precipitation is not systematic, i.e precipitation increases in some months and decreases in some other months. The percentage changes in precipitation increases from June to September and decreases from December-Feberuary for the future (2016–2100). The increment is most dramatic in August from the period 2016–2045 for both RCP8.5 and RCP4.5 scenarios in which the precipitation increases by 76.01% and 73.12% respectively. The decrease in precipitation reaches to a maximum of 18.32% (2046–2075) and 22.12% (2076–2100) for RCP8.5 scenario and 17.19% (2046–2075), 20.02% (2076–2100) for RCP4.5 scenario. Seasonally, the precipitation increases in Belg (Febrauary-May) and Bega (October -January) for all scenarios in all time horizons (exept, slight decreseas in 2050s for RCP8.5 scenario). The precipitation increases up to a maximum of 49% (2073–2100) for RCP4.5 scenario and 66% (2073–2100) for RCP4.5 scenario in the Belg season. And also, precipitation increases up 7% (2073–2100) for RCP4.5 scenario and 4% (2019–2045) for RCP4.5 scenario in the Bega season. Precipitation decreases in Kiremt (Jun-September) season by 8% (2019–2045) and 23% (2073–2100) for RCP4.5, 10% (2019–2045) and 23% (2073–2100) for RCP8.5 scenarios. Conclusion and Recommendations The trend analysis on the significant climate parameters indicated a slight decrease in rainfall, and an overall increase in the mean annual minimum and maximum temperatures in the study area for the last 32 years. For future scenarios, the result of this study showed a general increasing trend for precipitation, maximum and minimum temperatures in all three time periods (2030s, 2050s and 2080s). The mean monthly increment of maximum temperature ranges between + 1.7°C (2019–2045), + 2.0°C (2046–2072) and + 2.2°C (2073–2100) for RCP 4.5 scenarios as compared to the base period. And for RCP8.5 scenario, it ranges between + 1.3°C (2019–2045), + 1.9°C (2046–2072) and + 2.7°C (2073–2100). The highest increment of maximum temperature recorded in October month up to + 2.7°C in RCP8.5 scenario. The precipitation showed that increasing trends for the future time horizons mainly in Belg season (February to May) and Bega (October-January) season while it decreases in Kiremt season. The increment of precipitation in these seasons can be a good opportunity for agriculture; however, increment of temperature, may affect the area negatively. Future work needs to consider studying the effects of different climate change adaptation strategies. Abbreviations EIAR Ethiopian Institute of Agricultural Research FAO Food and Agriculture Organization. Declarations Acknowledgements We want to express our greatest appreciation to Ethiopian Institute of Agricultural Research (EIAR) for providing financial support. The opinion expressed herein are the authors’ own and do not necessarily express the view of EIAR. Authors' contributions Mekin Mohammed Yimam has designed and carried out the study. And Mekin Mohammed Yimam has analysed the data, and wrote the manuscript. Seyoum Bezabih contributed to the data collection and the interpretation of the results. Funding This work was supported by Ethiopian Institute of Agricultural Research (EIAR). Availability of data and materials The raw data supporting the conclusions of this article will be made available by the author, without any reservation. Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors have no competing interest to declare. Author details 1. Ethiopian Institute of Agricultural Research (EIAR), Fogera National Rice Research and Training Center, P.O. Box 1937, Ethiopia. 2. Ethiopian Institute of Agricultural Research (EIAR), Debre Markos Agricultural Research Center, Ethiopia. References IPCC, The IPCC's Fifth Assessment Report: What's in it for Africa? 2014. p. 70. Maharjan, L.D. and N. 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Taye, M., Hydrological modeling of climate change impact on selected catchment of Nile River basin. Journal of Hydrology and Earth System Sciences Discussions, 2010. 7 : p. 5441-5465. UNFCCC, C.C., Impacts, vulnerabilities and adaptation in developing countries . 2007: Germany. Shiferaw, A., et al., Impacts of Climate Change on Agriculture in Ethiopia: what, when, where and how. Ophthalmic Epidemiology, 2015. 22 (3): p. 162-169. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3812339","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":263909946,"identity":"eb9713a5-5322-4dbd-b88e-508d0156b684","order_by":0,"name":"Mekin Mohammed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYFACNoYDQJKxH8ROKCBFy8wGkBYDIrWAAOMGkEYGYrTotrclHi7M2Sa7+fzqxA8PDBjk+cUO4NdidubYgcMzt9023nbj7WYJoMMMZ85OIKDlRnrDYd5ttxO33Ti7AaQlweA2IS33n0O0bJ5xdvMP4rTcYDsA1rKBv3cbkbacSUsAaTGecYN3m0WCgQQRfjl+zPgzUItsf//ZzTd/VNjI80sT0IIAEmCVEsQqBwH+A6SoHgWjYBSMgpEEAKbRTsD28hMmAAAAAElFTkSuQmCC","orcid":"","institution":"Ethiopian Institute of Agricultural Research (EIAR), Fogera National Rice Research and Training Center","correspondingAuthor":true,"prefix":"","firstName":"Mekin","middleName":"","lastName":"Mohammed","suffix":""},{"id":263909948,"identity":"3ed61d8c-dc1e-4a1c-b022-a3c4c7de7506","order_by":1,"name":"Seyoum Bezabeh","email":"","orcid":"","institution":"Ethiopian Institute of Agricultural research (EIAR), Debre Markos Agricultural Research Center","correspondingAuthor":false,"prefix":"","firstName":"Seyoum","middleName":"","lastName":"Bezabeh","suffix":""}],"badges":[],"createdAt":"2023-12-27 13:02:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3812339/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3812339/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49020281,"identity":"bb1985b7-01a7-4a17-b482-da207e3f74e8","added_by":"auto","created_at":"2024-01-01 08:38:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":191063,"visible":true,"origin":"","legend":"\u003cp\u003eThe location of Guguf watershed\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3812339/v1/9d0a377a2d4d1abf66f54314.png"},{"id":49020274,"identity":"588c5cc8-961d-44e4-a10b-303fc66edfd4","added_by":"auto","created_at":"2024-01-01 08:38:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31414,"visible":true,"origin":"","legend":"\u003cp\u003eMean annual precipitation of the study area\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3812339/v1/da227d8998f3188042386860.png"},{"id":49020579,"identity":"78436d4d-53c5-4348-be76-79b1416dc5c7","added_by":"auto","created_at":"2024-01-01 08:46:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":328741,"visible":true,"origin":"","legend":"\u003cp\u003eSlope class of Guguf watershed\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3812339/v1/3aad0201c0fa36fefd1476d5.png"},{"id":49020582,"identity":"d5dca17a-2814-4ece-bfd0-0c65e37352e3","added_by":"auto","created_at":"2024-01-01 08:46:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":165787,"visible":true,"origin":"","legend":"\u003cp\u003eSoil map of Guguf watershed\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3812339/v1/70125f27b90783608d7ed25a.png"},{"id":49020580,"identity":"1f91e57b-dc27-4147-91a5-1ea6afe71cb7","added_by":"auto","created_at":"2024-01-01 08:46:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":260914,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual framework of a research\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3812339/v1/353a1497ed30fa29a5f0e78b.png"},{"id":49020583,"identity":"d6af615e-dc8b-412d-8770-fef97a65008c","added_by":"auto","created_at":"2024-01-01 08:46:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":66877,"visible":true,"origin":"","legend":"\u003cp\u003eCalibrated and observed mean daily minimum temperature (\u003csup\u003eo\u003c/sup\u003eC) (a) and mean daily maximum temperature (\u003csup\u003eo\u003c/sup\u003eC) (b), in the time step for the Maichew station.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3812339/v1/f3b07e4465b329d44db261f8.png"},{"id":49020275,"identity":"ac9e6916-5f63-4640-aee6-0105d70f055c","added_by":"auto","created_at":"2024-01-01 08:38:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":60969,"visible":true,"origin":"","legend":"\u003cp\u003eValidated and observed mean daily maximum temperature (a), minimum temperature (\u003csup\u003eo\u003c/sup\u003eC) (b) and mean monthly precipitation (\u003csup\u003eo\u003c/sup\u003eC) (mm), in the time step for the Maichew station.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3812339/v1/03e2746851480eed53fbbb3c.png"},{"id":49020648,"identity":"e6e29aa2-b272-43c1-8420-91e1ea6637fa","added_by":"auto","created_at":"2024-01-01 08:54:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":41703,"visible":true,"origin":"","legend":"\u003cp\u003eChange of downscaled monthly maximum temperature from the baseline period for canESM2 RCP4.5 (a) and RCP 8.5 (b)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3812339/v1/0ce5d7cd39031af1b5513a2a.png"},{"id":49020277,"identity":"e1800c6e-3b15-423f-9335-ad7e442f3419","added_by":"auto","created_at":"2024-01-01 08:38:44","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":43533,"visible":true,"origin":"","legend":"\u003cp\u003eChange of downscaled monthly minimum temperature from the baseline period for canESM2 RCP4.5 (a) and RCP8.5 (b)\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3812339/v1/b8b8bc820309f2e2bad4d22a.png"},{"id":49020282,"identity":"1ced39db-0ed9-4d54-b7cd-3e6d42b892e8","added_by":"auto","created_at":"2024-01-01 08:38:44","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":41690,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage change in monthly, seasonal and annual precipitations in the future (2019-2100) for RCP4.5 scenario (a) and RCP8.5 scenario (b) from the base period\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-3812339/v1/6e9f966449c473f47a82c4ad.png"},{"id":50573736,"identity":"9228926f-e9cb-4a76-a19d-93c41cf3b68c","added_by":"auto","created_at":"2024-02-02 16:52:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1552672,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3812339/v1/3491e65a-3d3a-4671-9f12-926767a739fd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Climate Change Trend Analysis and Future Projection in Guguf Watershed, Northern Ethiopia","fulltext":[{"header":"Introduction","content":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe Intergovernmental panel on Climate change (IPCC) defined as Climate change is the weather characteristic condition such as precipitation, air temperature, humidity, wind, sunshine, cloud cover and atmospheric pressure at a specific location determined over a long period of at least 30 years. Any changes in these phenomenon for several years is known as Climate change [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUsing watersheds as organizing units for planning and implementation of natural resources management is an approach being followed worldwide. It is because large regions can be divided along topographic lines that transcend administrative boundaries and the status and trends analysis can be done on the basis of entire natural systems in concert with social conditions. The communities within the watersheds can also better track and understand the impacts of their management activities on the larger system [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies at Bilate watershed in the Ethiopian Rift valley basin suggested that climate change could affect by decreasing of water resources based on the scenario development [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Similarly, in Geba river basin shows the implication of reduced water availability and impacts on agricultural production [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Both agree that adoption of water storage practice is very important. Moreover, research conducted on Central Rift Valley Basin shows the positive change of precipitation in future[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenerally rapid population growth and the expansion of farming and pastoralism under a drier, warmer climate regime could dramatically increase the number of at-risk people in Ethiopia during the next 20 years. Many areas of Ethiopia will maintain moist climate conditions, and agricultural development in these areas could help offset rainfall declines and reduced production in other areas [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch conducted to overcome uncertainties multi model average from LARS-WG and SDSM downscaling techniques has been used in the Upper Blue Nile River Basin. From that the positive change of precipitation in future can be a good opportunity for the farmers who are engaged in rain fed agriculture to maximize their agricultural production and to change their livelihoods [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is obvious that climate change is threatening every corner of the world. Understanding the cause and the driving forces for climate change and its effect on the water resources and agricultural production could help to develop strategies for adaptability and mitigation to tackle the problem while considering the threshold resilience limit of a certain agro-climatology.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn general, the aim of this study is to analyse the Climate Change Trend and Future Projection in Guguf watershed of Southern Tigray, Ethiopia. Specifically, the objectives of this study were (1) to analyse climate change trends for the last 32 years in the study area (2) project and predict climate change scenarios\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDescription of the Study area\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eLocation\u003c/h2\u003e \u003cp\u003eGuguf watershed is one of the several watersheds found in the South Tigray Zone that contributes to Denakil basin. Guguf is located between 12\u003csup\u003e0\u003c/sup\u003e48\u003csup\u003e'\u003c/sup\u003e50.507\u003csup\u003e''\u003c/sup\u003eN to 12\u003csup\u003e0\u003c/sup\u003e47\u003csup\u003e''\u003c/sup\u003e42.576\u003csup\u003e''\u003c/sup\u003eN latitudes and 39\u003csup\u003e0\u003c/sup\u003e30\u003csup\u003e'\u003c/sup\u003e36.705\u003csup\u003e''\u003c/sup\u003eE to 39\u003csup\u003e0\u003c/sup\u003e36\u003csup\u003e'\u003c/sup\u003e47.214\u003csup\u003e''\u003c/sup\u003e E longitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It covers 5861.7ha total area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eClimate\u003c/h2\u003e \u003cp\u003eAccording to the Traditional Ethiopian Agro-Ecological Zones classification, the study area is found in dry Woina-Dega agro-ecological zone, which is characterized by 759.4mm 1987\u0026ndash;2018) annual rainfall and 1814-3444masl. Based on altitude of the area, the watershed can be categorized under Woina-Dega and Dega. The annual mean minimum and maximum air temperature of the area is 10.2\u003csup\u003eo\u003c/sup\u003eC and 22.3\u003csup\u003eo\u003c/sup\u003eC, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eTopography\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe watershed marked topographic variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The slope class generated from 30\u0026times;30 m DEM by downloaded from USGS Earth explorer website and classify based on FAO slope classes. The dominant slope class are 15\u0026ndash;30% and 3\u0026ndash;8% those two slope classes covers 28% and 26% of the total area respectively. 8\u0026ndash;15% which covers 25%, \u0026gt;\u0026thinsp;30% covers 18% which is majorly the Northern, North Eastern, Central and South Eastern escarpments of the watershed area. And the remain 3% is included in 0\u0026ndash;3% slope class based on FAO slope classification for soil and water conservation[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSoil\u003c/h2\u003e \u003cp\u003eAccording to FAO (Food and Agriculture Organization) soil classification, the dominant soil type in Guguf watershed is Eutric cambisol (59.9%) which covers most parts of the watershed. Leptosols (30.1%) covers in the Southeast and Western part of watershed. Eutric regosoils (8%) covers Northern part of the watershed, Chromic Vertisols (2.1%) covers the outlet part of the watershed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003esoil type of Guguf watershed\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea in ha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea in %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromic vertisols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEutric cambisols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3509.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEutric regosols\u003c/p\u003e \u003cp\u003eLeptosols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e466.2\u003c/p\u003e \u003cp\u003e1764.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5861.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData types and source\u003c/h2\u003e \u003cp\u003eRelevant and appropriate data are very essential prior to simulation of any model in order to achieve the objective of the research. Therefore, the primary task of this study was getting applicable information specific to the model based on the core objective of this research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMeteorological data\u003c/h2\u003e \u003cp\u003eWeather data were used for statistical downscaling model (SDSM). Based on these objectives, meteorological data were collected from Ethiopia National Meteorological Agency (NMA). The long-term records of daily weather data for 32 years (1987–2018) were obtained for Maichew Meteorological stations located in the study watershed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGlobal Climate data\u003c/h2\u003e \u003cp\u003eClimate change scenario was obtained from GCM the World Climate Research Program’s (WCRP’s) Coupled Model Inter-Comparison Project phase 5 (CMIP5) multi-model dataset [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For this study, download canESM2 (Canadian Second Generation Earth System Model) which was freely available at the Canadian climate change scenario group website \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ccdsdscc.ec.gc.ca/?page=pred-canesm2\u003c/span\u003e\u003cspan address=\"http://ccdsdscc.ec.gc.ca/?page=pred-canesm2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData preparation\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eClimate change scenario and General Circulation Model (GCM) model setup\u003c/h2\u003e \u003cp\u003eFor this study, download GCM models canESM2 (Canadian Second Generation Earth System Model) which was freely available at the Canadian climate change scenario group website. And downscaled to the site level using Statistical Downscaling Model (SDSM). The model results were available for RCP 4.5 (medium to low emission) and RCP 8.5 (high to medium emissions) scenarios and the result were used to produce the future scenario. This model was applied in this study because of the following four reasons. Firstly, the GCM model is widely applied in many climate change impact studies. The results of canESM2 can be easily downscaled using SDSM [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and the model provides daily predictor variables which can be used for Statistical Downscaling Model. Secondly, it provides large scale daily predictor variables which could be used for statistical downscaling model (SDSM) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Thirdly, a single run was downloaded for each scenario, and data were extracted for the pixel containing the observation station. Lastly, the model has given 20 ensemble model result when statically downscaled and correctly used RCP scenario that have high climate mitigation policy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and RCPs offer a better understanding in terms of the concertation of future greenhouse gases for running climate models than previous scenarios [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAssessing precipitation and temperature trends\u003c/h2\u003e \u003cp\u003eThe Mann-Kendall trend test was used to test for the presence of trends in the precipitation and temperature data. The null hypothesis being that there is no trend in the time series whilst the alternative hypothesis was that there is a trend in the time series. The Mann-Kendall trend tests were performed in XLSTAT. The tests were carried out at 5% level of significance. The major variables for test interpretation were the p-value and the Sen’s slope.\u003c/p\u003e \u003cp\u003eThe MK test statistic (S) is calculated as follows:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eS= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sum }_{i=1}^{n-1}{\\sum }_{i=i+1}^{n}sign (Xj-Xj\\)\u003c/span\u003e\u003c/span\u003e 1\u003c/h2\u003e \u003cp\u003eWhere;\u003c/p\u003e \u003cp\u003eSign=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(\\begin{array}{c}\\{ifX\u0026gt;0\\\\ 0 ifX=0\\\\ -1 if X\u0026lt;0\\end{array}\\right)\\)\u003c/span\u003e\u003c/span\u003e 2\u003c/p\u003e \u003cp\u003eNote that if S \u0026gt; 0, then future observations in the time series tend to be larger than those that appear earlier in the time series and it is an indicator of an increasing trend, whereas the reverse is true if S \u0026lt; 0 and this indicates a decreasing trend.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDownscaling techniques using\u003c/b\u003e Statistical Downscaling Model (SDSM)\u003c/p\u003e \u003cp\u003eFor this study SDSM 4.2.9 decision support tool for the assessment of regional climate change impacts developed by [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] was used to downscale large scale predictors and it was freely downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.sdsm.org.uk\u003c/span\u003e\u003cspan address=\"http://www.sdsm.org.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSDSM develops statistical relationships, based on multiple linear regression techniques, between large scale (predictors) and local (predictand) that is precipitation and maximum and minimum temperature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eScreening the downscaled predictor variables\u003c/h2\u003e \u003cp\u003eThe choice of appropriate downscaling predictor variables was under taken through the screen variable option of SDSM. using correlation analysis, Partial correlation analysis, and scatter plot partial correlation analysis. It was done for selected predictors to see the level of correlation with each other. These statistics identify the amount of explanatory power of the predictor to explain the predictand [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Finally, the scatter plot was carried out in order to identify the nature of the association (linear, nonlinear, etc.), whether or not data transformation(s) may be needed, and the importance of outliers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of twenty six Predictor variables derived from canESM2 for the study area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor Variable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePredictor Variable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNcepmslpgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean sea level pressure\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNcepp5zhgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e500hpa divergence\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNcepp1_fgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurface air flow strength\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNcepp_fgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e850 hpa air flow strength\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNcepp1_ugl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurface zonal velocity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNcepp_ugl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e850 hpa zonal velocity\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNcepp1_vgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurface Meridians velocity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNcepp_vgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e850 hpa meridians velocity\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNcepp1_zgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurface velocity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNcepp_zgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e850 hpa vortices\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNcepp1thgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurface wind direction\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNcepp850gl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e850 hpa geo potential height\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNcepp1zhgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurface divergence\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNcepp8thgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e850 hpa wind direction\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNcepp5_fgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500hpa air flow strength\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNcepp8zhgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e850 hpa divergence\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNcepp5_ugl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 hpa zonal velocity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNcepprcpgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelative humidity at 500 hpa\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNcepp5_vgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 hpa meridians velocity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNceps500gl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecific humidity at 500hpa\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNcepp5_zgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 hpa vortices\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNceps850gl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecific humidity at 850 hpa\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNcepp500gl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500hpa geo potential height\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNcepshumgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurface specific humidity\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNcepp5thgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500hpa wind direction\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNceptempgl.dat\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean temperature at 2m\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eSDSM model has two processes called conditional and unconditional processes to be specified before the analysis takes place. In case of daily temperature where the predictand-predictor process is not regulated by intermediate process unconditional process was used, whereas for daily precipitation where the amounts depend on the occurrence of wet-day, the conditional process was chosen [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Significance value is used to test the significance of predictor–predictand correlations and it was set as the default of probability level at 0.05(p \u0026lt; 0.05).\u003c/p\u003e \u003cp\u003eSeveral analyses were made by selecting a maximum of 6 out of 26 predictor variables at a time till best predictor-predictand correlations were found even though up to 12 predictors are possible to select at a time [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCalibration and validation of SDSM\u003c/h2\u003e \u003cp\u003eThe model was calibrated for precipitation and maximum and minimum temperature (predictand variables) along with a set of predictor variables, and computed the parameters of multiple regression equations with an optimization algorithm. Daily data were used for model calibration for data representing the current climate condition of the period 19 years (1987–2005). Maichew Meteorological station was used for calibrating the predictor variables. For monthly models, different model parameters were used for each month [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eValidation was done based on thirty years simulation for years 2006–2018. Validation of the model was performed using the results of weather generator and independent observed data that were used for calibration. Finally, scenarios were generated for precipitation and maximum and minimum temperature predictand variables, both for base and future period, using canESM2 GCM model output for the two emission scenarios (RCP4.5 and RCP8.5 emission scenarios).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eScenarios generation\u003c/h2\u003e \u003cp\u003eFor this study, the model CanESM2 and scenario RCP 4.5 and RCP 8.5 were the two GCM output files used for the scenario generation. The mean of the twenty ensembles for the specified period was produced for maximum and minimum temperature and precipitation. The period from 1987–2018 were considered as a base period whereas the period from 2019–2100 were considered as future periods. The future periods were divided into three time horizons from 2030s (2019–2045), 2050s (2046–2072), and 2080s (2073–2100) and analyses were made for each time horizons.\u003c/p\u003e \u003cp\u003eThe structural skeleton of this study (input/output relationships) is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The Figure shows the schematic representations of the steps to be followed in this research.\u003c/p\u003e "},{"header":"RESULTS AND DISCUSSIONS","content":"\u003ch2\u003eData Quality Control and Adjustment for Model Input\u003c/h2\u003e\u003ch2\u003eFilling of missing data\u003c/h2\u003e\u003cp\u003eData collected from Meteorological stations might not be 100% complete and accurate. The missing data for the precipitation and temperature time series was filled using SPSS and the gaps were less than 1%.\u003c/p\u003e\u003ch2\u003eConsistency analysis\u003c/h2\u003e\u003cp\u003eThe consistencies of the data set of the given stations were checked using double mass-curve method with reference to their neighbourhood stations. The double mass curve was plotted by using the annual cumulative total rainfall of the base station as ordinate and the average annual cumulative total of neighbouring stations at abscissa.\u003c/p\u003e\u003ch2\u003eAssessing precipitation and temperature trends\u003c/h2\u003e\u003cp\u003eThe Mann-Kendall trend test was used to test for the presence of trends in the precipitation and temperature data. The null hypothesis being that there is no trend in the time series whilst the alternative hypothesis was that there is a trend in the time series. The Mann-Kendall trend tests were performed in XLSTAT. The tests were carried out at 5% level of significance. The major variables for test interpretation were the p-value and the Sen’s slope.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMann-Kendall trend test / Two-tailed test (Yearly) and Theil-Sen slope test of Precipitation and Temperature trends\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum temperature\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimum temperature\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKendall's tau\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.214\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-106\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e318.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e156.000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVar(S)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3802.667\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3802.667\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3802.667\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpha\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.685\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eTheil-Sen slope [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], also known as “Kendall’s slope” or “non-parametric linear regression slope”, is an alternative to the standard linear regression slope. It is popular in earth sciences (Meteorology and Climatology) for measuring temperatures and precipitations over time. As shown in the following table (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the slope shows the negative value. This showed that the amount of precipitation was decreasing with time. Maximum and minimum temperature shows significantly an increasing trend for the last 32 years (1987–2018).\u003c/p\u003e\u003cp\u003eHowever, the p-values were greater than the level of significance (0.05) which reveals that though the precipitation amounts were getting low, the differences were not statistically significant between the consecutive years.\u003c/p\u003e\u003cp\u003eSimilarly, [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] assessed the evidence of climate variability in the Northern part of Ethiopia indicated a slight decrease in rainfall, and an overall increase in the mean annual minimum and maximum temperatures. Moreover, [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] showed that mean annual precipitation shows a decreasing trend for the last 3 decades in Maichew station.\u003c/p\u003e\u003ch2\u003eModel calibration and Validation\u003c/h2\u003e\u003cp\u003eThe SDSM calibration was done for the period of 19 years (1987–2005) at a monthly model type in order to see the monthly temporal variations. The results showed that, the simulated precipitation, maximum and minimum temperature had good agreements with the observed results.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCalibrated, precipitation, maximum and minimum temperature at Maichew station\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictands\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalibration\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum temperature\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum temperature\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eValidation was done using an independent observed data for the period of thirty year from 2006 to 2018. Here also twenty ensembles (runs) of daily values were generated and the averages of these ensembles were taken for the comparison. The correlation coefficients that were found during the calibration are also maintained during the validation as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. A good agreement was also found between the observed and simulated precipitation (R\u003csup\u003e2\u003c/sup\u003e = 0.97 and 0.97 for calibration and validation) though it is a conditional process. And also there was a good agreement for minimum and maximum temperature were R\u003csup\u003e2\u003c/sup\u003e = 0.99 and 0.99 respectively during calibration.\u003c/p\u003e\u003ch2\u003eScenario generation\u003c/h2\u003e\u003cp\u003eThe climate scenario for the future period was developed from statistical downscaling using the GCM predictor variables for the two emission scenarios for 82 years based on the mean of 20 ensembles. And the analysis was done based on 27 years period from 2019–2045, 2046–2072 and 2073–2100. The IPCC recommends 1961–1990 as a climatological base period in impact assessment. Hence, for this research the period from 1987–2018 was taken as a base period with in which the comparison was made. The observed climatological data collected from Ethiopian Meteorological stations contain more consistent time series records from the period 1987 and onwards than the period before 1987 and this is why the period from 1987–2018 were taken as a base period.\u003c/p\u003e\u003ch2\u003eMaximum Temperature\u003c/h2\u003e\u003cp\u003eThe mean monthly, seasonal and annual change in maximum temperature for the future period (2016–2100) for both RCP4.5 and RCP8.5 scenarios are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e (a, b). As it can be seen from the Figure, the overall results (2019–2100) for annual mean maximum temperature showed an increasing trend for both scenarios (RCP 4.5 and RCP 8.5). Seasonally, Maximum temperature shows an increasing trend ranging between + 0.4°C and + 0.6°C in 2030s (2019–2045) and 2080s (2073–2100) respectively for RCP4.5 scenario. For RCP8.5 scenario, Maximum temperature shows an increasing trend in Bega season (October -January) ranging between + 1.0°C to + 2.0°C for 2030s (2019–2045) and 2080s (2073–2100) respectively. The mean monthly increment of maximum temperature ranges between + 1.7°C (2019–2045), + 2.0°C (2046–2072) and + 2.2°C (2073–2100) for RCP 4.5 scenarios as compared to the base period. While for RCP8.5 scenario, it ranges between + 1.3°C (2019–2045), + 1.9°C (2046–2072) and + 2.7°C (2073–2100). The highest increment of maximum temperature recorded in October month up to + 2.7°C in RCP8.5 scenario. The increment was not worth for both scenarios based on [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] in which the globally averaged surface air temperature was projected to warm 1.4°C to 5.8°C by 2100.\u003c/p\u003e\u003ch2\u003eMinimum temperature\u003c/h2\u003e\u003cp\u003eThe downscaled minimum temperature shows an increasing trend in all annual future period for both RCP4.5 and RCP8.5 scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). With respect to monthly minimum temperature, the downscaled minimum temperature scenario indicates that there might be an increasing trend (2019–2100) on the months of January, February, May, Jun, August, September and October for RCP4.5 scenario. But July (2019–2100), November (2019–2100), December (2019–2100) months shows a decreasing trend for RCP4.5 scenario. In the RCP8.5 scenario the projected minimum temperature shows an increasing trend in January, February, Jun, August-October months from 2019–2100. But, May, November and December months the minimum temperature shows a decreasing trend from 2019–2100. The highest increment of minimum temperature was projected on RCP8.5 scenario in the month of August + 0.8°C, + 1.4°C, and + 1.7°C for the 2030s, 2050s and 2080s periods respectively. The overall result of minimum temperature showed an increasing trend which is in line with previous studies conducted on Bilate watershed [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and Tikur wuha watershed [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] of the Central Rift Valley of Ethiopia.\u003c/p\u003e\u003ch2\u003ePrecipitation\u003c/h2\u003e\u003cp\u003eAs it can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e (a, b) the overall results (2019–2100) for annual percentage change in precipitation shows an increasing trend for both scenarios (RCP4.5 and RCP8.5). The increment of annual percentage change of precipitation ranges from + 1% (2019–2045) to + 11% (2073–2100) for RCP4.5 scenario. For RCP8.5 scenario the increment ranges between + 1% (2019–2045) and + 14% (2073–2100) which indicat similar result with [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] that rainfall increase or decrease (up to ± 20%) in 2100.\u003c/p\u003e\u003cp\u003eThe results of this study was thus in line with the previous researches done on Tana basin, upper Blue Nile basin, Central Rift Valley basin [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] assessed regional impacts and vulnerabilities to climate change in four regions including Africa and the results showed that annual mean rainfall would increase in East Africa due to climate change. Additionally, [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] conducted research around Adama district, most CMIP5 GCMs project an increase in rainfall at all locations (Nazereth, Wonji and Melkasa).\u003c/p\u003e\u003cp\u003eOn monthly basis, the percentage change in precipitation is not systematic, i.e precipitation increases in some months and decreases in some other months. The percentage changes in precipitation increases from June to September and decreases from December-Feberuary for the future (2016–2100). The increment is most dramatic in August from the period 2016–2045 for both RCP8.5 and RCP4.5 scenarios in which the precipitation increases by 76.01% and 73.12% respectively. The decrease in precipitation reaches to a maximum of 18.32% (2046–2075) and 22.12% (2076–2100) for RCP8.5 scenario and 17.19% (2046–2075), 20.02% (2076–2100) for RCP4.5 scenario.\u003c/p\u003e\u003cp\u003eSeasonally, the precipitation increases in Belg (Febrauary-May) and Bega (October -January) for all scenarios in all time horizons (exept, slight decreseas in 2050s for RCP8.5 scenario). The precipitation increases up to a maximum of 49% (2073–2100) for RCP4.5 scenario and 66% (2073–2100) for RCP4.5 scenario in the Belg season. And also, precipitation increases up 7% (2073–2100) for RCP4.5 scenario and 4% (2019–2045) for RCP4.5 scenario in the Bega season. Precipitation decreases in Kiremt (Jun-September) season by 8% (2019–2045) and 23% (2073–2100) for RCP4.5, 10% (2019–2045) and 23% (2073–2100) for RCP8.5 scenarios.\u003c/p\u003e"},{"header":"Conclusion and Recommendations","content":"\u003cp\u003eThe trend analysis on the significant climate parameters indicated a slight decrease in rainfall, and an overall increase in the mean annual minimum and maximum temperatures in the study area for the last 32 years. For future scenarios, the result of this study showed a general increasing trend for precipitation, maximum and minimum temperatures in all three time periods (2030s, 2050s and 2080s). The mean monthly increment of maximum temperature ranges between + 1.7°C (2019–2045), + 2.0°C (2046–2072) and + 2.2°C (2073–2100) for RCP 4.5 scenarios as compared to the base period. And for RCP8.5 scenario, it ranges between + 1.3°C (2019–2045), + 1.9°C (2046–2072) and + 2.7°C (2073–2100). The highest increment of maximum temperature recorded in October month up to + 2.7°C in RCP8.5 scenario. The precipitation showed that increasing trends for the future time horizons mainly in Belg season (February to May) and Bega (October-January) season while it decreases in Kiremt season. The increment of precipitation in these seasons can be a good opportunity for agriculture; however, increment of temperature, may affect the area negatively.\u003c/p\u003e\u003cp\u003eFuture work needs to consider studying the effects of different climate change adaptation strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEIAR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEthiopian Institute of Agricultural Research\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFAO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFood and Agriculture Organization.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to express our greatest appreciation to Ethiopian Institute of Agricultural Research (EIAR) for providing financial support. The opinion expressed herein\u0026nbsp;are the authors\u0026rsquo; own and do not necessarily express the view of EIAR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMekin Mohammed Yimam has designed and carried out the study. And Mekin Mohammed Yimam has analysed the data, and wrote the manuscript. Seyoum Bezabih contributed to the data collection and the interpretation of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Ethiopian Institute of Agricultural Research (EIAR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the author, without any reservation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Ethiopian Institute of Agricultural Research (EIAR), Fogera National Rice Research and Training Center, P.O. Box 1937, Ethiopia.\u003c/p\u003e\n\u003cp\u003e2. Ethiopian Institute of Agricultural Research (EIAR), Debre Markos Agricultural Research Center, Ethiopia.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIPCC, \u003cem\u003eThe IPCC\u0026apos;s Fifth Assessment Report: What\u0026apos;s in it for Africa?\u003c/em\u003e 2014. p. 70.\u003c/li\u003e\n\u003cli\u003eMaharjan, L.D. and N. Kathmandu, \u003cem\u003eNepal: Building Climate Resilience of Watersheds in Mountain Eco-Regions\u0026ndash;Climate Change and Vulnerability Mapping in Watersheds in Middle and High Mountains of Nepal.\u003c/em\u003e 2012.\u003c/li\u003e\n\u003cli\u003eTekle, A. \u003cem\u003eAssessment of climate change impact on water availability of Bilate watershed, Ethiopian Rift Valley Basin\u003c/em\u003e. in \u003cem\u003eAFRICON 2015\u003c/em\u003e. 2015. IEEE.\u003c/li\u003e\n\u003cli\u003e\u003cem\u003e\u0026lt;Abadi and kassa, 2014.pdf\u0026gt;.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eAshenafi, A.A., \u003cem\u003eModeling hydrological responses to changes in land cover and climate in Geba River Basin, Northern Ethiopia\u003c/em\u003e. 2014.\u003c/li\u003e\n\u003cli\u003eMohammed, M., B. Biazn, and M.D. Belete, \u003cem\u003eHydrological impacts of climate change in Tikur Wuha watershed, Ethiopian Rift Valley Basin.\u003c/em\u003e J Environ Earth Sci, 2020. \u003cstrong\u003e10\u003c/strong\u003e(2): p. 28-49.\u003c/li\u003e\n\u003cli\u003eChris, F., et al., \u003cem\u003eClimate Trend Analysis of Ethiopia: Famine Early Warning Systems Network-Informing Climate Change Adaptation Series.\u003c/em\u003e Fact Sheet, 2012. \u003cstrong\u003e3053\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eFenta Mekonnen, D. and M. 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Vol. 126. 2008: Iwmi.\u003c/li\u003e\n\u003cli\u003eTaye, M., \u003cem\u003eHydrological modeling of climate change impact on selected catchment of Nile River basin.\u003c/em\u003e Journal of Hydrology and Earth System Sciences Discussions, 2010. \u003cstrong\u003e7\u003c/strong\u003e: p. 5441-5465.\u003c/li\u003e\n\u003cli\u003eUNFCCC, C.C., \u003cem\u003eImpacts, vulnerabilities and adaptation in developing countries\u003c/em\u003e. 2007: Germany.\u003c/li\u003e\n\u003cli\u003eShiferaw, A., et al., \u003cem\u003eImpacts of Climate Change on Agriculture in Ethiopia: what, when, where and how.\u003c/em\u003e Ophthalmic Epidemiology, 2015. \u003cstrong\u003e22\u003c/strong\u003e(3): p. 162-169.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"canESM2, Climate change, GCM, Guguf watershed, Raya Valley, SDSM","lastPublishedDoi":"10.21203/rs.3.rs-3812339/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3812339/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccording to Intergovernmental panel on Climate Change (IPCC) Climate change is the weather characteristic condition such as precipitation, air temperature, humidity, wind, sunshine, cloud cover and atmospheric pressure at a specific location determined over a long period of at least 30 years. The main objective of this study was to analyse the climate trend and future projection in Guguf watershed of Southern Tigray, Ethiopia. 32 years (1987\u0026ndash;2018) Meteorological data were collected from Ethiopia National Meteorological Agency (NMA). Download canESM2 (Canadian Second Generation Earth System Model) which was freely available at the Canadian climate change scenario group website. The Mann-Kendal trend test was used to test for the presence of trends using XLSTAT. SDSM 4.2.9 decision support tool was used to downscale large scale predictors and project future climate change. The period from 1987\u0026ndash;2018 were considered as a base period whereas the period from 2019\u0026ndash;2100 were considered as future periods. Historically, slight decrease in rainfall, and an overall increase in the mean annual minimum and maximum temperatures in the study area for the last 32 years. The highest increment of maximum temperature recorded in October month up to +\u0026thinsp;2.7\u0026deg;C in RCP8.5 scenarios. The precipitation increases up to a maximum of 49% (2073\u0026ndash;2100) for RCP4.5 scenario and 66% (2073\u0026ndash;2100) for RCP4.5 scenario in the Belg. Precipitation decreases in Kiremt (Jun\u0026ndash;September) season by 8% (2019\u0026ndash;2045) and 23% (2073\u0026ndash;2100) for RCP4.5 scenarios. Future work needs to consider studying the effects of different climate change adaptation strategie.\u003c/p\u003e","manuscriptTitle":"Climate Change Trend Analysis and Future Projection in Guguf Watershed, Northern Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-01 08:38:39","doi":"10.21203/rs.3.rs-3812339/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae596cac-cc42-4309-b27d-a8c0a9c8fa15","owner":[],"postedDate":"January 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-09T08:00:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-01 08:38:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3812339","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3812339","identity":"rs-3812339","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}