Temporal and spatial distribution, variability, and trend of hydroclimate in the Dabus River Basin Upper Blue Nile, Ethiopia

Temporal and spatial distribution, variability, and trend of hydroclimate in the Dabus River Basin Upper Blue Nile, Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Temporal and spatial distribution, variability, and trend of hydroclimate in the Dabus River Basin Upper Blue Nile, Ethiopia Mekuria Tefera Tola, Kassahun Ture Bekitie, Tadesse Terefe Zeleke, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5446005/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background High-resolution local scale climate research approach is very effective in examining the existing climate change and predicting its risk. Thus, this study investigated the hydroclimate distribution, variation, trend, and abrupt change points, and considered more than the climate normal time range (1981 to 2020) to determine the climate change of the Dabus River Basin. The study employed different statistical, parametric, and nonparametric modified trend tests, and exact changing point detecting models. Results The result found the basin received 57.7% of the annual rainfall in June, July, and August. The standard anomaly index (SAI) value indicates 1999 and 2000 were the wettest years whereas 1982,1983,1984,1986 and 2015 were the driest years in the area. The basin experienced very fluctuating rainfall for the last four decades. Peaks of Precipitation Concentration Index (PCI) were observed in the years 1987,1991, 2002,2003,2006, and 2011 which indicates the strong irregular distribution of rainfall. The annual mean rainfall and maximum temperature (Tmax) increased significantly (p < 0.05), whereas the annual mean minimum temperature (Tmin), river flow, and river runoff decreased. In Dabus the abrupt increasing change point of annual rainfall was observed in 1996 whereas Tmax in 1993 and 1997. The abrupt decreasing change point of Tmin, river flow, and river runoff was observed in 1987, 1998, and 1999, respectively. Conclusions The study found the climate change in the basin due to the significant increase in temperature with fluctuating rainfall distribution as well as reduction of river flow and runoff. This climate change could upset agriculture, electric power production, and water demand in the basin. climate change Hydroclimate modified Mann Kendal test PCI Pettit test Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 1. Introduction Global warming, which affects the hydrological cycle results in a variety of climate change effects at the local, regional, and global levels (Bolan et al., 2023 ; Gashaw et al., 2023 ; Wang and Liu, 2023 ). The extreme change in temperature and global warming is a clear result of human activity (Wentz, 2022 zéquel et al., 2024; Labrousse et al., 2020) that exerts hydrological extreme events in different areas. Seidenfaden et al.(2021); Wedajo et al.( 2024) identified that even though it is well-known that spatiotemporal hydroclimate variation was mainly caused by global warming, there is great uncertainty in determining the factors that influence the existing conditions and projection of the local scale climate change. Asfaw et al. ( 2018 ); Gonfa et al.(2022) reported that studies on spatiotemporal dynamics of climatic change used to understand the changes that have already occurred and forecast expected changes that need adaptation measures. Therefore, a study on hydroclimate variation and its impact on different sectors in the sub-basin scale requires the attention of climate researchers. Africa's hydroclimatic characteristics range from the humid equatorial regions of West Africa to the arid deserts found in the northern and southern subtropics. Schaebitz et al.(2021). Climate-related research, (e.g Bedeke, 2023 ; Wentz, 2022 ; Mostefaoui et al., 2024; Sono et al., 2021) identified that several extreme hydrological events have occurred in Africa, making it one of the most vulnerable continents due to its geographical features, little capacity for adaptation, and highly sensitive socio-economic systems. Particularly, East Africa faced a persistent decline in seasonal and annual rainfall during the last four decades and this has had major consequences for regional water and food security (Anderson et al., 2021 ; Gebrechorkos et al., 2023). Therefore, detailed regional-level climate change information and awareness are crucial to ensure future adaptation plans as well as sustainable water availability in the region (Allan et al., 2020 ; Orkodjo et al., 2022 ; Gebrechorkos et al., 2020). Ethiopia experienced the hydro-meteorological extremes of climate modification due to its reliance on profoundly rain-fed farming (Abera and Shumate, 2021). Ethiopia’s watersheds are significantly impacted by current and anticipated alterations in temperature and rainfall. Different studies on Ethiopia’s basins confirmed this truth. Abera & Gebeyehu ( 2022) reported that in Ethiopia, the impacts of climate variation on stream flow extend beyond the scarcity of water to include an increase in the occurrence and strictness of extreme weather. Chanie ( 2024 ); Ketema & Dwarakish ( 2021 ). In Upper Blue Nile (UBN), the quantity and quality of water balance, as well as evapotranspiration, can all be directly impacted by variations in precipitation and temperature. (Mohammed et al., 2022 ). Kheireldin et al.(2016) reported extreme climate change-related occurrences are occurring in the Blue Nile Basin and could have an expected influence on the ecology, livelihood, agriculture, and livestock. Anose et al.(2022) found the current intensity of the drought may worsen the depletion of other natural resources and the water deficit, which may have a significant effect on the amount of water accessible for agriculture, hydropower production, and ecosystem services. Ketema & Dwarakish ( 2021 ); Wubneh et al. ( 2023 ) Changes in temperature within the watersheds could result in a diversified impact on water availability. Thus current and comprehensive studies are needed in Ethiopia because any changes made to the water bodies would have a big effect on the nation (Matthews and Vivoda, 2023 ). The Dabus River Basin, over its long journey, supports a variety of habitats and is essential for agriculture, the production of hydroelectric power, and different ways of serving the community’s life (Willemin and Backhaus, 2023). It is one of the main tributaries of the Blue Nile in Ethiopia and has a big contribution (Kedida and Arsano, 2024). In Ethiopia, particularly the Nile basin, there are several studies done. However, there was not much considered for this sub-basin. The previous research done in the basin by Bahiru et al.(2024); Diriba ( 2021 ); Mangel and Berhe ( 2021 ) focused on only the stream flow, never including other climate elements and long-range data of more than climate normal. It is essential to comprehend the variables influencing hydroclimate variability to create sustainable water management plans in dynamic settings (Wedajo et al., 2024 ). Nevertheless, the negative impact of climate change on this basin was not profoundly considered in most studies. Hence, this study has made an inclusive investigation on three hydroclimates and uses along the duration of climate data more than climate normal. The identification of local climate variability and change impacts at the basin scale is decisive because proper management of water resources is essential for maintaining the sustainability of water resource development projects, and searching for potential mitigation measures to prevent disastrous outcomes. Therefore, the objective of this study was to investigate the distribution, trends, variability, and climate change of monthly, seasonal, and annual hydroclimate data for four decades that were above the climate normal. Descriptive statistical models, variation detectors, parametric, modified Mann-Kendall test, sens slope, and abrupt change point (Pettit test) were used. Various techniques and different software were also applied which enable obtaining the best results. The results provide a significant understanding of existing hydroclimate variation and climatic conditions of the area. The finding will be crucial to developing adaptation strategies and management practices for the watershed and other associated places that have experienced the effects of climate variability and change. 2. Methods and materials 2.1. Description of the study area The Dabus River Basin is one of the largest tributaries of the Blue Nile in Ethiopia, and it contributes approximately one-third of the total flow of the upper Blue Nile. This makes the Dabus River Basin a vital water source used for irrigation, drinking water, and hydropower in the Blue Nile and the downstream countries. This sub-basin is located between 9°0′0″–11°50′0″ N latitude and 34°30′0″–35°58′40″ E longitude covering an area of 14,793.81 km 2 (Fig. 1 ). It originates in the central and southwestern parts of Wollega and covers two regions with seven woredas in Benishangul Gumuz and twelve woredas in Oromia. The elevation of the watershed ranges from 502 to 3032m (Fig. 1 ), with greater than 1500 m altitude in the eastern part, and less than 900 m altitude in the northern part. The climate of the study area ranges from cold in the highlands to moderately hot in the lowlands. 2.2 Data type and source This study used daily rainfall maximum temperature (Tmin), and minimum temperature (Tmax) of ten meteorological stations observational data were obtained from the Ethiopian National Meteorological (ENMA). The meteorological data were forty years period data from 1981 to 2020 (Table 1 ). The daily rainfall was observed with a spatial resolution of 0.05 degrees (4kmX4Km) and magnitude mm/day. The water data from the Ministry of Water Irrigation and Electric (MoWIE) which had the year length of the record was not consistence (Table 2 ). To analyze these data, different models and software were used. The daily river flow and data are only selected from five gauge stations that have full data. Table 1 Meteorological stations Dabus River Basin ID Stations latitude longitude Covered period 1 Mendi 9.79 35.2 1981–2020 2 Sharkole 10.48 34.62 1981–2020 3 Begi 9.2 34.64 1981–2020 4 Assossa 10.07 34.62 1981–2020 5 Nedjo 9.5 34.64 1981–2020 6 Aira 9.06 35.33 1981–2020 7 Gimbi 9.72 35.42 1981–2020 8 Abadi 11 35.07 1981–2020 9 Kamashe 10.5 35.4 1981–2020 10 Bure 10.04 35.41 1981–2020 In Dabus, most of the hydrological gauge station data has more than 15% of missing data. Therefore, in this study, only stations that have continued data were used to minimize the several factors that caused inconvenience. Table 2 Stations where the hydrological data collected ID Station name Lon Lat Drainage area (km 2 ) Covered period 1 Dabena 36.17 8.24 47 1990–2017 2 Didesa 36.25 8.41 9981 1990–2016 3 Hujur 35.20 9.38 94 1990–2011 4 Uke 36.31 9.26 4674 1990–2013 2.3 Methods 2.3.1 Statistical variability test hydroclimate variable test Several statistical techniques will be used to analyze the hydroclimate data, which generally fall into variability and trend analysis categories (Kiros et al., 2016 ; Koudahe et al., 2017 ). The mean, standard deviation (SD), median, and Coefficient of variance (CV) were used. The CV can be calculated using Eq. (1): \(\:CV=\frac{\sigma\:}{\mu\:}\:\times\:100\) 1 Where: \(\:\sigma\:\) is the standard deviation, and \(\:\mu\:\) is the mean, The degree of variability of rainfall was classified as low when CV < 20%, moderate when 20% < CV < 30%, high when 30% < CV < 40%, very high when 40% < CV 70% (Asfaw et al., 2018 ). 2.3.2. Standardized anomaly index The Standardized Anomaly Index (SAI) computes the basin’s negative and positive hydroclimate fluctuation anomalies. The SAI indicates the distance between the data and its mean value (Alemayehu and Bewket, 2017 ). It is calculated as follows: \(\:SAI=\left(\frac{x-\stackrel{-}{x}}{\sigma\:}\right)\) 2 Where: \(\:x\) is the rainfall or temperature data, \(\:\stackrel{-}{x}\) And \(\:\sigma\:\) are the mean and the standard deviation of the data, respectively. 2.3 The precipitation concentration index In this study, the Precipitation Concentration Index (PCI) was used to differentiate the normal and variation nature of the rainfall in the area. The PCI is used to analyze the seasonal or annual variability (heterogeneity pattern) of rainfall (De Luis et al. 2011). According to Oliver (1980), the PCI values were calculated annually according to Eq. (3) which represents 12 months of the year. Eq. (4) is used to calculate seasonal PCI. This equation was used to determine the PCI on a seasonal scale for the following months: spring (March to May or MAM), summer (June-August or JJA), autumn (September–November or SON), and winter (December–February or DJF). \(\:{PCI}_{annual}=\frac{\sum\:_{i=1}^{12}{P}_{i}^{2}}{{P}^{2}}*\:100\) 3 Where: \(\:{P\:}_{i}\) =the rainfall amount of the ith month. \(\:P\) =annual precipitation \(\:{\:\:PCI}_{seasonal}=\frac{\sum\:_{i=1}^{12}{P}_{i}^{2}}{{P}^{2}}*\:25\) 4 Where: \(\:{P\:}_{i}\) =the rainfall amount of the ith month. \(\:P\) =Total precipitation of the month Table 3 Precipitation concentration index (PCI) classification PCI Value Distribution of precipitation PCI 21 Strong irregular distribution Source, Oliver (1980) 2.3.1 Trend tests Several researchers agreed that there is no universal solution for serial correlation in the time series (Patakamuri et al., 2020 ). Trend tests reduce the effect of positive autocorrelation in the data, increasing the probability of detecting trends when none truly exists and vice versa. This study used parametric and non-parametric methods to perform hydroclimate data trend detection. Parametric tests are stronger, but the data must be normally distributed. Non-parametric tests are “distribution-free” methods, which do not rely on assumptions that the data are drawn from a given probability distribution (Amrender et al., 2015). Hydroclimate data are non-parametric, and the study applied mostly non-parametric models. The parametric method is the linear regression test, and non-parametric (rank-based) methods include the Mann-Kendal (Kendall, 1975 ; Mann, 1945 ). Modified Mann-Kendall, and trend-free pre-whitening Mann-Kendall trend tests. Hence, in this study parametric (linear regression) and non-parametric (modified Mann Kendal) trend tests were used. 2.3.2 The linear regression The linear regression model will measure the pattern or trend of variables over a long period(Kiros et al., 2016 ). It is calculated using Eq. (3). \(\:Y=a+bx\) 5 Where: \(\:Y\) indicates the trend value, \(\:a\) is the intercept, \(\:b\) is the slope of the trend, and \(\:{x}_{t}\) Is the time point. 2.3.3 Modified Mann-Kendall test (MMK-test) The Mann-Kendall ( MK) trend test is always affected by the autocorrelation of the time series. Modified Mann-Kendall (MMK) is recommended to correct autocorrelation effects in a given time series (Yue and Wang, 2002 ). Using the MK test with extensive auto-correlated hydrological data could result in overestimating or underestimating actual trends, which causes the failure of the trend test. Therefore, the MMK test calculated by \(\:V{\left(S\right)}^{*}=Var\left(S\right)\frac{n}{{n}^{*}}\) 6 where V(S)* is the modified variance, and the correction factor \(\:n/n*\) is computed by: \(\:\frac{n}{{n}^{*}}=1+\frac{2}{n\left(n-1\right)\left(n-2\right)}\sum\:_{h+1}^{n-1}\left(n-h\right)\left(n-h-1\right)\left(n-h-2\right){R}_{h}\) 7 Where: \(\:{R}_{h}\) is the autocorrelation coefficient of the ranked data Sen’s slope Estimator Sen’s slope (Sen, 1968 ) estimator was used to predict the magnitude of the trend. The non-parametric method can evaluate the change per unit of time. This technique assumes a linear trend in the time series. The slope ( \(\:{Q}_{i}\) ) of all pairs of data \(\:x\) can be calculated as \(\:{Q}_{i}=\frac{{x}_{j\:-{X}_{k}}}{j-k}\:,\:i=\text{1,2},\dots\:N,\:\:j>k\) 8 Where \(\:{x}_{j}\) and \(\:{x}_{k}\) are data values of time \(\:j\) and \(\:k\) , respectively. If there are n values in the time series, then as many as N = n(n-1)/2 slop estimates. \(\:{Q}_{i}\) .The Sen’s slope estimator is defined as the median of the \(\:N\:\) values of \(\:{Q}_{i}\) .The values of slopes are ranked from the smallest to the largest, and Sen’s slope estimator \(\:{Q}_{i}\) is calculated as If N is an odd observation: \(\:{Q}_{i}={Q}_{(N+1)/2}\) 9 If N is even observation: \(\:{Q}_{i}\) = \(\:\frac{1}{2}\left({Q}_{N/2}+{Q}_{\left(N+2\right)/2}\right)\) 10 2.3.4 Test for single change point (abrupt change) Extreme climate change and intensive human activities will cause abrupt changes in hydroclimate variables. Therefore, the Pettit test has been used in several climate studies to detect abrupt changes in the mean distribution of the variable of interest. The Pettit test is a distribution-free rank-based test used for detecting the exact time of change of the mean in the time series and gives information about the location of the shift. The test statistic \(\:{U}_{t,T}\) Is evaluated for all random variables from 1 to T; then, the most significant change point is selected where the value of \(\:⌊{U}_{t,,T}⌋\) is the largest(Jaiswal et al., 2015 ). To identify a change point, a statistical index \(\:{U}_{t}\) is defined as follows: \(\:{U}_{t,T}=\sum\:_{i=1}^{t}\sum\:_{j=1}^{T}Sgn({x}_{i}-{x}_{j}),\:1\le\:t\le\:T\) 11 Where similar to the MK test, \(\:Sgn\left(\theta\:\right)=\:\:\left\{\begin{array}{c}+1\:\:\:\:\:\:\:\:\:\theta\:>0\\\:\:\:\:0\:\:\:\:\:\:\:\theta\:=0\:\:\:\:\:\\\:-1\:\:\:\:\:\:\:\:\:\:\:\theta\:<0\end{array}\right.\) 12 The most probable change point is found where its value is (The break occurs in year k when). The test statistic \(\:{K}_{n}\) and the associated probability \(\:\left(P\right)\:\:\) Used in the Test are given as. \(\:{K}_{{t}_{0}}=\underset{1\le\:y\le\:n}{{max}}\left|{U}_{t,T}\right|\) 13 and the significance probability associated with the value \(\:{K}_{t}\) is evaluated as \(\:{p}_{\left({t}_{0}\right)}=2exp\left[\frac{-6{k}_{{t}_{o}}^{2}}{{T}^{3}+{T}^{2}}\right]\) 14 Where: \(\:{t}_{0}\:\) is concluded as a significant change point when \(\:\:\:{P}_{{t}_{0}}\le\:0.5.\:\) The value is then compared with the critical value (Pettitt, 1979 ). Given a certain significance level \(\:\alpha\:\) , if \(\:p<\alpha\:\) , we reject the null hypothesis and conclude that \(\:\:{x}_{t}\) is a significant change point at level \(\:\alpha\:\) (Du et al., 2013 ). 3. Result and discussion 3.1 Temporal hydroclimate distribution and variation in the basin 3.1.1 Monthly distribution and variation hydroclimate in the basin The monthly hydroclimate of the basin distribution shows different features (Table 4 ). The monthly rainfall of the basin is very low in January and maximum in August which ranges from 3.18 to 273.24mm/year, respectively. The monthly mean Tmin distribution ranges from 14.42 to 16.12°C/year in May and January, respectively. The monthly mean Tmax was observed to range from 26.34 to 32.39°C/year in July and March, respectively. More standard deviation (SD) was depicted in monthly rainfall which indicated a greater fluctuation in the monthly rainfall. The highest CV was recorded in the dry season months (November, December, January, and February), which were extremely variable CV > 70%. Similarly, Alemayehu et al.(2022)reported that the dry season months have a higher CV that indicates the year-to-year variation for these months is high. The wet season months except Jun were depicted as the lowest CV > 20% which has a moderate variation of rainfall. The wet season months (June, July, and August) contribute almost 60% of the annual rainfall of the basin. The high CV value exhibits the highest variation of rainfall in the studied area. Ayehu et al.(2021); Mohamed et al.(2022) found in line with this result, which stated the highest contribution of annual rainfall was obtained in (Jun. July, and August) in UBN. Alaminie et al.(2021); Mohamed et al.(2022) reported in agreement with this finding that the summer months account for roughly 68.2% of the total annual rainfall in the study area. Their finding also confirmed that 42.3% of the total rainfall occurs in July and August. A study done by Samy et al.(2019): Tadese et al. ( 2019 ) stated that at every rainfall station, December and January saw the lowest amount of rainfall, while August and September observed the highest amounts. In agreement with this result, Gashaw et al.(2023) found monthly, seasonal, and annual Tmax and Tmin showed less variability (< 20%). Table 4 Monthly variation rainfall, minimum and maximum temperature, and contribution to the annual in the basin Month Mean Mean Mean Rainfall SD CV % of annual contribution Tmin SD Tmax SD Jan 3.18 3.72 116.66 0.24 16.12 1.14 30.82 0.81 Feb 17.25 16.83 148.28 1.28 14.84 0.72 32.14 0.89 Mar 39.37 27.24 67.61 2.93 14.52 0.64 32.39 0.99 Apr 4.72 4.13 69.19 0.35 14.97 1.05 31.76 1.4 May 143.37 54.81 18.40 10.67 14.94 0.78 30.21 0.81 Jun 273.24 39.01 38.23 T 14.42 0.69 27.34 1.88 Jul 229.60 42.24 14.28 17.09 15.32 0.87 26.44 0.67 Aug 273.42 54.31 2 20.35 15.95 0.96 26.25 0.48 Sep 237.38 51.47 49.29 17.67 14.97 0.58 27.81 0.46 Oct 101.37 49.97 21.68 7.54 15.69 1 27.32 0.51 Nov 16.50 18.88 114.46 1.23 14.96 0.66 28.85 0.54 Dec 4.72 4.13 87.53 0.32 15.2 0.61 29.64 0.73 3.1.2 Seasonal variation hydroclimate in the basin Figure 2 . shows the seasonal rainfall distribution in the Dabus river basin. The basin received the highest rainfall in the summer, spring, and autumn seasons. Winter was the driest season for the basin. The seasonal rainfall contribution for annual rainfall was JJA (60.8%), MAM (11.9), SON(26.8%), and DJF(0.5%). The basin received the highest rainfall in the JJA. The area obtained the maximum rainfall above 1372.68mm/year in the southern east and central parts. The basin also received considerable rainfall in MAM and SON. The northern and northwest parts of the area received very little seasonal rainfall mainly in the MAM and DJF season. The very driest season of the whole basin was DJF about 2.99mm/year of seasonal rainfall was recorded. In agreement with this result, Cherinet et al.(2019) stated almost 49.3% of the region's total precipitation falls during the summer. The seasonal mean temperature distribution (Tmean = (Tmax + Tmin)/2) of the basin ranges from 12 to 34.15°C/year (Fig. 3 ). The maximum seasonal Tmean was observed in the northern and southwest pocket areas. Less seasonal mean temperature was depicted in the middle part of the basin. Spring and winter were the hottest seasons in the basin. The highest Tmean was recorded in the winter season in the northern and southwest parts of the basin. The highest Tmean was recorded in the northern, northwestern, and southmost parts of the basin which are in Abadi, Sharkole, Assosa, Kamashe, and Begi. In the central part of the basin Nedjo, Begi, Mendi, and Gimbi experienced less Tmean.Similar to this research findings several researchers (Asfaw et al., 2018 ; Chakilu et al., 2024 ; Gonfa et al., 2022 ; Zena et al., 2020 ) agreed the area was getting hotter. The seasonal mean of river flow of the basin ranges from 5.02 to 504.66 m 3 /s/ year and the river runoff ranges from 3,22 to 3806.83mm/ year (Table 4 ). The result of SD of the river flow indicates the highest variation among the rivers. The SD value has more differences and variations in the river runoff. The value of CV of river flow was observed above moderate variability > 20% exhibiting the highest flow variation among the rivers of the basin. The river runoff CV also showed the maximum variation of runoff except for the Didesa River which has 18.57%. Among the rivers of the basin except Didesa all had maximum flow in the summer season. The river runoff was high in summer and autumn seasons except Uke River which has maximum runoff in winter. The seasonal shift of river flow and river runoff also showed the same pattern as the seasonal rainfall of JJA and SON. In line with this result, Worku et al. ( 2021 ) detected that just three to five months of the year are in the rainy season which contributes to the highest river flow and runoff in the watershed. Table 4 seasonal variation of river flow and river runoff in Dabus River Basin. Station Seasons River flow(m 3 /s) River runoff(mm) Mean median SD CV% Mean median SD CV% Dabena Spring 13.62 8.94 19.09 140.10 304.03 170.31 524.86 172.63 Summer 183.89 181.10 38.67 21.03 3851.26 3806.8 879.46 22.84 Autumn 54.48 54.74 17.76 32.60 3368.73 3377.9 1001.1 29.72 Winter 12.28 12.22 3.54 28.85 260.63 259.90 75.82 29.09 Didesa Spring 86.62 72.80 69.37 80.09 48.32 40.31 38.09 78.84 Summer 86.62 72.80 69.37 80.09 409.55 411.95 76.05 18.57 Autumn 504.66 496.73 139.64 27.67 283.63 281.95 68.26 24.07 Winter 51.01 47.67 21.41 41.97 28.61 27.72 10.92 38.16 Hujura Spring 9.09 7.40 6.19 68.07 79.26 64.90 43.49 54.86 Summer 42.13 33.92 22.19 52.66 427.61 354.48 239.03 55.90 Autumn 36.19 36.86 18.25 50.41 387.58 392.10 190.05 49.03 Winter 6.38 6.72 3.00 47.00 95.26 72.86 103.51 108.67 Uke Spring 5.02 5.08 2.18 43.39 3.22 3.26 1.40 43.36 Summer 347.73 360.48 128.83 37.05 5.61 5.33 1.93 34.45 Autumn 307.30 301.39 72.05 23.44 65.75 64.48 15.41 23.44 Winter 25.90 24.88 8.76 33.81 74.47 71.13 27.51 36.94 3.2 Standard anomaly index of seasonal and annual hydroclimate 3.2.1 Seasonal and annual rainfall All the seasonal anomalies of the basin show increasing trends with different magnitudes. The autumn (SON) rainfall anomaly shows the greatest increase and the winter (DJF) had an almost negligible increase. The summer season (June to August) has the biggest contribution to rainfall of the basin which is typical for areas that experience monsoon during this time. The wettest years are shown at 37.5%,47.5%, and 40%, and very small numbers (0 to 0.4) in MAM, JJA, SON, and DJF seasons respectively (Fig. 4 ). This result indicates that even if the overall trend of seasonal rainfall anomaly was shown increasing the number of dry anomalies was greater in each season throughout the study period. The seasonal rainfall SAI was observed extremely wet in JJA and SON in the year 2000. In agreement with this result, Mera ( 2018 ) stated extreme drought was depicted in the basin in 2015 in JJA. In 2015, almost 10 million people were affected by the worst drought in decades, which struck north and central Ethiopia. Figure 5 depicts the annual rainfall anomaly of the Dabus River Basin. The trend of this anomaly graph shows an increasing trend and only 14.8% of the variability of the anomaly was expressed by the year. However, only 7.5% of it is above normal or wet, 27.5% is dry and the remaining almost 70.13% of it depicts the normal state. The annual rainfall Standard anomaly index (SAI) shows the year 1999 was the wettest year with a value of 2.37 whereas 1982,1983,1984,1986 and 2015 were the driest years in the basin. This result is in agreement with the study done by Moshe & Beza ( 2024 ) that stated according to the annual rainfall SAI results 15% of the study period was reasonably dry, 15% was wet, and 70% was in normal conditions. Kebede et al.(2020) also found the result the UBN experienced extreme drought from 1980 to 1996 and 2015. 3.2.2 Mean seasonal and annual temperature The seasonal mean temperature SAI of the Daubs basin was shown to increase starting from 2000 commonly in all seasons maximum in MAM and less in SON. In MAM 1998,2001, and 2002 were the extremely warmest years greater than three. Except for MAM the increase of seasonal Tmean was not significant, the magnitude ranges to in normal conditions. In the basin, extreme cooling was observed in the MAM season in 1985 which is less than − 2 (Fig. 6 ). The SAI of the annual Tmean of the basin indicates an increasing trend starting in 2000 (Fig. 7 ). However, The coldest year of the basin was observed in 1985 and the warmest years were 1998 and 2003. The SAI of the river flow shows a decreasing trend or drying tendency starting in 2003. Particularly, 2006 and 2017 were years which have a magnitude less than − 2 indicating extremely dry events (Fig. 8 ). The SAI of river runoff depicted an insignificant decreasing trend and extremely wet was observed in 1997. The anomaly of the basin river flow variation was very minimal and 67.71% of the anomaly was depicted in the drier condition. Supporting this study result Takele et al. ( 2022 ) stated that due to the increment of temperature and rainfall fluctuations may change the patterns and trends of hydrology and water resources in the basin. 3.3 Precipitation concentration index The PCI provides valuable insights into climate patterns and can help assess changes in precipitation distribution over time. The PCI is a crucial component for managing natural resources, planning water resources, and predicting the danger of floods or droughts(Zhang et al., 2019 ). The MAM season PCI indicates the precipitation distribution was uniform throughout the basin. The SON and DJF season PCI values are depicted the rainfall distribution was under moderate distribution (Fig. 9 ). While the rainfall distribution was uniform throughout the basin in MAM in which the PCI values were less than 10. In the basin, JJA’s rainfall distribution was significantly strong irregular distribution. In SON and DJF the distribution of rainfall was moderate and moderately irregular. The JJA rainfall distribution showed a maximum concentration that ranged from 18 to 28 causing the flood. The highest value of PCI depicts preserving consistent soil moisture levels can help agricultural operations(Pan et al., 2023 ). This tendency could help farmers in the area better arrange the dates of their planting and harvesting. The study done by Berihun et al.(2023) stated that the PCI result of the summer season showed the greatest seasonality a very erratic distribution of rainfall. The result reported by Mohamed et al.( 2022) was in line with this result in terms of MMA, SON, and DJF but their report was different from the result obtained by this study on the summer and annual PCI. It stated all under uniform distribution. Precipitation concentration index analysis is critical to track PCI trends to manage water resources and comprehend changes in climate patterns. The annual PCI result from 1980 to 2020 of the Dabus River Basin except 2014 observed significant irregular and strong irregular distribution precipitation which had a value greater than 16 (Fig. 10 ). In the basin, 30% of the PCI values indicate the highest concentration of rainfall which makes the area experience irregular rainfall. Mainly, peaks of PCI were observed in the years 1987,1991, 2002, 2003,2006, and 2011indicates the highest irregularities and concentration of rainfall. Getachew and Manjunatha ( 2022) reported in line with this result found there have been reasonable floods in the area, which have led to surface water body contamination and soil erosion. Similar researchers on UBN Moshe & Beza ( 2024 ) reported the area's rainfall distribution has a significant irregularity distribution over the relative study period. 3.4 Trend of hydroclimate 3.4.1 Temporal trend hydroclimate The monthly rainfall of the basin shows an increasing trend except for January and February. The trend of the monthly rainfall significantly increases in September and December with 0.20 and 0.12mm/ year, respectively. Monthly maximum temperature showed an increasing trend in all months and in March, July, and August month significant increase of Tmax was observed with the rate of 0.03,0.02, and 0.02°C/ year, respectively (Table 6 ). The trend of the monthly minimum temperature was not consistent.in January, July, August, and September the Tmin of the basin depicted decreasing with a range of -0.03 to -0.02°C/ year. Table 6 Temporal trend monthly rainfall. Minimum and Maximum Temperature variation in the basin Month Rainfall Tmax Tmin Tau Sens Slope P_Value Tau Sens Slope P_Value Tau Sens Slope P_Value Jan -0.18 -0.04 0.10 0.15 0.02 0.2 -0.13 -0.02 0.26 Feb -0.04 -0.05 0.70 0.23 0.03 0.05 0.08 0.01 0.51 Mar 0.08 0.29 0.45 0.4 0.05 0* 0.06 0.01 0.62 Apr 0.12 0.04 0.28 0.22 0.04 0.05 0.14 0.01 0.24 May 0.13 0.56 0.26 0.04 0.01 0.71 0 0 1 Jun 0.15 1.03 0.18 0.09 0.01 0.45 0.09 0.01 0.45 Jul 0.17 0.87 0.13 0.29 0.02 0.01* -0.17 -0.02 0.14 Aug 0.18 1.42 0.11 0.27 0.02 0.02* -0.23 -0.02 0.05* Sep 0.20 1.04 0.07 0.17 0.01 0.13 -0.25 -0.03 0.03* Oct 0.23 1.44 0.04* 0.06 0 0.62 0.11 0.01 0.35 Nov 0.24 0.27 0.03* 0.01 0 0.92 0 0 0.99 Dec 0.12 0.04 0.29 0.03 0 0.8 0.02 0 0.86 * stands for significant at P < 0.05 3.4.2 Temporal trend hydroclimate The seasonal rainfall of the Dabus River Basin was erratic and showed different values in each station in different seasons (Fig. 11 ). In the MAM season, the western part of the basin receives less rainfall than the eastern parts. In the MAM rainfall decreased from − 0.35 to 16.21mm/year, mainly in Begi and Gimbi stations, respectively.JJA was the rainy season of the basin, and showed a significant decrease at Abadi and Begi stations, with rates of -2.91 and − 1.15mm/year, respectively. A significant (P_value < 0.05) increasing trend was observed in JJA seasonal rainfall at Sharkole and Aira stations in the northwest and southeast of the basin, with a rate of 4.16 to 9.71mm/year (Fig. 11 ). Among the stations, Begi’s rainfall depicted a decreasing trend except in the SON. The SON season rainfall was sown almost increasing trend except for the northern and central of the basin. The Dabus River Basin had very low rainfall in the DJF season throughout the study period. The majority of the Basin's climatic stations indicated a decreasing trend in DJF rainfall (Mohammed et al., 2022 ). The result obtained confirmed that there was a rise in substantial seasonal rainfall observed in SON than in MAM depicting a seasonal shift in the basin. The seasonal Tmin of the Dabus River Basin was observed in both increasing and decreasing tendencies (Fig. 12 ). The trend of Tmin ranges from − 0.4 to 0.15°C/year.The significant decrease of Tmin was depicted in the summer and spring seasons. A significant increase in Tmin was observed in the autumn season with 0.16°C/year. In the winter season both increasing and decreasing of Tmin are observed throughout the basin. The significantly increasing seasonal Tmax was observed in the northern part of the basin in all seasons. The trend of seasonal Tmax ranged from − 0.04 to 0.15°C/year. The highest increment of T max was observed in the northern, northwestern, and southwest of the basin (Fig. 13 ). An insignificant decrease of annual Tmax was observed in the Winter season mainly in the southeast of the basin. In line with this result Alaminie et al.(2021); Asfaw et al., ( 2018 ); Kebede et al. ( 2020 ); Mohammed et al.(2022) reported the increase in Tmax in the area during the study time. In the Dabena seasonal river flow observed an insignificant increasing trend that ranged from 0.06 to 0.43 m 3 /s/year.It was maximum in summer and minimum in winter. The river runoff of Dabena significantly decreases in autumn with − 18.75mm/year whereas increases in spring, summer, and winter in the range of 2.32mm/year to 9.48mm/year. The river flow of the Didesa River was observed to increase in all seasons throughout the study period in the range of -1.24 to -0.48 m3/s/year insignificantly. In Dedesa the river runoff decreased insignificantly in all seasons except autumn which had an insignificant increase trend of 0.84mm/year. The river flow of Hujiru increases significantly by 0.31 m3/s/year in spring and increases insignificantly in the summer season. The seasonal river runoff increases insignificantly in the spring, summer, and winter seasons in Hujira, but increases significantly by 2mm/year. In Uke, the river flow increases only in spring, particularly in summer decreasing significantly in summer by -7.320.43 m 3 /s/year.Similarly in Uke, the river runoff increases only in spring, mainly in summer decreasing significantly by -1.57mm/year (Table 7 ). Table 7 Seasonal Trend of river flow and river runoff in Dabus River Basin. Station Seasons River flow River runoff Z Sens-slop Pvale Z Sens-slop Pvale Dabena Spring 0.93 0.09 0.35 1.62 2.45 0.1 Summer 0.43 0.43 0.66 0.47 9.48 0.64 Autumn 0.18 0.06 0.86 -2.41 -18.75 0.01* Winter 0.69 0.1 0.49 1.01 2.32 0.31 Didesa Spring -1.00 -1.24 0.32 -0.92 -0.56 0.36 Summer -1.00 -1.24 0.32 -0.05 -0.13 0.96 Autumn -0.15 -0.48 0.88 0.65 0.84 0.52 Winter -1.04 -0.52 0.30 -0.63 -0.09 0.53 Hujura Spring 2.43 0.31 0.02* 4.44 3.56 0.00* Summer 0.48 0.31 0.63 0.77 5.29 0.44 Autumn -0.37 -0.29 0.71 2.14 2.00 0.03* Winter -0.37 -0.29 0.71 4.44 3.56 0.34 Uke Spring 0.32 0.03 0.75 0.32 0.02 0.75 Summer -2.30 -7.32 0.02* -2.30 -1.57 0.02* Autumn -0.97 -1.54 0.33 -0.97 -0.33 0.33 Winter -0.97 -1.54 0.33 -0.97 -0.33 0.33 The basin had a mean annual rainfall of 1924.6.64 mm. The annual rainfall was observed the highest variation of rainfall with the value CV = 30.86%, and SD = 400.29 (Fig. 14 ). The annual rainfall of the Dabus River Basin has observed a significantly increasing trend (P \(\:\le\:0.05\) ). The trend of annual rainfall was significantly increased by 21.39mm/year and Tau=0.405. The annual rainfall was observed insignificant decreasing trend in Abadi, Begi, Bure, and Mendi stations. A significant increase in annual rainfall was observed in Aira station with 23.18mm/year The mean annual Tmax was 29.25, with little variation SD=0.53 and CV less than 20%. The annual Tmax of the basin was significantly increased with the rate of 0.017°C/year and Tau is positive 0.276. The mean annual Tmax of Dabus River was 29.25°C and The value SD = 0.53 which indicates less variation was shown on annual Tmax. The annual Tmin of the Dabus River Basin showed an insignificant decreasing tendency with the rate of -0.004°C/year. The annual mean of Tmin was 15.16°C and had the least variation annually because SD = 0.6 which depicted the magnitude of the variation of mean Tmin was almost the same. Ayehu et al.(2021); Gashaw et al.(2023) found an increasing trend in annual rainfall in UBN. In agreement with this study, Tegegn et al.(2024) reported that the Tmax increased significantly ranging from 0.027°C to 0.485°C, respectively. The result obtained was inconsistence with Tirfi and Oyekale ( 2022 ) that Tmin significantly increased by 0.13 in the western and northern parts of the country. Alemayehu et al., ( 2022 ) also found equivalent results stated in UBN the maximum temperature exhibited increasing (0.02°C per year). Generally, the above result indicates the area has been warming. Figure 15 shows the trend analysis of river flow and river run-off. Accordingly, the Mean annual river flow of the basin is 1322m 3 /s. The river flow of the basin had CV = 24.s moderate variation and high SD = 323.9 revealing the annual mean base. It depicted an increasing trend with the rate of -6.32 m 3 /s and had Tau = 0.13. The annual mean river runoff was 2209.05m and the highest version among the value mean (SD = 231.03). The trend of annual runoff in the basin decreased insignificantly with a rate of 0.89mm/year. The magnitude of CV was 10.46% depicting the less variability of the annual runoff. The trend result of the river flow and the river runoff manifested water shortage that has increased as a result of climate change and other driving forces. This result was reinforced by Ehtasham et al.(2024), and Wu et al.(2020) found that increasing temperature results in a decrease in water availability since it increases the rate of evaporation and raises water demand. Throughout the study period, there was a noticeable descending trend in the river discharge. Cherinet et al.(2019);Worku et al.(2021) found that significantly, the region's high-temperature increase throughout the research period led to a high degree of evaporation, which in turn produced a drop in water flow. Keeping in cognizance that rising temperatures have the impact of reducing river flow and runoff, and if these factors decline without causing a distinct shift in rainfall, the most likely influence may be the basin's land use and cover (Gebremicael et al., 2017 ; Werede et al., 2024 ; Xu et al., 2019 ). Mohammed et al.(2022) also stated that UBN was experiencing climate change, as evidenced by an increase in extreme rainfall and a warming trend in the extreme temperatures that affect the stream flow. 3.4.3 The changing point of hydroclimate The changing point test is a valuable instrument in aiding decision-making about hydroclimatological variables by detecting changing point variations. The mean annual rainfall value was observed to increase and change significantly in 1996. The change in mean annual rainfall before and after the abrupt point was 1673.87mm and 1930.55mm, respectively. The annual Tmax mean value was increasing significantly and had a changing point in 1993 and 1997. The change in mean annual Tmax before and after the abrupt point was 29.25°C and 30.24°C, respectively. The annual Tmin was observed to significantly change the point of mean in 1987. The annual rainfall and annual Tmax showed a significant increment of mean value after the change point, however, Tmin depicted a decreasing trend after the exact change point. The change in mean annual Tmin before and after the abrupt point was 15.67°C and 15.16°C, respectively (Fig. 16 ). Understanding the effects of climate change and human activity depends on the precise detection of abrupt changes in hydroclimatic time series, which is a crucial complement to the detection and attribution of hydroclimatic variability(Oteng Mensah et al., 2024 ; Xie et al., 2019 ). The abrupt change in annual river flow showed an insignificant decrease in mean value in the year 1998. The annual mean river runoff abrupt change also showed an insignificant decrease rate in the year 1999. Consistent with this result Cherinet et al. ( 2019 ) found in the Abay River basin that there was a noticeable descending trend in the river discharge, which abruptly reduced starting in 1992. The change of mean river flow before and after the abrupt point was 1450.973 m 3 /s and 1250.44 m3/s, respectively. The change of mean river runoff before and after the abrupt point was 2234.78mm and 2194.76mm, respectively (Fig. 17 ). Conclusion Even though global warming is the primary concern of climate studies, understanding local and regional climate change as well as its impact is very crucial. The climate change of an area is determined by several factors mainly the alteration of the long-term precipitation and temperature that brought hydrological extreme events. Using several testing methods and long-term rainfall and temperature including river flow and river runoff data, this study made a comprehensive investigation and obtained worthy results. Accordingly, the basin received 75.44% of the annual rainfall in the wet season (Jun, July, and August) including September. The study found the seasonal shift of rainfall from spring to autumn in the basin for the last four decades. The warmer area of the basin was recorded at the north, and south of the basin. The winter season was the warmest in the Dabus River Basin. The SAI value indicates 1999 and 2000 were the wettest year whereas 1982,1983,1984,1986 and 2015 were the driest years in the basin. The finding of distribution seasonal rainfall concentration shows the autumn and winter rainfall were moderate and moderately irregular, respectively. However, the rainfall concentration in summer was extremely irregular. The summer rainfall distribution showed a maximum concentration that ranged from 18 to 28 causing the flood. This results and the seasonal shift aware the farmers how to prepare their land for suitable crops to adapt to the variability of the soil moisture. The basin experienced very fluctuating rainfall in each year mainly peaks of PCI were observed in the years 1987,1991,2002,2003,2006, and 2011 which indicates the strong irregularities of concentration of rainfall. Except winter the seasonal rainfall was observed to increase. The basin's northern and western parts obtained less rainfall. A significant decrease in Tmin and an increasing trend of Tmax was observed in the spring and summer seasons. The annual rainfall and Tmax increased significantly whereas the annual Tmin, river flow, and river runoff decreased insignificantly.The abrupt changing point of annual rainfall was observed in 1996 and the Tmean mean was observed in 1993 and 1997.The annual mean decreasing point of Tmean, river flow, and river runoff was observed in 1987,1998, and 1999, respectively.The study found the increasing temperature with the fluctuating rainfall distribution has an impact on river flow and runoff. The study confirms further investigation into non-climatic issues is necessary, as the rise in annual rainfall did not explain the decline in the water balance in the basin. The reduction of river flow and river runoff could affect agricultural activity, electric power production, water demand, and other activities done in the basin. The change in the hydroclimate of the basin has an impact on water availability, distribution, production of hydroelectricity, irrigation, and overall agricultural activities of the Dabus River Basin. Further research into other climatic factors will be required as the study reveals that the drop in river flow and river runoff cannot be explained by the rise in annual rainfall in the basin. Declarations Ethics approval and consent to participate Not applicable Consent for publication All authors reviewed the manuscript and agreed to publication. Availability of data and materials No datasets were generated or analyzed during the current study. Competing Interest The authors declare no competing interests. Funding This study received no specific funding from public, commercial, or funding agencies. Author contributions: MTT : Conceptualization; Data Retrieval, Software, Data Analysis, writing discussion, Writing-original draft, writing review, and critical revisions. 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Tola","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIie3PMWrDMBSAYZuAp4SsApf0BAUZg9LBOAfpIiFQptLVg6HqIl/BOUTX0lHBoC6iXgUZai/t6u6hVM4aHKdbIfpBQpj38bDnuVz/sqC/FvZMpCT7xD78JzlKpBf3D9J8c9YTfi6ZxtGGV4dvJ8lNIVjYZXCxLCQKZ691+lxUdkue3A0RpJUCUsP4SmMWzvSOvmhiiWL3fIiYtQBbsSelh1U4DXYUSUt8Xg2Tjy9LfuBjOW+FJe8U1e0IMYECWw4xAHQSbYRMkRnbohm91QpGJfj0m05TjIzdgk/9y5uKTJbDazB/6CTO0hWq123T5ckgOYocJvG5432rvwy7XC7XZfQL+f9qZ9tAGUkAAAAASUVORK5CYII=","orcid":"","institution":"Wollega University","correspondingAuthor":true,"prefix":"","firstName":"Mekuria","middleName":"Tefera","lastName":"Tola","suffix":""},{"id":383304859,"identity":"c80759e4-b8cf-42b6-9adb-b4a05a7c01c9","order_by":1,"name":"Kassahun Ture Bekitie","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Kassahun","middleName":"Ture","lastName":"Bekitie","suffix":""},{"id":383304860,"identity":"ca5ee3c6-016b-4e12-b956-d7acb88fec16","order_by":2,"name":"Tadesse Terefe Zeleke","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Tadesse","middleName":"Terefe","lastName":"Zeleke","suffix":""},{"id":383304861,"identity":"6645c611-8b31-40fb-88a3-d460d65f9dc0","order_by":3,"name":"Fikru Abiko Anose","email":"","orcid":"","institution":"Wolkite University","correspondingAuthor":false,"prefix":"","firstName":"Fikru","middleName":"Abiko","lastName":"Anose","suffix":""}],"badges":[],"createdAt":"2024-11-13 10:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5446005/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5446005/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70319879,"identity":"7e59ba42-d04e-40d4-9367-af48abdabd89","added_by":"auto","created_at":"2024-12-02 06:33:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":124794,"visible":true,"origin":"","legend":"\u003cp\u003eThe Dabus River basin\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/cd56c315601d9308a5982287.png"},{"id":70319877,"identity":"1e09b053-00f6-4dfd-be02-8c71a2a0d8ec","added_by":"auto","created_at":"2024-12-02 06:33:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":133160,"visible":true,"origin":"","legend":"\u003cp\u003eThe seasonal rainfall distribution of in Dabus River Basin\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/2197563f15809f5efa458544.png"},{"id":70321409,"identity":"c813b818-b5d0-47d3-a612-ab09b331e958","added_by":"auto","created_at":"2024-12-02 06:49:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":132912,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of annual Tmean in the Dabus River Basin\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/980129f5ca322c6c9b28e755.png"},{"id":70322831,"identity":"e7fc201b-0c7a-4bf0-aabb-dcd99d9c5ac1","added_by":"auto","created_at":"2024-12-02 07:05:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":15035,"visible":true,"origin":"","legend":"\u003cp\u003eStandard anomaly index of Seasonal rainfall of the basin\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/249e6162836ee6eb1f7917b0.png"},{"id":70319888,"identity":"a1e31024-b441-46cd-9325-e504be584d3f","added_by":"auto","created_at":"2024-12-02 06:33:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":8581,"visible":true,"origin":"","legend":"\u003cp\u003eStandard anomaly index of annual rainfall of the basin\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/be9a50180467e50983af4fd5.png"},{"id":70319887,"identity":"6b6bb15d-f1a2-4891-9d54-edd79ba66c77","added_by":"auto","created_at":"2024-12-02 06:33:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":31886,"visible":true,"origin":"","legend":"\u003cp\u003eStandard anomaly index of seasonal mean temperature of the basin\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/54078761d2b1291df3d046a8.png"},{"id":70321201,"identity":"cb3a2706-5324-4698-a80f-be6fabcc8f25","added_by":"auto","created_at":"2024-12-02 06:41:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":8813,"visible":true,"origin":"","legend":"\u003cp\u003eStandard anomaly index of seasonal mean temperature of the basin\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/afa5220de90ba450081cf397.png"},{"id":70322830,"identity":"87fd657b-56b5-4c9a-bd1a-04e1ead2b66c","added_by":"auto","created_at":"2024-12-02 07:05:28","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":9834,"visible":true,"origin":"","legend":"\u003cp\u003eStandard anomaly index of seasonal mean river flow and river runoff in the basin\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/97fda298403fc1db777dd95d.png"},{"id":70321410,"identity":"20f5f658-d74b-4fc9-9b61-9ee8a13065b4","added_by":"auto","created_at":"2024-12-02 06:49:28","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":15606,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal precipitation index in the basin\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/16091a3f5671cf764ddb814a.png"},{"id":70321411,"identity":"5174dd88-2908-4558-aae6-aa80eef28ea6","added_by":"auto","created_at":"2024-12-02 06:49:28","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":68969,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual precputarti0n index in the basin\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/e36b27b6eb9f91048013f4d8.png"},{"id":70319881,"identity":"e3e42e37-1afd-4c25-9062-d822442e01d4","added_by":"auto","created_at":"2024-12-02 06:33:28","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":100605,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal trend of rainfall o the Dabus River Basin \u0026nbsp;(1981-2020)\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/e53b1cd25cff91eb3e0db0f5.png"},{"id":70321210,"identity":"5124ce7c-ce58-4ef1-8596-7292a10aedea","added_by":"auto","created_at":"2024-12-02 06:41:29","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":58047,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal trend of Tmin the Dabus River Basin (1981-2020)\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/a23a403708c5504b3ee077e1.png"},{"id":70321208,"identity":"0d09673b-3abc-4003-b5a1-fee13ae75277","added_by":"auto","created_at":"2024-12-02 06:41:28","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":84208,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal trend of Tmax the Dabus River Basin \u0026nbsp;(1981-2020)\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/00f713cbd82f12931fde9936.png"},{"id":70319884,"identity":"de9c2fa2-22ab-4ccc-807a-d2404f1e9f19","added_by":"auto","created_at":"2024-12-02 06:33:28","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":17379,"visible":true,"origin":"","legend":"\u003cp\u003eTrend of rainfall, Tmax, and Tmin in the Dabus River Basin\u003c/p\u003e","description":"","filename":"image14.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/6ab7faa6767afb8e54713ef7.png"},{"id":70319886,"identity":"42cc33b1-faf9-46b1-80ca-523ea727096b","added_by":"auto","created_at":"2024-12-02 06:33:28","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":13836,"visible":true,"origin":"","legend":"\u003cp\u003eTrend of river flow and river runoff in the Dabus River Basin\u003c/p\u003e","description":"","filename":"image15.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/9f7bf55f2f8d26a746790b36.png"},{"id":70321206,"identity":"691fc86d-191f-42d8-a7dd-904577c182c9","added_by":"auto","created_at":"2024-12-02 06:41:28","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":14586,"visible":true,"origin":"","legend":"\u003cp\u003ePettit test of rainfall, minimum and maximum temperature in the Dabus River Basin\u003c/p\u003e","description":"","filename":"image16.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/e3b6ffd6b6118ae5f4729510.png"},{"id":70322689,"identity":"3d47c38c-dcb4-464f-85f9-d25b4a03991a","added_by":"auto","created_at":"2024-12-02 06:57:28","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":13564,"visible":true,"origin":"","legend":"\u003cp\u003ePettit test of river runoff and river flow in the Dabus River Basin\u003c/p\u003e","description":"","filename":"image17.png","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/2543b313038f693f9b64c809.png"},{"id":72095527,"identity":"b82ec9fb-5417-49ca-9a7e-c321aa0966ed","added_by":"auto","created_at":"2024-12-22 09:31:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1881132,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5446005/v1/0b6f1857-6d79-4218-950b-6ce89765782b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Temporal and spatial distribution, variability, and trend of hydroclimate in the Dabus River Basin Upper Blue Nile, Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal warming, which affects the hydrological cycle results in a variety of climate change effects at the local, regional, and global levels (Bolan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gashaw et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang and Liu, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The extreme change in temperature and global warming is a clear result of human activity (Wentz, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003ez\u0026eacute;quel et al., 2024; Labrousse et al., 2020) that exerts hydrological extreme events in different areas. Seidenfaden et al.(2021); Wedajo et al.( 2024) identified that even though it is well-known that spatiotemporal hydroclimate variation was mainly caused by global warming, there is great uncertainty in determining the factors that influence the existing conditions and projection of the local scale climate change. Asfaw et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); Gonfa et al.(2022) reported that studies on spatiotemporal dynamics of climatic change used to understand the changes that have already occurred and forecast expected changes that need adaptation measures. Therefore, a study on hydroclimate variation and its impact on different sectors in the sub-basin scale requires the attention of climate researchers.\u003c/p\u003e \u003cp\u003eAfrica's hydroclimatic characteristics range from the humid equatorial regions of West Africa to the arid deserts found in the northern and southern subtropics. Schaebitz et al.(2021). Climate-related research, (e.g Bedeke, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wentz, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mostefaoui et al., 2024; Sono et al., 2021) identified that several extreme hydrological events have occurred in Africa, making it one of the most vulnerable continents due to its geographical features, little capacity for adaptation, and highly sensitive socio-economic systems. Particularly, East Africa faced a persistent decline in seasonal and annual rainfall during the last four decades and this has had major consequences for regional water and food security (Anderson et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gebrechorkos et al., 2023). Therefore, detailed regional-level climate change information and awareness are crucial to ensure future adaptation plans as well as sustainable water availability in the region (Allan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Orkodjo et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Gebrechorkos et al., 2020).\u003c/p\u003e \u003cp\u003eEthiopia experienced the hydro-meteorological extremes of climate modification due to its reliance on profoundly rain-fed farming (Abera and Shumate, 2021). Ethiopia\u0026rsquo;s watersheds are significantly impacted by current and anticipated alterations in temperature and rainfall. Different studies on Ethiopia\u0026rsquo;s basins confirmed this truth. Abera \u0026amp; Gebeyehu ( 2022) reported that in Ethiopia, the impacts of climate variation on stream flow extend beyond the scarcity of water to include an increase in the occurrence and strictness of extreme weather. Chanie (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Ketema \u0026amp; Dwarakish (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Upper Blue Nile (UBN), the quantity and quality of water balance, as well as evapotranspiration, can all be directly impacted by variations in precipitation and temperature. (Mohammed et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Kheireldin et al.(2016) reported extreme climate change-related occurrences are occurring in the Blue Nile Basin and could have an expected influence on the ecology, livelihood, agriculture, and livestock. Anose et al.(2022) found the current intensity of the drought may worsen the depletion of other natural resources and the water deficit, which may have a significant effect on the amount of water accessible for agriculture, hydropower production, and ecosystem services. Ketema \u0026amp; Dwarakish (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Wubneh et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) Changes in temperature within the watersheds could result in a diversified impact on water availability. Thus current and comprehensive studies are needed in Ethiopia because any changes made to the water bodies would have a big effect on the nation (Matthews and Vivoda, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Dabus River Basin, over its long journey, supports a variety of habitats and is essential for agriculture, the production of hydroelectric power, and different ways of serving the community\u0026rsquo;s life (Willemin and Backhaus, 2023). It is one of the main tributaries of the Blue Nile in Ethiopia and has a big contribution (Kedida and Arsano, 2024). In Ethiopia, particularly the Nile basin, there are several studies done. However, there was not much considered for this sub-basin. The previous research done in the basin by Bahiru et al.(2024); Diriba (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Mangel and Berhe (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) focused on only the stream flow, never including other climate elements and long-range data of more than climate normal. It is essential to comprehend the variables influencing hydroclimate variability to create sustainable water management plans in dynamic settings (Wedajo et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nevertheless, the negative impact of climate change on this basin was not profoundly considered in most studies. Hence, this study has made an inclusive investigation on three hydroclimates and uses along the duration of climate data more than climate normal. The identification of local climate variability and change impacts at the basin scale is decisive because proper management of water resources is essential for maintaining the sustainability of water resource development projects, and searching for potential mitigation measures to prevent disastrous outcomes.\u003c/p\u003e \u003cp\u003eTherefore, the objective of this study was to investigate the distribution, trends, variability, and climate change of monthly, seasonal, and annual hydroclimate data for four decades that were above the climate normal. Descriptive statistical models, variation detectors, parametric, modified Mann-Kendall test, sens slope, and abrupt change point (Pettit test) were used. Various techniques and different software were also applied which enable obtaining the best results. The results provide a significant understanding of existing hydroclimate variation and climatic conditions of the area. The finding will be crucial to developing adaptation strategies and management practices for the watershed and other associated places that have experienced the effects of climate variability and change.\u003c/p\u003e"},{"header":"2. Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Description of the study area\u003c/h2\u003e \u003cp\u003eThe Dabus River Basin is one of the largest tributaries of the Blue Nile in Ethiopia, and it contributes approximately one-third of the total flow of the upper Blue Nile. This makes the Dabus River Basin a vital water source used for irrigation, drinking water, and hydropower in the Blue Nile and the downstream countries. This sub-basin is located between 9\u0026deg;0\u0026prime;0\u0026Prime;\u0026ndash;11\u0026deg;50\u0026prime;0\u0026Prime; N latitude and 34\u0026deg;30\u0026prime;0\u0026Prime;\u0026ndash;35\u0026deg;58\u0026prime;40\u0026Prime; E longitude covering an area of 14,793.81 km\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It originates in the central and southwestern parts of Wollega and covers two regions with seven woredas in Benishangul Gumuz and twelve woredas in Oromia. The elevation of the watershed ranges from 502 to 3032m (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with greater than 1500 m altitude in the eastern part, and less than 900 m altitude in the northern part. The climate of the study area ranges from cold in the highlands to moderately hot in the lowlands.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data type and source\u003c/h2\u003e \u003cp\u003eThis study used daily rainfall maximum temperature (Tmin), and minimum temperature (Tmax) of ten meteorological stations observational data were obtained from the Ethiopian National Meteorological (ENMA). The meteorological data were forty years period data from 1981 to 2020 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The daily rainfall was observed with a spatial resolution of 0.05 degrees (4kmX4Km) and magnitude mm/day. The water data from the Ministry of Water Irrigation and Electric (MoWIE) which had the year length of the record was not consistence (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To analyze these data, different models and software were used. The daily river flow and data are only selected from five gauge stations that have full data.\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\u003eMeteorological stations Dabus River Basin\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003elongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCovered period\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\u003eMendi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1981\u0026ndash;2020\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\u003eSharkole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1981\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBegi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1981\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssossa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1981\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNedjo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1981\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAira\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1981\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGimbi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1981\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbadi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1981\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKamashe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1981\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1981\u0026ndash;2020\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\u003eIn Dabus, most of the hydrological gauge station data has more than 15% of missing data. Therefore, in this study, only stations that have continued data were used to minimize the several factors that caused inconvenience.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eStations where the hydrological data collected\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDrainage area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCovered period\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\u003eDabena\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1990\u0026ndash;2017\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\u003eDidesa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1990\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHujur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1990\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1990\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Methods\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Statistical variability test hydroclimate variable test\u003c/h2\u003e \u003cp\u003eSeveral statistical techniques will be used to analyze the hydroclimate data, which generally fall into variability and trend analysis categories (Kiros et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Koudahe et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The mean, standard deviation (SD), median, and Coefficient of variance (CV) were used. The CV can be calculated using Eq.\u0026nbsp;(1):\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:CV=\\frac{\\sigma\\:}{\\mu\\:}\\:\\times\\:100\\)\u003c/span\u003e \u003c/span\u003e 1\u003c/p\u003e \u003cp\u003eWhere: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e is the standard deviation, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003e is the mean, The degree of variability of rainfall was classified as low when CV\u0026thinsp;\u0026lt;\u0026thinsp;20%, moderate when 20% \u0026lt; CV\u0026thinsp;\u0026lt;\u0026thinsp;30%, high when 30% \u0026lt; CV\u0026thinsp;\u0026lt;\u0026thinsp;40%, very high when 40% \u0026lt; CV\u0026thinsp;\u0026lt;\u0026thinsp;70%, and extremely high when CV\u0026thinsp;\u0026gt;\u0026thinsp;70% (Asfaw et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Standardized anomaly index\u003c/h2\u003e \u003cp\u003eThe Standardized Anomaly Index (SAI) computes the basin\u0026rsquo;s negative and positive hydroclimate fluctuation anomalies. The SAI indicates the distance between the data and its mean value (Alemayehu and Bewket, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It is calculated as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:SAI=\\left(\\frac{x-\\stackrel{-}{x}}{\\sigma\\:}\\right)\\)\u003c/span\u003e \u003c/span\u003e 2\u003c/p\u003e \u003cp\u003eWhere: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e is the rainfall or temperature data,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e And \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sigma\\:\\)\u003c/span\u003e\u003c/span\u003e are the mean and the standard deviation of the data, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 The precipitation concentration index\u003c/h2\u003e \u003cp\u003eIn this study, the Precipitation Concentration Index (PCI) was used to differentiate the normal and variation nature of the rainfall in the area. The PCI is used to analyze the seasonal or annual variability (heterogeneity pattern) of rainfall (De Luis et al. 2011). According to Oliver (1980), the PCI values were calculated annually according to Eq.\u0026nbsp;(3) which represents 12 months of the year. Eq.\u0026nbsp;(4) is used to calculate seasonal PCI. This equation was used to determine the PCI on a seasonal scale for the following months: spring (March to May or MAM), summer (June-August or JJA), autumn (September\u0026ndash;November or SON), and winter (December\u0026ndash;February or DJF).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{PCI}_{annual}=\\frac{\\sum\\:_{i=1}^{12}{P}_{i}^{2}}{{P}^{2}}*\\:100\\)\u003c/span\u003e \u003c/span\u003e 3\u003c/p\u003e \u003cp\u003eWhere: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e =the rainfall amount of the ith month. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\)\u003c/span\u003e\u003c/span\u003e =annual precipitation\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\:\\:PCI}_{seasonal}=\\frac{\\sum\\:_{i=1}^{12}{P}_{i}^{2}}{{P}^{2}}*\\:25\\)\u003c/span\u003e \u003c/span\u003e4\u003c/p\u003e \u003cp\u003eWhere: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e =the rainfall amount of the ith month. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:P\\)\u003c/span\u003e\u003c/span\u003e =Total precipitation of the month\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003ePrecipitation concentration index (PCI) classification\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCI Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistribution of precipitation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCI\u0026thinsp;\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniform precipitation distribution\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate precipitation distribution\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIrregular distribution\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCI\u0026thinsp;\u0026gt;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrong irregular distribution\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eSource, Oliver (1980)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Trend tests\u003c/h2\u003e \u003cp\u003eSeveral researchers agreed that there is no universal solution for serial correlation in the time series (Patakamuri et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Trend tests reduce the effect of positive autocorrelation in the data, increasing the probability of detecting trends when none truly exists and vice versa. This study used parametric and non-parametric methods to perform hydroclimate data trend detection. Parametric tests are stronger, but the data must be normally distributed. Non-parametric tests are \u0026ldquo;distribution-free\u0026rdquo; methods, which do not rely on assumptions that the data are drawn from a given probability distribution (Amrender et al., 2015). Hydroclimate data are non-parametric, and the study applied mostly non-parametric models. The parametric method is the linear regression test, and non-parametric (rank-based) methods include the Mann-Kendal (Kendall, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Mann, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1945\u003c/span\u003e). Modified Mann-Kendall, and trend-free pre-whitening Mann-Kendall trend tests. Hence, in this study parametric (linear regression) and non-parametric (modified Mann Kendal) trend tests were used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 The linear regression\u003c/h2\u003e \u003cp\u003eThe linear regression model will measure the pattern or trend of variables over a long period(Kiros et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It is calculated using Eq.\u0026nbsp;(3).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Y=a+bx\\)\u003c/span\u003e \u003c/span\u003e 5\u003c/p\u003e \u003cp\u003eWhere: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Y\\)\u003c/span\u003e\u003c/span\u003e indicates the trend value, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:a\\)\u003c/span\u003e\u003c/span\u003e is the intercept, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:b\\)\u003c/span\u003e\u003c/span\u003e is the slope of the trend, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{t}\\)\u003c/span\u003e\u003c/span\u003eIs the time point.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Modified Mann-Kendall test (MMK-test)\u003c/h2\u003e \u003cp\u003eThe Mann-Kendall \u003cb\u003e(\u003c/b\u003eMK) trend test is always affected by the autocorrelation of the time series. Modified Mann-Kendall (MMK) is recommended to correct autocorrelation effects in a given time series (Yue and Wang, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Using the MK test with extensive auto-correlated hydrological data could result in overestimating or underestimating actual trends, which causes the failure of the trend test.\u003c/p\u003e \u003cp\u003eTherefore, the MMK test calculated by\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:V{\\left(S\\right)}^{*}=Var\\left(S\\right)\\frac{n}{{n}^{*}}\\)\u003c/span\u003e \u003c/span\u003e 6\u003c/p\u003e \u003cp\u003ewhere V(S)* is the modified variance, and the correction factor \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n/n*\\)\u003c/span\u003e\u003c/span\u003e is computed by:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{n}{{n}^{*}}=1+\\frac{2}{n\\left(n-1\\right)\\left(n-2\\right)}\\sum\\:_{h+1}^{n-1}\\left(n-h\\right)\\left(n-h-1\\right)\\left(n-h-2\\right){R}_{h}\\)\u003c/span\u003e \u003c/span\u003e 7\u003c/p\u003e \u003cp\u003eWhere: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{h}\\)\u003c/span\u003e\u003c/span\u003e is the autocorrelation coefficient of the ranked data\u003c/p\u003e \u003cp\u003eSen\u0026rsquo;s slope Estimator\u003c/p\u003e \u003cp\u003eSen\u0026rsquo;s slope (Sen, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1968\u003c/span\u003e) estimator was used to predict the magnitude of the trend. The non-parametric method can evaluate the change per unit of time. This technique assumes a linear trend in the time series. The slope (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{i}\\)\u003c/span\u003e\u003c/span\u003e) of all pairs of data \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e can be calculated as\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{i}=\\frac{{x}_{j\\:-{X}_{k}}}{j-k}\\:,\\:i=\\text{1,2},\\dots\\:N,\\:\\:j\u0026gt;k\\)\u003c/span\u003e \u003c/span\u003e 8\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{j}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{k}\\)\u003c/span\u003e\u003c/span\u003e are data values of time \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e, respectively. If there are n values in the time series, then as many as N\u0026thinsp;=\u0026thinsp;n(n-1)/2 slop estimates. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{i}\\)\u003c/span\u003e\u003c/span\u003e .The Sen\u0026rsquo;s slope estimator is defined as the median of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:N\\:\\)\u003c/span\u003e\u003c/span\u003evalues of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{i}\\)\u003c/span\u003e\u003c/span\u003e.The values of slopes are ranked from the smallest to the largest, and Sen\u0026rsquo;s slope estimator\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{i}\\)\u003c/span\u003e\u003c/span\u003e is calculated as\u003c/p\u003e \u003cp\u003eIf N is an odd observation: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{i}={Q}_{(N+1)/2}\\)\u003c/span\u003e\u003c/span\u003e 9\u003c/p\u003e \u003cp\u003eIf N is even observation:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{i}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{2}\\left({Q}_{N/2}+{Q}_{\\left(N+2\\right)/2}\\right)\\)\u003c/span\u003e\u003c/span\u003e 10\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Test for single change point (abrupt change)\u003c/h2\u003e \u003cp\u003eExtreme climate change and intensive human activities will cause abrupt changes in hydroclimate variables. Therefore, the Pettit test has been used in several climate studies to detect abrupt changes in the mean distribution of the variable of interest. The Pettit test is a distribution-free rank-based test used for detecting the exact time of change of the mean in the time series and gives information about the location of the shift. The test statistic\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{U}_{t,T}\\)\u003c/span\u003e\u003c/span\u003e Is evaluated for all random variables from 1 to T; then, the most significant change point is selected where the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lfloor;{U}_{t,,T}\u0026rfloor;\\)\u003c/span\u003e\u003c/span\u003e is the largest(Jaiswal et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To identify a change point, a statistical index \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{U}_{t}\\)\u003c/span\u003e\u003c/span\u003e is defined as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{U}_{t,T}=\\sum\\:_{i=1}^{t}\\sum\\:_{j=1}^{T}Sgn({x}_{i}-{x}_{j}),\\:1\\le\\:t\\le\\:T\\)\u003c/span\u003e \u003c/span\u003e 11\u003c/p\u003e \u003cp\u003eWhere similar to the MK test,\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Sgn\\left(\\theta\\:\\right)=\\:\\:\\left\\{\\begin{array}{c}+1\\:\\:\\:\\:\\:\\:\\:\\:\\:\\theta\\:\u0026gt;0\\\\\\:\\:\\:\\:0\\:\\:\\:\\:\\:\\:\\:\\theta\\:=0\\:\\:\\:\\:\\:\\\\\\:-1\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\theta\\:\u0026lt;0\\end{array}\\right.\\)\u003c/span\u003e \u003c/span\u003e 12\u003c/p\u003e \u003cp\u003eThe most probable change point is found where its value is (The break occurs in year k when). The test statistic \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{K}_{n}\\)\u003c/span\u003e\u003c/span\u003e and the associated probability \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(P\\right)\\:\\:\\)\u003c/span\u003e\u003c/span\u003eUsed in the Test are given as.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{K}_{{t}_{0}}=\\underset{1\\le\\:y\\le\\:n}{{max}}\\left|{U}_{t,T}\\right|\\)\u003c/span\u003e \u003c/span\u003e 13\u003c/p\u003e \u003cp\u003eand the significance probability associated with the value \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{K}_{t}\\)\u003c/span\u003e\u003c/span\u003e is evaluated as\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{\\left({t}_{0}\\right)}=2exp\\left[\\frac{-6{k}_{{t}_{o}}^{2}}{{T}^{3}+{T}^{2}}\\right]\\)\u003c/span\u003e \u003c/span\u003e 14\u003c/p\u003e \u003cp\u003eWhere: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}_{0}\\:\\)\u003c/span\u003e\u003c/span\u003eis concluded as a significant change point when\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:{P}_{{t}_{0}}\\le\\:0.5.\\:\\)\u003c/span\u003e\u003c/span\u003eThe value is then compared with the critical value (Pettitt, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Given a certain significance level \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003e, if \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\u0026lt;\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003e, we reject the null hypothesis and conclude that\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{x}_{t}\\)\u003c/span\u003e\u003c/span\u003e is a significant change point at level \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003e (Du et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Result and discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Temporal hydroclimate distribution and variation in the basin\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Monthly distribution and variation hydroclimate in the basin\u003c/h2\u003e \u003cp\u003eThe monthly hydroclimate of the basin distribution shows different features (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The monthly rainfall of the basin is very low in January and maximum in August which ranges from 3.18 to 273.24mm/year, respectively. The monthly mean Tmin distribution ranges from 14.42 to 16.12°C/year in May and January, respectively. The monthly mean Tmax was observed to range from 26.34 to 32.39°C/year in July and March, respectively. More standard deviation (SD) was depicted in monthly rainfall which indicated a greater fluctuation in the monthly rainfall. The highest CV was recorded in the dry season months (November, December, January, and February), which were extremely variable CV \u0026gt; 70%. Similarly, Alemayehu et al.(2022)reported that the dry season months have a higher CV that indicates the year-to-year variation for these months is high. The wet season months except Jun were depicted as the lowest CV \u0026gt; 20% which has a moderate variation of rainfall. The wet season months (June, July, and August) contribute almost 60% of the annual rainfall of the basin. The high CV value exhibits the highest variation of rainfall in the studied area. Ayehu et al.(2021); Mohamed et al.(2022) found in line with this result, which stated the highest contribution of annual rainfall was obtained in (Jun. July, and August) in UBN. Alaminie et al.(2021); Mohamed et al.(2022) reported in agreement with this finding that the summer months account for roughly 68.2% of the total annual rainfall in the study area. Their finding also confirmed that 42.3% of the total rainfall occurs in July and August. A study done by Samy et al.(2019): Tadese et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) stated that at every rainfall station, December and January saw the lowest amount of rainfall, while August and September observed the highest amounts. In agreement with this result, Gashaw et al.(2023) found monthly, seasonal, and annual Tmax and Tmin showed less variability (\u0026lt; 20%).\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=\"char\" char=\".\" 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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\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\u003eMonthly variation rainfall, minimum and maximum temperature, and contribution to the annual in the basin\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% of annual\u003c/p\u003e \u003cp\u003econtribution\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTmin\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTmax\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116.66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeb\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e32.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMar\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e32.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApr\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e31.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e143.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJun\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e273.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJul\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e229.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAug\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e273.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSep\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e237.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOct\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNov\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDec\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29.64\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Seasonal variation hydroclimate in the basin\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. shows the seasonal rainfall distribution in the Dabus river basin. The basin received the highest rainfall in the summer, spring, and autumn seasons. Winter was the driest season for the basin. The seasonal rainfall contribution for annual rainfall was JJA (60.8%), MAM (11.9), SON(26.8%), and DJF(0.5%). The basin received the highest rainfall in the JJA. The area obtained the maximum rainfall above 1372.68mm/year in the southern east and central parts. The basin also received considerable rainfall in MAM and SON. The northern and northwest parts of the area received very little seasonal rainfall mainly in the MAM and DJF season. The very driest season of the whole basin was DJF about 2.99mm/year of seasonal rainfall was recorded. In agreement with this result, Cherinet et al.(2019) stated almost 49.3% of the region's total precipitation falls during the summer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe seasonal mean temperature distribution (Tmean = (Tmax + Tmin)/2) of the basin ranges from 12 to 34.15°C/year (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The maximum seasonal Tmean was observed in the northern and southwest pocket areas. Less seasonal mean temperature was depicted in the middle part of the basin. Spring and winter were the hottest seasons in the basin. The highest Tmean was recorded in the winter season in the northern and southwest parts of the basin. The highest Tmean was recorded in the northern, northwestern, and southmost parts of the basin which are in Abadi, Sharkole, Assosa, Kamashe, and Begi. In the central part of the basin Nedjo, Begi, Mendi, and Gimbi experienced less Tmean.Similar to this research findings several researchers (Asfaw et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chakilu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gonfa et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zena et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) agreed the area was getting hotter.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe seasonal mean of river flow of the basin ranges from 5.02 to 504.66 m\u003csup\u003e3\u003c/sup\u003e/s/ year and the river runoff ranges from 3,22 to 3806.83mm/ year (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The result of SD of the river flow indicates the highest variation among the rivers. The SD value has more differences and variations in the river runoff. The value of CV of river flow was observed above moderate variability \u0026gt; 20% exhibiting the highest flow variation among the rivers of the basin. The river runoff CV also showed the maximum variation of runoff except for the Didesa River which has 18.57%. Among the rivers of the basin except Didesa all had maximum flow in the summer season. The river runoff was high in summer and autumn seasons except Uke River which has maximum runoff in winter. The seasonal shift of river flow and river runoff also showed the same pattern as the seasonal rainfall of JJA and SON. In line with this result, Worku et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) detected that just three to five months of the year are in the rainy season which contributes to the highest river flow and runoff in the watershed.\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eseasonal variation of river flow and river runoff in Dabus River Basin.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSeasons\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eRiver flow(m\u003csup\u003e3\u003c/sup\u003e/s)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eRiver runoff(mm)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emedian\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCV%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003emedian\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCV%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDabena\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e304.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e170.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e524.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e172.63\u003c/p\u003e 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align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e260.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e259.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e75.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29.09\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDidesa\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.62\u003c/p\u003e \u003c/td\u003e\u003ctd 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\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e283.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e281.95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.07\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e38.16\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHujura\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e64.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e54.86\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e427.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e354.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e239.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55.90\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e387.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e392.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e190.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e49.03\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e103.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e108.67\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eUke\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e43.36\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e347.73\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e360.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128.83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e34.45\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e307.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e301.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e64.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.44\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e71.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e36.94\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Standard anomaly index of seasonal and annual hydroclimate\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Seasonal and annual rainfall\u003c/h2\u003e \u003cp\u003eAll the seasonal anomalies of the basin show increasing trends with different magnitudes. The autumn (SON) rainfall anomaly shows the greatest increase and the winter (DJF) had an almost negligible increase. The summer season (June to August) has the biggest contribution to rainfall of the basin which is typical for areas that experience monsoon during this time. The wettest years are shown at 37.5%,47.5%, and 40%, and very small numbers (0 to 0.4) in MAM, JJA, SON, and DJF seasons respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This result indicates that even if the overall trend of seasonal rainfall anomaly was shown increasing the number of dry anomalies was greater in each season throughout the study period. The seasonal rainfall SAI was observed extremely wet in JJA and SON in the year 2000. In agreement with this result, Mera (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) stated extreme drought was depicted in the basin in 2015 in JJA. In 2015, almost 10\u0026nbsp;million people were affected by the worst drought in decades, which struck north and central Ethiopia.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e depicts the annual rainfall anomaly of the Dabus River Basin. The trend of this anomaly graph shows an increasing trend and only 14.8% of the variability of the anomaly was expressed by the year. However, only 7.5% of it is above normal or wet, 27.5% is dry and the remaining almost 70.13% of it depicts the normal state. The annual rainfall Standard anomaly index (SAI) shows the year 1999 was the wettest year with a value of 2.37 whereas 1982,1983,1984,1986 and 2015 were the driest years in the basin. This result is in agreement with the study done by Moshe \u0026amp; Beza (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) that stated according to the annual rainfall SAI results 15% of the study period was reasonably dry, 15% was wet, and 70% was in normal conditions. Kebede et al.(2020) also found the result the UBN experienced extreme drought from 1980 to 1996 and 2015.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Mean seasonal and annual temperature\u003c/h2\u003e \u003cp\u003eThe seasonal mean temperature SAI of the Daubs basin was shown to increase starting from 2000 commonly in all seasons maximum in MAM and less in SON. In MAM 1998,2001, and 2002 were the extremely warmest years greater than three. Except for MAM the increase of seasonal Tmean was not significant, the magnitude ranges to in normal conditions. In the basin, extreme cooling was observed in the MAM season in 1985 which is less than − 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe SAI of the annual Tmean of the basin indicates an increasing trend starting in 2000 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). However, The coldest year of the basin was observed in 1985 and the warmest years were 1998 and 2003.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe SAI of the river flow shows a decreasing trend or drying tendency starting in 2003. Particularly, 2006 and 2017 were years which have a magnitude less than − 2 indicating extremely dry events (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The SAI of river runoff depicted an insignificant decreasing trend and extremely wet was observed in 1997. The anomaly of the basin river flow variation was very minimal and 67.71% of the anomaly was depicted in the drier condition. Supporting this study result Takele et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) stated that due to the increment of temperature and rainfall fluctuations may change the patterns and trends of hydrology and water resources in the basin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Precipitation concentration index\u003c/h2\u003e \u003cp\u003eThe PCI provides valuable insights into climate patterns and can help assess changes in precipitation distribution over time. The PCI is a crucial component for managing natural resources, planning water resources, and predicting the danger of floods or droughts(Zhang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The MAM season PCI indicates the precipitation distribution was uniform throughout the basin. The SON and DJF season PCI values are depicted the rainfall distribution was under moderate distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). While the rainfall distribution was uniform throughout the basin in MAM in which the PCI values were less than 10. In the basin, JJA’s rainfall distribution was significantly strong irregular distribution. In SON and DJF the distribution of rainfall was moderate and moderately irregular. The JJA rainfall distribution showed a maximum concentration that ranged from 18 to 28 causing the flood. The highest value of PCI depicts preserving consistent soil moisture levels can help agricultural operations(Pan et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This tendency could help farmers in the area better arrange the dates of their planting and harvesting. The study done by Berihun et al.(2023) stated that the PCI result of the summer season showed the greatest seasonality a very erratic distribution of rainfall. The result reported by Mohamed et al.( 2022) was in line with this result in terms of MMA, SON, and DJF but their report was different from the result obtained by this study on the summer and annual PCI. It stated all under uniform distribution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrecipitation concentration index analysis is critical to track PCI trends to manage water resources and comprehend changes in climate patterns. The annual PCI result from 1980 to 2020 of the Dabus River Basin except 2014 observed significant irregular and strong irregular distribution precipitation which had a value greater than 16 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). In the basin, 30% of the PCI values indicate the highest concentration of rainfall which makes the area experience irregular rainfall. Mainly, peaks of PCI were observed in the years 1987,1991, 2002, 2003,2006, and 2011indicates the highest irregularities and concentration of rainfall. Getachew and Manjunatha ( 2022) reported in line with this result found there have been reasonable floods in the area, which have led to surface water body contamination and soil erosion. Similar researchers on UBN Moshe \u0026amp; Beza (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported the area's rainfall distribution has a significant irregularity distribution over the relative study period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Trend of hydroclimate\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Temporal trend hydroclimate\u003c/h2\u003e \u003cp\u003eThe monthly rainfall of the basin shows an increasing trend except for January and February. The trend of the monthly rainfall significantly increases in September and December with 0.20 and 0.12mm/ year, respectively. Monthly maximum temperature showed an increasing trend in all months and in March, July, and August month significant increase of Tmax was observed with the rate of 0.03,0.02, and 0.02°C/ year, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The trend of the monthly minimum temperature was not consistent.in January, July, August, and September the Tmin of the basin depicted decreasing with a range of -0.03 to -0.02°C/ year.\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=\"char\" char=\".\" 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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTemporal trend monthly rainfall. Minimum and Maximum Temperature variation in the basin\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eTmax\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eTmin\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTau\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSens Slope\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP_Value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTau\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSens Slope\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP_Value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTau\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSens Slope\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP_Value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJan\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeb\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMar\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApr\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJun\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJul\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAug\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.05*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSep\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOct\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNov\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDec\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e* stands for significant at P \u0026lt; 0.05\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Temporal trend hydroclimate\u003c/h2\u003e \u003cp\u003eThe seasonal rainfall of the Dabus River Basin was erratic and showed different values in each station in different seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). In the MAM season, the western part of the basin receives less rainfall than the eastern parts. In the MAM rainfall decreased from − 0.35 to 16.21mm/year, mainly in Begi and Gimbi stations, respectively.JJA was the rainy season of the basin, and showed a significant decrease at Abadi and Begi stations, with rates of -2.91 and − 1.15mm/year, respectively. A significant (P_value \u0026lt; 0.05) increasing trend was observed in JJA seasonal rainfall at Sharkole and Aira stations in the northwest and southeast of the basin, with a rate of 4.16 to 9.71mm/year (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Among the stations, Begi’s rainfall depicted a decreasing trend except in the SON. The SON season rainfall was sown almost increasing trend except for the northern and central of the basin. The Dabus River Basin had very low rainfall in the DJF season throughout the study period. The majority of the Basin's climatic stations indicated a decreasing trend in DJF rainfall (Mohammed et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The result obtained confirmed that there was a rise in substantial seasonal rainfall observed in SON than in MAM depicting a seasonal shift in the basin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe seasonal Tmin of the Dabus River Basin was observed in both increasing and decreasing tendencies (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). The trend of Tmin ranges from − 0.4 to 0.15°C/year.The significant decrease of Tmin was depicted in the summer and spring seasons. A significant increase in Tmin was observed in the autumn season with 0.16°C/year. In the winter season both increasing and decreasing of Tmin are observed throughout the basin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe significantly increasing seasonal Tmax was observed in the northern part of the basin in all seasons. The trend of seasonal Tmax ranged from − 0.04 to 0.15°C/year. The highest increment of T max was observed in the northern, northwestern, and southwest of the basin (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). An insignificant decrease of annual Tmax was observed in the Winter season mainly in the southeast of the basin. In line with this result Alaminie et al.(2021); Asfaw et al., (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); Kebede et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Mohammed et al.(2022) reported the increase in Tmax in the area during the study time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the Dabena seasonal river flow observed an insignificant increasing trend that ranged from 0.06 to 0.43 m\u003csup\u003e3\u003c/sup\u003e/s/year.It was maximum in summer and minimum in winter. The river runoff of Dabena significantly decreases in autumn with − 18.75mm/year whereas increases in spring, summer, and winter in the range of 2.32mm/year to 9.48mm/year. The river flow of the Didesa River was observed to increase in all seasons throughout the study period in the range of -1.24 to -0.48 m3/s/year insignificantly. In Dedesa the river runoff decreased insignificantly in all seasons except autumn which had an insignificant increase trend of 0.84mm/year. The river flow of Hujiru increases significantly by 0.31 m3/s/year in spring and increases insignificantly in the summer season. The seasonal river runoff increases insignificantly in the spring, summer, and winter seasons in Hujira, but increases significantly by 2mm/year. In Uke, the river flow increases only in spring, particularly in summer decreasing significantly in summer by -7.320.43 m\u003csup\u003e3\u003c/sup\u003e/s/year.Similarly in Uke, the river runoff increases only in spring, mainly in summer decreasing significantly by -1.57mm/year (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSeasonal Trend of river flow and river runoff in Dabus River Basin.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSeasons\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eRiver flow\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eRiver runoff\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSens-slop\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePvale\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSens-slop\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePvale\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDabena\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-18.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDidesa\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHujura\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eUke\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eThe basin had a mean annual rainfall of 1924.6.64 mm. The annual rainfall was observed the highest variation of rainfall with the value CV = 30.86%, and SD = 400.29 (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e). The annual rainfall of the Dabus River Basin has observed a significantly increasing trend (P\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:0.05\\)\u003c/span\u003e\u003c/span\u003e). The trend of annual rainfall was significantly increased by 21.39mm/year and Tau=0.405. The annual rainfall was observed insignificant decreasing trend in Abadi, Begi, Bure, and Mendi stations. A significant increase in annual rainfall was observed in Aira station with 23.18mm/year The mean annual Tmax was 29.25, with little variation SD=0.53 and CV less than 20%. The annual Tmax of the basin was significantly increased with the rate of 0.017°C/year and Tau is positive 0.276. The mean annual Tmax of Dabus River was 29.25°C and The value SD = 0.53 which indicates less variation was shown on annual Tmax. The annual Tmin of the Dabus River Basin showed an insignificant decreasing tendency with the rate of -0.004°C/year. The annual mean of Tmin was 15.16°C and had the least variation annually because SD = 0.6 which depicted the magnitude of the variation of mean Tmin was almost the same. Ayehu et al.(2021); Gashaw et al.(2023) found an increasing trend in annual rainfall in UBN. In agreement with this study, Tegegn et al.(2024) reported that the Tmax increased significantly ranging from 0.027°C to 0.485°C, respectively. The result obtained was inconsistence with Tirfi and Oyekale (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) that Tmin significantly increased by 0.13 in the western and northern parts of the country. Alemayehu et al., (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also found equivalent results stated in UBN the maximum temperature exhibited increasing (0.02°C per year). Generally, the above result indicates the area has been warming.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e shows the trend analysis of river flow and river run-off. Accordingly, the Mean annual river flow of the basin is 1322m\u003csup\u003e3\u003c/sup\u003e/s. The river flow of the basin had CV = 24.s moderate variation and high SD = 323.9 revealing the annual mean base. It depicted an increasing trend with the rate of -6.32 m\u003csup\u003e3\u003c/sup\u003e/s and had Tau = 0.13. The annual mean river runoff was 2209.05m and the highest version among the value mean (SD = 231.03). The trend of annual runoff in the basin decreased insignificantly with a rate of 0.89mm/year. The magnitude of CV was 10.46% depicting the less variability of the annual runoff. The trend result of the river flow and the river runoff manifested water shortage that has increased as a result of climate change and other driving forces. This result was reinforced by Ehtasham et al.(2024), and Wu et al.(2020) found that increasing temperature results in a decrease in water availability since it increases the rate of evaporation and raises water demand. Throughout the study period, there was a noticeable descending trend in the river discharge. Cherinet et al.(2019);Worku et al.(2021) found that significantly, the region's high-temperature increase throughout the research period led to a high degree of evaporation, which in turn produced a drop in water flow. Keeping in cognizance that rising temperatures have the impact of reducing river flow and runoff, and if these factors decline without causing a distinct shift in rainfall, the most likely influence may be the basin's land use and cover (Gebremicael et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Werede et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Mohammed et al.(2022) also stated that UBN was experiencing climate change, as evidenced by an increase in extreme rainfall and a warming trend in the extreme temperatures that affect the stream flow.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3 The changing point of hydroclimate\u003c/h2\u003e \u003cp\u003eThe changing point test is a valuable instrument in aiding decision-making about hydroclimatological variables by detecting changing point variations. The mean annual rainfall value was observed to increase and change significantly in 1996. The change in mean annual rainfall before and after the abrupt point was 1673.87mm and 1930.55mm, respectively. The annual Tmax mean value was increasing significantly and had a changing point in 1993 and 1997. The change in mean annual Tmax before and after the abrupt point was 29.25°C and 30.24°C, respectively. The annual Tmin was observed to significantly change the point of mean in 1987. The annual rainfall and annual Tmax showed a significant increment of mean value after the change point, however, Tmin depicted a decreasing trend after the exact change point. The change in mean annual Tmin before and after the abrupt point was 15.67°C and 15.16°C, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnderstanding the effects of climate change and human activity depends on the precise detection of abrupt changes in hydroclimatic time series, which is a crucial complement to the detection and attribution of hydroclimatic variability(Oteng Mensah et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The abrupt change in annual river flow showed an insignificant decrease in mean value in the year 1998. The annual mean river runoff abrupt change also showed an insignificant decrease rate in the year 1999. Consistent with this result Cherinet et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found in the Abay River basin that there was a noticeable descending trend in the river discharge, which abruptly reduced starting in 1992. The change of mean river flow before and after the abrupt point was 1450.973 m\u003csup\u003e3\u003c/sup\u003e/s and 1250.44 m3/s, respectively. The change of mean river runoff before and after the abrupt point was 2234.78mm and 2194.76mm, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eEven though global warming is the primary concern of climate studies, understanding local and regional climate change as well as its impact is very crucial. The climate change of an area is determined by several factors mainly the alteration of the long-term precipitation and temperature that brought hydrological extreme events. Using several testing methods and long-term rainfall and temperature including river flow and river runoff data, this study made a comprehensive investigation and obtained worthy results. Accordingly, the basin received 75.44% of the annual rainfall in the wet season (Jun, July, and August) including September. The study found the seasonal shift of rainfall from spring to autumn in the basin for the last four decades. The warmer area of the basin was recorded at the north, and south of the basin. The winter season was the warmest in the Dabus River Basin. The SAI value indicates 1999 and 2000 were the wettest year whereas 1982,1983,1984,1986 and 2015 were the driest years in the basin. The finding of distribution seasonal rainfall concentration shows the autumn and winter rainfall were moderate and moderately irregular, respectively. However, the rainfall concentration in summer was extremely irregular. The summer rainfall distribution showed a maximum concentration that ranged from 18 to 28 causing the flood. This results and the seasonal shift aware the farmers how to prepare their land for suitable crops to adapt to the variability of the soil moisture. The basin experienced very fluctuating rainfall in each year mainly peaks of PCI were observed in the years 1987,1991,2002,2003,2006, and 2011 which indicates the strong irregularities of concentration of rainfall. Except winter the seasonal rainfall was observed to increase. The basin's northern and western parts obtained less rainfall. A significant decrease in Tmin and an increasing trend of Tmax was observed in the spring and summer seasons. The annual rainfall and Tmax increased significantly whereas the annual Tmin, river flow, and river runoff decreased insignificantly.The abrupt changing point of annual rainfall was observed in 1996 and the Tmean mean was observed in 1993 and 1997.The annual mean decreasing point of Tmean, river flow, and river runoff was observed in 1987,1998, and 1999, respectively.The study found the increasing temperature with the fluctuating rainfall distribution has an impact on river flow and runoff. The study confirms further investigation into non-climatic issues is necessary, as the rise in annual rainfall did not explain the decline in the water balance in the basin. The reduction of river flow and river runoff could affect agricultural activity, electric power production, water demand, and other activities done in the basin. The change in the hydroclimate of the basin has an impact on water availability, distribution, production of hydroelectricity, irrigation, and overall agricultural activities of the Dabus River Basin. Further research into other climatic factors will be required as the study reveals that the drop in river flow and river runoff cannot be explained by the rise in annual rainfall in the basin.\u003c/p\u003e"},{"header":"Declarations","content":"\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\u003eAll authors reviewed the manuscript and agreed to publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analyzed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no specific funding from public, commercial, or funding agencies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions: MTT\u003c/strong\u003e: Conceptualization;\u0026nbsp;Data Retrieval, Software, Data Analysis, writing discussion, Writing-original draft, writing review, and critical revisions. \u003cstrong\u003eKTB\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eTTZ\u003c/strong\u003e: Design Methodology, Data Interpretation, Supervision, and \u0026nbsp;Review.\u003cstrong\u003eFAA\u003c/strong\u003e: editing, and validation, and Critical Revisions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors greatly appreciate the National Meteorology Agency of Ethiopia for providing meteorological data and the Ministry of Water, Irrigation, and Electricity of Ethiopia for providing hydrological data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbera FF, Shumete A (2021) Optimal Operation of Cascade Reservoir Systems under Climate Change: Case Study of Tekeze Hydropower Reservoir in the Tributary of the Blue Nile River. 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Int J Climatol 39:4648\u0026ndash;4658\u003c/span\u003e\u003c/li\u003e\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":"climate change, Hydroclimate, modified Mann Kendal test, PCI, Pettit test","lastPublishedDoi":"10.21203/rs.3.rs-5446005/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5446005/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHigh-resolution local scale climate research approach is very effective in examining the existing climate change and predicting its risk. Thus, this study investigated the hydroclimate distribution, variation, trend, and abrupt change points, and considered more than the climate normal time range (1981 to 2020) to determine the climate change of the Dabus River Basin. The study employed different statistical, parametric, and nonparametric modified trend tests, and exact changing point detecting models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe result found the basin received 57.7% of the annual rainfall in June, July, and August. The standard anomaly index (SAI) value indicates 1999 and 2000 were the wettest years whereas 1982,1983,1984,1986 and 2015 were the driest years in the area. The basin experienced very fluctuating rainfall for the last four decades. Peaks of Precipitation Concentration Index (PCI) were observed in the years 1987,1991, 2002,2003,2006, and 2011 which indicates the strong irregular distribution of rainfall. The annual mean rainfall and maximum temperature (Tmax) increased significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas the annual mean minimum temperature (Tmin), river flow, and river runoff decreased. In Dabus the abrupt increasing change point of annual rainfall was observed in 1996 whereas Tmax in 1993 and 1997. The abrupt decreasing change point of Tmin, river flow, and river runoff was observed in 1987, 1998, and 1999, respectively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe study found the climate change in the basin due to the significant increase in temperature with fluctuating rainfall distribution as well as reduction of river flow and runoff. This climate change could upset agriculture, electric power production, and water demand in the basin.\u003c/p\u003e","manuscriptTitle":"Temporal and spatial distribution, variability, and trend of hydroclimate in the Dabus River Basin Upper Blue Nile, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 06:33:23","doi":"10.21203/rs.3.rs-5446005/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":"e64ecac6-5f00-466f-bdd3-f26486cd8474","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-22T09:23:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-02 06:33:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5446005","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5446005","identity":"rs-5446005","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}