Comparative trend analysis of hydroclimatic variables for sustainable water resource management in Jebba Dam in the Niger River Basin

Comparative trend analysis of hydroclimatic variables for sustainable water resource management in Jebba Dam in the Niger River Basin | 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 Comparative trend analysis of hydroclimatic variables for sustainable water resource management in Jebba Dam in the Niger River Basin Emmanuel Olorunyomi Aremu, Agnidé Emmanuel Lawin, David Olatunde Olukanni, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8978959/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 Analyzing the trends of key hydroclimatic variables can enhance sustainable water resource management in the face of a changing climate, water availability, and their associated uncertainty. This is critical for hydropower generation, especially cascaded dams like Jebba Hydropower Station (JHS). In this study, we analyzed the trends of hydroclimatic variables – rainfall, temperature, and associated hydropower generation variables using satellite and observed data from 1981–2022. We employed statistical tests, the standardized precipitation index (SPI), Modified Mankendall (MMK) trend test, homogeneity tests, and Innovative Trend analysis (ITA). The study reveals that the annual mean rainfall in Niger River Basin (NRB) and Jebba hydropower station has been experiencing a normal variability with high, moderate, and normal variability detected for different seasons. Furthermore, the SPI reveals that the Ilorin and Bida rainfall station has the highest and the lowest negative anomaly, with 66% and 40%, respectively. The most prolonged dry period was 1981–1989 in the NRB, followed by 1992–1996 in the Ilorin station. The MMK and ITA reveal similar results, showing that Ilorin and Bida rainfall, Jebba dam’s inflow, outflow, turbine discharge, and energy generation have increased significantly; nevertheless, evaporation loss decreases while other variables show no trend. Likewise, the homogeneity test, notably the Pettit test, reveals that abrupt changes (change year) are detected in all variables in the JHS and neighboring rainfall stations except Bida, Minna, and Lokoja rainfall. The study concludes that the significant increase in the hydropower generation is attributed to the increase in the water availability (rainfall and reservoir inflow) in the hydropower station. Environmental Engineering Trend Analysis hydroclimatic Hydropower Jebba dam Mankendall Homogeneity Innovative Trend Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1.0 Introduction Hydropower is a renewable energy source fueled by water and is environmentally friendly, especially in the face of future climate change. It reduces carbon emissions by generating energy from generators attached to water turbines while boosting the rotational power of the water turbines by using a head. Because it has a higher energy density than other alternative energy sources, it is considered a resource with high development value (Jung et al. 2021). The link between climate, water, and energy nexus, especially rainfall and temperature in hydropower dam river catchments, greatly influences water availability and electricity generation and supply. To ensure water security, it is crucial that a population build capacity to safeguard sustainable access to adequate quantities of acceptable quality water for sustaining livelihoods, human well-being, energy generation, and socio-economic development, ensuring protection against water-borne pollution and water-related disasters, and for preserving ecosystems in a climate of peace and political stability (UN-Water, 2013). As described by Fung (2009), the effect of climate change on river basin water availability is determined by two factors: changes in climatic variables that drive hydrological processes, such as precipitation, solar radiation, and temperature, and the basin's vulnerability to these changes. Furthermore, Gain et al. (2012) stated that climate change affects the earth's ecosystems and, hence, people's livelihoods and well-being, often through water (e.g., floods and droughts). Aside from climate change, present demographic trends, economic growth, and related land-use changes all influence the growing demand for water resources. Bates (2008), reported that scientists within the Intergovernmental Panel on Climate Change (IPCC) expect that the present increase in greenhouse gas concentrations will have direct first-order effects (increase in ambient air temperature, changes in evaporation, precipitation, streamflow, and sea-level rise on the global hydrological cycle, with impacts on water availability and demand. The availability of water resources in West Africa is critical for economic growth and social well-being. West Africa has already witnessed a sea-level rise, significant shoreline erosion, increased temperatures, erratic rainfall, dwindling water availability, and more due to climate change (Babalola et al., 2021). In this regard, it is critical to examine the trends of hydroclimatic variables to gain a more explicit and quantitative understanding of the available water resources for various functions, such as hydropower generation. Statistical and trend analysis is one of the most essential measures in studying climate time series data (Hussain & Mahmud, 2019). In a study done byTadese et al. (2019),statistical and trend analysis was used to characterize, quantify, and validate the variability and trend of hydro-climatic variables in the Awash River Basin, Ethiopia. Methods such as coefficient of variation (CV), standardized anomaly index (SAI), and graphical methods were used to test for variability of rainfall and streamflow in the study area. In trend analysis, both parametric and non-parametric tests are often utilized. Data must be independent and regularly distributed for use in parametric testing. On the other hand, non-parametric trend tests just demand that the data be independent and can tolerate outliers (Hamed & Rao, 1998). However, parametric tests are more powerful than non-parametric ones (Hussain & Mahmud, 2019). Several tests are used worldwide for trend analysis. One of them is the Mann–Kendall test (Mann, 1945; Kendall, 1975). Mann–Kendall test is a popular non-parametric method for detecting significant trends in time series. On the other hand, the original Mann-Kendall test did not account for serial correlation or seasonality effects (Bari et al., 2016; Hirsch et al., 1982). However, in many real-life circumstances, the observed data are autocorrelated, which can lead to a misunderstanding of trend test results(Cox et al., 1955). Water quality, hydrologic, climatic, and other natural time series, on the other hand, are all seasonal. Several modified Mann-Kendall tests have been created to address the limitations of the original Mann-Kendall test (Hamed & Rao, 1998). In a study conducted by Lawin et al., (2018), to assess climate and land-use change impacts on future flows in the Bétérou basin. Based on the combination of climate change scenarios with land use/cover change scenarios, the future flows in Ouémé River at the Bétérou outlet were estimated. Their findings, compared to the reference period of 2002–2004, indicated that Ouémé River at the Bétérou outlet will experience an increased discharge for all time horizons until 2050 and for all climate and land-use change combined scenarios. Also, in West Africa Babalola et al. (2021),did similar studies to assess climate change impacts on the seasonal river discharge in two rivers in West Africa, namely, the Niger and the Hadejia-Jama’are Komadugu Yobe Basin. Their results of the multi-model median regarding climate change show that climate change impacted the temporal pattern of future river discharge in the river basins. It was noted that the basins influenced by precipitation continuously increase streamflow volumes during the later part of the high-flow season. In the face of a changing climate, measures for adaptation to climate change are necessary to enhance the adjustment of a system to reduce the effects, exploit new possibilities, and or cope with the consequences. Even with highly severe emission control policies, it is expected that greenhouse gas concentrations might keep rising(Abbas et al., 2018). For basin-scale trend analysis, which includes characterization, variability, and trend for river basins. Satellite data, such as rainfall estimates, are sometimes obtained and used as an alternative to supplement in-situ observations (Obahoundje et al., 2022). This is due to insufficient long-term data from weather stations that could cover the entire basin. Many satellite-based rainfall products with long-time series have coarse spatial and temporal resolutions and are not homogeneous. In Nigeria, where Jebba Hydropower Station is located, Ogbu et al. (2020) carried out a study in which the capabilities of Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Tropical Application of Meteorology Using Satellite Data and Ground-Based Observations(TAMSAT) to reproduce local rainfall characteristics in Nigeria from 1983 to 2013 were evaluated at a point-location scale. It was concluded that CHIRPS performed better than other products in reproducing local rainfall climatology in most locations. CHIRPS data has been validated with in situ observed data in previous studies over and within regions in West Africa. For example,Didi Sacré Regis et al. (2020) validated the CHIPRS data over the Sahel and Guinea Coast when it was used to investigate climate change in West Africa. Furthermore,Obahoundje et al. (2020) also validated CHIRPS data in a study over the Bandama and Mono river basins, showing a strong correlation and lowest mean absolute error compared to GPCP and CRU precipitation products. In previous studies, various statistical tests, such as Mankendall trend and homogeneity tests, have been used to determine the trends of hydroclimatic variables. However, to enhance adequate planning and gain more perspectives on the trend of hydroclimatic variables and its effect on water resources availability for energy generation in Jebba hydropower station, this study uses a comparative trend analysis which includes modified Mankendall (MMK) trend test, Innovative trend analysis (ITA), and homogeneity tests (such as Petitt Test, Standard Normal Homogeneity Test, Buishand Test) to obtain insights for the development of a sustainable water resource management in Jebba dam, Nigeria. 2.0 Methods and Tools 2.1 Study Area and data The Niger River Basin (NRB) is the second-largest river in Africa, covering an area of 2.27 million km² and is located between latitudes 5° N and 24° N and longitudes 12° W and 17° E. The basin traverses four countries, which are Guinea, Mali, Niger and Nigeria. It is delineated by an unusual flow through ten shared countries, namely Algeria, Benin, Burkina Faso, Cameroon, Chad, Cote d’Ivoire, Guinea, Mali, Niger, and Nigeria. Out of the active river basins, Nigeria has 562,372 km², accounting for 44.2% of the total active basins. According to (Andersen et al., 2005), the basin has two seasonal variations: a rainy summer and a dry winter, except for Nigeria, which has four seasons. A significant part of the basin has major reservoirs in the lower basin, namely, Kanji, Jebba, and Shiroro reservoirs in Nigeria. The country has about 60 large dams; the three dams mentioned earlier are the most prominent, and it has a total water storage of 34,800 (MCM: million cubic meters) in the area under study (JICO, 2014). The study focuses on the Jebba reservoir, as displayed in Fig. 1 . It is the country’s second-biggest hydropower dam and has six-generation turbines, each with a rated capacity of 96.4 megawatts, for a maximum installed output of 578.4 megawatts. Hydroclimatic data for the Jebba hydropower dam were obtained from Mainstream Energy Solution, which is in charge of the Jebba Hydropower Station. The available data consists of a monthly rainfall time series, average maximum temperature, reservoir inflow, reservoir outflow, evaporation loss, and turbine discharge data. Observed rainfall data for Jebba dam neighboring stations (Ilorin, Bida, Minna, and Lokoja) were obtained from NiMET (Nigerian Meteorological Service Agency). For basin scale analysis, the rainfall data from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) version 2 developed by the Climate Hazards Group of the University of California was used. The daily rainfall dataset was downloaded at a basic scale utilizing the feature of custom polygon region for Niger River Basin (NRB) in the Google Climate Engine App using the link https://app.climateengine.com/climateEngine . The length of the data collected was from 1981–2022. The CHIRPS dataset is a quasi-global rainfall dataset covering 50° S to 50° N and spanning from 1981 to near present. It incorporates 0.05° x 0.05° resolution satellite imagery with in situ station data to create gridded rainfall time series suitable for trend analysis and seasonal drought monitoring (Chris, Funk et al., 2014). It can be freely accessed at http://chg.geog.ucsb.edu/data/chirps/ . To ensure data quality, the datasets were visually inspected to check for outliers, and the data was graphically visualized with time series plots using Microsoft Excel. Also, the CHIRPS rainfall dataset was selected based on three criteria: its long-term data availability (1981–2022), which is more than 40 years, zero percentage of missing data, and good representativeness of the entire river basin. Table 1 presents the spatial and temporal resolution of the datasets used in the study and their corresponding years of record. Table 1 Spatial and temporal resolution of datasets used in the study Variable Data Type Resolution Temporal Spatial Source Rainfall CHIRPS Daily 1981–2022 Niger River Basin USGS Rainfall Observed Daily 1992–2021 Ilorin Station NiMET Rainfall Observed Daily 1992–2021 Bida Station NiMET Rainfall Observed Daily 1992–2021 Minna Station NiMET Rainfall Observed Daily 1992–2021 Lokoja Station NiMET Rainfall Observed Monthly 1988–2018 Jebba Dam JHS Max. Temperature Observed Monthly 1988–2018 Jebba Dam JHS Reservoir Inflow Observed Monthly 1988–2018 Jebba Dam JHS Reservoir Outflow Observed Monthly 1988–2018 Jebba Dam JHS Lake evaporation Observed Monthly 1988–2018 Jebba Dam JHS Turbine Discharge Observed Monthly 1984–2009 Jebba Dam JHS Energy generated Observed Monthly 1988–2018 Jebba Dam JHS CHIRPS : Climate Hazards Group Infrared Precipitation with Stations; USGS : United States Geological Survey NiMET : Nigerian Meteorological Agency; JHS : Jebba Hydropower Station; 2.4 Statistical and Trend Analysis The statistical analysis of the hydro-climatic data was undertaken to characterize, quantify, and evaluate the variability and trend in the river basin and the hydropower station. The variability and trend were tested using different graphical and statistical methods, including coefficient of variation (CV), Standardized Precipitation Index (SPI), Modified-Mankendall (MMK) trend test, Innovative Trend Analysis (ITA), and homogeneity tests such as Pettit’s Test, Standard Normal Homogeneity Test (SNHT) and Buishand’s range test for breakpoint detection. 2.4.1 Coefficient of Variation (CV) This study used the coefficient of variation as a statistical measure to determine the variation of the rainfall data about the mean. it is the ratio of standard deviation to the mean using Eq. ( 1 ). For this study, a CV of 30 was considered normal, moderate, and highly variable, respectively. In previous studies, such as (Asfaw et al., 2018; Bekele et al., 2017; Bewket & Conway, 2007; Tadese et al., 2019b), CV was used to characterize rainfall variability. $$\text{C}\text{V}=\frac{\sigma}{x̅}\times100\%$$ 1 where σ and x̅ denote the standard deviation and mean of rainfall, respectively. 2.4.2 Standardized Precipitation Index (SPI) Standardized Precipitation Index is a tool recommended by the World Meteorological Organization (WMO) and widely used for quantifying the precipitation deficit over different timescales from 3 to 48 months (Svoboda et al., 2012). SPI has been used for the analysis of the interannual rainfall variability in previous studies (Lawin et al., 2019). This study obtained the SPI for the annual rainfall records using Eq. ( 2 ). $$I\left(i\right)=\frac{{x}_{i}-\stackrel{-}{{x}_{m}}}{\sigma},$$ 2 where I(i), x i , ͞ x m, and σ are, respectively, the standardized index of the year I, the value for the year I, and the average, and the standard deviation of the time series. Table 3 presents the guidelines for analyzing and interpreting SPI values(Svoboda et al., 2012). Table 3 Standardized precipitation index (SPI) values and their meanings SPI Value Meaning 2.0 and plus Extremely wet 1.5 to 1.99 Very wet 1.0 to 1.49 Moderately wet −0.99 to 0.99 Near Normal −1 to − 1.49 Moderately dry −1.5 to − 1.99 Severely dry −2 and less Extremely dry 2.4.3 Modified Mankendall (MMK) Trend Test The Mankendall test is a non-parametric trend detection method (Mann, 1945; Kendall, 1975). It is a commonly used tool for detecting changes in climatic and hydrologic time series (Hamed & Rao, 1998). However, Modified Mankendall (MMK), a modified version of the Mann-Kendall test based on the modified variance (S), which is robust in the presence of autocorrelation and does not assume a specific distribution, is used where autocorrelation is detected. (Hamed & Rao, 1998). This method has been used to detect trends in many climatic and hydrologic variables, such as rainfall, temperature, and runoff. (e.g Pingale et al., 2016; Aziz & Obuobie, 2017; Abungba et al., 2020). In this study, the MK statistic (S) was calculated following the method described by Mann (1945) and Kendall (1975). $$S=\sum_{k=1}^{n-1}\sum_{j=k+1}^{n}\text{Sgn}\left({x}_{j}-{x}_{k}\right)\left(3\right)$$ Where x j and x k are sequential data values for the time series data of length n, the test statistic represents the number of positive differences minus the number of negative differences for all the differences between adjacent points in the time series considered. It equates to the sum of the Sgn series, which is defined as: $$\text{S}\text{g}\text{n}\left({x}_{j}-{x}_{k}\right)=\left\{\begin{array}{c}1\text{}\text{i}\text{f}\text{}{x}_{j}>{x}_{k}\\0\text{}\text{i}\text{f}\text{}{x}_{j}={x}_{k}\\-1\text{}\text{i}\text{f}\text{}{x}_{j}<{x}_{k}\end{array}\right.$$ 4 The mean and variance of S, E(S), and V(S), respectively, under the null hypothesis, Ho, of randomness, given the possibility that there may be ties in the x values is given as; $$E\left(S\right)=0$$ 5 $$V\left(S\right)=\frac{1}{18}\left\{n(n-1)(2n+5)-\sum_{i=1}^{n}{t}_{i}\left[\left({t}_{i}-1\right)\left(2{t}_{i}+5\right)\right]\right\}$$ 6 where t is the extent of any given tie. Σti denotes the summation over all ties and is only used if the data series contains tied values. The standard normal variate Z is calculated by $$Z=\{\begin{array}{c}\frac{S-1}{\sqrt{V\left(S\right)}}\text{i}\text{f}S>0\\0\text{i}\text{f}S=0\\\frac{S+1}{\sqrt{V\left(S\right)}}\text{i}\text{f}S<0\end{array}$$ 7 There are two advantages of using this test. First, it is a non-parametric test and does not require the data to be normally distributed. Second, the test has low sensitivity to abrupt breaks due to inhomogeneous time series. According to this test, the null hypothesis H 0 assumes that there is no trend (the data is independent and randomly ordered), and this is tested against the alternative hypothesis H 1 , which assumes a trend. The non-parametric trend analysis has a Python package called pyMankendall implemented in pure Python, which brings together almost all types of Mann-Kendall tests and was developed to help researchers check Mann-Kendall trends in Python. It uses a vectorization approach to increase its performance. Currently, the package has 11 Mann-Kendall Tests and 2 Sen’s slope estimator functions. A brief description of all the functions can be found in (Hussain & Mahmud 2019). The pyMankendall python package was used for trend analysis in this study. 2.4.4 Innovative Trend Analysis (ITA) The Innovative Trend Analysis (ITA) is a novel trend detection technique introduced by (Şen, 2012). This method is based on creating subsection time series plots on a cartesian coordinate system, where trend-free time series appear along a 45° straight line as seen. This method divides a time series into two equal parts, which are separately sorted in ascending order. Then, the first sub-series is located on the X-axis, and the second sub-series is located on the Y-axis. There is no trend if the investigated data are collected on the 1:1 line. If data fall above the 1:1 line or below the 1:1 line, then an upward or downward trend exists in the time series(Şen, 2014; Cui et al., 2017). The absolute value of the difference between the y and x values of a point is the distance from the 1:1 line. (Wu & Qian, 2017; Cui et al., 2017). The difference denotes the magnitude of an upward or downward trend. Therefore, the mean difference denotes the general trend of a time series. The trend indicator is given by: $$D=\frac{1}{n}\sum_{i=1}^{n}\frac{10\left({y}_{i}-{x}_{i}\right)}{\stackrel{-}{x}}$$ 8 where D is the trend indicator, and a positive value for D indicates an increasing trend, whereas a negative value indicates a decreasing trend; n is the number of observations of each sub-series, and x̅ is the average of the first sub-series (Wu & Qian, 2017; Cui et al., 2017). If the original time series has odd observations, the first observation is discarded before dividing to fully utilize the latest data. 2.4.5 Homogeneity test A homogeneity test was done to detect change points in the hydro-climatic data. Three homogeneity tests, namely Pettitt’s test, Standard Normal Homogeneity Test (SNHT), and Buishand’s range statistics, were used in the study. These methods have been utilized in prior studies, such asTaxak et al., (2014). The Pettitt test, which is a rank-based method for finding significant changes in the mean of time series data when the change point is unknown (Pettitt, 1979). It is known for its robustness to changes in the distributional form of the data and its relative power compared to tests such as Wilcoxon-Mann-Whitney and cumulative sum and deviation. It has also been widely applied in detecting changes in climatic and hydrological time series data (Zhang et al., 2016). On the other hand, the SNHT uses a series of ratios to compare the observations of a measuring station to the average of several stations, which are then standardized to obtain the Xi series. The null and alternative hypotheses are given as; Ho: The T variables Xi follow an N (0, 1) distribution. Ha: Between times 1 and n, the variables follow an N(µ1,1) distribution, and between n + 1 and T, they follow an N(µ2,1) distribution. The Pettitt’s statistic is defined by; $${T}_{0}=\underset{1\let\leT}{\text{m}\text{a}\text{x}}\left[v{z}_{1}^{2}+(n-v){z}_{1}^{2}\right]$$ 9 With $$\left\{\begin{array}{c}{z}_{1}=\frac{1}{v}\sum_{i=1}^{v}{x}_{t}\\{z}_{2}=\frac{1}{n-v}\sum_{t=v+1}^{T}{x}_{i}\end{array}\right.$$ 10 The T 0 statistic is derived by comparing the likelihood of two alternative models. The model analogous to Ha estimates i1 and i2 while defining the n parameter, maximizing the likelihood, SNHT is more sensitive to breaks at the beginning and end of a time series (Costa & Soares, 2009). Buishand's range test is suitable for variables following any distribution form, and its properties have mainly been studied in normal cases. For this study, Buishand focuses on the two-tailed test and the Q statistic (Buishand, 1982). For Q statistic, the null and alternative hypotheses are given by; $${S}_{0}^{\text{*}}=0,{S}_{k}^{\text{*}}={z}_{1}=\sum_{i=1}^{k}\left({x}_{i}-u\right),k=\text{1,2},\dots.T$$ 11 And $${S}_{0}^{\text{*}}={S}_{k}^{\text{*}}/\sigma$$ 12 The Buishand's Q statistics follow; $$Q=\underset{1\let\leT}{max}\left|{S}_{k}^{**}\right|$$ 13 These methods have been used previously to analyze climate data and investigate hydro-climatological climate change signals and variability (Taxak et al., 2014). The homogeneity tests have a Python package called Pyhomogeneity implemented in pure Python, which brings together almost all types of homogeneity tests (Pettit’s, SNHT, Buishard). A brief description of all the functions can be found in (Hussain & Mahmud, 2019). This study used The Pyhomogeneity Python package for change point detection analysis. 3.0 Results and discussion 3.1 Statistical Analysis In Niger River Basin and Jebba Hydropower Station, the annual mean rainfall and seasonal mean rainfall of AMJ and JAS had a CV of < 20 for NRB, which indicates a normal variability. On the other hand, JFM and OND for Niger River Basin and JAS for Jebba Hydropower Station had a CV of 20–30% (moderate variability). In comparison, Jebba Hydropower Station had a CV > 30% (high variability), for JFM and OND season (Table 3 ). In this study, high seasonal rainfall variability was only detected for the Jebba Hydropower Station, while moderate variability was detected for the Niger River Basin. Table 3 Summary of descriptive statistics of annual and seasonal rainfall Region/Station Annual JFM AMJ JAS OND Niger River Basin Mean 652.68 10.10 165.6 452.2 51.8 STD 49.14 2.86 16.23 36.95 14.21 CV% 7.52 28.46 9.80 8.69 27.42 Jebba Hydropower Station Mean 1123.243 17.90 403.45 612.77 89.12 STD 150.2136 33.91 112.00 125.14 47.78 CV% 13.37321 189.48 15.77 20.42 53.62 STD : Standard deviation, CV : Coefficient of variation, JFM : January, February, March, AMJ; April, May, June, JAS ; July August, September, OND : October, November, December. 3.2 Inter-Annual Variability The inter-annual variability of rainfall using a standardized precipitation index for NRB, JHS, and selected rainfall stations neighboring Jebba Hydropower Station is shown in Fig. 7 . The figure shows that the negative anomaly was 47% in the Niger River Basin, 45% in Jebba Station, 40% in Bida Station, 66% in Ilorin Station, 60% in Minna Station, and 46% in Lokoja Station. The Ilorin and Bida Stations recorded the highest and lowest negative anomalies, respectively. The most prolonged dry period was 1981–1989 in the Niger River Basin, followed by 1992–1996 in the Ilorin station. Also, the driest year was in 1983 in the Niger River basin, followed by 1998 in the Bida station. The highest rainfall excess happened in 2016 at the Ilorin station, followed by the Minna rainfall station in the year 2020. The results of the Jebba dam rainfall, inflow, and energy, standardized anomaly Index (SAI) show that the longest negative and positive anomalies for both inflow and energy generation were from 1988 to 1994 and 2007 to 2014 respectively, while the rainfall had the longest negative and positive anomalies from 1992 to 1997 and 2010 to 2014 respectively Figure 7 Standardized Anomaly Index (SAI) for annual and seasonal rainfall time series for Niger River Basin (NRB) and Jebba Hydropower Station (JHS). Figure 8 Standardized Anomaly Index (SAI) for annual rainfall, reservoir inflow and energy generation in Jebba Hydropower Station (JHS). 3.3 Modified Mankendall and Innovative Trend Analysis The Modified Man-Kendall trend test results of the hydroclimatic variables in the NRB and JHS are displayed in Table 3 . The results show an increasing trend for rainfall in Niger River Basin and Ilorin Station, reservoir inflow, reservoir outflow, turbine discharge, and energy generation, while evaporation loss shows a decreasing trend; however, rainfall in Bida, Minna, Lokoja, and Jebba stations and the maximum temperature at JHS have no trend. The Innovative Trend Analysis shows a similar result as displayed in Fig. 8 . The rainfall in the Niger River Basin, Ilorin Station, JHS reservoir inflow, reservoir outflow, turbine discharge, and energy generation with the majority of the points falling on the increasing triangle, while evaporation shows a decreasing trend with the majority of the points falling on the decreasing triangle. However, annual rainfall in Minna, Lokoja, Jebba, and maximum temperature in JHS show no trend. Only Bida station shows a variation showing an increasing trend in ITA but no trend in the Man-Kendall Test. Table 3 Modified Mann-Kendall (at 5% significant level) and Innovative Trend Analysis (D) Statistics for Jebba Hydropower Station and Neighbouring Stations Region/Station Hydroclimatic Variable P-value Zs Sen-slope ITA (D) Trend Niger River Basin Rainfall (mm) 0.0063 2.7310 1.6943 1.0837 +Ve Ilorin Station Rainfall (mm) 0.0027 2.9973 29.925 29.4835 +Ve Bida Station Rainfall (mm) 0.1007 1.6413 9.6739 13.3128 Non Minna Station Rainfall (mm) 1.0000 0.0000 0.1000 3.6800 Non Lokoja Station Rainfall (mm) 0.5207 0.6422 3.0706 4.6538 Non Jebba Station Rainfall (mm) 0.0891 1.6996 4.6371 0.8491 Non Jebba Station Max. Temperature (℃) 0.1258 1.5305 0.3260 0.2577 Non Jebba Station Reservoir Inflow (m 3 /s) 0.0005 3.4672 232.8695 198.3489 +Ve Jebba Station Reservoir Outflow (m 3 /s) 0.0005 3.4672 232.8571 197.9468 +Ve Jebba Station Evaporation loss (m 3 /s) 0.0065 -2.7194 0.7421 -0.97665 -Ve Jebba Station Turbine discharge (m 3 /s) 0.0000 4.7610 24.6500 25.0171 +Ve Jebba Station Energy Generation (MWh) 0.0001 3.8071 46.0553 40.5681 +Ve D – Trend Detector for Innovative Trend Analysis (A)(( (B) 3.4 Homogeneity test Table 5 summarizes the results of the homogeneity tests (Pettit, Standard Normal Homogeneity, Buishand’s range) for the hydro-climatic variables at the Jebba hydropower station and neighboring rainfall stations. The Homogeneity test results of the Pettit Test showed that all variables experienced change years except rainfall in Bida and Minna Station. In contrast, the SNHT showed that all variables experienced change years except rainfall in Bida, Minna, Lokoja, and Jebba Station. The Buishand range test also shows a similar result, showing that all variables experienced change years except rainfall in Minna and Lokoja Station. Jointly, all three tests showed no change in the year in rains at the Minna Station. True (P 0.05), means the variable exhibit a homogenous trend. 4.0 Conclusion In this study, statistical tools such as the coefficient of variation, Standardized Precipitation Index (SPI), and trend analysis tests, namely the Modified Mankendall (MMK), Innovative Trend Analysis (ITA), and homogeneity tests, have been used for comparative trends assessment of the hydroclimatic variables in the study area. The study shows that the annual mean rainfall in Niger River Basin and Jebba hydropower station has been experiencing a normal variability while high, moderate, and normal variability was detected for different seasons in the hydropower station and the river basin. Furthermore, the standardized precipitation index (SPI) reveals that the Ilorin and Bida rainfall station has the highest and the lowest negative anomaly, with 66% and 40%, respectively. The most prolonged dry period was 1981–1989 in the Niger River Basin, followed by 1992–1996 in the Ilorin station. Also, the driest year was in 1983 in the Niger River basin, followed by 1998 in the Bida station. In the comparative assessment, the modified Mankendall trend test and the Innovative Trend Analysis (ITA) had similar results, with both tests showing that Ilorin and Bida rainfall, Jebba dam’s inflow, outflow, turbine discharge, and energy generation have increased significantly; nevertheless, evaporation loss shows a decreasing trend while other variables show no trend. Likewise, the change point detection using the Pettit test reveals that abrupt changes (change year) are detected in all variables in the Jebba hydropower station and neighboring rainfall stations except Bida, Minna, and Lokoja rainfall. The study concludes that the variability in rainfall affects water availability in the hydropower station, contributing to the increasing trend of energy generation. A decrease in rainfall in the river basin and the hydropower station could result in drought, affecting the quantity of reservoir inflow and negatively impacting energy generation at the station. Therefore, there is a need to build resilience and adaptive measures for sustainable water management to enhance water security and mitigate the impact of climate change. Declarations Acknowledgment This study forms part of a Ph.D. research under the West African Science Service Canter on Climate Change and Adapted Land Use (WASCAL) program of Climate Change and Water Resources at the University of Abomey-Calavi, Republic of Benin and funded by the German Ministry of Education and Research (BMBF). References Abbas, N., Wasimi, S. A., Al-Ansari, N., & Sultana, N. (2018). WATER RESOURCES PROBLEMS OF IRAQ: CLIMATE CHANGE ADAPTATION AND MITIGATION. In JOURNAL OF ENVIRONMENTAL HYDROLOGY Open Access Online Journal of the International Association for Environmental Hydrology (Vol. 26). Abungba, J. A., Khare, D., Pingale, S. M., Adjei, K. A., Gyamfi, C., & Odai, S. N. (2020). Assessment of Hydro-climatic Trends and Variability over the Black Volta Basin in Ghana. Earth Systems and Environment , 4 (4), 739–755. https://doi.org/10.1007/s41748-020-00171-9 Andersen, I., Dione, O., Jarosewich-holder, M., & Olivry Edited Katherin George Golitzen D I R E, J. B. (2005). The Niger River Basin AVision for Sustainable Management . Asfaw, A., Simane, B., Hassen, A., & Bantider, A. (2018). Variability and time series trend analysis of rainfall and temperature in northcentral Ethiopia: A case study in Woleka sub-basin. Weather and Climate Extremes , 19 , 29–41. https://doi.org/10.1016/j.wace.2017.12.002 Aziz, F., & Obuobie, E. (2017). Trend analysis in observed and projected precipitation and mean temperature over the Black Volta Basin, West Africa. In 1400| International Journal of Current Engineering and Technology (Vol. 7, Issue 4). http://inpressco.com/category/ijcet Babalola, T. E., Oguntunde, P. G., Ajayi, A. E., & Akinluyi, F. O. (2021). Future Climate Change Impacts on River Discharge Seasonality for Selected West African River Basins . www.intechopen.com Bari, S. H., R. M. T. U., H. M. A., & H. M. M. (2016). Analysis of seasonal and annual rainfall trends in the northern region of Bangladesh. Atmospheric Research , 176 . Bates, B. C. ; K. Z. W. ; W. S. ; P. J. P. (2008). Climate Change and Water. Technical Paper for Intergovernmental Panel on Climate Change, Geneva, Switzerland . Bekele, D., Alamirew, T., Kebede, A., Zeleke, G., & Melese, A. M. (2017). Analysis of rainfall trend and variability for agricultural water management in awash river Basin, Ethiopia. Journal of Water and Climate Change , 8 (1), 127–141. https://doi.org/10.2166/wcc.2016.044 Bewket, W., & Conway, D. (2007). A note on the temporal and spatial variability of rainfall in the drought-prone Amhara region of Ethiopia. In International Journal of Climatology (Vol. 27, Issue 11, pp. 1467–1477). https://doi.org/10.1002/joc.1481 Buishand. (1982). Some Methods for Testing The Homogeneity of Rainfall Records . Chris C. Funk, Pete J. Peterson, Martin F. Landsfeld, Diego H. Pedreros, James P. Verdin, James D. Rowland, Bo E. Romero, Gregory J. Husak, Joel C. Michaelsen, & Andrew P. Verdin. (2014). Quasi-Global Precipitation Time Series for Drought Monitoring. U.S. Geological Survey . https://doi.org/10.3133/ds832 Costa, A. C., & Soares, A. (2009). Homogenization of climate data: Review and new perspectives using geostatistics. In Mathematical Geosciences (Vol. 41, Issue 3, pp. 291–305). https://doi.org/10.1007/s11004-008-9203-3 Cox, D. R., & S. A. (1955). Some quick sign tests for trend in location and dispersion.. Biometrika , 42 ((1/2)), 80–95. Cui, L., Wang, L., Lai, Z., Tian, Q., Liu, W., & Li, J. (2017). Innovative trend analysis of annual and seasonal air temperature and rainfall in the Yangtze River Basin, China during 1960–2015. Journal of Atmospheric and Solar-Terrestrial Physics , 164 , 48–59. https://doi.org/10.1016/j.jastp.2017.08.001 Didi Sacré Regis, M., Mouhamed, L., Kouakou, K., Adeline, B., Arona, D., Saint, C. H. J., Claude, K. K. A., Jean, C. T. H., Salomon, O., & Issiaka, S. (2020). Using the CHIRPS dataset to investigate historical changes in precipitation extremes in West Africa. Climate , 8 (7). https://doi.org/10.3390/CLI8070084 Emmanuel, L. A., M’Po, Y. N. T., Biaou, C. A., Komi, K., Hounguè, R., Yao, K. B., & Afouda, A. A. (2018). Mid-Century daily discharge scenarios based on climate and land use change in Ouémé River Basin at Bétérou Outlet. Hydrology , 5 (4). https://doi.org/10.3390/hydrology5040069 Fung, F. A. I., L. A. N. A., & N. M. (2009). Introduction . Gain, A. K., Giupponi, C., & Renaud, F. G. (2012). Climate change adaptation and vulnerability assessment of water resources systems in developing countries: A generalized framework and a feasibility study in Bangladesh. Water (Switzerland) , 4 (2), 345–366. https://doi.org/10.3390/w4020345 Hamed, K. H., & R. A. R. (1998). A modified Mann–Kendall trend test for autocorrelated data. Journal of Hydrology , 204 . Hamed, K. H., & Rao, A. R. (1998). Hydrology A modified Mann-Kendall trend test for autocorrelated data. In Journal of Hydrology (Vol. 204). Hirsch, R. M., S. J. R., & S. R. A. (1982). Techniques of trend analysis for monthly water quality data. Water Resources Research , 18 (1), 107–201. Hussain, Md., & Mahmud, I. (2019). pyMannKendall: a python package for non parametric Mann Kendall family of trend tests. Journal of Open Source Software , 4 (39), 1556. https://doi.org/10.21105/joss.01556 Jung, S., Bae, Y., Kim, J., Joo, H., Kim, H., & Jung, J. (2021). Analysis of small hydropower generation potential: (1) estimation of the potential in ungaged basins. Energies , 14 (11). https://doi.org/10.3390/en14112977 Kendall, M. (1975a). Rank correlation measures.. Charles Griffin ,, 202 (15). Kendall, M. (1975b). Rank Correlation Methods,. Charles Griffin & Company Ltd. London UK. Lawin, A. E., Hounguè, N. R., Biaou, C. A., & Badou, D. F. (2019). Statistical analysis of recent and future rainfall and temperature variability in the Mono River watershed (Benin, Togo). Climate , 7 (1). https://doi.org/10.3390/cli7010008 Mann, H. B. (1945). Nonparametric tests against trend. Econometrica. Journal of the Econometric Society ,. Obahoundje, S., Bi, V. H. N., Kouassi, K. L., Ta, M. Y., Amoussou, E., & Diedhiou, A. (2020). Validation of Three Satellite Precipitation Products in Two South-Western African Watersheds: Bandama (Ivory Coast) and Mono (Togo). Atmospheric and Climate Sciences , 10 (04), 597–613. https://doi.org/10.4236/acs.2020.104031 Pettitt, A. N. (1979). A Non-Parametric Approach to the Change-Point Problem. In Source: Journal of the Royal Statistical Society. Series C (Applied Statistics) (Vol. 28, Issue 2). Pingale, S. M., Khare, D., Jat, M. K., & Adamowski, J. (2016). Trend analysis of climatic variables in an arid and semi-arid region of the Ajmer District, Rajasthan, India. Journal of Water and Land Development , 28 (1), 3–18. https://doi.org/10.1515/jwld-2016-0001 Şen, Z. (2012). Innovative Trend Analysis Methodology. Journal of Hydrologic Engineering , 17 (9), 1042–1046. https://doi.org/10.1061/(asce)he.1943-5584.0000556 Şen, Z. (2014). Trend Identification Simulation and Application. Journal of Hydrologic Engineering , 19 (3), 635–642. https://doi.org/10.1061/(asce)he.1943-5584.0000811 Svoboda, M., Hayes, M., & Wood, D. (2012). Standardized Precipitation Index User Guide . Hydrology Commons, Other Earth Sciences Commons. https://digitalcommons.unl.edu/droughtfacpub Tadese, M. T., Kumar, L., Koech, R., & Zemadim, B. (2019a). Hydro-climatic variability: A characterisation and trend study of the Awash River Basin, Ethiopia. Hydrology , 6 (2). https://doi.org/10.3390/hydrology6020035 Tadese, M. T., Kumar, L., Koech, R., & Zemadim, B. (2019b). Hydro-climatic variability: A characterisation and trend study of the Awash River Basin, Ethiopia. Hydrology , 6 (2). https://doi.org/10.3390/hydrology6020035 Taxak, A. K., Murumkar, A. R., & Arya, D. S. (2014). Long term spatial and temporal rainfall trends and homogeneity analysis in Wainganga basin, Central India. Weather and Climate Extremes , 4 , 50–61. https://doi.org/10.1016/j.wace.2014.04.005 UN-Water. (2013). What is Water Security? Infographic. UN-Water, Geneva, Switzerland. . Retrieved from: www.unwater.org/publications/water-security-infographic Wu, H., & Qian, H. (2017). Innovative trend analysis of annual and seasonal rainfall and extreme values in Shaanxi, China, since the 1950s. International Journal of Climatology , 37 (5), 2582–2592. https://doi.org/10.1002/joc.4866 Zhang, H., Wang, B., Lan, T., Shi, J., & Lu, S. (2016). Change-point detection and variation assessment of the hydrologic regime of the Wenyu River. Toxicological and Environmental Chemistry , 98 (3–4), 358–375. https://doi.org/10.1080/02772248.2015.1123480 Table 5 Table 5 is available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files Table5.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8978959","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597623759,"identity":"5f01e85d-2355-4362-92d1-d1b504547906","order_by":0,"name":"Emmanuel Olorunyomi Aremu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBADOQaGBCiTnUgtxggtzERqSWwgWgv/7MNHN/Mw2KT3s+cYMPPU1MnzNzMwPq74hVuLxLm0tNs8DGm5M3veALUcO2w44zADs+HZPjzWnOExA2o5nLvhBtCWHLYDCQyHGdgkG3tw65A/w/8NqOV/uj1Yy7+6BHlCWgzO8LABtRxIMJAAasltY04wAGlp+IFbi+EZNrObcwySDWeceVZw+G/fYcONhxmbDRsbcGuRO8P87MabCjt5/vbkjQ9nfKuTlzvefPBhwx883oc4D0IdgFCMDQyMbYS0YAKCtoyCUTAKRsEIAgDsmU28rjocWgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0008-1067-1816","institution":"Graduate Research Program (GRP) Climate Change and Water Resources, West African Science Service Centre on Climate Change and Adapted Land Use (WASCAL), University of Abomey-Calavi, Benin","correspondingAuthor":true,"prefix":"","firstName":"Emmanuel","middleName":"Olorunyomi","lastName":"Aremu","suffix":""},{"id":597623760,"identity":"3b3e7627-e738-4459-af34-713b89169a85","order_by":1,"name":"Agnidé Emmanuel 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Map\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8978959/v1/7e334d2cf61676ce56964b41.png"},{"id":103570273,"identity":"bc2f3cf3-a42f-426b-b6a9-93d8e39ea893","added_by":"auto","created_at":"2026-02-27 08:07:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":276834,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 7\u003c/strong\u003e: Standardized Anomaly Index (SAI) for annual and seasonal rainfall time series for Niger River Basin (NRB) and Jebba Hydropower Station (JHS).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8978959/v1/34fc3052acbe8c48b3248514.png"},{"id":103570275,"identity":"085310f2-05d5-416f-91f5-e3fa9c7654a8","added_by":"auto","created_at":"2026-02-27 08:07:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":165243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 8\u003c/strong\u003e: Standardized Anomaly Index (SAI) for annual rainfall, reservoir inflow and energy generation in Jebba Hydropower Station (JHS).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8978959/v1/79c1b98144d9481dac9731f4.png"},{"id":103570276,"identity":"c814cb32-c060-4984-bbc3-a17a6fa1f7ec","added_by":"auto","created_at":"2026-02-27 08:07:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1095254,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 7:\u003c/strong\u003e Modified Mankendall Trend Test For (A) Rainfall (B) Other Jebba Dam Variables\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8978959/v1/a45554832acff5185b955355.png"},{"id":103570277,"identity":"73fbcf6a-b4cb-4167-9b4b-4bdf5625f487","added_by":"auto","created_at":"2026-02-27 08:07:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":886573,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 8:\u003c/strong\u003e Innovative Trend Test For (A) Rainfall (B) Other Jebba Dam Variables\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8978959/v1/7452f1b892e7f1e8c2d06e26.png"},{"id":103570279,"identity":"2bfb5e5e-6619-4764-b2bd-d249ea7853d3","added_by":"auto","created_at":"2026-02-27 08:07:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1237527,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4: Homogeneity Test Results of Jebba Reservoir Hydroclimatic Variable\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8978959/v1/c834384540fb1b7cfbba7a99.png"},{"id":104398158,"identity":"5d62c028-0b9c-419c-bcda-fb52ac5696a7","added_by":"auto","created_at":"2026-03-11 12:00:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5427796,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8978959/v1/918f5eec-d97f-45fe-8337-b19ae9c3425c.pdf"},{"id":103570274,"identity":"30d25806-56cd-4043-8255-ffc742e7890c","added_by":"auto","created_at":"2026-02-27 08:07:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18872,"visible":true,"origin":"","legend":"","description":"","filename":"Table5.docx","url":"https://assets-eu.researchsquare.com/files/rs-8978959/v1/97850e6212786e36ede1d594.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eComparative trend analysis of hydroclimatic variables for sustainable water resource management in Jebba Dam in the Niger River Basin\u003c/p\u003e","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eHydropower is a renewable energy source fueled by water and is environmentally friendly, especially in the face of future climate change. It reduces carbon emissions by generating energy from generators attached to water turbines while boosting the rotational power of the water turbines by using a head. Because it has a higher energy density than other alternative energy sources, it is considered a resource with high development value (Jung et al. 2021). The link between climate, water, and energy nexus, especially rainfall and temperature in hydropower dam river catchments, greatly influences water availability and electricity generation and supply. To ensure water security, it is crucial that a population build capacity to safeguard sustainable access to adequate quantities of acceptable quality water for sustaining livelihoods, human well-being, energy generation, and socio-economic development, ensuring protection against water-borne pollution and water-related disasters, and for preserving ecosystems in a climate of peace and political stability (UN-Water, 2013). As described by Fung (2009), the effect of climate change on river basin water availability is determined by two factors: changes in climatic variables that drive hydrological processes, such as precipitation, solar radiation, and temperature, and the basin's vulnerability to these changes. Furthermore, Gain et al. (2012) stated that climate change affects the earth's ecosystems and, hence, people's livelihoods and well-being, often through water (e.g., floods and droughts). Aside from climate change, present demographic trends, economic growth, and related land-use changes all influence the growing demand for water resources. Bates (2008), reported that scientists within the Intergovernmental Panel on Climate Change (IPCC) expect that the present increase in greenhouse gas concentrations will have direct first-order effects (increase in ambient air temperature, changes in evaporation, precipitation, streamflow, and sea-level rise on the global hydrological cycle, with impacts on water availability and demand. The availability of water resources in West Africa is critical for economic growth and social well-being. West Africa has already witnessed a sea-level rise, significant shoreline erosion, increased temperatures, erratic rainfall, dwindling water availability, and more due to climate change (Babalola et al., 2021).\u003c/p\u003e \u003cp\u003eIn this regard, it is critical to examine the trends of hydroclimatic variables to gain a more explicit and quantitative understanding of the available water resources for various functions, such as hydropower generation. Statistical and trend analysis is one of the most essential measures in studying climate time series data (Hussain \u0026amp; Mahmud, 2019). In a study done byTadese et al.\u0026nbsp;(2019),statistical and trend analysis was used to characterize, quantify, and validate the variability and trend of hydro-climatic variables in the Awash River Basin, Ethiopia. Methods such as coefficient of variation (CV), standardized anomaly index (SAI), and graphical methods were used to test for variability of rainfall and streamflow in the study area. In trend analysis, both parametric and non-parametric tests are often utilized. Data must be independent and regularly distributed for use in parametric testing. On the other hand, non-parametric trend tests just demand that the data be independent and can tolerate outliers (Hamed \u0026amp; Rao, 1998). However, parametric tests are more powerful than non-parametric ones (Hussain \u0026amp; Mahmud, 2019). Several tests are used worldwide for trend analysis. One of them is the Mann\u0026ndash;Kendall test (Mann, 1945; Kendall, 1975). Mann\u0026ndash;Kendall test is a popular non-parametric method for detecting significant trends in time series. On the other hand, the original Mann-Kendall test did not account for serial correlation or seasonality effects (Bari et al., 2016; Hirsch et al., 1982). However, in many real-life circumstances, the observed data are autocorrelated, which can lead to a misunderstanding of trend test results(Cox et al., 1955). Water quality, hydrologic, climatic, and other natural time series, on the other hand, are all seasonal. Several modified Mann-Kendall tests have been created to address the limitations of the original Mann-Kendall test (Hamed \u0026amp; Rao, 1998).\u003c/p\u003e \u003cp\u003eIn a study conducted by Lawin et al., (2018), to assess climate and land-use change impacts on future flows in the B\u0026eacute;t\u0026eacute;rou basin. Based on the combination of climate change scenarios with land use/cover change scenarios, the future flows in Ou\u0026eacute;m\u0026eacute; River at the B\u0026eacute;t\u0026eacute;rou outlet were estimated. Their findings, compared to the reference period of 2002\u0026ndash;2004, indicated that Ou\u0026eacute;m\u0026eacute; River at the B\u0026eacute;t\u0026eacute;rou outlet will experience an increased discharge for all time horizons until 2050 and for all climate and land-use change combined scenarios. Also, in West Africa Babalola et al. (2021),did similar studies to assess climate change impacts on the seasonal river discharge in two rivers in West Africa, namely, the Niger and the Hadejia-Jama\u0026rsquo;are Komadugu Yobe Basin. Their results of the multi-model median regarding climate change show that climate change impacted the temporal pattern of future river discharge in the river basins. It was noted that the basins influenced by precipitation continuously increase streamflow volumes during the later part of the high-flow season. In the face of a changing climate, measures for adaptation to climate change are necessary to enhance the adjustment of a system to reduce the effects, exploit new possibilities, and or cope with the consequences. Even with highly severe emission control policies, it is expected that greenhouse gas concentrations might keep rising(Abbas et al., 2018).\u003c/p\u003e \u003cp\u003eFor basin-scale trend analysis, which includes characterization, variability, and trend for river basins. Satellite data, such as rainfall estimates, are sometimes obtained and used as an alternative to supplement in-situ observations (Obahoundje et al., 2022). This is due to insufficient long-term data from weather stations that could cover the entire basin. Many satellite-based rainfall products with long-time series have coarse spatial and temporal resolutions and are not homogeneous. In Nigeria, where Jebba Hydropower Station is located, Ogbu et al. (2020) carried out a study in which the capabilities of Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Tropical Application of Meteorology Using Satellite Data and Ground-Based Observations(TAMSAT) to reproduce local rainfall characteristics in Nigeria from 1983 to 2013 were evaluated at a point-location scale. It was concluded that CHIRPS performed better than other products in reproducing local rainfall climatology in most locations. CHIRPS data has been validated with in situ observed data in previous studies over and within regions in West Africa. For example,Didi Sacr\u0026eacute; Regis et al. (2020) validated the CHIPRS data over the Sahel and Guinea Coast when it was used to investigate climate change in West Africa. Furthermore,Obahoundje et al. (2020) also validated CHIRPS data in a study over the Bandama and Mono river basins, showing a strong correlation and lowest mean absolute error compared to GPCP and CRU precipitation products.\u003c/p\u003e \u003cp\u003eIn previous studies, various statistical tests, such as Mankendall trend and homogeneity tests, have been used to determine the trends of hydroclimatic variables. However, to enhance adequate planning and gain more perspectives on the trend of hydroclimatic variables and its effect on water resources availability for energy generation in Jebba hydropower station, this study uses a comparative trend analysis which includes modified Mankendall (MMK) trend test, Innovative trend analysis (ITA), and homogeneity tests (such as Petitt Test, Standard Normal Homogeneity Test, Buishand Test) to obtain insights for the development of a sustainable water resource management in Jebba dam, Nigeria.\u003c/p\u003e"},{"header":"2.0 Methods and Tools","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area and data\u003c/h2\u003e \u003cp\u003eThe Niger River Basin (NRB) is the second-largest river in Africa, covering an area of 2.27\u0026nbsp;million km\u0026sup2; and is located between latitudes 5\u0026deg; N and 24\u0026deg; N and longitudes 12\u0026deg; W and 17\u0026deg; E. The basin traverses four countries, which are Guinea, Mali, Niger and Nigeria. It is delineated by an unusual flow through ten shared countries, namely Algeria, Benin, Burkina Faso, Cameroon, Chad, Cote d\u0026rsquo;Ivoire, Guinea, Mali, Niger, and Nigeria. Out of the active river basins, Nigeria has 562,372 km\u0026sup2;, accounting for 44.2% of the total active basins. According to (Andersen et al., 2005), the basin has two seasonal variations: a rainy summer and a dry winter, except for Nigeria, which has four seasons. A significant part of the basin has major reservoirs in the lower basin, namely, Kanji, Jebba, and Shiroro reservoirs in Nigeria. The country has about 60 large dams; the three dams mentioned earlier are the most prominent, and it has a total water storage of 34,800 (MCM: million cubic meters) in the area under study (JICO, 2014). The study focuses on the Jebba reservoir, as displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. It is the country\u0026rsquo;s second-biggest hydropower dam and has six-generation turbines, each with a rated capacity of 96.4 megawatts, for a maximum installed output of 578.4 megawatts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHydroclimatic data for the Jebba hydropower dam were obtained from Mainstream Energy Solution, which is in charge of the Jebba Hydropower Station. The available data consists of a monthly rainfall time series, average maximum temperature, reservoir inflow, reservoir outflow, evaporation loss, and turbine discharge data. Observed rainfall data for Jebba dam neighboring stations (Ilorin, Bida, Minna, and Lokoja) were obtained from NiMET (Nigerian Meteorological Service Agency). For basin scale analysis, the rainfall data from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) version 2 developed by the Climate Hazards Group of the University of California was used. The daily rainfall dataset was downloaded at a basic scale utilizing the feature of custom polygon region for Niger River Basin (NRB) in the Google Climate Engine App using the link \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://app.climateengine.com/climateEngine\u003c/span\u003e\u003cspan address=\"https://app.climateengine.com/climateEngine\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The length of the data collected was from 1981\u0026ndash;2022. The CHIRPS dataset is a quasi-global rainfall dataset covering 50\u0026deg; S to 50\u0026deg; N and spanning from 1981 to near present. It incorporates 0.05\u0026deg; x 0.05\u0026deg; resolution satellite imagery with in situ station data to create gridded rainfall time series suitable for trend analysis and seasonal drought monitoring (Chris, Funk et al., 2014). It can be freely accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://chg.geog.ucsb.edu/data/chirps/\u003c/span\u003e\u003cspan address=\"http://chg.geog.ucsb.edu/data/chirps/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. To ensure data quality, the datasets were visually inspected to check for outliers, and the data was graphically visualized with time series plots using Microsoft Excel. Also, the CHIRPS rainfall dataset was selected based on three criteria: its long-term data availability (1981\u0026ndash;2022), which is more than 40 years, zero percentage of missing data, and good representativeness of the entire river basin. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the spatial and temporal resolution of the datasets used in the study and their corresponding years of record.\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\u003eSpatial and temporal resolution of datasets used in the study\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTemporal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpatial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHIRPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1981\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNiger River Basin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUSGS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1992\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIlorin Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNiMET\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1992\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBida Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNiMET\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1992\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinna Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNiMET\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1992\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLokoja Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNiMET\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1988\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJebba Dam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJHS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax. Temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1988\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJebba Dam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJHS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReservoir Inflow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1988\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJebba Dam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJHS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReservoir Outflow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1988\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJebba Dam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJHS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLake evaporation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1988\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJebba Dam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJHS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurbine Discharge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1984\u0026ndash;2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJebba Dam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJHS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy generated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1988\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJebba Dam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJHS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eCHIRPS\u003c/b\u003e: Climate Hazards Group Infrared Precipitation with Stations; \u003cb\u003eUSGS\u003c/b\u003e: United States Geological Survey \u003cb\u003eNiMET\u003c/b\u003e: Nigerian Meteorological Agency; \u003cb\u003eJHS\u003c/b\u003e: Jebba Hydropower Station;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical and Trend Analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis of the hydro-climatic data was undertaken to characterize, quantify, and evaluate the variability and trend in the river basin and the hydropower station. The variability and trend were tested using different graphical and statistical methods, including coefficient of variation (CV), Standardized Precipitation Index (SPI), Modified-Mankendall (MMK) trend test, Innovative Trend Analysis (ITA), and homogeneity tests such as Pettit\u0026rsquo;s Test, Standard Normal Homogeneity Test (SNHT) and Buishand\u0026rsquo;s range test for breakpoint detection.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Coefficient of Variation (CV)\u003c/h2\u003e \u003cp\u003eThis study used the coefficient of variation as a statistical measure to determine the variation of the rainfall data about the mean. it is the ratio of standard deviation to the mean using Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For this study, a CV of \u0026lt;\u0026thinsp;20, \u0026ge; 20\u0026thinsp;\u0026le;\u0026thinsp;30, and \u0026gt;\u0026thinsp;30 was considered normal, moderate, and highly variable, respectively. In previous studies, such as (Asfaw et al., 2018; Bekele et al., 2017; Bewket \u0026amp; Conway, 2007; Tadese et al., 2019b), CV was used to characterize rainfall variability.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\text{C}\\text{V}=\\frac{\\sigma}{x̅}\\times100\\%$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere σ and \u003cem\u003ex̅\u003c/em\u003e denote the standard deviation and mean of rainfall, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Standardized Precipitation Index (SPI)\u003c/h2\u003e \u003cp\u003eStandardized Precipitation Index is a tool recommended by the World Meteorological Organization (WMO) and widely used for quantifying the precipitation deficit over different timescales from 3 to 48 months (Svoboda et al., 2012). SPI has been used for the analysis of the interannual rainfall variability in previous studies (Lawin et al., 2019). This study obtained the SPI for the annual rainfall records using Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$I\\left(i\\right)=\\frac{{x}_{i}-\\stackrel{-}{{x}_{m}}}{\\sigma},$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere I(i), \u003cem\u003ex\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e, ͞\u003cem\u003ex\u003c/em\u003e\u003csub\u003em,\u003c/sub\u003e and σ are, respectively, the standardized index of the year I, the value for the year I, and the average, and the standard deviation of the time series. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the guidelines for analyzing and interpreting SPI values(Svoboda et al., 2012).\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandardized precipitation index (SPI) values and their meanings\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\u003eSPI Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeaning\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.0 and plus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtremely wet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.5 to 1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery wet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.0 to 1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerately wet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus;0.99 to 0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNear Normal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus;1 to \u0026minus;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerately dry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus;1.5 to \u0026minus;\u0026thinsp;1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeverely dry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus;2 and less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtremely dry\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=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Modified Mankendall (MMK) Trend Test\u003c/h2\u003e \u003cp\u003eThe Mankendall test is a non-parametric trend detection method (Mann, 1945; Kendall, 1975). It is a commonly used tool for detecting changes in climatic and hydrologic time series (Hamed \u0026amp; Rao, 1998). However, Modified Mankendall (MMK), a modified version of the Mann-Kendall test based on the modified variance (S), which is robust in the presence of autocorrelation and does not assume a specific distribution, is used where autocorrelation is detected. (Hamed \u0026amp; Rao, 1998). This method has been used to detect trends in many climatic and hydrologic variables, such as rainfall, temperature, and runoff. (e.g Pingale et al., 2016; Aziz \u0026amp; Obuobie, 2017; Abungba et al., 2020). In this study, the MK statistic (S) was calculated following the method described by Mann (1945) and Kendall (1975).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$S=\\sum_{k=1}^{n-1}\\sum_{j=k+1}^{n}\\text{Sgn}\\left({x}_{j}-{x}_{k}\\right)\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003ex\u003c/em\u003e\u003csub\u003ej\u003c/sub\u003e and \u003cem\u003ex\u003c/em\u003e\u003csub\u003ek\u003c/sub\u003e are sequential data values for the time series data of length n, the test statistic represents the number of positive differences minus the number of negative differences for all the differences between adjacent points in the time series considered. It equates to the sum of the Sgn series, which is defined as:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\text{S}\\text{g}\\text{n}\\left({x}_{j}-{x}_{k}\\right)=\\left\\{\\begin{array}{c}1\\text{}\\text{i}\\text{f}\\text{}{x}_{j}\u0026gt;{x}_{k}\\\\0\\text{}\\text{i}\\text{f}\\text{}{x}_{j}={x}_{k}\\\\-1\\text{}\\text{i}\\text{f}\\text{}{x}_{j}\u0026lt;{x}_{k}\\end{array}\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe mean and variance of S, E(S), and V(S), respectively, under the null hypothesis, Ho, of randomness, given the possibility that there may be ties in the x values is given as;\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$E\\left(S\\right)=0$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$V\\left(S\\right)=\\frac{1}{18}\\left\\{n(n-1)(2n+5)-\\sum_{i=1}^{n}{t}_{i}\\left[\\left({t}_{i}-1\\right)\\left(2{t}_{i}+5\\right)\\right]\\right\\}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere t is the extent of any given tie. Σti denotes the summation over all ties and is only used if the data series contains tied values. The standard normal variate Z is calculated by\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$Z=\\{\\begin{array}{c}\\frac{S-1}{\\sqrt{V\\left(S\\right)}}\\text{i}\\text{f}S\u0026gt;0\\\\0\\text{i}\\text{f}S=0\\\\\\frac{S+1}{\\sqrt{V\\left(S\\right)}}\\text{i}\\text{f}S\u0026lt;0\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThere are two advantages of using this test. First, it is a non-parametric test and does not require the data to be normally distributed. Second, the test has low sensitivity to abrupt breaks due to inhomogeneous time series. According to this test, the null hypothesis H\u003csub\u003e0\u003c/sub\u003e assumes that there is no trend (the data is independent and randomly ordered), and this is tested against the alternative hypothesis H\u003csub\u003e1\u003c/sub\u003e, which assumes a trend. The non-parametric trend analysis has a Python package called pyMankendall implemented in pure Python, which brings together almost all types of Mann-Kendall tests and was developed to help researchers check Mann-Kendall trends in Python. It uses a vectorization approach to increase its performance. Currently, the package has 11 Mann-Kendall Tests and 2 Sen\u0026rsquo;s slope estimator functions. A brief description of all the functions can be found in (Hussain \u0026amp; Mahmud 2019). The pyMankendall python package was used for trend analysis in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4 Innovative Trend Analysis (ITA)\u003c/h2\u003e \u003cp\u003eThe Innovative Trend Analysis (ITA) is a novel trend detection technique introduced by (Şen, 2012). This method is based on creating subsection time series plots on a cartesian coordinate system, where trend-free time series appear along a 45\u0026deg; straight line as seen. This method divides a time series into two equal parts, which are separately sorted in ascending order. Then, the first sub-series is located on the X-axis, and the second sub-series is located on the Y-axis. There is no trend if the investigated data are collected on the 1:1 line. If data fall above the 1:1 line or below the 1:1 line, then an upward or downward trend exists in the time series(Şen, 2014; Cui et al., 2017). The absolute value of the difference between the y and x values of a point is the distance from the 1:1 line. (Wu \u0026amp; Qian, 2017; Cui et al., 2017). The difference denotes the magnitude of an upward or downward trend. Therefore, the mean difference denotes the general trend of a time series. The trend indicator is given by:\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$D=\\frac{1}{n}\\sum_{i=1}^{n}\\frac{10\\left({y}_{i}-{x}_{i}\\right)}{\\stackrel{-}{x}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere D is the trend indicator, and a positive value for D indicates an increasing trend, whereas a negative value indicates a decreasing trend; n is the number of observations of each sub-series, and x̅ is the average of the first sub-series (Wu \u0026amp; Qian, 2017; Cui et al., 2017). If the original time series has odd observations, the first observation is discarded before dividing to fully utilize the latest data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.5 Homogeneity test\u003c/h2\u003e \u003cp\u003eA homogeneity test was done to detect change points in the hydro-climatic data. Three homogeneity tests, namely Pettitt\u0026rsquo;s test, Standard Normal Homogeneity Test (SNHT), and Buishand\u0026rsquo;s range statistics, were used in the study. These methods have been utilized in prior studies, such asTaxak et al., (2014). The Pettitt test, which is a rank-based method for finding significant changes in the mean of time series data when the change point is unknown (Pettitt, 1979). It is known for its robustness to changes in the distributional form of the data and its relative power compared to tests such as Wilcoxon-Mann-Whitney and cumulative sum and deviation. It has also been widely applied in detecting changes in climatic and hydrological time series data (Zhang et al., 2016). On the other hand, the SNHT uses a series of ratios to compare the observations of a measuring station to the average of several stations, which are then standardized to obtain the Xi series. The null and alternative hypotheses are given as;\u003c/p\u003e \u003cp\u003eHo: The T variables Xi follow an N (0, 1) distribution.\u003c/p\u003e \u003cp\u003eHa: Between times 1 and n, the variables follow an N(\u0026micro;1,1) distribution, and between n\u0026thinsp;+\u0026thinsp;1 and T, they follow an N(\u0026micro;2,1) distribution. The Pettitt\u0026rsquo;s statistic is defined by;\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$${T}_{0}=\\underset{1\\let\\leT}{\\text{m}\\text{a}\\text{x}}\\left[v{z}_{1}^{2}+(n-v){z}_{1}^{2}\\right]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWith\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equ9\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$\\left\\{\\begin{array}{c}{z}_{1}=\\frac{1}{v}\\sum_{i=1}^{v}{x}_{t}\\\\{z}_{2}=\\frac{1}{n-v}\\sum_{t=v+1}^{T}{x}_{i}\\end{array}\\right.$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe T\u003csub\u003e0\u003c/sub\u003e statistic is derived by comparing the likelihood of two alternative models. The model analogous to Ha estimates i1 and i2 while defining the n parameter, maximizing the likelihood, SNHT is more sensitive to breaks at the beginning and end of a time series (Costa \u0026amp; Soares, 2009). Buishand's range test is suitable for variables following any distribution form, and its properties have mainly been studied in normal cases. For this study, Buishand focuses on the two-tailed test and the Q statistic (Buishand, 1982).\u003c/p\u003e \u003cp\u003eFor Q statistic, the null and alternative hypotheses are given by;\u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e\n$${S}_{0}^{\\text{*}}=0,{S}_{k}^{\\text{*}}={z}_{1}=\\sum_{i=1}^{k}\\left({x}_{i}-u\\right),k=\\text{1,2},\\dots.T$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e11\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAnd\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equ11\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ11\" name=\"EquationSource\"\u003e\n$${S}_{0}^{\\text{*}}={S}_{k}^{\\text{*}}/\\sigma$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e12\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Buishand's Q statistics follow;\u003cdiv id=\"Equ12\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ12\" name=\"EquationSource\"\u003e\n$$Q=\\underset{1\\let\\leT}{max}\\left|{S}_{k}^{**}\\right|$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e13\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThese methods have been used previously to analyze climate data and investigate hydro-climatological climate change signals and variability (Taxak et al., 2014). The homogeneity tests have a Python package called Pyhomogeneity implemented in pure Python, which brings together almost all types of homogeneity tests (Pettit\u0026rsquo;s, SNHT, Buishard). A brief description of all the functions can be found in (Hussain \u0026amp; Mahmud, 2019). This study used The Pyhomogeneity Python package for change point detection analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3.0 Results and discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Statistical Analysis\u003c/h2\u003e \u003cp\u003eIn Niger River Basin and Jebba Hydropower Station, the annual mean rainfall and seasonal mean rainfall of AMJ and JAS had a CV of \u0026lt;\u0026thinsp;20 for NRB, which indicates a normal variability. On the other hand, JFM and OND for Niger River Basin and JAS for Jebba Hydropower Station had a CV of 20\u0026ndash;30% (moderate variability). In comparison, Jebba Hydropower Station had a CV\u0026thinsp;\u0026gt;\u0026thinsp;30% (high variability), for JFM and OND season (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In this study, high seasonal rainfall variability was only detected for the Jebba Hydropower Station, while moderate variability was detected for the Niger River Basin.\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\u003eSummary of descriptive statistics of annual and seasonal rainfall\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion/Station\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eJFM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAMJ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOND\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNiger River Basin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e652.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e165.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e452.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e49.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCV%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e7.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJebba Hydropower Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1123.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e17.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e403.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e612.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e89.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e150.2136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e112.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e125.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCV%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e13.37321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e189.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSTD\u003c/b\u003e: Standard deviation, \u003cb\u003eCV\u003c/b\u003e: Coefficient of variation, \u003cb\u003eJFM\u003c/b\u003e: January, February, March, \u003cb\u003eAMJ;\u003c/b\u003e April, May, June, \u003cb\u003eJAS\u003c/b\u003e; July August, September, \u003cb\u003eOND\u003c/b\u003e: October, November, December.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Inter-Annual Variability\u003c/h2\u003e \u003cp\u003eThe inter-annual variability of rainfall using a standardized precipitation index for NRB, JHS, and selected rainfall stations neighboring Jebba Hydropower Station is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The figure shows that the negative anomaly was 47% in the Niger River Basin, 45% in Jebba Station, 40% in Bida Station, 66% in Ilorin Station, 60% in Minna Station, and 46% in Lokoja Station. The Ilorin and Bida Stations recorded the highest and lowest negative anomalies, respectively. The most prolonged dry period was 1981\u0026ndash;1989 in the Niger River Basin, followed by 1992\u0026ndash;1996 in the Ilorin station. Also, the driest year was in 1983 in the Niger River basin, followed by 1998 in the Bida station. The highest rainfall excess happened in 2016 at the Ilorin station, followed by the Minna rainfall station in the year 2020. The results of the Jebba dam rainfall, inflow, and energy, standardized anomaly Index (SAI) show that the longest negative and positive anomalies for both inflow and energy generation were from 1988 to 1994 and 2007 to 2014 respectively, while the rainfall had the longest negative and positive anomalies from 1992 to 1997 and 2010 to 2014 respectively\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003c/strong\u003e \u003cp\u003eStandardized Anomaly Index (SAI) for annual and seasonal rainfall time series for Niger River Basin (NRB) and Jebba Hydropower Station (JHS).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u003c/strong\u003e \u003cp\u003eStandardized Anomaly Index (SAI) for annual rainfall, reservoir inflow and energy generation in Jebba Hydropower Station (JHS).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Modified Mankendall and Innovative Trend Analysis\u003c/h2\u003e \u003cp\u003eThe Modified Man-Kendall trend test results of the hydroclimatic variables in the NRB and JHS are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The results show an increasing trend for rainfall in Niger River Basin and Ilorin Station, reservoir inflow, reservoir outflow, turbine discharge, and energy generation, while evaporation loss shows a decreasing trend; however, rainfall in Bida, Minna, Lokoja, and Jebba stations and the maximum temperature at JHS have no trend. The Innovative Trend Analysis shows a similar result as displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The rainfall in the Niger River Basin, Ilorin Station, JHS reservoir inflow, reservoir outflow, turbine discharge, and energy generation with the majority of the points falling on the increasing triangle, while evaporation shows a decreasing trend with the majority of the points falling on the decreasing triangle. However, annual rainfall in Minna, Lokoja, Jebba, and maximum temperature in JHS show no trend. Only Bida station shows a variation showing an increasing trend in ITA but no trend in the Man-Kendall Test.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eModified Mann-Kendall (at 5% significant level) and Innovative Trend Analysis (D) Statistics for Jebba Hydropower Station and Neighbouring Stations\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion/Station\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydroclimatic Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSen-slope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eITA (D)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTrend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNiger River Basin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainfall (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+Ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlorin Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainfall (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.9973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.4835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+Ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBida Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainfall (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.6739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.3128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinna Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainfall (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.6800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLokoja Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainfall (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJebba Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainfall (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJebba Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax. Temperature (℃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJebba Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReservoir Inflow (m\u003csup\u003e3\u003c/sup\u003e/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.4672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e232.8695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e198.3489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+Ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJebba Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReservoir Outflow (m\u003csup\u003e3\u003c/sup\u003e/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.4672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e232.8571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e197.9468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+Ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJebba Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvaporation loss (m\u003csup\u003e3\u003c/sup\u003e/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.7194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.97665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-Ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJebba Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbine discharge (m\u003csup\u003e3\u003c/sup\u003e/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.6500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.0171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+Ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJebba Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnergy Generation (MWh)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.0553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.5681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+Ve\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eD\u003c/b\u003e \u0026ndash; Trend Detector for Innovative Trend Analysis\u003c/p\u003e \u003cp\u003e(A)((\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(B)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Homogeneity test\u003c/h2\u003e \u003cp\u003eTable 5 summarizes the results of the homogeneity tests (Pettit, Standard Normal Homogeneity, Buishand\u0026rsquo;s range) for the hydro-climatic variables at the Jebba hydropower station and neighboring rainfall stations.\u0026nbsp;The Homogeneity test results of the Pettit Test showed that all variables experienced change years except rainfall in Bida and Minna Station. In contrast, the SNHT showed that all variables experienced change years except rainfall in Bida, Minna, Lokoja, and Jebba Station. The Buishand range test also shows a similar result, showing that all variables experienced change years except rainfall in Minna and Lokoja Station. Jointly, all three tests showed no change in the year in rains at the Minna Station.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrue (P \u0026lt; 0.05),\u003c/strong\u003e means the variable exhibit a non-homogenous trend \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFalse (P \u0026gt; 0.05),\u003c/strong\u003e means the variable exhibit a homogenous trend.\u003c/p\u003e\n"},{"header":"4.0 Conclusion","content":"\u003cp\u003eIn this study, statistical tools such as the coefficient of variation, Standardized Precipitation Index (SPI), and trend analysis tests, namely the Modified Mankendall (MMK), Innovative Trend Analysis (ITA), and homogeneity tests, have been used for comparative trends assessment of the hydroclimatic variables in the study area. The study shows that the annual mean rainfall in Niger River Basin and Jebba hydropower station has been experiencing a normal variability while high, moderate, and normal variability was detected for different seasons in the hydropower station and the river basin. Furthermore, the standardized precipitation index (SPI) reveals that the Ilorin and Bida rainfall station has the highest and the lowest negative anomaly, with 66% and 40%, respectively. The most prolonged dry period was 1981\u0026ndash;1989 in the Niger River Basin, followed by 1992\u0026ndash;1996 in the Ilorin station. Also, the driest year was in 1983 in the Niger River basin, followed by 1998 in the Bida station. In the comparative assessment, the modified Mankendall trend test and the Innovative Trend Analysis (ITA) had similar results, with both tests showing that Ilorin and Bida rainfall, Jebba dam\u0026rsquo;s inflow, outflow, turbine discharge, and energy generation have increased significantly; nevertheless, evaporation loss shows a decreasing trend while other variables show no trend. Likewise, the change point detection using the Pettit test reveals that abrupt changes (change year) are detected in all variables in the Jebba hydropower station and neighboring rainfall stations except Bida, Minna, and Lokoja rainfall. The study concludes that the variability in rainfall affects water availability in the hydropower station, contributing to the increasing trend of energy generation. A decrease in rainfall in the river basin and the hydropower station could result in drought, affecting the quantity of reservoir inflow and negatively impacting energy generation at the station. Therefore, there is a need to build resilience and adaptive measures for sustainable water management to enhance water security and mitigate the impact of climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgment\u003c/h2\u003e \u003cp\u003eThis study forms part of a Ph.D. research under the West African Science Service Canter on Climate Change and Adapted Land Use (WASCAL) program of Climate Change and Water Resources at the University of Abomey-Calavi, Republic of Benin and funded by the German Ministry of Education and Research (BMBF).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbas, N., Wasimi, S. A., Al-Ansari, N., \u0026amp; Sultana, N. (2018). WATER RESOURCES PROBLEMS OF IRAQ: CLIMATE CHANGE ADAPTATION AND MITIGATION. 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(2016). Change-point detection and variation assessment of the hydrologic regime of the Wenyu River. \u003cem\u003eToxicological and Environmental Chemistry\u003c/em\u003e, \u003cem\u003e98\u003c/em\u003e(3\u0026ndash;4), 358\u0026ndash;375. https://doi.org/10.1080/02772248.2015.1123480\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 5","content":"\u003cp\u003eTable 5 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Université d'Abomey-Calavi","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Trend Analysis, hydroclimatic, Hydropower, Jebba dam, Mankendall, Homogeneity, Innovative Trend Analysis","lastPublishedDoi":"10.21203/rs.3.rs-8978959/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8978959/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnalyzing the trends of key hydroclimatic variables can enhance sustainable water resource management in the face of a changing climate, water availability, and their associated uncertainty. This is critical for hydropower generation, especially cascaded dams like Jebba Hydropower Station (JHS). In this study, we analyzed the trends of hydroclimatic variables \u0026ndash; rainfall, temperature, and associated hydropower generation variables using satellite and observed data from 1981\u0026ndash;2022. We employed statistical tests, the standardized precipitation index (SPI), Modified Mankendall (MMK) trend test, homogeneity tests, and Innovative Trend analysis (ITA). The study reveals that the annual mean rainfall in Niger River Basin (NRB) and Jebba hydropower station has been experiencing a normal variability with high, moderate, and normal variability detected for different seasons. Furthermore, the SPI reveals that the Ilorin and Bida rainfall station has the highest and the lowest negative anomaly, with 66% and 40%, respectively. The most prolonged dry period was 1981\u0026ndash;1989 in the NRB, followed by 1992\u0026ndash;1996 in the Ilorin station. The MMK and ITA reveal similar results, showing that Ilorin and Bida rainfall, Jebba dam\u0026rsquo;s inflow, outflow, turbine discharge, and energy generation have increased significantly; nevertheless, evaporation loss decreases while other variables show no trend. Likewise, the homogeneity test, notably the Pettit test, reveals that abrupt changes (change year) are detected in all variables in the JHS and neighboring rainfall stations except Bida, Minna, and Lokoja rainfall. The study concludes that the significant increase in the hydropower generation is attributed to the increase in the water availability (rainfall and reservoir inflow) in the hydropower station.\u003c/p\u003e","manuscriptTitle":"Comparative trend analysis of hydroclimatic variables for sustainable water resource management in Jebba Dam in the Niger River Basin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 08:07:01","doi":"10.21203/rs.3.rs-8978959/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":"96687350-6721-431d-92ea-782c2b3bb7de","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63597373,"name":"Environmental Engineering"}],"tags":[],"updatedAt":"2026-02-27T08:07:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 08:07:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8978959","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8978959","identity":"rs-8978959","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}