Corresponding author’s email address: fayomiuche@gmail.com 732 ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY & ENVIRONMENT ORIGINAL RESEARCH ARTICLE USING CLOUD-BASED APPLICATION FOR ASSESSMENT OF EXTREME THREAT OF CLIMATE IMPACT ON WATER RESOURCES: A CASE OF VAAL RIVER CATCHMENT, SOUTH AFRICA N. Upasana1, E. K. Onyari1, G. U. Fayomi 1* and I. R. Bodunrin 1 1 Department of Civil and Environmental Engineering and Building Sciences, University of South Africa, Florida Science Campus, Cnr Christian de Wet Road and Pioneer Avenue, Johannesburg, South Africa *Corresponding author’s email: fayomiuche@gmail.com ARTICLE INFORMATION ABSTRACT Climate change impact on water resources is becoming a global challenge with persistent escalation of threating weather and changes in precipitation patterns. The study evaluates the extreme impacts of climate change in Vaal River in South Africa. The channel of Vaal River is an area with a critical water resource which supports agricultural, industrial and domestic activities. A three-decade dataset (1993-2023) of precipitation, temperature, streamflow, combined with surface water changes was engaged to examine climate impact on Vaal River water resources. Modified Normalized Difference Water Index (MNDWI) derived from Landsat-5 (1994–2012) and Landsat-8 (2013-2023) imagery on the Google Earth Platform (GEE) was also used to examine the seasonal water stress and availability. The results revealed increasing temperatures and irregular precipitation which are indications of climate altering condition resulting to decreased surface water coverage especially during dry seasons. MNDWI result records a fluctuating value indicating variations in both seasons. Further result revealed weak correlation between precipitation and streamflow, driven by anthropogenic regulations and land use, The MNDWI wet season's linear trendline showed a positive slope of 0.0046 and R² value of 0.26 suggesting water availability over time. Similarly, polynomial trendline for the MNDWI dry season equally shows a moderate coefficient R² value 0.38 Adaptive strategies, including Integrated Water Resource Management (IWRM) and advanced monitoring systems that will include stakeholders, planners and policy makers at all levels are recommended to mitigate these impacts. More effort should be channeled towards sustainable solutions such as tree planting, tree box filter and afforestation practices to reduce the effect of climate change on water resources. Received: 21st May 2025 Revised: 11th July 2025 Accepted: 13th July 2025 Keywords: Climate change Water resources Hydrological cycle Surface water Geospatial techniques Cloud-based applications © 2025 Faculty of Engineering, University of Maiduguri, Nigeria. All rights reserved. 1.0 Introduction Climate change is increasingly posing a significant challenge to the sustainability of global water resources. It has a variety of effects on water systems, including changes in precipitation patterns, more intense extreme weather, changes in the availability of water, and a decline in water quality (Chaturvedi et al., 2021; Xia et al., 2015). These changes threaten water security, with profound effect on ecosystems, human health survival, agricultural production and industrial activities (Raneesh, 2024; Yang et al., 2020). According to the Intergovernmental Panel on Climate Change (IPCC, 2021), heavy precipitation events have increased in frequency and intensity in many regions of the world. Conversely, areas like Southern Africa, North America, and the Mediterranean are seeing severe drought conditions and decreasing rainfall conditions (IPCC, 2021; Teweldebrihan and Dinka, 2024). This trend is indicative of more widespread disturbances to the global water cycle, which climate change is constantly changing (Bodunrin and Onyari, 2024). The hydrological cycle is becoming more intense due to rising temperatures, which causes precipitation changes to become more noticeable. Some regions are experiencing prolonged drought and increasing water scarcity, while others are getting more rainfall and even flooding (Chaturvedi et al., 2021; Xia et al., 2019). AZOJETE September 2025. Vol.21(3):732-747 Published by the Faculty of Engineering, University of Maiduguri, Maiduguri, Nigeria. Print ISSN: 1596-2490, Electronic ISSN: 2545-5818 https://doi.org/10.63958/AZOJETE/2025/21/03/005 www.azojete.com.ng mailto:fayomiuche@gmail.com mailto:fayomiuche@gmail.com http://www.azojete.com.ng/ Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(3): 732-747. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: fayomiuche@gmail.com 733 South African's climate is influenced by its distinct location situated within the sub-tropical belt, which is encircled by an ocean on all side of the country (Dlamini et al., 2023; Akanbi et al., 2021; Hosea & Khalema, 2020; INR, 2019). The nation is also regarded as semi-arid region because of its diverse rainfall patterns and distribution (Botai et al., 2018; Teweldebrihan and Dinka, 2024, Department of Water & Sanitation, 2018). South Africa has previously experienced sporadic droughts, and climate forecasts which suggest that such events will likely grow more regular and severe in the future (Fyna and Abiye, 2022; Schulze, 2000). This circumstance presents serious difficulties for urban water supply, agriculture, and water management (Gyamfi et al., 2016). Utilization of Remote Sensing (RS) methods, such as satellite imagery and Geographic Information Systems (GIS) is essential for tracking changes in water bodies (Cui et al. 2022; Bodunrin and Onyari, 2024; Dlamini et al., 2023). The major benefit of using remote sensing application is that it provides a wealth of data and a variety of observations (Garajeh et al., 2024; Cui et al., 2022). However, the recent cloud computing platform known as (GEE) is helpful in processing and storing enormous amounts of RS images with the use of cloud storage (Wang et al., 2018). GEE platform comprises of a varied range of satellites (Landsat, Sentinel, and MODIS are among the current used satellites) for temperature, precipitation, and land cover. They are also used in boundary detection, vegetation cover mapping, population and settlement detection (Akhundazadah et al., 2020; Wang et al., 2018; Remilekun et al., 2021). The Vaal River is among South Africa’s most commercial water resources that is threatened by the adverse impact of climate change (Bodunrin and Onyari, 2024). These threats include drought, rising temperatures, and changing precipitation patterns, and it is predicted that more frequent extreme weather events will make already-existing problems with water quality and quantity worse in nearby future (Raneesh, 2024; Akanbi et al., 2021). These alterations pose serious threats to the river's ecological well-being and capacity to supply sufficient, clean water for its usage in home, industrial, and agricultural processes (Chaturvedi et al., 2021, Shah, 2024). Therefore, this study examined the extreme impact of climate change on Vaal River water resources situated in the North-Eastern region of South Africa. The objectives is to evaluate the effects of seasonal variations over a three-decade period utilizing the temperature and precipitation climate components. 2. Materials and Method The Vaal River catchment was delineated using digitized shapefile downloaded from Google earth with the combination of satellite imagery derived from (GEE) and Quantum Geographic Information System (QGIS). The identified recording stations usually known as a permanent structure with sensor equipment or other devices used for continuous track and documentation of different environmental and hydrological factors connected with the behaviour of the catchment. It involves the use of cardinal co-ordinates from Microsoft Excel and QGIS, to identify the proximity along the Vaal River. The cloud computing platform, (GEE) was equally utilized to study the surface water changes of the Vaal River using Landsat-5 imagery featuring collection-2, Tier-1, Level-2 (TOA). The imagery was downloaded from 1993–2012 (19 year duration). Similarly, Landsat-8 (Operational Land Imager) was employed from 2013 –2024 (12years duration) featuring collection-2, Tier-1, Level-2 (TOA). These Landsat imageries was used to observe changes in surface water for three stipulated decades. Both Landsat 5 and Landsat 8 (OLI) have proven to be effective in monitoring surface water availability since inception. Modified Normalized Difference Water Index (MNDWI) was employed to examine the water level for both wet (January – March) and dry (May – July) seasons, using reflectance wavelength of the green and short- wave infrared (SWIR) bands shown in equation (1) & (2) (Remilekun et al. 2021). Annual precipitation and temperature data from 1993- 2023 (3 decades duration) was obtained from South African Weather Services (SAWS), while historical annual total flows of Vaal River were extracted from the Department of Water and Sanitation website (DWS/hydrology), for catchment recording station. Data derived from temperature and precipitation was analysed in Microsoft Excel where trend analysis was observed. However, normal ratio equation was utilized to account for the missing precipitation values (Haji, 2011; Chabalala et al., 2019). Equation 3 displayed the formular used in accounting for missing values. Table 1 and Table 2 revealed data derived from temperature and precipitation. Landsat 5 = MNDWI = (𝑃𝑔𝑟𝑒𝑒𝑛− 𝑃𝑆𝑊𝐼𝑅) (𝑃𝑔𝑟𝑒𝑒𝑛+ 𝑃𝑆𝑊𝐼𝑅) = (Band2 −Band7) (Band2 − Band7) 1 Landsat 8 = MNDWI = (𝑃𝑔𝑟𝑒𝑒𝑛− 𝑃𝑆𝑊𝐼𝑅) (𝑃𝑔𝑟𝑒𝑒𝑛+ 𝑃𝑆𝑊𝐼𝑅) = (Band3 −Band7) (Band3 − Band7) 2 http://www.azojete.com.ng/ mailto:fayomiuche@gmail.com Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(3): 732-747. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: fayomiuche@gmail.com 734 𝑃𝐴 = ∑ 𝑅𝐴 𝑁𝑅𝑖 × 𝑃𝑖 𝑁 𝑖−1 𝑛 3 where: PA is the estimate for the ungagged station, Pi is the rainfall at the surrounding stations, N RA is the normal annual rainfall at station A, N Ri is the normal annual rainfall at station I and n is the number of surrounding stations that data are used for estimation. 2.1 Study Area The Vaal River is the largest tributary of the Orange River and is a critical water resource for South Africa traversing various climatic and geographical zones. It originates in Ermelo, in the Mpumalanga Province, west of Swaziland, and extends over approximately 1, 210 km (Bodunrin and Onyari, 2024). The river forms part of the Upper, Middle, and Lower Vaal basins, each influenced by distinct climatic and anthropogenic factors (Masindi and Abiye, 2018). Figure 1 displayed the location of Vaal River within the urbanised environment. Figure 1: Vaal River location (Bodunrin and Onyari, 2024) The climate of the Vaal River in South Africa is sub-tropical dry savannah, with a pronounced seasonal rainfall pattern that peak in the summer (January to March) and an average annual evaporation rate that is much higher than its rainfall. It featured an average of 570 mm of rainfall per year; the river's watershed is primarily semi-arid. The summer months of November through January see the highest temperatures, while the winter months can have below-freezing temperatures. The average yearly temperature is approximately 15°C. (Jury, 2016; Bodunrin and Onyari, 2024). The Vaal River basin supports significant economic activities renowned to South Africa such as mining, agriculture, and industrial activities, particularly in the urbanized area of Johannesburg and Vereeniging (Masindi and Abiye, 2018; Wepener et al., 2011; Haji, 2011). The Vaal River basin delineation is presented in Figure 2. Table 1 present the annual average precipitation from recording stations along the Vaal River while Table 2 describe the annual average temperature from recording stations. The mean rainfall is relatively consistent across the study area with significant variability observed. This equally suggests average temperature increase over time within the catchment. The temperature also shows less variation in comparison to precipitation however, peaks are observed in some years. Average temperature http://www.azojete.com.ng/ mailto:fayomiuche@gmail.com Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(3): 732-747. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: fayomiuche@gmail.com 735 Figure 2: Vaal River delineation Table 1: Annual Average Precipitation from Recording Stations Along the Vaal River Year Barrage RWB Potchefs Troom Bothavile Balkfrontein Bloemloef Leeu heuwel Vaal River estate Doug laspol Mean SD Variance 1993 63.98 60.96 45.94 45.94 23.79 22.76 29.45 41.83 15.73 247.57 1994 46.14 38.53 21.49 21.49 22.73 18.50 20.54 27.06 9.94 98.90 1995 66.48 57.07 39.68 39.68 44.52 14.38 24.08 40.84 16.55 274.02 1996 59.77 67.50 23.56 23.56 48.26 11.41 9.68 34.82 21.73 472.07 1997 62.24 54.48 7.74 7.74 34.61 21.33 22.14 30.04 19.96 398.22 1998 57.53 59.92 2.13 2.13 37.67 13.46 22.00 27.83 22.58 510.02 1999 45.81 44.91 12.75 12.75 30.43 29.93 25.54 28.88 12.41 154.09 2000 104.62 72.78 22.18 22.18 38.88 45.13 30.38 48.02 28.17 793.53 2001 93.02 67.77 32.16 32.16 55.09 44.08 40.53 52.11 20.43 417.47 2002 51.10 52.73 25.21 25.21 46.33 32.16 24.08 36.69 11.96 143.00 2003 46.56 40.63 15.49 15.49 32.83 13.00 12.98 25.28 13.31 177.05 2004 57.73 49.94 20.04 20.04 36.43 18.92 23.26 32.34 14.82 219.51 2005 42.96 45.20 19.49 19.49 27.49 12.33 5.75 24.67 13.77 189.67 2006 85.99 62.13 36.23 36.23 61.64 29.36 34.33 49.42 19.37 375.20 2007 36.08 48.58 15.02 15.02 34.04 35.73 19.47 29.13 11.87 140.88 2008 70.79 46.03 17.93 17.93 31.99 17.33 22.55 32.08 18.52 343.09 2009 82.03 58.42 60.78 60.78 47.64 26.24 27.38 51.89 18.46 340.72 2010 77.80 58.18 62.73 62.73 46.83 33.67 27.64 52.80 16.41 269.42 2011 57.48 55.30 70.60 70.60 79.69 42.88 29.28 57.98 16.25 264.18 2012 57.34 41.07 42.30 42.30 18.71 34.85 28.40 37.85 11.30 127.80 2013 50.08 33.91 31.08 31.08 17.77 15.67 6.50 26.58 13.34 178.04 2014 61.70 43.18 44.47 44.47 36.57 26.71 19.64 39.53 12.66 160.32 2015 31.60 34.83 18.67 18.67 16.18 19.47 10.71 21.44 7.97 63.52 2016 60.66 54.34 41.07 41.07 45.65 26.33 17.20 40.90 13.98 195.37 2017 53.66 59.40 32.90 32.90 31.33 28.21 25.06 37.64 12.32 151.73 2018 44.81 38.85 34.87 34.87 23.33 12.83 15.35 29.27 11.31 127.88 2019 38.90 54.37 53.25 53.25 24.04 21.13 9.78 36.39 16.87 284.57 2020 60.41 48.33 69.37 69.37 44.39 44.25 17.59 50.53 16.79 282.03 2021 60.95 72.20 51.35 51.35 47.66 53.79 53.79 55.87 7.65 58.48 2022 72.73 69.13 46.99 33.04 50.91 31.97 32.74 48.22 15.95 254.46 2023 57.08 49.58 13.40 36.82 22.30 17.74 14.52 30.21 16.42 269.71 Mean 59.94 52.91 33.25 33.56 37.41 26.31 22.66 SD 16.14 10.63 18.00 17.48 13.89 11.23 9.99 Variance 260.44 112.95 323.98 305.49 193.01 126.05 99.73 (Source: South African Weather Services-SAWS, 2024). http://www.azojete.com.ng/ mailto:fayomiuche@gmail.com Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(3): 732-747. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: fayomiuche@gmail.com 736 Table 2: Annual Average Temperature from Recording Stations Along the Vaal River Year Secunda Verneengine Portchel Stroom Klerksdorp Bothaville Balkfontein Bloemhot Postmas burg Mean SD Varia nce 1993 15.97 16.70 17.77 17.41 18.20 18.37 18.15 17.51 0.82 0.68 1994 15.28 16.34 16.68 16.50 16.55 17.57 17.77 16.67 0.77 0.59 1995 15.94 16.99 17.72 17.22 17.29 18.34 17.96 17.35 0.72 0.52 1996 15.58 16.22 17.06 17.12 17.40 17.45 17.86 16.95 0.73 0.53 1997 15.70 16.37 17.69 17.26 17.63 17.88 18.07 17.23 0.81 0.65 1998 16.75 17.30 17.96 18.73 17.89 18.31 18.74 17.95 0.68 0.46 1999 16.65 17.64 18.81 18.78 18.42 18.83 18.83 18.28 0.78 0.60 2000 15.43 16.87 17.65 18.88 17.44 17.67 17.36 17.33 0.96 0.92 2001 16.27 17.28 18.64 17.92 17.24 17.72 17.38 17.49 0.67 0.45 2002 16.48 17.53 17.34 17.01 17.81 18.56 17.58 17.47 0.60 0.36 2003 17.28 17.55 18.57 17.14 18.07 18.49 17.46 17.79 0.54 0.29 2004 17.15 15.38 17.90 16.23 18.68 18.22 17.66 17.32 1.08 1.16 2005 19.37 16.97 17.63 16.57 19.06 18.73 17.55 17.98 1.00 1.00 2006 16.04 16.65 18.37 17.64 19.02 17.50 16.80 17.43 0.96 0.92 2007 16.44 17.29 17.98 18.25 19.08 18.12 17.62 17.83 0.77 0.59 2008 16.73 17.25 18.07 18.50 17.82 18.71 18.15 17.89 0.65 0.42 2009 16.47 16.94 17.88 17.79 16.03 18.69 18.55 17.48 0.95 0.90 2010 17.26 17.43 18.38 17.98 18.22 18.95 18.89 18.16 0.61 0.37 2011 16.59 16.56 17.29 17.29 17.72 17.70 17.64 17.26 0.46 0.21 2012 17.05 16.71 18.98 18.88 18.32 18.35 18.77 18.15 0.84 0.71 2013 16.94 16.97 18.89 18.98 16.98 18.59 17.54 17.84 0.88 0.77 2014 16.02 15.90 17.67 18.78 18.02 18.58 17.40 17.48 1.06 1.13 2015 17.16 17.85 18.66 19.70 18.98 20.13 19.63 18.87 0.99 0.98 2016 17.08 17.47 18.85 19.79 19.36 19.53 18.79 18.70 0.96 0.93 2017 16.64 17.30 18.40 18.90 18.15 18.34 20.13 18.27 1.03 1.07 2018 17.00 17.44 18.80 19.13 18.41 18.27 21.05 18.58 1.22 1.48 2019 17.45 18.00 19.14 19.53 18.88 19.37 19.58 18.85 0.76 0.58 2020 16.84 17.16 17.89 17.95 17.65 18.06 18.23 17.68 0.47 0.22 2021 16.85 17.04 18.41 18.18 17.56 17.95 17.48 17.64 0.53 0.29 2022 17.03 17.27 17.89 18.23 18.13 17.87 19.18 17.94 0.65 0.42 2023 17.04 17.91 18.66 18.87 18.63 18.98 19.01 18.44 0.67 0.44 Mean 16.66 17.04 18.12 18.10 18.02 18.38 18.28 SD 0.75 0.58 0.60 0.G5 0.76 0.60 0.G3 Varia nce 0.57 0.34 0.36 0.G0 0.57 0.37 0.86 (Source: South African Weather Services-SAWS, 2024). 3. Results and Discussion A statistical analysis of the mean, standard deviation, and variance indicated the volatility of the stream flow in the Vaal River given the hydrological trend analysis. The mean rainfall is relatively consistent across the study area. However, some variability is observed. The upstream Barrage-RWB and Potchefstroom stations have a higher means of 59.94 mm and 52.91 mm respectively, in comparison to the downstream Douglas-POL station with 22.66 mm. The streamflow analysis implies that some stations receive more consistent and substantial rainfall than other stations illustrating changes in water availability per season. This result also aligned with the study of Garajeh et al., (2024); Assiri et al., (2024) and Mwelwa et al., (2024) where streamflow varies across stations indicating water presence variation per time. The condition is likely influenced by localized climatic conditions as depicted by Chabalala et al., (2019, and Pham Phuoc, (2014) and Jury, (2016). However, the variance shows a similar trend with the standard deviation, being higher upstream and reducing downstream, suggesting rainfall patterns are more predictable downstream. The linear regression line for precipitation illustrates increase in the precipitation pattern over the years (y = 0.2307). signalling significant inter-annual variability with peaks in 2001, 2006, 2009, 2010, and 2011 and notable declines in 2005, and 2015. Between 2019 – 2023, the trend was discreetly increasing after the dip in 2018, Figure 3. The linear regression for temperature displayed an upward trend (y = 0.0369), indicating a moderate correlation. This equally suggests average temperature increase over time within the catchment. Although the temperature shows less variation in comparison to precipitation, peaks are observed in 2015, 2019, and 2023, contrastingly hollows that occur in 1994, 1996, 2011, and 2014. The recent years, 2019 – 2023, remain consistently high indicating possible warming trends as depicted by Jury, (2016) and Chabalala et al., (2019). http://www.azojete.com.ng/ mailto:fayomiuche@gmail.com Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(3): 732-747. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: fayomiuche@gmail.com 737 Figure 3: Trend Analysis of Average Annual Precipitation and Temperature. Precipitation and temperature relationship showed a consistent increase independently from each other, (Figure 4a & 4b). Similarly, 1998, 1999 and 2015 have low precipitation but contrasting temperatures indicating possible changes on precipitation variability outlined also by Merolla (2011); Olabanji et al., (2020). The years 2010 – 2011 indicate high precipitation coinciding with moderate temperatures. The weak correlation testing between precipitation and streamflow revealed that there is no significant relationship between the two variables indicating the dominance of other factors impacting streamflow along with temperature. Figure 4 (b) revealed gradual rising of both standard deviation and variance trend lines, indicating a minor upward trend in variability. This implies that precipitation variability recorded some growth in some of the years. For instance, 2003, 2008 and 2017 reported significant rise in standard deviation and variance. The linear correlation relationship R² showed a weak value however, with an increasing trend suggesting that precipitation is gradually getting more erratic across stations in the region. Although, there is no significant change in the average rainfall but, the distribution and consistency showed unpredictable. This result is consistent with studies by Kusangaya et al., 2014; Van-Rooyen et al., 2018, who identified rising temperatures and irregular precipitation as key indicators of climate stress on water systems. (a) (b) Figure 4: a) Mean monthly average temperature across all stations b) Mean annual precipitation across all stations 1993- 2023. 3.1 Trend Analysis of Precipitation, Temperature and Streamflow The temperature and streamflow trend displayed a notable variability over the three decades, the statistical analysis of temperature pattern revealed significant fluctuation across the study period indicating possible http://www.azojete.com.ng/ mailto:fayomiuche@gmail.com Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(3): 732-747. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: fayomiuche@gmail.com 738 streamflow variations with inferences of climate variability on water availability. The red line shows the dispersion, or annual variance, in temperatures across all stations. Significant peaks coincide with increases in global warming which occur between 2015 and 2019 (Akanbi et al., 2021; Masindi & Abiye, 2018; Kusangaya et al., 2014). The variance and standard deviation lines from 2003, 2010–2011, and 2015–2017. Notable peaks, suggesting localized or regional climate anomalies. However, 2020-2023 recorded moderated standard deviation indicating stable temperature lately. The region's climate is changing, with occasional extremes, as seen by rising mean temperatures and greater variability in some years. Higher fluctuation and sudden shifts in variance are indicators of unpredictable temperature patterns that may affect water resources, agriculture, and human comfort. The findings of this study, which are related to earlier research on the Vaal River, show that the extreme climatic impact causes streamflow fluctuation (Akanbi et al., 2021; Masindi & Abiye, 2018; Okoye et al., 2022). Figure 5 (a & b) revealed trend analysis of streamflow and precipitation of two recording station which also indicate very weak correlation (a) (b) Figure 5: Trend analysis of streamflow and precipitation in a) De Vaal and b) Bloemloef Station 3.2 Modified Normalized Difference Water Index (MNDWI) The investigation of the MNDWI data offers additional information about the effects of climate change on the Vaal River's water resources presented in Table 3. MNDWI values for wet and dry seasons displayed consistent variations throughout the study period with value ranging from 0.08 to -0.12. The wet season's MNDWI value was often greater than the dry season's, indicating that there was more water present during wet periods due to heavy rainfall that overflowed the water body level. Additionally, there are records of lower values during the dry season, which indicates substantial water stress and shrinkages. This phenomenon can be ascribed to the impact of rising temperatures on river flow. The MNDWI wet season's linear trendline showed a positive slope of 0.0046 and R² value of 0.26 suggesting water availability over time. Similarly, polynomial trend line for the MNDWI dry season equally shows a moderate degree of fit to the trend with coefficient R² value 0.38 as shown in Figure 6. Water presence fluctuation across the study period is shown by the chosen year trend of MNDWI. The trend analysis of the yearly MNDWI value over the Vaal River recorded varying values that could be attributed to the effects of climate variability as well as variations in surface water. This outcome aligned with previous studies on fluctuating values caused by climate factors (Botai et al., (2018); Cui et al., (2022); Yang et al., (2021); Peterson et al., (2017). However, less negative indices are shown in the Landsat-5 data for 1993, 1998, and 2007, indicating more water presence. Similarly, the Landsat-8 output for 2014, 2019, and 2023 shows a sharp decrease especially in 2019, hence reduced water presence dominates recent years from 2020-2023. http://www.azojete.com.ng/ mailto:fayomiuche@gmail.com Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(3): 732-747. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: fayomiuche@gmail.com 739 Table 3: MNDWI Output for 30-Year Study Period - Wet Season Year Season Data Range Mean-Band 2(L5)/ 3(L8) Mean-Band 7 MNDWI- wet 1993 1995 1997 1998 2001 2007 2008 Wet (Jan- Mar) Landsat 5 (1993- 2012) -0.263 0.4393 -0.227 0.5037 -0.289 0.2605 -0.276 0.2455 -0.224 0.547 -0.241 0.1902 -0.206 0.1902 12734.36 12416.83 11003.47 12656.35 13540.35 11836.83 13900.97 15040.48 14618.14 12478.04 13343.74 12815.85 13466.16 13635.66 -0.08 -0.08 -0.06 -0.03 0.03 -0.06 0.01 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 wet (Jan- Mar) Landsat 8 (2013- 2023) -0.278 0.3912 -0.262 0.4013 -0.285 0.3897 -0.236 0.2 -0.172 0.5523 -0.247 0.215 -0.242 0.1896 -0.184 0.287 -0.176 0.207 -0.217 0.4663 14408.21 12570.73 13398.34 13729.75 16660.48 11655.11 13133.99 12196.94 12065.55 13042.10 14267.39 13756.27 15341.64 13755.35 14911.23 13840.72 13489.97 12564.52 12357.42 13313.41 0.00 -0.05 -0.07 0.00 0.06 -0.09 -0.01 -0.01 -0.01 -0.01 MNDWI Output for 30-Year Study Period - Dry Season. Year Season Data Range Mean-Band 2(L5)/ 3(L8) Mean-Band 7 MNDWI- wet 1993 1994 1995 1997 1998 2004 2006 2007 2008 2009 Dry (May- Jul) Landsat 5 (1993- 2012) -0.26 0.684 -0.237 0.2774 -0.27 0.2415 -0.243 0.363 -0.252 0.2289 -0.218 0.2451 -0.262 0.1159 -0.244 0.2162 -0.27 0.1695 -0.228 0.2216 13900.97 11362.02 11176.40 12436.26 14023.81 12285.56 11299.31 11334.42 11617.07 12786.83 13635.66 14020.44 14614.56 13346.93 14730.29 14425.95 13965.71 13367.03 14412.21 13853.82 0.01 -0.10 -0.13 -0.04 -0.02 -0.08 - 0.12 -0.08 -0.11 -0.01 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Dry (May- Jul) Landsat 8 (2013- 2023) -0.268 0.2029 -0.29 0.2089 -0.284 0.1255 -0.286 0.3519 -0.239 0.2172 -0.271 0.2054 -0.26 0.2272 -0.269 0.228 -0.257 0.2012 -0.279 0.202 10447.41 10662.95 12374.70 12014.70 11211.60 12003.61 10332.69 11924.68 104412.62 12878.24 13828.34 13642.83 14855.08 14398.08 13646.35 13756.39 13034.96 13503.83 12894.20 13540.92 - 0.14 -0.12 -0.09 -0.09 -0.10 -0.07 -0.12 -0.06 -0.11 -0.03 3.3 Correlation between MNDWI, Temperature and Precipitation The outcome of MNDWI in the wet season varies between positive and negative values, indicating periods of water presence and water stress, Figure 7. The MNDWI during the dry season consistently displays negative values, indicating a reduction in the amount of available water. However, MNDWI exhibits a more noticeable fall during the dry seasons, which could result from the combined effects of climate induced factors such as higher evaporation, decreased inflows as well as human-induced water abstractions (Enriquez, 2022; Zhang et al., 2014). As anticipated, wet seasons consistently have higher water presence than dry seasons. The difference was more noticeable between 2010 and 2019, suggesting that the dry seasons throughout that decade, Vaal River might have witness severe water stress. Higher precipitation or surface water retention is probably correlated with higher MNDWI values. Similarly, the dry season MNDWI values correlates with temperature changes, as increased temperature further reduces the value during the dry seasons. However, higher temperature is also noted to worsen water stress in the Vaal River, likely due to cumulative evaporation and http://www.azojete.com.ng/ mailto:fayomiuche@gmail.com Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(3): 732-747. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: fayomiuche@gmail.com 740 reduced water flows. The clear evidence of variability, with significant fluctuations over the year highlights the sensitivity of the Vaal River system to climate induced changes. Figure 6: MNDWI correlation (wet and dry season) Figure 7: MNDWI Performance on Vaal River The dry season low values indicate that surface water availability was substantially decreased particularly from the year 2000 to 2010. However, the most significant decline of water presence was witnessed in 2014 and 2019 with value range of (-0.12), indicating widespread shrinkage and water stress, due to increased temperatures, reduced precipitation and increased water extraction. This report aligned with the study of Chabalala et al., (2019) and Akhundzadah et al., (2020), who confirmed with temporal correlation that high temperatures during the wet season correlate with less positive values due to accelerated evaporation and increased water stress. The mapping of MNDWI in the Vaal River basin aided in providing more insight into water shrinkage and stress over the study period using both Landsat 5 and Landsat 8 (OLI). The distribution and dynamics of surface water along the river are spatially revealed by the mapping Figure 8 (a &b). The span value ranges from positive to negative outcome indicates fluctuating water availability. The key observations in the maps are indicative of the water body having positive MNDWI values or showing areas with significant moisture content aligning with previous studies of (Kaplan, 2020; Bhaga et al., 2021; Botai et al., 2018; Zhang et al., 2014). From the map, areas with blue / purple background are areas with strong water presence indicating water availability http://www.azojete.com.ng/ mailto:fayomiuche@gmail.com Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(3): 732-747. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: fayomiuche@gmail.com 741 in the Vaal River. Wet season have more record of blue / purple background with significant evidence in 2009, while other wet season such as 1998, 2007, 2014 and 2019 displayed fluctuation in water presence. Reduction in blue background areas or changes to other colours are signals of water stress and shrinkage The yellow / red background represents sparse or no water suggesting water stress usually caused by climate change. It is observed that the wet season MNDWI exhibits a strong dependence on precipitation than the dry season MNDWI, demonstrating rainfall critical role in replenishing water bodies during the wet season as depicted by Mpanyaro et al., (2024); Zhang et al., (2020). The dry season MNDWI correlates with temperature changes, as increased temperatures further reduce the MNDWI values during the dry seasons. This reflects reduced water availability and increased evaporation during low rainfall seasons Figure 8(a & b). Dry season mapping reports more of yellow and red background indicating sparce and shrinkage however, significant evidence was noticed in 2023 reflecting increasing climate impact on surface water availability. The outcome of this study aligned with studies by Wang et al., (2018) and Banda et al., (2024) who utilized remote sensing to demonstrate the sensitivity of surface water dynamics to climate variability. MNDWI Reflectance Map – Wet Season Legend Landsat-5: 1993 – wet season. Landsat-5: 1998 – wet season. Landsat-5: 2007 – wet season. http://www.azojete.com.ng/ mailto:fayomiuche@gmail.com Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(3): 732-747. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: fayomiuche@gmail.com 742 Landsat-8: 2014 – wet season. Landsat-8: 2019 – wet season Figure 8a: MNDWI for selected years (wet season) MNDWI Reflectance Map – Dry Season Legend Landsat-5: 1993 – dry season. http://www.azojete.com.ng/ mailto:fayomiuche@gmail.com Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(3): 732-747. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: fayomiuche@gmail.com 743 Landsat-5: 2007 – dry season. Landsat-8: 2014 – dry season. Landsat-8: 2019 – dry season. Landsat-5: 1998 – dry season. http://www.azojete.com.ng/ mailto:fayomiuche@gmail.com Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(3): 732-747. ISSN 1596-2490; e-ISSN2545-5818; www.azojete.com.ng Corresponding author’s email address: fayomiuche@gmail.com 744 Landsat- 8: 2023 – dry season. Figure 8b: MNDWI for selected years (dry season) Significant climatic change impact was found in the Vaal River basin throughout a thirty-year period, as evidenced by changes in temperature, precipitation, and streamflow variability that affected water resources both geographically and temporally. Higher altitude regions had lower climates and more reliable precipitation, based on data gathered upstream, downstream regions, which are defined by lower altitudes, had higher temperatures and less predictable precipitation. The outcome from MNDWI revealed seasonal variations in the water surface area, with noticeable drops in water availability especially during dry season resulting from combined impacts of evaporation, rising temperatures, and decreased inflows. These findings aligned with the report of climate change on the hydrological cycle, consistent with studies by Kusangaya et al., (2014) and Van- Rooyen et al., (2018), where increasing temperatures and uneven precipitation was confirmed as key indicators of climate stress on water systems. Significant variations across the study years demonstrated the variability of precipitation patterns, following periods of high rainfall contributing a major impact on the availability of water resources. Water availability is uneven throughout the basin as a result of this fluctuation. Significant variations between wet and dry seasons were equally found when streamflow report was examined using recording stations connected with precipitation data. The weak relationship recorded on streamflow analysis can also be attributed to human interventions impacting on the surface water aside from climate effects. The outcome of streamflow variability correlates with studies by Wang et al., (2018) and Akhundzadah, et al., (2020) who utilized remote sensing to demonstrate the sensitivity of streamflow dynamics to climate variability. 4. Conclusion This study highlights the significant impact of climate change on the water resources of the Vaal River basin using combined consequences of rising temperatures, erratic precipitation patterns, and streamflow effects. Notable patterns across the three-decade study period demonstrate the catchment's vulnerability to climate change. The investigation showed decreased water availability during dry seasons and an increased trend in temperatures, which was especially noticeable downstream intensifying water scarcity. Trends in precipitation showed notable interannual fluctuation, with rainy years increasing water availability within the Vaal River channel. Additionally, the MNDWI analysis offers a temporal and spatial perspective for comprehending surface dynamics. It displayed seasonal variations, with inconsistent recovery during the wet season, which is hampered by rising temperatures, and decreases in water bodies during the dry season. Variability in intensity indicates that certain parts reflect water more strongly than others. The findings are consistent with regional and international studies, highlighting the urgent need for flexible water management techniques. IWRM strategy, entails equitable water allocation through effective maximization of its use to balance the demands of household, industrial, and agricultural applications while maintaining environmental flow releases. Additional effort should be channeled towards sustainable remedies that will support the reduction of climate change effect on water resources while further research is proposed using high-resolution satellite imagery from Sentinel imagery and Landsat 8 (OLI) to improve the understanding and clarity of water stressors in the catchment. References Akhundzadah, NA., Soltani, S. and Aich, V. 2020. Impacts of climate change on the water resources of the Kunduz River Basin, Afghanistan. Climate, 8(102): 1-19. http://www.azojete.com.ng/ mailto:fayomiuche@gmail.com Arid Zone Journal of Engineering, Technology and Environment, June 2025; Vol. 21(3): 732-747. 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