Linking Land Use and Precipitation Changes to Water Quality changes in Lake Victoria Using Remote Sensing

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Thus, we analyzed spatial and temporal patterns of land cover, precipitation, and water quality changes in the Lake Victoria basin from 2000 to 2022 using processed remote sensing (RS) data. Focusing on chlorophyll-a (Chl-a) and turbidity (TUR) in Lake Victoria, we used statistical metrics (correlation coefficient, trend analysis, change budget, and intensity analysis) to understand the relationship between land use and precipitation changes in the basin with changes in Chl-a and TUR at two major pollution hotspots on the lake i.e. Winam Gulf and Inner Murchison Bay (IMB). Results show that the Chl-a and TUR concentrations in the Winam gulf increase with increases in precipitation. Through increases in precipitation, the erosion risks are increased and transport of nutrients from land to the lake system, promoting algal growth and turbidity. In the IMB, Chl-a and TUR concentrations decrease with increase in precipitation, possibly due to dilution, but peak during moderate rainfall. Interestingly, LULC changes showed no substantial correlation with water quality changes at selected hotspot areas even though LULC change analysis showed a notable 300% increase in built-up areas across the Lake Victoria basin. These findings underscore the dominant influence of precipitation changes over LULC changes on the water quality of Lake Victoria for the selected hotspot areas. climate change land use change remote sensing Lake Victoria Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The deterioration of water quality (WQ) in freshwater bodies is a pervasive and escalating global issue, detrimentally impacting ecosystems (Peters & Meybeck, 2000; Kundu et al., 2017). Contributory factors to this decline encompass the continual rise in population, urbanization, industrialization, and the influence of climate change (Bhateria & Jain, 2016; Me et al., 2018; Razman et al., 2023). Over the last fifty years, Lake Victoria, the largest tropical freshwater lake in the world, has faced threats from nutrient sources such as surface runoff, wastewater, agricultural waste, and atmospheric deposition, endangering both local communities and its biodiversity (Kayombo & Jorgensen, 2006). The lake’s ecosystem has experienced significant and alarming changes such as algal blooms, declining water transparency, water hyacinth, over-fishing, introduction of exotic fish species, and oxygen depletion (Achieng, 1990; Aloyce et al., 2001; Njiru et al., 2008). These changes can be attributed to high population densities that put a strain on the lake's natural resources, resulting in land degradation (Wang et al., 2012). This has in turn impacted the hydrology of multiple rivers in the basin and, as a result, affected the lake's dynamics (Olang & Fürst, 2011). Changes in land use, which are largely controlled by human activities, have played a substantial role in the degradation of the lake's water quality (Juma et al., 2014). Over 60% of the Lake Victoria Basin (LVB) experiences degradation, attributed to diverse land use alterations, such as wetland reclamation. These changes lead to heightened sediment and nutrient loading into the lake through surface runoff, aerial deposition, and river inflow (Scheren et al., 2000; Nyamweya et al., 2023). Several studies have shown that significant amounts of pesticides and agrochemicals have been detected in the water and sediments as a result of agricultural intensification in the region (Osano et al., 2003; Getenga et al., 2004; Musa et al., 2011). Eutrophication of the lake has also been aided by water contamination from municipal and industrial waste, and improper solid waste management particularly in the bays and gulfs (Nyenje et al., 2010; Oguttu et al., 2018; Olokotum et al., 2021). Additionally, small-scale gold mining in some parts of the Tanzanian catchment could lead to mercury discharges into the lake water if mining wastes are not properly contained (Campbell et al., 2003). Environmental changes, particularly those associated with climate conditions such as changes in precipitation, temperature, and hydroclimatic extremes (e.g. floods, droughts, and heatwaves) significantly influence water quality. Numerous studies indicate that weather exacerbates the most severe impacts on water quality (Mimikou et al., 2000; Whitehead et al., 2009; EPA, 2016; Amanullah et al., 2020; Shinhu et al., 2023). Increased rainfall variability impacts drinking water quality significantly leading to notable changes in water quality parameters, often shifting from clear to turbid water (Bastiancich et al., 2022; Turyasingura et al., 2023). It also promotes the prevalence of cyanobacteria, worsening eutrophication and impacting the physical, chemical, and biological parameters, as well as nutrient availability (Ojok et al., 2017). Thus, as land use/land cover (LULC) and climate changes continue to evolve, it is likely that conditions favoring the degradation of water quality will occur more often. Consequently, there is need to monitor water quality to ensure its sustainability for multiple purposes such as human consumption, agriculture, energy, and biodiversity. As a transboundary resource, the LVB region has yet to implement the basin-wide regulatory measures, technological breakthroughs, and planning necessary to slow the rate at which the lake's WQ deteriorates (Semyalo, 2021). Some member states face cost constraints in conducting continuous monitoring and relying solely on field samples may not adequately capture the geographical and temporal diversity necessary for comprehensive lake water quality monitoring and management (Dube et al., 2015). Hence, the need to incorporate remote sensing as a useful technology for monitoring WQ parameters (Sent et al., 2021). Remote sensing (RS) offers significant benefits over traditional methods by providing a comprehensive view of water quality, facilitating improved monitoring of spatial and temporal variations. The growing interest in the usage of RS data is based on its technological advances, affordability and good spatio-temporal resolution that permits getting information over wide areas (Mashala et al., 2023). Additionally, it provides access to historical data which allows us to track the changes and patterns of many WQ factors over time (Werdell et al., 2018). It also serves as a valuable resource for planning field surveys and collecting samples, as well as offering reliable assessments of optically active components required to define water quality (Dube et al., 2015). RS products are indeed a valuable alternative to in-situ measurements; however, their complexity arises, from having distinct underlying assumptions, computation algorithms of parameters and possible improper spatial and temporal image resolutions (Corbari et al., 2016). That is why it is important to validate the remote sensing data with in-situ measurements whenever possible, prior to use in analysis (Wu et al., 2019). Numerous studies have utilized RS data to evaluate water quality in various sections of Lake Victoria(Juma et al., 2014; Sichangi & Makokha, 2017; Mutyaba et al., 2018). Additionally, RS has been instrumental in analyzing the impacts of climate change (Awange et al., 2013), and LULC changes (Kiggundu et al., 2018; Mugo et al., 2020; Onyango et al., 2021) in the LVB. The results all showed increasing trends of these global changes which pose a serious threat to the environment and water quality. The diverse and successful usage of RS data to assess water quality and to explore impacts of climate and LULC changes in the LVB shows the potential of utilizing RS to fulfill the objective of this study which is to analyze water quality changes in Lake Victoria by utilizing existing RS products of precipitation, land use and water quality. While some studies such as Mugo et al. (2020) and Nyamweya et al. (2023) agree that the key drivers of water quality decline in Lake Victoria are climate and land use change; distinct relationships between these changes and the water quality of the lake are yet to be. This study examines RS data of Chlorophyll-a (Chl-a) and turbidity (TUR) concentrations across the lake over the period (2000 – 2022), exploring the trends and variability of these concentrations in relation to changes in precipitation and LULC in the LVB. The study places emphasis on two key regions, i.e. the Winam gulf in Kenya and inner Murchison Bay in Uganda, which experience poor water quality and have undergone major LULC changes in recent times. Additionally, the study validates the RS data against in-situ measurements, thereby enhancing the discussion on the feasibility and accuracy of using RS technologies for water quality monitoring. 2. Materials and methods 2.1 Study area Lake Victoria, spanning an area of 68,800 km 2 , is shared by three nations (Tanzania 49%, Uganda 45%, and Kenya 6%), with a catchment area of 194,000 square kilometers spread across five countries (Juma et al., 2014). Its climate ranges from tropical rain forest with year-round rainfall (117 km 3 /year) over the lake to a semi-arid climate with occasional droughts in some parts, and temperatures ranging from 12 - 26 0 C (Miriti, 2022). The LVB experiences rainfall in two distinct seasons with the "long rains" season spanning from March to May (MAM) and the "short rains" occurring in October, November, and early December (OND) (Nicholson, 2015). These are influenced by different large-scale forces such as zonal winds over the central Indian Ocean and inter-tropical convergences (Nicholson, 2017). On the other hand, the driest months tend to be June, July, and August. The soil types in the LVB are diverse and heavily influenced by the Great Rift Valley's volcanic activity whereas montane forests, savannahs, grasslands, wetlands, woodlands, and croplands are among the vegetation types found throughout the basin (Odada et al., 2009). LVB is densely populated, with 300 people per km 2 , growing by 3.5% annually (Marcus, 2022). Major cities such as Jinja, Kisumu, Mwanza have expanded, alongside new towns on the lake shore (Nyamweya et al., 2020). Fig. 1 shows the LVB, its major tributary rivers and elevation from the Shuttle Radar Topography Mission (SRTM). 2.2 Datasets used in the study In this study, turbidity and chlorophyll-a were the water quality parameters considered as these can be directly derived from ocean-color satellite remote sensing data. Chlorophyll-a indicates phytoplankton abundance and biomass, reflecting trophic status (Keukelaere & Knaeps, 2021), while turbidity indicates water clarity, affected by factors like river run-off, phytoplankton growth, climate, and watershed changes (Crétaux et al., 2020). Satellites like MODIS, MERIS, Sentinel-2, and Landsat enable accurate analysis of WQ parameters through the connection established between in-situ measurements and emitted/reflected radiation in spectral bands such as the green and infrared bands (Watanabe et al., 2018; Papenfus et al., 2020; Ambrose-Igho et al., 2021). Chl-a and TUR are derived from Lake Water-Leaving Reflectance (LWLR); an important indicator of biogeochemical processes and habitats in the water column (Crétaux et al., 2020), using globally validated algorithms (Dogliotti et al., 2015; Keukelaere & Knaeps, 2021). Two RS WQ products were used i.e. (1) “ESA” data from the Lakes Project of the European Space Agency Climate Change Initiative (ESA CCI-Lakes) and (2) “VITO” data which is a Lake WQ product from Copernicus Global Land Service (CGLS). The VITO data comprised of monthly turbidity and trophic state index (TSI) at a spatial resolution of 300m derived from the OLCI sensor on board of Sentinel-3. TSI measures phytoplankton productivity and eutrophication. The VITO data were obtained from the Copernicus Global Land Service website (https://land.copernicus.eu/global/products/lwq) for the period of 2016 – 2022. The retrieval algorithms for this dataset are stipulated in Warren et al. (2021). Chl-a was derived from TSI according to the table adapted from (Simis, 2020) shown in Supplementary Material (Table S1). ESA data records of turbidity and chlorophyll-a at a spatial resolution of 100m and daily temporal resolution were acquired from the ESA website (https://climate.esa.int/en/projects/lakes/data/) from 2000 – 2012 (derived from the MERIS sensor on board ESA's ENVISAT satellite) and 2016 – 2019 (derived from the OLCI sensor on board of Sentinel-3). The “ Algorithm Theoretical Basis Document ” for this data product, readily available on website, provides a full explanation of the algorithms and corrections used to create these estimates. Both datasets were already preprocessed and ready for use. Chlorophyll-a estimates were measured in mgm -3 whereas turbidity in NTU. Past records of in-situ measurements of Chl-a and TUR data were collected from the National Water and Sewerage Corporation (NWSC), Uganda and these were used to validate RS Chl-a and TUR data. The measurements, available irregularly, were gathered monthly from March 2013 to June 2022 at 26 sampling locations in the Inner Murchison Bay (IMB). Approximately 7% of the data was missing, reflecting occasional gaps in the monthly records. Monthly precipitation records at 0.05° spatial resolution for the period of 2000 to 2022 were retrieved from the CHIRPS website (https://www.chc.ucsb.edu/data/chirps) and used to analyze changes in rainfall across the LVB over time. Annual land cover maps at a spatial resolution of 300m were obtained from the Land cover dataset from the European Space Agency Climate Change Initiative (ESA-CCI). These were acquired from the website (https://www.esa-landcover-cci.org) for the years 2000, 2005, 2010, 2015 and 2020. 2.3 Validation of WQ RS with in-situ measurements We carried out an accuracy assessment and validation of the RS water quality data using past in-situ measurements. WQ parameters from the 26 sampling locations in the IMB were averaged at a monthly scale and compared with the ESA and VITO RS data. The sampling locations can be seen in Fig. S1(Supplementary Material). The RS Chl-a and TUR raster files were realigned and resampled to the same pixel size (0.00833 o ) at a monthly timestep. Statistical metrics, such as mean, median, standard deviation, correlation coefficients, time series and graphical criteria were used to compare the RS and in-situ data. The evaluation aimed at assessing the accuracy and correlation between in-situ and RS data. 2.4 Assessment of water quality in the lake A visual assessment of maps and time series was also carried out using processed RS data of chlorophyll-a and turbidity to assess the ecological status of the lake from 2005 to 2022. The analysis involved both ESA and VITO data due to the challenges of missing data and the limited timeframe of the satellite data available. Pollution hotspots, notably the Winam Gulf and the IMB, were identified across the lake, prompting a detailed study to investigate the links between land use changes, precipitation patterns, and water quality variations in these regions. The Winam Gulf and IMB consistently exhibited elevated levels of chlorophyll-a and turbidity throughout the study period. 2.5 Spatial and temporal variability of rainfall This analysis involved the use of monthly mean CHIRPS precipitation raster files spanning from 2000 to 2022. The mean annual precipitation and coefficient of variation (CV) over LVB were computed. The CV was computed as the ratio of the standard deviation to the mean and was used to classify the degree of variability of rainfall events as less (CV < 20), moderate (20 < CV 30) (Nkwasa et al., 2022). The Mann-Kendall (MK) (Mann, 1945; Kendall, 1975) test was then applied to the data to identify trends. This test has previously been used to analyze temporal trends of climatic variables such as precipitation and temperature (H. Wang et al., 2012; F. Wang, 2018; Mallick et al., 2021). Since the test is nonparametric, the data does not have to adhere to a normal distribution. However, it does presuppose that there is no autocorrelation in the time series. Typically, trends are considered significant when they achieve a 95% confidence level (Buo et al., 2021). The magnitude of the trends was also calculated using the nonparametric Theil-Sen estimator(Sen, 1968) which is computed by taking the median of the slopes between each pair of points in the time series data. 2.6 Land use/cover change analysis The nomenclature of the land cover maps was reclassified from the 36 original classes in the LVB to 8 major land classes i.e., agriculture, forest, grassland, wetland, built-up, sparse vegetation, bare area, and open water as shown in Table S2, Supplementary Material (Mousivand & Arsanjani, 2019). This was done to accommodate classes that are relevant to the study area and represent specific land use changes related to ongoing human activities. To visually depict LULC changes over time, maps were created to highlight areas that experienced growth and those that remained unchanged. Using the GIS vector geoprocessing tool, land cover shapefiles from two time periods (e.g. 2000-2010, 2010-2020 or 2000-2020) were intersected to identify classes that showed no changes in each area. This intersection indicated the absence of change in the land cover class. The intersected land cover classes were then reclassified as "no change" in the resulting map, which represented the "no change" classes and newly gained areas from the recent land cover map e.g. for 2010 for the period of 2000-2010. 2.6.1 LULC change matrices. Land cover change matrices are used to analyse how different land cover areas have changed over time. This involves comparing maps of the same location from two distinct points in time and generating a cross tabulation matrix. The matrix shows the area that has changed between different land cover categories. Diagonal entries indicate land persistence, while off-diagonal entries indicate land cover change (Aldwaik & Pontius, 2012). Transition matrices have been widely used in landscape ecology and land use/cover change studies (Han et al., 2009; Takada et al., 2010; Romero-Ruiz et al., 2012). Three levels of analysis exist i.e. interval, categorical, and transition levels. The interval level examines changes between two time periods, the categorical level assesses the intensity of transformation between categories, and the transition level focuses on the dynamics and intensity of transitions within a category relative to others. Annual change intensities are computed at the interval level, while the magnitude and intensity of gross gains and losses are evaluated at the category level. The transition level investigates changes in categories, their variations, and identifies frequently targeted or avoided categories. These analyses compare observed intensities to uniform measures of transition (Alo & Pontius, 2008; Aldwaik & Pontius, 2012). Areal percentage changes for 3 distinct time periods; 2000-2010, 2010-2020 and 2000-2020, were calculated using Equation 1. Transition matrices were then generated using the Semi-Automatic Classification Plugin in QGIS (Congedo, 2016). 2.6.2 Change budget and intensity analysis The analysis of relative gain, loss, and persistence among different land cover classes offers deeper insights into the dynamics of LULC changes. To assess the change budget, transition matrices were employed to calculate the uniform gain, loss, and persistence of various land cover classes using the following equations. To conduct the intensity analysis, the average values of gains and losses were calculated and then employed to distinguish between active and dormant changes. An active category change is identified when the intensity surpasses the uniform line, indicating a relatively rapid change. On the other hand, a dormant change is observed when the intensity falls below the uniform line, indicating a relatively slow change (Huang et al., 2012). 2.7 Linking impacts of changes in climate and LULC to changes in lake water quality To assess the effects of climate and land use/cover changes on water quality, Chl-a and TUR time series data were extracted from prominent pollution hotspots in Lake Victoria, namely the IMB in Uganda and the Winam Gulf in Kenya. These areas are both ecologically important, pollution-prone (as seen in the annual average chlorophyll-a map for the lake in Fig. 2), and subject to land use and precipitation changes, making them ideal for studying the relationship between these factors and water quality (Calamari et al., 2006; Kabenge et al., 2016). They also serve as major sources for water abstraction for local communities and major cities like Kampala and Kisumu (Olokotum et al., 2020). The extracted data was plotted and analyzed to understand the trends and variability in water quality parameters and to relate them to climate and land use changes. 3. Results 3.1 Accuracy assessment and Validation of RS data The following statistical metrics in Table 1 were obtained when comparing in-situ and satellite data of chlorophyll-a and turbidity in the IMB for the year 2018. Table 1: Statistical comparison of the WQ parameters for VITO and ESA data for 2018 Chlorophyll-a (mg/m 3 ) Statistical Metric In-situ VITO ESA Mean 217.55 122.05 101.17 Median 179.79 103.84 93.69 Standard Deviation 129.47 70.63 59.03 Correlation Coefficient 0.41 0.73 Turbidity (NTU) Mean 58.41 62.52 52.18 Median 59.82 68.49 54.53 Standard Deviation 22.78 25.07 10.04 Correlation Coefficient 0.61 0.51 In terms of correlation, for the year 2018, the chlorophyll-a data from ESA showed the highest correlation at 0.73, while that from VITO had the lowest correlation at 0.41. Significant variations from the in-situ measurements were observed more in Chl-a than TUR for the mean, median, and standard deviation for both the RS datasets. The datasets were visually compared with in-situ data across varying time spans (Fig.S2, Supplementary Material), contingent upon data availability, specifically VITO (2016 to 2022) and ESA (2016 to 2019). Chl-a values from VITO were generally underestimated compared to the in-situ measurements, while the turbidity values showed fewer underestimations. Overall, graphically, TUR had a better fit than Chl-a, and ESA data matched more closely with in-situ values than VITO data. 3.2 Lake Victoria water quality analysis Fig. 3 displays the spatial distribution of chlorophyll-a categorized according to trophic levels (Table S1, Supplementary Material) adapted from (Simis, 2020) and turbidity in the lake for five different years. Most of the lake, particularly the inner waters, falls under the oligotrophic category, indicating good water quality. However, the shores consistently exhibit eutrophic-mesotrophic conditions throughout the years. In 2010, highly mesotrophic conditions were observed, particularly in the Winam Gulf and along the lake's shores. Overall, there is an improvement in water quality spatially from 2020 to 2022, with much of the lake shifting towards oligotrophic conditions. TUR classifications were determined based on guidelines established by organizations such as the Environmental Protection Agency (EPA) and World Health Organization (WHO). Most of the lake’s extent exhibits excellent water quality, with TUR values below 1 NTU. Along the shores, the water quality ranges from good to fair. From 2005 to 2010, there was a decline in water quality, particularly in the northeastern part of the lake, where it changed from excellent to good-fair. The Winam Gulf in Kenya had the poorest water quality, reaching its lowest levels in 2010 and 2020. However, in 2022, there was an improvement, with WQ nearing excellent levels throughout the lake. In summary, the lake's overall water quality has remained relatively stable, particularly in the inner waters. Nevertheless, certain areas, such as the shores, IMB and the Winam Gulf, displayed significant trends that were examined further. 3.3 Precipitation Analysis The mean annual precipitation was computed as the mean of the yearly totals over the time frame of 23 years. From Fig. 4, the minimum amount of rainfall received was 658 mm whereas maximum was 2409 mm experienced in the northeastern part of the basin which showed a high spatial variability across the basin. The coefficient of variation (CV) reflects the interannual variability of annual precipitation as shown in the Figure 4. The moderate fluctuations (CV > 20) were found in areas with the lowest mean annual precipitation, while the lowest variations (CV< 20) were found in regions with the highest annual precipitation. This suggests that water availability is more unpredictable in regions with low yearly precipitation due to high variances around the low precipitation. The monthly mean rainfall across the LVB ranges from 35mm in July to 202mm in April depicting the two rainy seasons with the “long rains” between March and May and the “short rains” between October and December as shown in Table 2. The mean annual rainfall averaged over the catchment was 1273 mm for the period 2000-2022 with the highest rainfall received in 2020 (Fig.S3, Supplementary Material). Table 2: Monthly mean precipitation (mm) over the LVB Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 86 81 149 202 141 49 35 60 77 117 146 128 Trend analysis was conducted using the Mann-Kendall test at a monthly scale to assess seasonal trends. The trends were depicted with a range of -15mm to 25mm per year. It was observed that in the first wet season (MAM), there was a generally decreasing trend, with March showing the strongest decline. In the second wet season (OND), the trend started with a decrease and then transitioned to an increase towards the end. During the dry season (late June to September), there was an overall increasing trend across the basin, but with a decrease in the upper part. Notably, there was a clear spatial pattern in the Ugandan part of the LVB, with a significant decreasing trend in the first wet season and an increasing trend in the second wet season. The Sen’s slope parameter was also computed, and it showed where and when precipitation changed the most in mm/year. A decreasing trend during the first wet season, and an increasing trend at the start of dry season were observed. Majorly there was no trend in November which then shifted to an increasing trend in December. The complete overview of results from these tests can be found in the Fig.S4 and Fig. S5, Supplementary Material. 3.4 LULC change analysis Table 3 presents the percentage changes in land cover class areas over time. Positive values indicate an expansion in coverage, while negative values indicate a reduction. Built-up areas had the highest increment in coverage at 300% between 2000 and 2020. On the other hand, bare land suffered the largest loss, at approximately 50%, mostly occurring between 2010 and 2020. The increase in forest cover between 2010 and 2020 can be attributed to reafforestation efforts and government policies promoting forest conservation in the basin. Table 3: Percentage change in area for the different time periods Land cover classes Percentage change in area 2000 - 2010 2010 - 2020 2000 – 2020 Agriculture 0.75 -0.85 -0.11 Forest -3.50 4.85 1.18 Grassland -2.10 -1.21 -3.28 Wetland -0.34 -0.63 -0.97 Built-Up 136.18 69.28 299.81 Sparse Vegetation -1.05 -0.97 -2.01 Bare Land -2.16 -48.78 -49.89 Water Body 0.02 0.01 0.03 Transition matrices were generated to further analyze the LULC changes over different time periods (Table S3, Supplementary Materials). Similar findings were obtained, such as built-up areas having the highest uniform gain at 50% whereas all the other classes gained about 13% between 2000 and 2020. Bare land and water suffered the highest uniform losses at 25% and 27% respectively. The uniform gain in built-up areas was higher in the first decade, reaching 30%. Overall, the transition matrices demonstrated conservative changes in most of the classes across all time periods, with similar uniform losses and gains observed, except for built-up areas and bare land. Results from the change budget analysis (Fig. S6) also showed that built-up areas experienced the greatest gain, while bare land encountered the highest loss during the analyzed period. The intensity of transitions for the different land cover classes between 2000 and 2020 was also analyzed (Fig. S7). The average loss and gain of the different classes determined the uniform loss and gain at 17% and 14% respectively. Only the gain of built-up areas can be termed as active as its intensity of transition was above the uniform line. The rest were dormant gains. For the losses, bare land suffered an active loss whereas the rest were dormant. Fig. 5 displays the gain in the land cover changes in the LVB between 2000 and 2020, indicating persistence of most classes. Agricultural areas increased in Kenya, while built-up areas expanded in the Winam gulf and along the shores of the lake in Uganda (Kampala and Wakiso districts). 3.5 Linking changes in precipitation and land use/cover to water quality 3.5.1 Winam Gulf in Kenya Table 4 shows the mean annual rainfall, Chl-a and TUR concentrations and the changes in percentage for the areal coverage of agricultural and built-up areas in the Winam Gulf starting with 2000 as the benchmark. Table 4: Mean annual rainfall, Chl-a, and TUR concentrations, LULC changes over the years in the Winam Gulf Year 2005 2010 2016 2020 2022 Rainfall (mm) 1177 1519 1235 2255 1909 Chl-a (mg/m 3 ) 23.94 40.86 13.47 15.88 12.05 TUR (NTU) 12.80 33.23 14.15 26.40 3.19 Percentage increment in area (%) starting from 2000 Land cover classes Agriculture 1.3 0.5 -0.1 -0.2 Built-Up 237.3 49.4 29.2 13.9 The levels of TUR and Chl-a increased in correlation with higher rainfall, reaching their peak values in 2010. In 2022, TUR showed significant improvement, indicating good water quality (1-5 NTU). However, the gulf remained mesotrophic-eutrophic for Chl-a despite having the lowest value in recent years. Fig. 6 shows the seasonal variations of turbidity and chlorophyll-a with precipitation. Chl-a increases during the first wet season, decreases during the dry season, and peaks again at the start of the second wet season. TUR follows a similar pattern. For the comparison with the LULC changes in the gulf, particular attention was given to agricultural and urban areas. These areas were considered of utmost importance due to their high population density, intensive land use practices, and significant dependence on water resources (Calamari et al., 2006). There was an overall steep increment especially from 2000 to 2005. 3.5. Inner Murchison Bay in Uganda Table 5 shows the mean annual rainfall together with the Chl-a and TUR concentrations in the IMB obtained from the RS data. It also shows the change in area as a percentage for agricultural and built-up areas in the IMB starting from 2000. Table 5: Mean annual rainfall, Chl-a, and TUR concentrations and LULC changes over the years in the IMB. Year 2005 2010 2016 2020 2022 Rainfall(mm) 1191 1386 1173 1764 1538 Chl-a (mg/m 3 ) 66.61 65.95 79.02 42.28 26.44 TUR (NTU) 15.00 22.62 22.92 12.96 18.54 Percentage increment in area (%) starting from 2000 Land cover classes 2005 2010 2015 2020 Agriculture -34.0 -17.8 -8.6 -14.3 Built-Up 68.0 12.8 4.2 5.7 Chl-a concentrations ranged between 20-80 mg/m 3 (eutrophic conditions) which are attributed to pollution from nearby towns and agricultural areas. These concentrations decreased with increased rainfall. In 2016, the IMB had the highest TUR and Chl-a concentrations. TUR concentrations ranged from 6 to 25 NTU (fair water quality) which can be attributed to suspended matter after heavy rainfall from urbanized cities. In 2022, TUR significantly improved to 1-5 NTU, indicating good water quality. However, the bay still had mesotrophic-eutrophic conditions based on Chl-a levels. Built-up areas increased significantly over time, particularly from 2000 to 2005, while agricultural areas decreased. Chl-a concentrations decreased after 2016, possibly due to reduced agricultural land use. The increase in TUR levels, particularly in 2010 and 2022, can be attributed to rapid urban growth in the bay. Fig. 7 illustrates the seasonal variations of Chl-a and TUR in the IMB. Chl-a concentrations increased at the start of the MAM season, declining towards the end, peaking in the dry season, and decreasing again in the OND season. Turbidity was highest during the OND season and lowest at the end of MAM season, gradually increasing in the dry season. These trends indicated that Chl-a and TUR were lowest during heavy rainfall and highest during moderate rainfall. Due to the availability of in-situ data for the IMB from 2013 to 2022, a similar analysis was done to compare the findings with those from the RS data. The findings were similar those obtained with the RS data as Chl-a and TUR decrease with heavy precipitation and only peak during moderate rainfall events. TUR performed better as it had a better correlation with similar pattern and trend between the RS and in-situ monthly concentrations. 4. Discussion 4.1 Water quality in Lake Victoria The RS data used in this study was first validated using in-situ measurements of the IMB. From the statistical and graphical comparisons, ESA RS data showed a stronger correlation with in-situ measurements compared to VITO data, possibly due to differences in retrieval algorithms and spatial resolution. TUR measurements outperformed Chl-a due to the presence of suspended sediment in the lake, making Chl-a measurement challenging especially in turbid and eutrophic areas (Ambrose-Igho et al., 2021; Baltodano et al., 2022). There are a variety of reasons that can account for the poor correlation between the RS and in-situ datasets. Potential sources of error include differences in spatial and temporal resolution, transformation from TSI to Chl-a for VITO data and the poor quality of in-situ data as it had missing values. It is also important to note that the accuracy of satellite-derived Chl-a and TUR estimates can be affected by cloud cover, atmospheric correction, and calibration errors (Matthews et al., 2012), while in-situ measurements can be affected by sensor drift, calibration and measurement errors, and environmental factors such as fouling (Mills & Fones, 2012). However, despite the above challenges, the combination of statistical metrics and visualizations provided a comprehensive understanding of the correlation between the satellite and in-situ data. Improving the spatiotemporal coverage of in-situ measurements and optimizing retrieval algorithms for RS would improve the correlation (Garaba et al., 2014; Gidudu et al., 2018). Enhancements in the precision of RS methods, both in terms of radiometric, spatial, and spectral aspects, along with the growing abundance of satellite images captured across various locations and time periods, will also continue elevating the capabilities for extensive monitoring of optically active water quality parameters such as turbidity, chlorophyll-a (Gholizadeh et al., 2016). Majority of the lake classified as oligotrophic from 2005 to 2022 with eutrophic-mesotrophic conditions along the shores indicating increased nutrient loading which is likely attributable to the increment in rainfall extremes that have become more frequent in recent years (Evans et al., 2020). Most vulnerable part of the lake is the Winam gulf in Kenya which had the mesotrophic conditions in 2010 as well as very high turbidity levels in 2010 and 2020 that can be credited to rapid urban development and agricultural practice (Fusilli et al., 2014). Turbidity in the lake remained relatively stable in the inner waters over the study period, but the shores experienced significant variations and poor water quality likely caused by climate change coupled with human activities. Hence, the need to always quantify impacts of land cover transformations on hydrological factors such as runoff (Baltodano et al., 2022). Additionally, it is worth noting that TUR and Chl-a exhibited a nearly similar temporal and spatial distribution pattern suggesting that TUR may primarily be influenced by algae rather than sediment load (Ambrose-Igho et al., 2021). 4.2 Precipitation variability across the LVB and its impacts on water quality The analysis showed a high spatial variability of precipitation across the whole LVB for the period of study with the lowest amount received in the Southeastern part. The highest rainfall was recorded in 2020 whereas the lowest in 2000 and 2005. The LVB has reportedly been receiving an increase in rainfall over the past decades as shown in previous studies (Kizza et al., 2009; Awange et al., 2013; Akurut et al., 2014; Nkwasa et al., 2022). The Mann-Kendall (MK) test showed that the “long rains” season received less rainfall over the years with the “short rains” season receiving more especially in December. The dry season was also becoming wetter, more so in July indicating a shift in the on-set and cessations of the MAM and OND seasons (Evans et al., 2020). This shows that there are climatic changes taking place across the basin similar to the findings from the MK test done by Kizza et al. (2009), that showed positive trends dominating the Lake Victoria Basin over the 20 th century. The comparison of the chlorophyll-a concentrations and turbidity of the Winam Gulf in Kenya to precipitation showed that the water quality parameters increased with rainfall. This rise is possibly due to runoff carrying sediments and nutrients from agricultural areas, a major land use around the gulf. Consequently, nutrient flux promotes eutrophication, reflected in elevated chlorophyll-a and higher turbidity. The improvement in TUR concentrations in 2022 can be attributed to reduced rainfall hence less sediment transport. The seasonal analysis showed that for Chl-a, an increase in rainfall resulted in higher concentrations which could be explained by the increased nutrient availability and hence subsequent algae growth. A similar relationship was obtained with TUR. This trend is ascribed to the impact of intense precipitation events on erosion, particularly in urban and agricultural areas, as well as flooding, which agitates sediment that has deposited on the bed of the tributary rivers or lake(Rui et al., 2012). On the other hand, Chl-a concentration in the IMB decreased with an increase in rainfall. The observed phenomenon is explained by the fact excessive rainfall increases nutrient runoff, promoting algae growth and consequently elevating Chl-a levels. However, the downside is that excessive algae growth leads to oxygen depletion due to the decomposition of organic matter, limiting further growth and resulting in a reduction in Chl-a concentration (Varadharajan & Soundarapandian, 2014). Furthermore, excessive rainfall and runoff also reduce light penetration due to heightened turbidity as well as altering water temperature and salinity, further impacting phytoplankton growth, and resulting in decreased chlorophyll-a levels (Acharyya et al., 2020; Nair & Nayak, 2023). We also note that built-up areas dominate the IMB, likely having a modest impact on Chl-a concentration. This may also explain reduced Chl-a during increased rainfall. The seasonal analysis with precipitation data indicated that turbidity levels were lowest during periods of abundant precipitation and highest with moderate rainfall. This could be explained by the fact that during heavy rainfall events, the rapid flux of water can effectively dilute the concentration of suspended particles, causing a temporary spike in turbidity. On the other hand, during moderate rainfall events, the slower water flow and increased sediment accumulation lead to a more sustained increase in turbidity. Seasonal precipitation compared to in-situ and RS data for the IMB showed similar results. TUR trends for the in-situ and RS datasets were more similar than those of Chl-a because of the challenges encountered in retrieving Chl-a using satellites particularly in eutrophic waters (Ambrose-Igho et al., 2021). This analysis further supports the reliability of RS data as a complement to ground-based data, particularly in data scarce regions. 4.3 LULC changes in the LVB and their influence on water quality in the lake The basin's land cover changes were mostly conservative during 2000-2020, with minimal losses and gains observed except for built-up areas and bare land. The land cover changes align with prior research, with slight variations in the land classification, spatial and temporal scales (Berakhi et al., 2015; Kiggundu et al., 2018; Mugo et al., 2020; Onyango et al., 2021). The increase in built-up areas in the Lake Victoria Basin is attributed to population growth, urbanization, and government policies promoting infrastructure development (Onyango & Opiyo, 2022). Conversely, the loss of bare land can be explained by the increase in constructed areas. Similar drivers have been demonstrated to influence extensive LULC changes in various regions of the LVB (Ebanyat et al., 2010; Wasige et al., 2013). The increase in forested areas is accredited to afforestation drivers in the region since the 1980s (Mugure & Oino, 2013). For the LULC change analysis in the Winam Gulf, there was a slight increment in agricultural areas from 2005 till 2010. An increase in agricultural fields leads to increased runoff of nutrients like nitrogen and phosphorus which result into algae blooms that cause loss of biodiversity, alterations in food chain dynamics, and deterioration of fisheries (Verschuren et al., 2002; Wasige et al., 2013). The decline in Chl-a concentrations after 2010 can be attributed to the reduction in agricultural areas near the gulf. In contrast, the rise in turbidity between 2016 and 2020 can be attributed to increased built-up areas, which intensify stormwater runoff and flooding events. The comparison of water quality parameters with LULC changes in the IMB revealed a significant reduction in Chl-a levels possibly due to decreased agricultural land use and nutrient inputs as we observe a reduction in the agricultural areas from 2000 to 2020. The increase in TUR levels, especially in 2010 and 2022, could be explained by the increase in built-up areas in the IMB during the last two decades thus increased sediment transport. Similar findings were reported by Bhaskar & Gidudu (2020) showing a substantial 119% rise in impervious surface area and a 12% decline in vegetative cover between 1995 and 2019, that was followed by an increase in Chl-a concentration in the bay. 4.4 Limitations The results of this research, while aligning with previous studies, are hampered by several limitations. Firstly, the short time span for satellite and in-situ data available restricted the analysis of trends. Secondly, only the influence of precipitation and LULC changes on water quality in the Winam Gulf and IMB was examined. While the increased built-up areas in the Winam Gulf and the prominent agricultural practices may contribute to TUR and Chl-a concentrations, substantial rainfall was a better explanatory factor for the water quality patterns and trends. This showed that rainfall plays a crucial role in influencing water quality dynamics, particularly in areas with unrestricted human activities like urbanization and agriculture (Tuladhar et al., 2019). In the inner Murchison Bay, high Chl-a levels could not be solely explained by land use changes, suggesting additional pollution sources such as industries and sewage effluents to be responsible for the elevated Chl-a and TUR levels. Furthermore, examining water quality from the tributary rivers of the lake is essential to comprehensively understand the influence of land cover changes on water quality, as rivers serve as conduits for transporting nutrients and pollutants from land to the lake. In addition, mixing processes in the lake could be a factor to consider as a study by Hecky (1993) showed that some of the causes of degradation in Lake Victoria are shallow mixing depths because of climate change and low flushing times. Okungu et al. (2005) suggested that winds from a southerly direction dominated mixing and nutrient transport processes at Winam Gulf, whereas winds from the northeast were less important in the annual transport of nutrients and sediments. A mixing box model by Gikuma-Njuru et al. (2013) also revealed that most nutrients from river inflows and municipal sources entering the Winam Gulf were retained within the gulf, with only a small portion transferred to the main lake. For the IMB, wind-induced mixing influenced its water quality (Ssebiyonga et al., 2013; Akurut et al., 2017). Thus, it is vital to explore the wind dynamics over the lake as well the flushing and exchange patterns between the bays, gulfs, and the offshore waters. There are quite a variety of other natural and human-induced factors that influence the Chl-a and TUR concentrations of a water body such as excessive nutrient load from point and diffuse sources, raw or semi-treated effluents from industries and wastewater treatment facilities, physical factors such as the lake’s flushing rate, loss of biodiversity, climate change etc. According to Kaijanangoma (2020), effluents discharged into Lake Victoria are primarily raw or partially treated. Treatment ponds installed at most wastewater treatment facilities and industries are inadequate in size with a short retention time. As a result, raw and semi-treated wastewater is released into the lake, leading to contamination and harm to fish breeding sites due to high BOD. While nutrient load is a key driver of eutrophication, factors like temperature, precipitation, wind velocity, and solar radiation can increase the risk. Climate change, characterized by shifts in precipitation, temperature, wind speed, and solar radiation, directly impacts water quality by altering stream flow and water temperatures (Rui et al., 2012; Rolighed et al., 2016). Studies conducted by Cooke et al. (2016) and Krzyk et al. (2015) revealed that certain lakes are exposed to tidal effects and contamination because of their outlets. These lakes undergo frequent renewal, making them less susceptible to eutrophication. Consequently, the polluted water in such lakes is efficiently purified due to its short residence time. Thus, while this analysis examines land use and precipitation, it is important to acknowledge that other environmental factors specific to the lake's location can also influence water quality (Liu et al., 2010). 5. Conclusion This study aimed at using satellite data to link land use and precipitation changes in the Lake Victoria basin to water quality changes in the lake with a special focus on two water pollution hotspots namely the Winam Gulf and inner Murchison Bay. Analyzing the relationship between precipitation changes and Chl-a and TUR concentrations in different parts of the lake highlighted the significant impact of rainfall on water quality as it accounts for approximately 80% of the water input to the lake and influences runoff and erosion processes. Elevated rainfall leads to increased runoff and erosion, transporting pollutants and nutrients from the land into the lake, notably in expansive agricultural areas like the Winam Gulf. Urban areas, such as the IMB, characterized by extensive impervious surfaces, experience increased turbidity. Intense rainfall, on the other hand, can also induce sediment dilution in the lake, as noted in the IMB. Therefore, the extent of rainfall's influence on water quality is affected by various factors, such as rainfall intensity and duration, land use practices, and lake characteristics. Agricultural and built-up areas also provide sources of nutrients and pollutants, which with heavy rains, end up in our water bodies and contribute to an increase in TUR and Chl-a concentrations as witnessed in the Winam gulf and IMB. The analysis revealed that while land use changes had a positive correlation with Chl-a and TUR concentrations in the Winam Gulf and inner Murchison Bay, changes in rainfall provided a better explanation for the observed patterns. However, it is important to recognize the complex interaction between land use, urbanization, and climate change in affecting turbidity and chlorophyll-a levels in lakes. Effective water management strategies may need to address multiple factors simultaneously. With the availability of remote sensing data, it is crucial to explore other factors like wind that influence water quality in lakes and rivers. Future research could investigate the impact of wastewater treatment effluents and wind speed on chlorophyll-a and turbidity respectively. Improving algorithms for retrieving water quality parameters from RS data would also enhance the accuracy of results and their alignment with ground measurements, increasing reliability and validity. Declarations Funding The authors would like to thank the Research Foundation – Flanders (FWO) for funding the International Coordination Action (ICA) “Open Water Network: Open Data and Software tools for water resources management” (project code G0E2621N) and the AXA chair program on Water Quality and global change for funding this research. Data Availability All datasets used in this study are openly available and are accessible from these links: “VITO” RS data for turbidity and trophic state index is available from https://land.copernicus.eu/global/products/lwq “ESA” RS data for turbidity and chlorophyll-a is available from https://climate.esa.int/en/projects/lakes/data/ CHIRPS rainfall data is available from https://www.chc.ucsb.edu/data/chirps ESA-CCI Land cover maps are available from https://www.esa-landcover-cci.org Analysis and processing of data was done using Python scripts. Scripts can be obtained from the corresponding author upon request. Competing interests The authors declare no conflict of interest. Author Contributions Maria Theresa Nakkazi: formulated study, data collection & analysis, results interpretation, and manuscript preparation. Albert Nkwasa: formulated study, results interpretation, and manuscript—review and editing. Analy Baltodano Martínez: data collection, manuscript—review and editing. 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Supplementary Files SupplementaryMaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 25 Oct, 2024 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 15 May, 2024 Reviews received at journal 13 May, 2024 Reviews received at journal 11 Apr, 2024 Reviewers agreed at journal 08 Apr, 2024 Reviewers agreed at journal 08 Apr, 2024 Reviewers agreed at journal 08 Apr, 2024 Reviewers agreed at journal 27 Feb, 2024 Reviewers agreed at journal 26 Feb, 2024 Reviewers agreed at journal 25 Feb, 2024 Reviewers invited by journal 23 Feb, 2024 Editor assigned by journal 18 Feb, 2024 Submission checks completed at journal 18 Feb, 2024 First submitted to journal 17 Jan, 2024 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. 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16:44:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3873388/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3873388/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-024-13261-2","type":"published","date":"2024-10-25T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51450395,"identity":"ecf26fdb-9af4-409f-b1b8-ea32615d0e89","added_by":"auto","created_at":"2024-02-21 20:33:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":170097,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Lake Victoria basin, with major rivers and surface elevation\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3873388/v1/7675bc4ff65f99dbbf7776ab.png"},{"id":51450305,"identity":"01388791-7c72-4e92-bf6b-90202b979b6d","added_by":"auto","created_at":"2024-02-21 20:25:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":163015,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual average Chl-a map for Lake Victoria showing the locations of the IMB and Winam Gulf\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3873388/v1/2a7f2dca73a8bec985b9dfc7.png"},{"id":51450394,"identity":"3fa27cc7-9be6-496d-a3eb-dae0b47e6204","added_by":"auto","created_at":"2024-02-21 20:33:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":340532,"visible":true,"origin":"","legend":"\u003cp\u003eWater Quality maps based on the classification of Chl-a and TUR for 5 different years.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3873388/v1/8d64f982e0dbc73f806988d9.jpg"},{"id":51450312,"identity":"df282fe3-1246-4f91-adbe-ddbce309ef6b","added_by":"auto","created_at":"2024-02-21 20:25:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":340718,"visible":true,"origin":"","legend":"\u003cp\u003eMean annual precipitation(left) and CV of precipitation over the LVB\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3873388/v1/7275d735588ad40d96d48ac1.jpg"},{"id":51450307,"identity":"ee0fc0ca-f9b4-4913-8d4e-7e60020f242a","added_by":"auto","created_at":"2024-02-21 20:25:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":456401,"visible":true,"origin":"","legend":"\u003cp\u003eLand cover classes that gained area in the period 2000-2020 in the LVB.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3873388/v1/9e961f066e662ef41722a5ab.png"},{"id":51450310,"identity":"abb0f066-318e-42b3-a0d1-890fe2395188","added_by":"auto","created_at":"2024-02-21 20:25:15","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":178964,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variations of Chl-a and TUR in the Winam Gulf\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3873388/v1/dda4f41620aa4f2a95d98a2c.jpg"},{"id":51450311,"identity":"f967da45-17c4-469c-9956-1b11fabf44d7","added_by":"auto","created_at":"2024-02-21 20:25:15","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":263852,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variations of Chl-a(a) and TUR(b) in the IMB\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3873388/v1/f127aff9c1fd8c0d1f19d67a.jpg"},{"id":67682720,"identity":"da3b34ba-2ea8-478b-8f91-58076c29813f","added_by":"auto","created_at":"2024-10-28 16:14:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2869342,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3873388/v1/ed56f5a2-05a8-4c34-b881-24a24a48f982.pdf"},{"id":51450396,"identity":"04228f59-1079-44ec-9a28-9b95381cbd46","added_by":"auto","created_at":"2024-02-21 20:33:15","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":653880,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3873388/v1/8a4d625c23cad39fb90660ac.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Linking Land Use and Precipitation Changes to Water Quality changes in Lake Victoria Using Remote Sensing","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003eThe deterioration of water quality (WQ) in freshwater bodies is a pervasive and escalating global issue, detrimentally impacting ecosystems (Peters \u0026amp; Meybeck, 2000; Kundu et al., 2017). Contributory factors to this decline encompass the continual rise in population, urbanization, industrialization, and the influence of climate change (Bhateria \u0026amp; Jain, 2016; Me et al., 2018; Razman et al., 2023). Over the last fifty years, Lake Victoria, the largest tropical freshwater lake in the world, has faced threats from nutrient sources such as surface runoff, wastewater, agricultural waste, and atmospheric deposition, endangering both local communities and its biodiversity (Kayombo \u0026amp; Jorgensen, 2006). The lake\u0026rsquo;s ecosystem has experienced significant and alarming changes such as algal blooms, declining water transparency, water hyacinth, over-fishing, introduction of exotic fish species, and oxygen depletion (Achieng, 1990; Aloyce et al., 2001; Njiru et al., 2008). These changes can be attributed to high population densities that put a strain on the lake\u0026apos;s natural resources, resulting in land degradation (Wang et al., 2012). This has in turn impacted the hydrology of multiple rivers in the basin and, as a result, affected the lake\u0026apos;s dynamics (Olang \u0026amp; F\u0026uuml;rst, 2011).\u003c/p\u003e\n\u003cp\u003eChanges in land use, which are largely controlled by human activities, have played a substantial role in the degradation of the lake\u0026apos;s water quality (Juma et al., 2014). Over 60% of the Lake Victoria Basin (LVB) experiences degradation, attributed to diverse land use alterations, such as wetland reclamation. These changes lead to heightened sediment and nutrient loading into the lake through surface runoff, aerial deposition, and river inflow (Scheren et al., 2000; Nyamweya et al., 2023). Several studies have shown that significant amounts of pesticides and agrochemicals have been detected in the water and sediments as a result of agricultural intensification in the region (Osano et al., 2003; Getenga et al., 2004; Musa et al., 2011). Eutrophication of the lake has also been aided by water contamination from municipal and industrial waste, and improper solid waste management particularly in the bays and gulfs (Nyenje et al., 2010; Oguttu et al., 2018; Olokotum et al., 2021). Additionally, small-scale gold mining in some parts of the Tanzanian catchment could lead to mercury discharges into the lake water if mining wastes are not properly contained (Campbell et al., 2003).\u003c/p\u003e\n\u003cp\u003eEnvironmental changes, particularly those associated with climate conditions such as changes in precipitation, temperature, and hydroclimatic extremes (e.g. floods, droughts, and heatwaves) significantly influence water quality. Numerous studies indicate that weather exacerbates the most severe impacts on water quality (Mimikou et al., 2000; Whitehead et al., 2009; EPA, 2016; Amanullah et al., 2020; Shinhu et al., 2023). Increased rainfall variability impacts drinking water quality significantly leading to notable changes in water quality parameters, often shifting from clear to turbid water (Bastiancich et al., 2022; Turyasingura et al., 2023). It also promotes the prevalence of cyanobacteria, worsening eutrophication and impacting the physical, chemical, and biological parameters, as well as nutrient availability (Ojok et al., 2017). Thus, as land use/land cover (LULC) and climate changes continue to evolve, it is likely that conditions favoring the degradation of water quality will occur more often. Consequently, there is need to monitor water quality to ensure its sustainability for multiple purposes such as human consumption, agriculture, energy, and biodiversity. \u003c/p\u003e\n\u003cp\u003eAs a transboundary resource, the LVB region has yet to implement the basin-wide regulatory measures, technological breakthroughs, and planning necessary to slow the rate at which the lake\u0026apos;s WQ deteriorates (Semyalo, 2021). Some member states face cost constraints in conducting continuous monitoring and relying solely on field samples may not adequately capture the geographical and temporal diversity necessary for comprehensive lake water quality monitoring and management (Dube et al., 2015). Hence, the need to incorporate remote sensing as a useful technology for monitoring WQ parameters (Sent et al., 2021).\u003c/p\u003e\n\u003cp\u003eRemote sensing (RS) offers significant benefits over traditional methods by providing a comprehensive view of water quality, facilitating improved monitoring of spatial and temporal variations. The growing interest in the usage of RS data is based on its technological advances, affordability and good spatio-temporal resolution that permits getting information over wide areas (Mashala et al., 2023). Additionally, it provides access to historical data which allows us to track the changes and patterns of many WQ factors over time (Werdell et al., 2018). It also serves as a valuable resource for planning field surveys and collecting samples, as well as offering reliable assessments of optically active components required to define water quality (Dube et al., 2015). RS products are indeed a valuable alternative to in-situ measurements; however, their complexity arises, from having distinct underlying assumptions, computation algorithms of parameters and possible improper spatial and temporal image resolutions (Corbari et al., 2016). That is why it is important to validate the remote sensing data with in-situ measurements whenever possible, prior to use in analysis (Wu et al., 2019). \u003c/p\u003e\n\u003cp\u003eNumerous studies have utilized RS data to evaluate water quality in various sections of Lake Victoria(Juma et al., 2014; Sichangi \u0026amp; Makokha, 2017; Mutyaba et al., 2018). Additionally, RS has been instrumental in analyzing the impacts of climate change (Awange et al., 2013), and LULC changes (Kiggundu et al., 2018; Mugo et al., 2020; Onyango et al., 2021) in the LVB. The results all showed increasing trends of these global changes which pose a serious threat to the environment and water quality. The diverse and successful usage of RS data to assess water quality and to explore impacts of climate and LULC changes in the LVB shows the potential of utilizing RS to fulfill the objective of this study which is to analyze water quality changes in Lake Victoria by utilizing existing RS products of precipitation, land use and water quality.\u003c/p\u003e\n\u003cp\u003eWhile some studies such as Mugo et al. (2020) and Nyamweya et al. (2023) agree that the key drivers of water quality decline in Lake Victoria are climate and land use change; distinct relationships between these changes and the water quality of the lake are yet to be. This study examines RS data of Chlorophyll-a (Chl-a) and turbidity (TUR) concentrations across the lake over the period (2000 \u0026ndash; 2022), exploring the trends and variability of these concentrations in relation to changes in precipitation and LULC in the LVB. The study places emphasis on two key regions, i.e. the Winam gulf in Kenya and inner Murchison Bay in Uganda, which experience poor water quality and have undergone major LULC changes in recent times. Additionally, the study validates the RS data against in-situ measurements, thereby enhancing the discussion on the feasibility and accuracy of using RS technologies for water quality monitoring.\u003c/p\u003e"},{"header":"2.\tMaterials and methods ","content":"\u003ch2\u003e2.1 Study area\u003c/h2\u003e\n\u003cp\u003eLake Victoria, spanning an area of 68,800 km\u003csup\u003e2\u003c/sup\u003e, is shared by three nations (Tanzania 49%, Uganda 45%, and Kenya 6%), with a catchment area of 194,000 square kilometers spread across five countries (Juma et al., 2014). Its climate ranges from tropical rain forest with year-round rainfall (117 km\u003csup\u003e3\u003c/sup\u003e/year) over the lake to a semi-arid climate with occasional droughts in some parts, and temperatures ranging from 12 - 26\u003csup\u003e0\u003c/sup\u003eC (Miriti, 2022). The LVB experiences rainfall in two distinct seasons with the \"long rains\" season spanning from March to May (MAM) and the \"short rains\" occurring in October, November, and early December (OND) (Nicholson, 2015). These are influenced by different large-scale forces such as zonal winds over the central Indian Ocean and inter-tropical convergences (Nicholson, 2017). On the other hand, the driest months tend to be June, July, and August. The soil types in the LVB are diverse and heavily influenced by the Great Rift Valley's volcanic activity whereas montane forests, savannahs, grasslands, wetlands, woodlands, and croplands are among the vegetation types found throughout the basin (Odada et al., 2009). LVB is densely populated, with 300 people per km\u003csup\u003e2\u003c/sup\u003e, growing by 3.5% annually (Marcus, 2022). Major cities such as Jinja, Kisumu, Mwanza have expanded, alongside new towns on the lake shore (Nyamweya et al., 2020). Fig. 1 shows the LVB, its major tributary rivers and elevation from the Shuttle Radar Topography Mission (SRTM).\u003c/p\u003e\n\u003ch2\u003e2.2 Datasets used in the study\u003c/h2\u003e\n\u003cp\u003eIn this study, turbidity and chlorophyll-a were the water quality parameters considered as these can be directly derived from ocean-color satellite remote sensing data. Chlorophyll-a indicates phytoplankton abundance and biomass, reflecting trophic status (Keukelaere \u0026amp; Knaeps, 2021), while turbidity indicates water clarity, affected by factors like river run-off, phytoplankton growth, climate, and watershed changes (Crétaux et al., 2020). Satellites like MODIS, MERIS, Sentinel-2, and Landsat enable accurate analysis of WQ parameters through the connection established between in-situ measurements and emitted/reflected radiation in spectral bands such as the green and infrared bands (Watanabe et al., 2018; Papenfus et al., 2020; Ambrose-Igho et al., 2021). Chl-a and TUR are derived from Lake Water-Leaving Reflectance (LWLR); an important indicator of biogeochemical processes and habitats in the water column (Crétaux et al., 2020), using globally validated algorithms (Dogliotti et al., 2015; Keukelaere \u0026amp; Knaeps, 2021).\u003c/p\u003e\n\u003cp\u003eTwo RS WQ products were used i.e. (1) “ESA” data from the Lakes Project of the European Space Agency Climate Change Initiative (ESA CCI-Lakes) and (2) “VITO” data which is a Lake WQ product from Copernicus Global Land Service (CGLS). The VITO data comprised of monthly turbidity and trophic state index (TSI) at a spatial resolution of 300m derived from the OLCI sensor on board of Sentinel-3. TSI measures phytoplankton productivity and eutrophication. The VITO data were obtained from the Copernicus Global Land Service website (https://land.copernicus.eu/global/products/lwq) for the period of 2016 – 2022. The retrieval algorithms for this dataset are stipulated in Warren et al. (2021). Chl-a was derived from TSI according to the table adapted from (Simis, 2020) shown in Supplementary Material (Table S1). ESA data records of turbidity and chlorophyll-a at a spatial resolution of 100m and daily temporal resolution were acquired from the ESA website (https://climate.esa.int/en/projects/lakes/data/) from 2000 – 2012 (derived from the MERIS sensor on board ESA's ENVISAT satellite) and 2016 – 2019 (derived from the OLCI sensor on board of Sentinel-3). The “\u003cem\u003eAlgorithm Theoretical Basis\u003c/em\u003e \u003cem\u003eDocument\u003c/em\u003e” for this data product, readily available on website, provides a full explanation of the algorithms and corrections used to create these estimates. Both datasets were already preprocessed and ready for use. Chlorophyll-a estimates were measured in mgm\u003csup\u003e-3\u003c/sup\u003e whereas turbidity in NTU.\u003c/p\u003e\n\u003cp\u003ePast records of in-situ measurements of Chl-a and TUR data were collected from the National Water and Sewerage Corporation (NWSC), Uganda and these were used to validate RS Chl-a and TUR data. The measurements, available irregularly, were gathered monthly from March 2013 to June 2022 at 26 sampling locations in the Inner Murchison Bay (IMB). Approximately 7% of the data was missing, reflecting occasional gaps in the monthly records.\u003c/p\u003e\n\u003cp\u003eMonthly precipitation records at 0.05° spatial resolution for the period of 2000 to 2022 were retrieved from the CHIRPS website (https://www.chc.ucsb.edu/data/chirps) and used to analyze changes in rainfall across the LVB over time. Annual land cover maps at a spatial resolution of 300m were obtained from the Land cover dataset from the European Space Agency Climate Change Initiative (ESA-CCI). These were acquired from the website (https://www.esa-landcover-cci.org) for the years 2000, 2005, 2010, 2015 and 2020.\u003c/p\u003e\n\u003ch2\u003e2.3 Validation of WQ RS with in-situ measurements\u003c/h2\u003e\n\u003cp\u003eWe carried out an accuracy assessment and validation of the RS water quality data using past in-situ measurements. WQ parameters from the 26 sampling locations in the IMB were averaged at a monthly scale and compared with the ESA and VITO RS data. The sampling locations can be seen in Fig. S1(Supplementary Material). The RS Chl-a and TUR raster files were realigned and resampled to the same pixel size (0.00833\u003csup\u003eo\u003c/sup\u003e) at a monthly timestep. Statistical metrics, such as mean, median, standard deviation, correlation coefficients, time series and graphical criteria were used to compare the RS and in-situ data. The evaluation aimed at assessing the accuracy and correlation between in-situ and RS data.\u003c/p\u003e\n\u003ch2\u003e2.4 Assessment of water quality in the lake\u003c/h2\u003e\n\u003cp\u003eA visual assessment of maps and time series was also carried out using processed RS data of chlorophyll-a and turbidity to assess the ecological status of the lake from 2005 to 2022. The analysis involved both ESA and VITO data due to the challenges of missing data and the limited timeframe of the satellite data available. Pollution hotspots, notably the Winam Gulf and the IMB, were identified across the lake, prompting a detailed study to investigate the links between land use changes, precipitation patterns, and water quality variations in these regions. The Winam Gulf and IMB consistently exhibited elevated levels of chlorophyll-a and turbidity throughout the study period.\u003c/p\u003e\n\u003ch2\u003e2.5 Spatial and temporal variability of rainfall\u003c/h2\u003e\n\u003cp\u003eThis analysis involved the use of monthly mean CHIRPS precipitation raster files spanning from 2000 to 2022. The mean annual precipitation and coefficient of variation (CV) over LVB were computed. The CV was computed as the ratio of the standard deviation to the mean and was used to classify the degree of variability of rainfall events as less (CV \u0026lt; 20), moderate (20 \u0026lt; CV \u0026lt; 30), and high (CV \u0026gt; 30) (Nkwasa et al., 2022).\u003c/p\u003e\n\u003cp\u003eThe Mann-Kendall (MK) (Mann, 1945; Kendall, 1975) test was then applied to the data to identify trends. This test has previously been used to analyze temporal trends of climatic variables such as precipitation and temperature (H. Wang et al., 2012; F. Wang, 2018; Mallick et al., 2021). Since the test is nonparametric, the data does not have to adhere to a normal distribution. However, it does presuppose that there is no autocorrelation in the time series. Typically, trends are considered significant when they achieve a 95% confidence level (Buo et al., 2021). The magnitude of the trends was also calculated using the nonparametric Theil-Sen estimator(Sen, 1968) which is computed by taking the median of the slopes between each pair of points in the time series data.\u003c/p\u003e\n\u003ch2\u003e2.6 Land use/cover change analysis\u003c/h2\u003e\n\u003cp\u003eThe nomenclature of the land cover maps was reclassified from the 36 original classes in the LVB to 8 major land classes i.e., agriculture, forest, grassland, wetland, built-up, sparse vegetation, bare area, and open water as shown in Table S2, Supplementary Material (Mousivand \u0026amp; Arsanjani, 2019). This was done to accommodate classes that are relevant to the study area and represent specific land use changes related to ongoing human activities.\u003c/p\u003e\n\u003cp\u003eTo visually depict LULC changes over time, maps were created to highlight areas that experienced growth and those that remained unchanged. Using the GIS vector geoprocessing tool, land cover shapefiles from two time periods (e.g. 2000-2010, 2010-2020 or 2000-2020) were intersected to identify classes that showed no changes in each area. This intersection indicated the absence of change in the land cover class. The intersected land cover classes were then reclassified as \"no change\" in the resulting map, which represented the \"no change\" classes and newly gained areas from the recent land cover map e.g. for 2010 for the period of 2000-2010.\u003c/p\u003e\n\u003ch3\u003e2.6.1 LULC change matrices.\u003c/h3\u003e\n\u003cp\u003eLand cover change matrices are used to analyse how different land cover areas have changed over time. This involves comparing maps of the same location from two distinct points in time and generating a cross tabulation matrix. The matrix shows the area that has changed between different land cover categories. Diagonal entries indicate land persistence, while off-diagonal entries indicate land cover change (Aldwaik \u0026amp; Pontius, 2012). Transition matrices have been widely used in landscape ecology and land use/cover change studies (Han et al., 2009; Takada et al., 2010; Romero-Ruiz et al., 2012).\u003c/p\u003e\n\u003cp\u003eThree levels of analysis exist i.e. interval, categorical, and transition levels. The interval level examines changes between two time periods, the categorical level assesses the intensity of transformation between categories, and the transition level focuses on the dynamics and intensity of transitions within a category relative to others. Annual change intensities are computed at the interval level, while the magnitude and intensity of gross gains and losses are evaluated at the category level. The transition level investigates changes in categories, their variations, and identifies frequently targeted or avoided categories. These analyses compare observed intensities to uniform measures of transition (Alo \u0026amp; Pontius, 2008; Aldwaik \u0026amp; Pontius, 2012). Areal percentage changes for 3 distinct time periods; 2000-2010, 2010-2020 and 2000-2020, were calculated using Equation 1. Transition matrices were then generated using the Semi-Automatic Classification Plugin in QGIS (Congedo, 2016).\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"729\" height=\"43\"\u003e\u003c/p\u003e\n\u003ch3\u003e2.6.2 Change budget and intensity analysis\u003c/h3\u003e\n\u003cp\u003eThe analysis of relative gain, loss, and persistence among different land cover classes offers deeper insights into the dynamics of LULC changes. To assess the change budget, transition matrices were employed to calculate the uniform gain, loss, and persistence of various land cover classes using the following equations.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"729\" height=\"146\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTo conduct the intensity analysis, the average values of gains and losses were calculated and then employed to distinguish between active and dormant changes. An active category change is identified when the intensity surpasses the uniform line, indicating a relatively rapid change. On the other hand, a dormant change is observed when the intensity falls below the uniform line, indicating a relatively slow change (Huang et al., 2012).\u003c/p\u003e\n\u003ch2\u003e2.7 Linking impacts of changes in climate and LULC to changes in lake water quality\u003c/h2\u003e\n\u003cp\u003eTo assess the effects of climate and land use/cover changes on water quality, Chl-a and TUR time series data were extracted from prominent pollution hotspots in Lake Victoria, namely the IMB in Uganda and the Winam Gulf in Kenya. These areas are both ecologically important, pollution-prone (as seen in the annual average chlorophyll-a map for the lake in Fig. 2), and subject to land use and precipitation changes, making them ideal for studying the relationship between these factors and water quality (Calamari et al., 2006; Kabenge et al., 2016). They also serve as major sources for water abstraction for local communities and major cities like Kampala and Kisumu (Olokotum et al., 2020). The extracted data was plotted and analyzed to understand the trends and variability in water quality parameters and to relate them to climate and land use changes.\u003c/p\u003e"},{"header":"3.\tResults ","content":"\u003ch2\u003e3.1 Accuracy assessment and Validation of RS data\u003c/h2\u003e\n\u003cp\u003eThe following statistical metrics in Table 1 were obtained when comparing in-situ and satellite data of chlorophyll-a and turbidity in the IMB for the year 2018.\u003c/p\u003e\n\u003cp\u003eTable 1: Statistical comparison of the WQ parameters for VITO and ESA data for 2018\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.19867549668874%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.80132450331126%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChlorophyll-a (mg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.27363184079602%\" valign=\"top\"\u003e\n \u003cp\u003eStatistical Metric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.076285240464344%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;In-situ \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVITO\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.749585406301826%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eESA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.27363184079602%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.076285240464344%\" valign=\"top\"\u003e\n \u003cp\u003e217.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\" valign=\"top\"\u003e\n \u003cp\u003e122.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.749585406301826%\" valign=\"top\"\u003e\n \u003cp\u003e101.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.27363184079602%\" valign=\"top\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.076285240464344%\" valign=\"top\"\u003e\n \u003cp\u003e179.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\" valign=\"top\"\u003e\n \u003cp\u003e103.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.749585406301826%\" valign=\"top\"\u003e\n \u003cp\u003e93.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.27363184079602%\" valign=\"top\"\u003e\n \u003cp\u003eStandard Deviation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.076285240464344%\" valign=\"top\"\u003e\n \u003cp\u003e129.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\" valign=\"top\"\u003e\n \u003cp\u003e70.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.749585406301826%\" valign=\"top\"\u003e\n \u003cp\u003e59.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.27363184079602%\" valign=\"top\"\u003e\n \u003cp\u003eCorrelation Coefficient\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.076285240464344%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\" valign=\"top\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.749585406301826%\" valign=\"top\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.19867549668874%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.80132450331126%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTurbidity (NTU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.27363184079602%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.076285240464344%\" valign=\"top\"\u003e\n \u003cp\u003e58.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\" valign=\"top\"\u003e\n \u003cp\u003e62.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.749585406301826%\" valign=\"top\"\u003e\n \u003cp\u003e52.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.27363184079602%\" valign=\"top\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.076285240464344%\" valign=\"top\"\u003e\n \u003cp\u003e59.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\" valign=\"top\"\u003e\n \u003cp\u003e68.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.749585406301826%\" valign=\"top\"\u003e\n \u003cp\u003e54.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.27363184079602%\" valign=\"top\"\u003e\n \u003cp\u003eStandard Deviation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.076285240464344%\" valign=\"top\"\u003e\n \u003cp\u003e22.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\" valign=\"top\"\u003e\n \u003cp\u003e25.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.749585406301826%\" valign=\"top\"\u003e\n \u003cp\u003e10.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.27363184079602%\" valign=\"top\"\u003e\n \u003cp\u003eCorrelation Coefficient\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.076285240464344%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.90049751243781%\" valign=\"top\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.749585406301826%\" valign=\"top\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn terms of correlation, for the year 2018, the chlorophyll-a data from ESA showed the highest correlation at 0.73, while that from VITO had the lowest correlation at 0.41. Significant variations from the in-situ measurements were observed more in Chl-a than TUR for the mean, median, and standard deviation for both the RS datasets. The datasets were visually compared with in-situ data across varying time spans (Fig.S2, Supplementary Material), contingent upon data availability, specifically VITO (2016 to 2022) and ESA (2016 to 2019). Chl-a values from VITO were generally underestimated compared to the in-situ measurements, while the turbidity values showed fewer underestimations. Overall, graphically, TUR had a better fit than Chl-a, and ESA data matched more closely with in-situ values than VITO data.\u003c/p\u003e\n\u003ch2\u003e3.2 Lake Victoria water quality analysis\u003c/h2\u003e\n\u003cp\u003eFig. 3 displays the spatial distribution of chlorophyll-a categorized according to trophic levels (Table S1, Supplementary Material) adapted from (Simis, 2020) and turbidity in the lake for five different years. Most of the lake, particularly the inner waters, falls under the oligotrophic category, indicating good water quality. However, the shores consistently exhibit eutrophic-mesotrophic conditions throughout the years. In 2010, highly mesotrophic conditions were observed, particularly in the Winam Gulf and along the lake\u0026apos;s shores. Overall, there is an improvement in water quality spatially from 2020 to 2022, with much of the lake shifting towards oligotrophic conditions.\u003c/p\u003e\n\u003cp\u003eTUR classifications were determined based on guidelines established by organizations such as the Environmental Protection Agency (EPA) and World Health Organization (WHO). Most of the lake\u0026rsquo;s extent exhibits excellent water quality, with TUR values below 1 NTU. Along the shores, the water quality ranges from good to fair. From 2005 to 2010, there was a decline in water quality, particularly in the northeastern part of the lake, where it changed from excellent to good-fair. The Winam Gulf in Kenya had the poorest water quality, reaching its lowest levels in 2010 and 2020. However, in 2022, there was an improvement, with WQ nearing excellent levels throughout the lake.\u003c/p\u003e\n\u003cp\u003eIn summary, the lake\u0026apos;s overall water quality has remained relatively stable, particularly in the inner waters. Nevertheless, certain areas, such as the shores, IMB and the Winam Gulf, displayed significant trends that were examined further.\u003c/p\u003e\n\u003ch2\u003e3.3 Precipitation Analysis\u003c/h2\u003e\n\u003cp\u003eThe mean annual precipitation was computed as the mean of the yearly totals over the time frame of 23 years. From Fig. 4, the minimum amount of rainfall received was 658 mm whereas maximum was 2409 mm experienced in the northeastern part of the basin which showed a high spatial variability across the basin.\u003c/p\u003e\n\u003cp\u003eThe coefficient of variation (CV) reflects the interannual variability of annual precipitation as shown in the Figure 4. The moderate fluctuations (CV \u0026gt; 20) were found in areas with the lowest mean annual precipitation, while the lowest variations (CV\u0026lt; 20) were found in regions with the highest annual precipitation. This suggests that water availability is more unpredictable in regions with low yearly precipitation due to high variances around the low precipitation.\u003c/p\u003e\n\u003cp\u003eThe monthly mean rainfall across the LVB ranges from 35mm in July to 202mm in April depicting the two rainy seasons with the \u0026ldquo;long rains\u0026rdquo; between March and May and the \u0026ldquo;short rains\u0026rdquo; between October and December as shown in Table 2. The mean annual rainfall averaged over the catchment was 1273 mm for the period 2000-2022 with the highest rainfall received in 2020 (Fig.S3, Supplementary Material).\u003c/p\u003e\n\u003cp\u003eTable 2: Monthly mean precipitation (mm) over the LVB\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"636\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003eJan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003eFeb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003eMar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003eApr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003eMay\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003eJune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003eAug\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003eSept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003eOct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003eNov\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003eDec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTrend analysis was conducted using the Mann-Kendall test at a monthly scale to assess seasonal trends. The trends were depicted with a range of -15mm to 25mm per year. It was observed that in the first wet season (MAM), there was a generally decreasing trend, with March showing the strongest decline. In the second wet season (OND), the trend started with a decrease and then transitioned to an increase towards the end. During the dry season (late June to September), there was an overall increasing trend across the basin, but with a decrease in the upper part. Notably, there was a clear spatial pattern in the Ugandan part of the LVB, with a significant decreasing trend in the first wet season and an increasing trend in the second wet season. The Sen\u0026rsquo;s slope parameter was also computed, and it showed where and when precipitation changed the most in mm/year. A decreasing trend during the first wet season, and an increasing trend at the start of dry season were observed. Majorly there was no trend in November which then shifted to an increasing trend in December. The complete overview of results from these tests can be found in the Fig.S4 and Fig. S5, Supplementary Material.\u003c/p\u003e\n\u003ch2\u003e3.4 LULC change analysis\u003c/h2\u003e\n\u003cp\u003eTable 3 presents the percentage changes in land cover class areas over time. Positive values indicate an expansion in coverage, while negative values indicate a reduction. Built-up areas had the highest increment in coverage at 300% between 2000 and 2020. On the other hand, bare land suffered the largest loss, at approximately 50%, mostly occurring between 2010 and 2020. The increase in forest cover between 2010 and 2020 can be attributed to reafforestation efforts and government policies promoting forest conservation in the basin.\u003c/p\u003e\n\u003cp\u003eTable 3: Percentage change in area for the different time periods\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.179445350734095%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLand cover classes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"69.8205546492659%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage change in area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.80373831775701%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2000 - 2010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.51401869158879%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2010 - 2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.6822429906542%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2000 \u0026ndash; 2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.179445350734095%\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.412724306688418%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.796084828711255%\"\u003e\n \u003cp\u003e-0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.611745513866232%\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.179445350734095%\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.412724306688418%\"\u003e\n \u003cp\u003e-3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.796084828711255%\"\u003e\n \u003cp\u003e4.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.611745513866232%\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.179445350734095%\"\u003e\n \u003cp\u003eGrassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.412724306688418%\"\u003e\n \u003cp\u003e-2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.796084828711255%\"\u003e\n \u003cp\u003e-1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.611745513866232%\"\u003e\n \u003cp\u003e-3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.179445350734095%\"\u003e\n \u003cp\u003eWetland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.412724306688418%\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.796084828711255%\"\u003e\n \u003cp\u003e-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.611745513866232%\"\u003e\n \u003cp\u003e-0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.179445350734095%\"\u003e\n \u003cp\u003eBuilt-Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.412724306688418%\"\u003e\n \u003cp\u003e136.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.796084828711255%\"\u003e\n \u003cp\u003e69.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.611745513866232%\"\u003e\n \u003cp\u003e299.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.179445350734095%\"\u003e\n \u003cp\u003eSparse Vegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.412724306688418%\"\u003e\n \u003cp\u003e-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.796084828711255%\"\u003e\n \u003cp\u003e-0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.611745513866232%\"\u003e\n \u003cp\u003e-2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.179445350734095%\"\u003e\n \u003cp\u003eBare Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.412724306688418%\"\u003e\n \u003cp\u003e-2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.796084828711255%\"\u003e\n \u003cp\u003e-48.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.611745513866232%\"\u003e\n \u003cp\u003e-49.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.179445350734095%\"\u003e\n \u003cp\u003eWater Body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.412724306688418%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.796084828711255%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.611745513866232%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTransition matrices were generated to further analyze the LULC changes over different time periods (Table S3, Supplementary Materials). Similar findings were obtained, such as built-up areas having the highest uniform gain at 50% whereas all the other classes gained about 13% between 2000 and 2020. Bare land and water suffered the highest uniform losses at 25% and 27% respectively. The uniform gain in built-up areas was higher in the first decade, reaching 30%. Overall, the transition matrices demonstrated conservative changes in most of the classes across all time periods, with similar uniform losses and gains observed, except for built-up areas and bare land. Results from the change budget analysis (Fig. S6) also showed that built-up areas experienced the greatest gain, while bare land encountered the highest loss during the analyzed period. The intensity of transitions for the different land cover classes between 2000 and 2020 was also analyzed (Fig. S7). The average loss and gain of the different classes determined the uniform loss and gain at 17% and 14% respectively. Only the gain of built-up areas can be termed as active as its intensity of transition was above the uniform line. The rest were dormant gains. For the losses, bare land suffered an active loss whereas the rest were dormant. Fig. 5 displays the gain in the land cover changes in the LVB between 2000 and 2020, indicating persistence of most classes. Agricultural areas increased in Kenya, while built-up areas expanded in the Winam gulf and along the shores of the lake in Uganda (Kampala and Wakiso districts).\u003c/p\u003e\n\u003ch2\u003e3.5 Linking changes in precipitation and land use/cover to water quality\u003c/h2\u003e\n\u003ch3\u003e3.5.1 Winam Gulf in Kenya\u003c/h3\u003e\n\u003cp\u003eTable 4 shows the mean annual rainfall, Chl-a and TUR concentrations and the changes in percentage for the areal coverage of agricultural and built-up areas in the Winam Gulf starting with 2000 as the benchmark.\u003c/p\u003e\n\u003cp\u003eTable 4: Mean annual rainfall, Chl-a, and TUR concentrations, LULC changes over the years in the Winam Gulf\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.757475083056477%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.757475083056477%\"\u003e\n \u003cp\u003eRainfall (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e1177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e1519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e1235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e2255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e1909\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.757475083056477%\"\u003e\n \u003cp\u003eChl-a (mg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e23.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e40.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e13.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e15.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e12.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.757475083056477%\"\u003e\n \u003cp\u003eTUR (NTU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e12.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e33.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e14.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e26.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.795341098169718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.20465890183029%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage increment in area (%) starting from 2000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.757475083056477%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLand cover classes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.757475083056477%\" valign=\"top\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\" valign=\"top\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\" valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\" valign=\"top\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\" valign=\"top\"\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.757475083056477%\" valign=\"top\"\u003e\n \u003cp\u003eBuilt-Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\" valign=\"top\"\u003e\n \u003cp\u003e237.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\" valign=\"top\"\u003e\n \u003cp\u003e49.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\" valign=\"top\"\u003e\n \u003cp\u003e29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\" valign=\"top\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.448504983388704%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe levels of TUR and Chl-a increased in correlation with higher rainfall, reaching their peak values in 2010. In 2022, TUR showed significant improvement, indicating good water quality (1-5 NTU). However, the gulf remained mesotrophic-eutrophic for Chl-a despite having the lowest value in recent years. Fig. 6 shows the seasonal variations of turbidity and chlorophyll-a with precipitation. Chl-a increases during the first wet season, decreases during the dry season, and peaks again at the start of the second wet season. TUR follows a similar pattern.\u003c/p\u003e\n\u003cp\u003eFor the comparison with the LULC changes in the gulf, particular attention was given to agricultural and urban areas. These areas were considered of utmost importance due to their high population density, intensive land use practices, and significant dependence on water resources (Calamari et al., 2006). There was an overall steep increment especially from 2000 to 2005.\u003c/p\u003e\n\u003ch3\u003e3.5. Inner Murchison Bay in Uganda\u003c/h3\u003e\n\u003cp\u003eTable 5 shows the mean annual rainfall together with the Chl-a and TUR concentrations in the IMB obtained from the RS data. It also shows the change in area as a percentage for agricultural and built-up areas in the IMB starting from 2000.\u003c/p\u003e\n\u003cp\u003eTable 5: Mean annual rainfall, Chl-a, and TUR concentrations and LULC changes over the years in the IMB.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.165820642978005%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.165820642978005%\"\u003e\n \u003cp\u003eRainfall(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e1191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e1386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e1173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e1764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e1538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.165820642978005%\"\u003e\n \u003cp\u003eChl-a (mg/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e66.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e65.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e79.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e42.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e26.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.165820642978005%\"\u003e\n \u003cp\u003eTUR (NTU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e15.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e22.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e22.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e12.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e18.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.165820642978005%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"62.267343485617594%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage increment in area (%) starting from 2000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.165820642978005%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLand cover classes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.165820642978005%\"\u003e\n \u003cp\u003eAgriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\" valign=\"top\"\u003e\n \u003cp\u003e-34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\" valign=\"top\"\u003e\n \u003cp\u003e-17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\" valign=\"top\"\u003e\n \u003cp\u003e-8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\" valign=\"top\"\u003e\n \u003cp\u003e-14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.165820642978005%\"\u003e\n \u003cp\u003eBuilt-Up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\" valign=\"top\"\u003e\n \u003cp\u003e68.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\" valign=\"top\"\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\" valign=\"top\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\" valign=\"top\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.566835871404399%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eChl-a concentrations ranged between 20-80 mg/m\u003csup\u003e3\u003c/sup\u003e (eutrophic conditions) which are attributed to pollution from nearby towns and agricultural areas. These concentrations decreased with increased rainfall. In 2016, the IMB had the highest TUR and Chl-a concentrations. TUR concentrations ranged from 6 to 25 NTU (fair water quality) which can be attributed to suspended matter after heavy rainfall from urbanized cities. In 2022, TUR significantly improved to 1-5 NTU, indicating good water quality. However, the bay still had mesotrophic-eutrophic conditions based on Chl-a levels. Built-up areas increased significantly over time, particularly from 2000 to 2005, while agricultural areas decreased. Chl-a concentrations decreased after 2016, possibly due to reduced agricultural land use. The increase in TUR levels, particularly in 2010 and 2022, can be attributed to rapid urban growth in the bay. Fig. 7 illustrates the seasonal variations of Chl-a and TUR in the IMB. Chl-a concentrations increased at the start of the MAM season, declining towards the end, peaking in the dry season, and decreasing again in the OND season. Turbidity was highest during the OND season and lowest at the end of MAM season, gradually increasing in the dry season. These trends indicated that Chl-a and TUR were lowest during heavy rainfall and highest during moderate rainfall.\u003c/p\u003e\n\u003cp\u003eDue to the availability of in-situ data for the IMB from 2013 to 2022, a similar analysis was done to compare the findings with those from the RS data. The findings were similar those obtained with the RS data as Chl-a and TUR decrease with heavy precipitation and only peak during moderate rainfall events. TUR performed better as it had a better correlation with similar pattern and trend between the RS and in-situ monthly concentrations.\u003c/p\u003e"},{"header":"4.\tDiscussion","content":"\u003ch2\u003e4.1 Water quality in Lake Victoria\u003c/h2\u003e\n\u003cp\u003eThe RS data used in this study was first validated using in-situ measurements of the IMB. From the statistical and graphical comparisons, ESA RS data showed a stronger correlation with in-situ measurements compared to VITO data, possibly due to differences in retrieval algorithms and spatial resolution. TUR measurements outperformed Chl-a due to the presence of suspended sediment in the lake, making Chl-a measurement challenging especially in turbid and eutrophic areas (Ambrose-Igho et al., 2021; Baltodano et al., 2022). There are a variety of reasons that can account for the poor correlation between the RS and in-situ datasets. Potential sources of error include differences in spatial and temporal resolution, transformation from TSI to Chl-a for VITO data and the poor quality of in-situ data as it had missing values. It is also important to note that the accuracy of satellite-derived Chl-a and TUR estimates can be affected by cloud cover, atmospheric correction, and calibration errors (Matthews et al., 2012), while in-situ measurements can be affected by sensor drift, calibration and measurement errors, and environmental factors such as fouling (Mills \u0026amp; Fones, 2012). However, despite the above challenges, the combination of statistical metrics and visualizations provided a comprehensive understanding of the correlation between the satellite and in-situ data. Improving the spatiotemporal coverage of in-situ measurements and optimizing retrieval algorithms for RS would improve the correlation (Garaba et al., 2014; Gidudu et al., 2018). Enhancements in the precision of RS methods, both in terms of radiometric, spatial, and spectral aspects, along with the growing abundance of satellite images captured across various locations and time periods, will also continue elevating the capabilities for extensive monitoring of optically active water quality parameters such as turbidity, chlorophyll-a (Gholizadeh et al., 2016).\u003c/p\u003e\n\u003cp\u003eMajority of the lake classified as oligotrophic from 2005 to 2022 with eutrophic-mesotrophic conditions along the shores indicating increased nutrient loading which is likely attributable to the increment in rainfall extremes that have become more frequent in recent years (Evans et al., 2020). Most vulnerable part of the lake is the Winam gulf in Kenya which had the mesotrophic conditions in 2010 as well as very high turbidity levels in 2010 and 2020 that can be credited to rapid urban development and agricultural practice (Fusilli et al., 2014). Turbidity in the lake remained relatively stable in the inner waters over the study period, but the shores experienced significant variations and poor water quality likely caused by climate change coupled with human activities. Hence, the need to always quantify impacts of land cover transformations on hydrological factors such as runoff (Baltodano et al., 2022). Additionally, it is worth noting that TUR and Chl-a exhibited a nearly similar temporal and spatial distribution pattern suggesting that TUR may primarily be influenced by algae rather than sediment load (Ambrose-Igho et al., 2021).\u003c/p\u003e\n\u003ch2\u003e4.2 Precipitation variability across the LVB and its impacts on water quality\u003c/h2\u003e\n\u003cp\u003eThe analysis showed a high spatial variability of precipitation across the whole LVB for the period of study with the lowest amount received in the Southeastern part. The highest rainfall was recorded in 2020 whereas the lowest in 2000 and 2005. The LVB has reportedly been receiving an increase in rainfall over the past decades as shown in previous studies (Kizza et al., 2009; Awange et al., 2013; Akurut et al., 2014; Nkwasa et al., 2022). The Mann-Kendall (MK) test showed that the \u0026ldquo;long rains\u0026rdquo; season received less rainfall over the years with the \u0026ldquo;short rains\u0026rdquo; season receiving more especially in December. The dry season was also becoming wetter, more so in July indicating a shift in the on-set and cessations of the MAM and OND seasons (Evans et al., 2020). This shows that there are climatic changes taking place across the basin similar to the findings from the MK test done by Kizza et al. (2009), that showed positive trends dominating the Lake Victoria Basin over the 20\u003csup\u003eth\u003c/sup\u003e century. \u003c/p\u003e\n\u003cp\u003eThe comparison of the chlorophyll-a concentrations and turbidity of the Winam Gulf in Kenya to precipitation showed that the water quality parameters increased with rainfall. This rise is possibly due to runoff carrying sediments and nutrients from agricultural areas, a major land use around the gulf. Consequently, nutrient flux promotes eutrophication, reflected in elevated chlorophyll-a and higher turbidity. The improvement in TUR concentrations in 2022 can be attributed to reduced rainfall hence less sediment transport. The seasonal analysis showed that for Chl-a, an increase in rainfall resulted in higher concentrations which could be explained by the increased nutrient availability and hence subsequent algae growth. A similar relationship was obtained with TUR. This trend is ascribed to the impact of intense precipitation events on erosion, particularly in urban and agricultural areas, as well as flooding, which agitates sediment that has deposited on the bed of the tributary rivers or lake(Rui et al., 2012). On the other hand, Chl-a concentration in the IMB decreased with an increase in rainfall. The observed phenomenon is explained by the fact excessive rainfall increases nutrient runoff, promoting algae growth and consequently elevating Chl-a levels. However, the downside is that excessive algae growth leads to oxygen depletion due to the decomposition of organic matter, limiting further growth and resulting in a reduction in Chl-a concentration (Varadharajan \u0026amp; Soundarapandian, 2014). Furthermore, excessive rainfall and runoff also reduce light penetration due to heightened turbidity as well as altering water temperature and salinity, further impacting phytoplankton growth, and resulting in decreased chlorophyll-a levels (Acharyya et al., 2020; Nair \u0026amp; Nayak, 2023). We also note that built-up areas dominate the IMB, likely having a modest impact on Chl-a concentration. This may also explain reduced Chl-a during increased rainfall. \u003c/p\u003e\n\u003cp\u003eThe seasonal analysis with precipitation data indicated that turbidity levels were lowest during periods of abundant precipitation and highest with moderate rainfall. This could be explained by the fact that during heavy rainfall events, the rapid flux of water can effectively dilute the concentration of suspended particles, causing a temporary spike in turbidity. On the other hand, during moderate rainfall events, the slower water flow and increased sediment accumulation lead to a more sustained increase in turbidity. Seasonal precipitation compared to in-situ and RS data for the IMB showed similar results. TUR trends for the in-situ and RS datasets were more similar than those of Chl-a because of the challenges encountered in retrieving Chl-a using satellites particularly in eutrophic waters (Ambrose-Igho et al., 2021). This analysis further supports the reliability of RS data as a complement to ground-based data, particularly in data scarce regions.\u003c/p\u003e\n\u003ch2\u003e4.3 LULC changes in the LVB and their influence on water quality in the lake\u003c/h2\u003e\n\u003cp\u003eThe basin\u0026apos;s land cover changes were mostly conservative during 2000-2020, with minimal losses and gains observed except for built-up areas and bare land. The land cover changes align with prior research, with slight variations in the land classification, spatial and temporal scales (Berakhi et al., 2015; Kiggundu et al., 2018; Mugo et al., 2020; Onyango et al., 2021). The increase in built-up areas in the Lake Victoria Basin is attributed to population growth, urbanization, and government policies promoting infrastructure development (Onyango \u0026amp; Opiyo, 2022). Conversely, the loss of bare land can be explained by the increase in constructed areas. Similar drivers have been demonstrated to influence extensive LULC changes in various regions of the LVB (Ebanyat et al., 2010; Wasige et al., 2013). The increase in forested areas is accredited to afforestation drivers in the region since the 1980s (Mugure \u0026amp; Oino, 2013). \u003c/p\u003e\n\u003cp\u003eFor the LULC change analysis in the Winam Gulf, there was a slight increment in agricultural areas from 2005 till 2010. An increase in agricultural fields leads to increased runoff of nutrients like nitrogen and phosphorus which result into algae blooms that cause loss of biodiversity, alterations in food chain dynamics, and deterioration of fisheries (Verschuren et al., 2002; Wasige et al., 2013). The decline in Chl-a concentrations after 2010 can be attributed to the reduction in agricultural areas near the gulf. In contrast, the rise in turbidity between 2016 and 2020 can be attributed to increased built-up areas, which intensify stormwater runoff and flooding events. The comparison of water quality parameters with LULC changes in the IMB revealed a significant reduction in Chl-a levels possibly due to decreased agricultural land use and nutrient inputs as we observe a reduction in the agricultural areas from 2000 to 2020. The increase in TUR levels, especially in 2010 and 2022, could be explained by the increase in built-up areas in the IMB during the last two decades thus increased sediment transport. Similar findings were reported by Bhaskar \u0026amp; Gidudu (2020) showing a substantial 119% rise in impervious surface area and a 12% decline in vegetative cover between 1995 and 2019, that was followed by an increase in Chl-a concentration in the bay. \u003c/p\u003e\n\u003ch2\u003e4.4 Limitations\u003c/h2\u003e\n\u003cp\u003eThe results of this research, while aligning with previous studies, are hampered by several limitations. Firstly, the short time span for satellite and in-situ data available restricted the analysis of trends. Secondly, only the influence of precipitation and LULC changes on water quality in the Winam Gulf and IMB was examined. While the increased built-up areas in the Winam Gulf and the prominent agricultural practices may contribute to TUR and Chl-a concentrations, substantial rainfall was a better explanatory factor for the water quality patterns and trends. This showed that rainfall plays a crucial role in influencing water quality dynamics, particularly in areas with unrestricted human activities like urbanization and agriculture (Tuladhar et al., 2019). In the inner Murchison Bay, high Chl-a levels could not be solely explained by land use changes, suggesting additional pollution sources such as industries and sewage effluents to be responsible for the elevated Chl-a and TUR levels. Furthermore, examining water quality from the tributary rivers of the lake is essential to comprehensively understand the influence of land cover changes on water quality, as rivers serve as conduits for transporting nutrients and pollutants from land to the lake. \u003c/p\u003e\n\u003cp\u003eIn addition, mixing processes in the lake could be a factor to consider as a study by Hecky (1993) showed that some of the causes of degradation in Lake Victoria are shallow mixing depths because of climate change and low flushing times. Okungu et al. (2005) suggested that winds from a southerly direction dominated mixing and nutrient transport processes at Winam Gulf, whereas winds from the northeast were less important in the annual transport of nutrients and sediments. A mixing box model by Gikuma-Njuru et al. (2013) also revealed that most nutrients from river inflows and municipal sources entering the Winam Gulf were retained within the gulf, with only a small portion transferred to the main lake. For the IMB, wind-induced mixing influenced its water quality (Ssebiyonga et al., 2013; Akurut et al., 2017). Thus, it is vital to explore the wind dynamics over the lake as well the flushing and exchange patterns between the bays, gulfs, and the offshore waters. \u003c/p\u003e\n\u003cp\u003eThere are quite a variety of other natural and human-induced factors that influence the Chl-a and TUR concentrations of a water body such as excessive nutrient load from point and diffuse sources, raw or semi-treated effluents from industries and wastewater treatment facilities, physical factors such as the lake\u0026rsquo;s flushing rate, loss of biodiversity, climate change etc. According to Kaijanangoma (2020), effluents discharged into Lake Victoria are primarily raw or partially treated. Treatment ponds installed at most wastewater treatment facilities and industries are inadequate in size with a short retention time. As a result, raw and semi-treated wastewater is released into the lake, leading to contamination and harm to fish breeding sites due to high BOD. While nutrient load is a key driver of eutrophication, factors like temperature, precipitation, wind velocity, and solar radiation can increase the risk. Climate change, characterized by shifts in precipitation, temperature, wind speed, and solar radiation, directly impacts water quality by altering stream flow and water temperatures (Rui et al., 2012; Rolighed et al., 2016). Studies conducted by Cooke et al. (2016) and Krzyk et al. (2015) revealed that certain lakes are exposed to tidal effects and contamination because of their outlets. These lakes undergo frequent renewal, making them less susceptible to eutrophication. Consequently, the polluted water in such lakes is efficiently purified due to its short residence time. Thus, while this analysis examines land use and precipitation, it is important to acknowledge that other environmental factors specific to the lake\u0026apos;s location can also influence water quality (Liu et al., 2010).\u003c/p\u003e"},{"header":"5.\tConclusion ","content":"\u003cp\u003eThis study aimed at using satellite data to link land use and precipitation changes in the Lake Victoria basin to water quality changes in the lake with a special focus on two water pollution hotspots namely the Winam Gulf and inner Murchison Bay. Analyzing the relationship between precipitation changes and Chl-a and TUR concentrations in different parts of the lake highlighted the significant impact of rainfall on water quality as it accounts for approximately 80% of the water input to the lake and influences runoff and erosion processes. Elevated rainfall leads to increased runoff and erosion, transporting pollutants and nutrients from the land into the lake, notably in expansive agricultural areas like the Winam Gulf. Urban areas, such as the IMB, characterized by extensive impervious surfaces, experience increased turbidity. Intense rainfall, on the other hand, can also induce sediment dilution in the lake, as noted in the IMB. Therefore, the extent of rainfall\u0026apos;s influence on water quality is affected by various factors, such as rainfall intensity and duration, land use practices, and lake characteristics. Agricultural and built-up areas also provide sources of nutrients and pollutants, which with heavy rains, end up in our water bodies and contribute to an increase in TUR and Chl-a concentrations as witnessed in the Winam gulf and IMB.\u003c/p\u003e\n\u003cp\u003eThe analysis revealed that while land use changes had a positive correlation with Chl-a and TUR concentrations in the Winam Gulf and inner Murchison Bay, changes in rainfall provided a better explanation for the observed patterns. However, it is important to recognize the complex interaction between land use, urbanization, and climate change in affecting turbidity and chlorophyll-a levels in lakes. Effective water management strategies may need to address multiple factors simultaneously. With the availability of remote sensing data, it is crucial to explore other factors like wind that influence water quality in lakes and rivers. Future research could investigate the impact of wastewater treatment effluents and wind speed on chlorophyll-a and turbidity respectively. Improving algorithms for retrieving water quality parameters from RS data would also enhance the accuracy of results and their alignment with ground measurements, increasing reliability and validity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Research Foundation \u0026ndash; Flanders (FWO) for funding the International Coordination Action (ICA) \u0026ldquo;Open Water Network: Open Data and Software tools for water resources management\u0026rdquo; (project code G0E2621N) and the AXA chair program on Water Quality and global change for funding this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets used in this study are openly available and are accessible from these links:\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;VITO\u0026rdquo; RS data for turbidity and trophic state index is available from https://land.copernicus.eu/global/products/lwq\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;ESA\u0026rdquo; RS data for turbidity and chlorophyll-a is available from https://climate.esa.int/en/projects/lakes/data/\u003c/p\u003e\n\u003cp\u003eCHIRPS rainfall data is available from https://www.chc.ucsb.edu/data/chirps\u003c/p\u003e\n\u003cp\u003eESA-CCI Land cover maps are available from https://www.esa-landcover-cci.org\u003c/p\u003e\n\u003cp\u003eAnalysis and processing of data was done using Python scripts. Scripts can be obtained from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaria Theresa Nakkazi: formulated study, data collection \u0026amp; analysis, results interpretation, and manuscript preparation. Albert Nkwasa: formulated study, results interpretation, and manuscript\u0026mdash;review and editing. Analy Baltodano Mart\u0026iacute;nez: data collection, manuscript\u0026mdash;review and editing. Ann van Griensven: study supervision, funding, results interpretation, and manuscript\u0026mdash;review and editing. All authors read and approved the contents of the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcharyya, T., Mishra, M., \u0026amp; Kar, D. (2020). 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Advances in quantitative remote sensing product validation: Overview and current status. \u003cem\u003eEarth-Science Reviews\u003c/em\u003e, \u003cem\u003e196\u003c/em\u003e, 102875. https://doi.org/10.1016/j.earscirev.2019.102875\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"climate change, land use change, remote sensing, Lake Victoria ","lastPublishedDoi":"10.21203/rs.3.rs-3873388/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3873388/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Due to the continued increase in land use changes and changing climatic patterns in the Lake Victoria basin, understanding the impacts of these changes on the water quality of Lake Victoria is imperative for safeguarding the integrity of the freshwater ecosystem. Thus, we analyzed spatial and temporal patterns of land cover, precipitation, and water quality changes in the Lake Victoria basin from 2000 to 2022 using processed remote sensing (RS) data. Focusing on chlorophyll-a (Chl-a) and turbidity (TUR) in Lake Victoria, we used statistical metrics (correlation coefficient, trend analysis, change budget, and intensity analysis) to understand the relationship between land use and precipitation changes in the basin with changes in Chl-a and TUR at two major pollution hotspots on the lake i.e. Winam Gulf and Inner Murchison Bay (IMB). Results show that the Chl-a and TUR concentrations in the Winam gulf increase with increases in precipitation. Through increases in precipitation, the erosion risks are increased and transport of nutrients from land to the lake system, promoting algal growth and turbidity. In the IMB, Chl-a and TUR concentrations decrease with increase in precipitation, possibly due to dilution, but peak during moderate rainfall. Interestingly, LULC changes showed no substantial correlation with water quality changes at selected hotspot areas even though LULC change analysis showed a notable 300% increase in built-up areas across the Lake Victoria basin. These findings underscore the dominant influence of precipitation changes over LULC changes on the water quality of Lake Victoria for the selected hotspot areas.","manuscriptTitle":"Linking Land Use and Precipitation Changes to Water Quality changes in Lake Victoria Using Remote Sensing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-21 20:25:10","doi":"10.21203/rs.3.rs-3873388/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-16T02:11:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-13T10:34:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-11T14:53:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3b2f1218-0056-44c2-bdf0-5c028bef4ecf","date":"2024-04-09T01:46:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76382ff4-534c-4bbf-b9a4-9046d8dc49c2","date":"2024-04-08T19:28:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"d93742c0-607d-420c-ba2b-0f2181b4123c","date":"2024-04-08T08:27:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1ac0f24d-056a-4a56-b35c-ed5ead316d45","date":"2024-02-27T16:15:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5b631c6f-8c1e-469a-89dc-623cb064c957","date":"2024-02-26T07:18:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c2b73e19-5afd-4708-9573-512f3b23eaee","date":"2024-02-26T03:39:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-23T19:13:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-19T03:15:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-19T03:15:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2024-01-17T16:28:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2eb2738f-c077-4578-b3f4-ce8a515ec5be","owner":[],"postedDate":"February 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-28T16:11:08+00:00","versionOfRecord":{"articleIdentity":"rs-3873388","link":"https://doi.org/10.1007/s10661-024-13261-2","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2024-10-25 15:57:49","publishedOnDateReadable":"October 25th, 2024"},"versionCreatedAt":"2024-02-21 20:25:10","video":"","vorDoi":"10.1007/s10661-024-13261-2","vorDoiUrl":"https://doi.org/10.1007/s10661-024-13261-2","workflowStages":[]},"version":"v1","identity":"rs-3873388","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3873388","identity":"rs-3873388","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}