Projecting models for river changes and evaluation of water quality of the savannah river networks - Adamu, Aliyu Gaddafi (2020)
This study seeks to project river Galma changes in the savannah region of Nigeria. The total area which was covered by water at River Galma has greatly decreased over years, which trigger more water pollution-associated issues in the area. The rapid landuse changes within the river basin has directly and indirectly caused long term implication towards the river quality degradation, therefore the Markov Chain Model (MC) and Cellular Automated Markov model (CA-Markov) used in this study will be useful in predicting future changes in the study area. The satellite image dataset of 2004, 2011 and 2018 was used in 2025-projection of river changes by incorporating both landuse changes pattern and water quality trends by adopting the MC and CA-Markov model. The hydrological factors which characterize the river responses were analysed with specific focus on the flow and water quality relationship, apart from the climatological influences towards this dam-regulated river system. A set of statistical tools, namely hierarchical cluster analysis (HCA), independent samples test (t-test), and Principal Component Analysis (PCA) was used on top of the standard water quality index (WQI) calculation to characterize the by-season and by-location variation of the water quality in the area. The shrinking pattern of water-covered area between the comparable satellite images in 7 years-interval period shows a decreasing trend in total water covered area (6.08 km2 in 2004, 4.71km2 in 2011, and 4.63 km2 in 2018), and projected to further reduced in 2025 (4.39 km2 ), which support by the decreasing trend in the river flow pattern over the tested dataset. The landuse prediction (surface total area changes) was made based on the dramatic changes of expanding agricultural-associated landuse type over that 14 years stretch of landuse changes trend, in exponential. The study area is considered as data-scarce in term of water quality, hence the prediction of future water quality status was made based on the complete one water cycle year from the fieldwork activities in 2018-2019. The WQI revealed the pollution increased in a downstream ward pattern in both wet and dry season, taking the downstream most sampling station (Sp. 15) as the most polluted section (WQI of 102.24 and 118.20 in wet and dry season respectively). The upstream most sampling station (Sp.1), on contrary has better water quality status (62.74 and 75.14 in both wet and dry seasons respectively), which justify the landuse activities in the vicinity of the tested river section. T-test shows that nine (9) out of eighteen (18) water parameters were statistically significant (p< 0.05) in contributing the overall water quality status of the river for both tested seasons, with increased pollutant concentration values were observed during the dry seasons which can be directly linked to less rainfall-runoff interaction within the study area. The polluted most section (Sp.15) might have receiving direct contribution of pollutant-laden runoff from the nearby industrial and agricultural activities, besides the cumulative effects of the pollution from the upstream region at this downstream section. The upper section of the river basin was dominated by agricultural activities as the dominant landuse, hence can be associated directly with the significantly high concentration of agricultural-related pollutants such as nutrients load (magnesium, sulphate and phosphate). Different clusters were found during wet and dry season HACA classification analysis, with distinct and consistent pollution categories were discovered for Cluster 1 (Sp5, Sp6, Sp7 and Sp9 located middle of the river) and the remaining sampling points indicates difference in cluster 2, 3 and 4 in both tested season. To complement the HACA by-location pollution classification, the PCA was useful in determining the specific prominent pollutants that responsible in characterizing the overall state of water quality condition at polluted stations. The PCA suggests that 79.33% and 80.09% of the total variance in the variables from the respective wet and dry season samples can be directly associated with agriculture activities, with the turbidity, phosphate, magnesium, and Sulphate as the consistent pollutants type from both tested season. Similarly the LULC result show that for 14 years (2004 to 2018) the river decreases 1.45km2 , which signify a reduction in size from (6.08 km2 to 4.63 km2 ) at a declining rate of 0.104 per annum resulting from agriculture and human influence on the river. By this investigation, the results will provide a better understanding of the changes, spatial variation, and recent water quality status of the river in order to implement appropriate strategies to minimize changes and improve water quality management efforts in the river basin. Thus, Projecting models of land use change, trend test, inferential statistics, WQI, environmetric multivariate statistical techniques, and mapping for environmental management should be employed in monitoring as they provide a detailed explanation in managing river resources, by making land use decisions that will preserve natural areas of River Galma watershed.