Quality and quantity of streamflow are crucial components in the management and control of water resources according which are challenging due to their non-stationarity and uncertainty path. As industrial and economic growth intensifies, greenhouse gases are released into the atmosphere, leading to a shift in global warming and climate patterns. Brahmani River, Odisha is one of the most important sources of water, and river water is mostly used for drinking, agriculture, and animal activity. As a result, this watershed faces significant challenges such as flooding, drought, irrigation, and water supply scarcity, as well as health issues stemming from climate change within the community. Conventional hydro-chemical data have been used to anticipate the spatial variability of surface water and evaluate the origins of its contamination, in conjunction with mathematical, statistical, and geostatistical models. For this purpose, 10 years (2013-2023) dataset of 15 water quality (WQ) variables, covering 7 monitoring stations, and approximately 200 observations was used. The surface water (SW) quality map was developed by geographical information system (GIS) interface as per weighted arithmetic (WA), synthetic pollution index (SPI), numerow's pollution index (NPI), comprehensive pollution index (CPI) and overall index of pollution (OIP). The WQ index (I) values were found ranging from 49-72.02 (WA), 0.31-0.68 (SPI), 6-29.91 (NPI), 0.46-1.90 (CPI) and 0.45-4.40 (OIP). The visualization of the WQI distributions using GIS software recommends pointing the source of pollution to WQ degradation regions. Multivariate Statistical Approach was employed to evaluate the water's quality, ascertain the relationship between the river's physical and chemical characteristics and the local water type trend. Meanwhile, discriminant analysis (DA), hierarchical clustering (HC), and principal component analysis (PCA) were employed to classify chemical properties and identify the factors affecting quality variations. Conversely, HC analysis applied to data on water quality, generates three sample clusters, each of which is adequately explained by the PCA variables. The Cluster-I comprises 57.14% of water samples, which is suitable for irrigation as well as drinking. The spatial distribution of water quality reveals that the area's four areas are mostly related to the drinkable and irrigation water. These patches could be used to provide the local populace with drinking water. The HC finding was confirmed using DA, which became easier to identify the factors separating the observed groups. Thus, DA produced eleven parameters for water quality that includes, EC, Ca2+, alkalinity, K+, F-, Cl-, SO42-, NO3-, Na+, TDS, and PO43-, enabling 100% accurate allocations in river spatial analysis. As per PCA, the two factors that were extracted and dealt with 82.61% and 9.24% of the overall disparity. The PCA results shown that the primary causes influencing the river's WQ are weathering, leaching, and human activities. Additionally, the factor score plot distinguished between the polluted and potentially declining WQ areas. However, Ordinary kriging interpolation methods were applied to the parameters pertaining to the quality of SW, and the optimal interpolation model was subsequently found by cross-validation using the nugget, sill, average standard error (ASE), and root mean square error (RMSE) criteria. Over the specified sample time, the best results are obtained at the circular, gaussian, and exponential distributions. Furthermore, the upstream and downstream portions of the basin, where the landfill was located, showed the highest levels of SW contamination and deteriorated WQ on the spatial distribution maps. Also, more than 50% of the research region had poor and extremely poor WQ, thus implies inappropriate for drinking. From the results, it has been concluded that in the current location, it discloses that St-(1), (2), (3) and (7) comes under extremely polluted group. The main problems, which persist despite a significant amount of riverside development, are the partial or complete discharge of domestic sewage into the river without proper treatment. Therefore, based on the observed trends of decreased streamflow volume, recommendations for the study area include the development of water sources such as micro dams, ponds, and water wells, implementation of water harvesting techniques, improvement of land use and land cover practices, proper utilization and management of available discharge, drought assessment, and environmental impact assessment. Results obtained from this study indicate that the proposed hybrid model can capture the non-linear characteristics of a streamflow process in terms of water quality and quantity simultaneously and thus provide more accurate predicting results without frquency decomposing.
