@ARTICLE{Marselina_Mariana_Spatial_2021, author={Marselina, Mariana and Sabar, Anwar and Fahimah, Nurul}, number={No 49}, pages={111-120}, journal={Journal of Water and Land Development}, howpublished={online}, year={2021}, publisher={Polish Academy of Sciences; Institute of Technology and Life Sciences - National Research Institute}, abstract={Developments in agriculture, industry, and urban life have caused the deterioration of water resources, such as rivers and reservoirs in terms of their quality and quantity. This includes the Saguling Reservoir located in the Citarum Basin, Indonesia. A review of previous studies reveals that the water quality index ( WQI) is efficient for the identification of pollution sources, as well as for the understanding of temporal and spatial variations in reservoir water quality. The NSFWQI (The National Sanitation Foundation water quality index) is one of WQI calculation methods. The NSFWQI is commonly used as an indi-cator of surface water quality. It is based on nitrate, phosphate, turbidity, temperature, faecal coliform, pH, DO, TDS, and BOD. The average NSFWQI has been 48.42 during a dry year, 43.97 during a normal year, and 45.82 during a wet year. The WQI helped to classify water quality in the Saguling Reservoir as “bad”. This study reveals that the strongest and most significant correlation between the parameter concentration and the WQI is the turbidity concentration, for which the coeffi-cient correlation is 0.821 in a dry year, and faecal coli, for which the coefficient correlation is 0.729 in a dry year. Both parameters can be used to calculate the WQI. The research also included a nitrate concentration distribution analysis around the Saguling Reservoir using the Inverse Distance Weighted method.}, type={Article}, title={Spatial and temporal assessment of surface water quality using water quality index. The Saguling Reservoir, Indonesia}, URL={http://www.czasopisma.pan.pl/Content/119857/13%20Marselina%20et%20al%20739.pdf}, doi={10.24425/jwld.2021.137103}, keywords={inverse distance weight, spatial and temporal assessment, surface water, water quality index}, }