Assessing the impacts of land use land cover and climatic changes on the cost of water treatment

Abstract

This research aimed at assessing the impacts of land use land cover and climate changes on the cost of water treatment in river Malaba catchment. The MOLUSCE plugin (version 2.18.16) in QGIS Desktop software was used to predict future land use land cover (LULC) changes from 2035 to 2050 using the Cellular Automata-Artificial Neural Network (CA-ANN) model and weather data was projected using a statistical downscaling model (SDSM version 4.2) considering two climate change scenarios (RCP4.5 and RCP8.5). ArcSWAT2012.10.26 in ArcGIS 10.8.2 was used to build a rainfall-runoff model for sediment modelling. The model considered four scenarios and used 10m resolution DEM data to delineate the watershed into 26 subbasins with an automatic delineator. HRUs were defined by setting threshold proportions for land use, soil, and slope classifications. Daily rainfall and temperature data for 1990 to 2005 were used for weather input. The SWAT simulation was run for 1990 to 2020 with a 3-year warmup period for model stabilization. Calibration, validation, and sensitivity analysis were performed using SWAT-CUP software version 5.1.6. Water samples were collected at various land use locations along the river to analyse physical, chemical, and bacterial properties. Statistical analysis (ANOVA at 95% confidence interval) was conducted using Excel to assess how land use variations affect water quality parameters. Data on treatment costs influenced by water quality was obtained from the National Water and Sewerage Corporation (Tororo) and similarly analyzed using ANOVA at a 95% confidence interval. sediment yield for priority intervention. Four best management practices (BMPs) (filter strips, sedimentation ponds, and no-tillage) were evaluated for their effectiveness in reducing sediment. The study found that sedimentation ponds and filter strips were most significant in reducing sediment .Four different BMP scenarios were simulated in SWAT to determine BMPs effectiveness and found out that filter stripes reduce sediment by 30%, sedimentation ponds by 24%, no tillage is by 16% and reforestation by 12% and the significance test was performed which showed sedimentation pond and filter strips were significant at 5% interval and the design of filter strip was done with the width(Filter.wt) of 50ft. Results showed that these changes worsen water quality, leading to increased treatment costs. Water quality parameters like EC, pH, and turbidity were significantly affected by land use and climate change. Treatment costs for aluminum sulfate, HTH, and polymer were higher during the rainy season and in years with high rainfall. The study recommends watershed restoration and specific BMPs like filter strips and sedimentation ponds to reduce sediment and mitigate the impact on water quality and treatment costs.