Estimating Point and Nonpoint Source Pollutant Flux by Integrating Various Models, a Case Study of the Lake Hawassa Watershed in Ethiopia’s Rift Valley Basin

Abstract

Increasing pollutant emissions in the Lake Hawassa watershed (LHW) has led to a severe water quality deterioration. Allocation and quantification of responsible pollutant fluxes are suffering from scarce data. In this study, a combination of various models with monitoring data has been applied to determine the fluxes for Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD5), Total Dissolved Solid (TDS), Total Nitrogen (TN), Nitrate and Nitrite-nitrogen (NOx-N), Total Phosphorous (TP) and phosphate (PO4-P). Water, wastewater and stormwater samples were collected and analyzed at eight monitoring stations from rivers and point sources and six monitoring stations of stormwater samples. The flow simulated with soil and water assessment tool (SWAT) could be very well calibrated and validated with gauge data. This flow from SWAT model, measured flow during monitoring and pollutant concentrations were used in FLUX32 to estimate pollutant fluxes of main rivers and point sources in LHW. The formulas provided by Ethiopian Roads Authority and Gumbel’s theory of rainfall frequency analysis was employed to determine the 2-years return period rainfall depth for the City of Hawassa. The integration of HEC-GeoHMS and SCS-CN with the catchment area enabled to determine stormwater pollution load of Hawassa City. The estimated pollutant flux at each monitoring stations showed that the pollutant contribution from the point and nonpoint sources prevailing in the study area, where the maximum fluxes were observed at Tikur-Wuha sub-catchments. This station was located downstream of the two point sources and received flow from the upper streams where agricultural use is predominant. Furthermore, Hawassa city has been identified as a key pollutant load driver, owing to increased impacts from clearly identified point sources and stormwater pollutant flux from major outfalls. Agricultural activities, on the other hand, covers a large portion of the catchment and contributes significant amount to the overall load that reaches the lake. Thus, mitigation measures that are focused on pollutant flux reduction to the lake Hawassa have to target on the urban and agricultural activities.

Pollutant transport model with large time‐scale and estimation of land‐use export coefficients at a watershed level

“…The ranges of the empirical values of the land export coefficients for farmland, urban, and primary vegetation areas obtained from the literature are listed in Table 5 (Lencha et al, 2022; Povilaitis, 2008; Shrestha et al, 2008; Ukita & Nakanishi, 1999; Vassiljev et al, 2008; Yan et al, 2019).…”

Agricultural land use and lake morphology explain substantial variation in water quality across Canada

“…However, mitigation of point sources does not always result in reduced nutrient and algal biomass concentrations in lakes, even in cases where combined (N and P) nutrient loading reduction programs have been applied to lakes and their catchments (Özkundakci et al 2011; McCrackin et al 2017). Nonpoint, or diffuse, sources of nutrient inputs remain the main cause of eutrophication worldwide (Reid et al 2019) partly due to the difficulties in quantifying and regulating them: fluxes can vary temporally (Medalie et al 2012) and spatially (Lencha et al 2022) and are often regulated by management strategies with conflicting purposes such as food production and natural resource protection (Wunderlich and Martinez 2018). Furthermore, annual inputs of N and P from agricultural activities often exceed the removal by harvested crops (MacDonald et al 2011; Spiess 2011), leading to a surplus of highly mobile nutrients in soils (GOC 2016) that can leach into downstream water bodies (Cassman and Dobermann 2022).…”

Water Quality Modeling and Monitoring

“…Advanced models or modeling methods also allow researchers, engineers, and managers to better understand water quality dynamics and spatial distributions in watersheds and waterbodies that discrete data collections or monitoring cannot reveal. Point and non-point source pollutant fluxes including chemical oxygen demand (COD), biochemical oxygen demand (BOD 5 ), total dissolved solids (TDS), TN, nitrate and nitrite-nitrogen (NOx-N), total phosphorous (TP), and phosphate (PO 4 -P) were estimated by integrating several models in the Lake Hawassa watershed in Ethiopia's Rift Valley Basin [12]. The integration of HEC-GeoHMS and SCS-CN with the catchment area enabled the stormwater pollution load of Hawassa to be [12].…”

“…Point and non-point source pollutant fluxes including chemical oxygen demand (COD), biochemical oxygen demand (BOD 5 ), total dissolved solids (TDS), TN, nitrate and nitrite-nitrogen (NOx-N), total phosphorous (TP), and phosphate (PO 4 -P) were estimated by integrating several models in the Lake Hawassa watershed in Ethiopia's Rift Valley Basin [12]. The integration of HEC-GeoHMS and SCS-CN with the catchment area enabled the stormwater pollution load of Hawassa to be [12]. Advances in machine learning techniques can serve practical water management needs such as salinity level estimation in California's Sacramento-San Joaquin Delta [13].…”