Modelling Diffuse Pollution on Watersheds Using a Gis-linked Basin Scale Hydrologic/water. Quality Model. R. Salvetti 1 and R.

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1 Modelling Diffuse Pollution on Watersheds Using a Gis-linked Basin Scale Hydrologic/water Quality Model A. Azzellino 1, M. Acutis 2, L. Bonomo 1, E. Calderara 1, R. Salvetti 1 and R. Vismara 1 1. Politecnico di Milano (Technical( University), D.I.I.A.R. Envir. Division,, Piazza Leonardo da Vinci, , Milano, Italy 2. Milano University, Crop Science Dept.. (Di ProVe), Italy

2 Background Diffuse pollution enters the environment by multiple pathways. Non point sources are generally estimated by using area- and pollutant- specific emission factors, function of land use (e.g. Autorità di Bacino del Po) Despite the different temporal scale, instream direct measurements, when available, have often suggested that non-point indirect estimates may lead to large overestimations or to misleading results.

3 Aims of the Study To combine a water quality model, (USEPA- QUAL2E), with a GIS-linked physical-based model (SWAT) in order to assess the source apportionment of point and non-point emissions to the global quality of the rivers. to simulate the factors driving diffuse pollution in large and complex watersheds. To validate the models by comparing their predictions over a regional scale with instream direct measurements available from ARPA monitoring stations.

4 Aim of the Study: Validation of the source apportionment Validation studies on diffuse pollution are not easy to design no readily available tool enables to simulate the river water quality during rainfall events at large scales (i.e. basin-scale) and over the instantaneous temporal scale which is typical of water quality direct measurements Average annual basis Dry-weather scenario (i.e. point emissions over the mean annual flow regime) Wet-weather scenario (i.e. point and non point emissions during rainfall events)

5 Study Area Lombardy Cherio river Watershed 60 km Mincio river Watershed

6 Cherio River Watershed Drainage area: 150 km 2 Watershed covers both the mountainous and the plain zone (approximately half and half of the percentage area) Dominant coltures: Maize (59%), Wheat (30%) Other (11%)

7 Mincio River Watershed Drainage area: 310 km 2 Plain zone within the Po and Mincio Rivers, dominated by a very intensive agricultural land use Dominant coltures: Maize (70%), Wheat (30%) Garda Lake Mincio River Mantua Lakes Po River

8 QUAl2E Modeling Reach3 Tratto 3 Reach1 Tratto 1 Cherio a Casazza Tratto2 Reach2 T. Drione Reach4 Tratto 4 T. Grone Reach8 Tratto 8 T. Tadone Reach5 Tratto 5 Reaches Tratto Cherio Tratto Reach6 6 T. Malmera Reach11 Tratto 11 T. Tirna Reach11 Tratto 12 Cherio a Palosco ASSUMPTIONS Constant streamflow (i.e. Median of 3 years monthly measurements) Constant emissions (point loads calculated on the basis of annual emission factors)

9 Input for Point Emissions Point Sources Emission Factors and Average Treatment Removals used to estimate the domestic wastewater loads. Pollutant Emission Imhoff Removal A.S.* Removal A.S + N Removal A.S. + N, P Removal % % % % BOD 5 60 g BOD PE -1 d COD 129 g COD PE -1 d Total-N 12.3 g N PE -1 d Total-P 1.8 g PE -1 d *A.S.: Activated Sludge PE: Population Equivalent

10 QUAL2E Validation Mean Annual Dry-weather Scenario Cherio River Point load effect simulation was validated for every parameter considered by comparing simulated values with the median of 3 years monthly monitoring campaign (ARPA) Then the same emission factors for point loads, validated with QUAL2E, were used for the SWAT modeling mg/l mg/l Direct Measuremets Qual2E Simulation SWAT simulation N-NH4 N-NO3 P-PO4 P-tot Direct measurements QUAL2E simulation SWAT Simulation N-NH4 N-NO3 P-PO4 Mincio River Point loads control the river quality even in watershed characterized by extensive agricultural land use

11 SWAT SIMULATION: INPUTS DEM grid at a 40 m spacing ERSAL maps on land use and soil characteristics. ERSAL land use codes were translated into the SWAT classes of land cover Weather database generated on the basis of site specific average monthly temperature and precipitation parameters (i.e.min,max, std deviation) Three lakes: Endine and Garda Lakes headwaters of Cherio and Mincio rivers Mantoa lakes within Mincio Watershed.

12 SWAT SIMULATION: Irrigation network Irrigation network

13 SWAT SIMULATION: METHODS Irrigation network The plain zone of the study area, and particularly the Mincio Basin area, is characterised by a very complex system of irrigation channels that overlays the natural hydrographic network. A pseudo-hydrography was generated by means of the Watershed Delineation tool ( Burn In option). Such a simplification reflects reasonably the average pathways of the diffuse pollutants to the main stream Pseudo-Hydrography Generated by the SWAT WD tool

14 Results SWAT simulations Cherio River NO3-N NH4+-N Ptot Diffuse load is much lower in Cherio than in Mincio Basin Diffuse loads increase downstream Diffuse NO3-N load (kg/month) SWAT subbasin Diffuse load NH4+-N and Ptot (kg/month) The presence of Mantoa lakes within Mincio basin smoothes the downstream trend Diffuse NO3-N load (kg/month) NO3-N NH4_N P-PO Diffuse load NH4+-N and Ptot (kg/month) Mincio River SWAT subbasin Mantoa Lakes

15 Results Comparison with the indirect emission estimates approach QUAL2E has been used to simulate the water quality corresponding to streamflows higher Monthly than trend the of 75 non percentile. point loads 200 Non point loads were either estimated by means of SWAT modeling or by 15 using AdBPo indirect emission factors rain (mm) Pollutant 100 Arable Grasslands Forest Other Nitrogen percolation and runoff kg N (ha -1 y -1 ) Phosphorous erosion and runoff kg P (ha -1 y ) % of the total annual load 0 Point loads were kept constant although diluted of 25% April May June July August September October 0 AdBPo diffuse loads, being evaluated on an annual basis, were distributed following the monthly precipitation trend over the monthly average of rainy days. Similarly the SWAT predictions were distributed over the same interval of rainy days.

16 Results Comparison with the indirect emission estimates approach Cherio River Direct Measurement SWAT simulation AdBPo Direct Measurement SWAT simulation AdBPo N-NO3 (mg/l) P-tot (mg/l) Direct Measurement SWAT simulation AdBPo Direct Measurement SWAT simulation AdBPo N-NO3 (mg/l) P-tot (mg/l) Indirect emission factors may lead to remarkable overestimations Mincio River

17 Conclusions The integration of QUAL2E and SWAT enabled to quantify overestimations of the diffuse loads contribution It offers the opportunity to adjust the source apportionment of point and non point contributions to the instream total load within the SWAT model. SWAT predictions resulted to be much more accurate than the indirect emission estimates for both the studied watersheds.