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Optimising Predictions of Sediment and Nutrient Loads Using AnnAGNPS

Optimising Predictions of Sediment and Nutrient Loads Using AnnAGNPS. Lu Y. , J. Dela-Cruz and P. Scanes Waters & Catchments Science Section Department of Environment & Conservation PO Box A290, Sydney South NSW1232 Yi.Lu@environment.nsw.gov.au. Introduction.

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Optimising Predictions of Sediment and Nutrient Loads Using AnnAGNPS

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  1. Optimising Predictions of Sediment and Nutrient Loads Using AnnAGNPS Lu Y. , J. Dela-Cruz and P. Scanes Waters & Catchments Science Section Department of Environment & Conservation PO Box A290, Sydney South NSW1232 Yi.Lu@environment.nsw.gov.au

  2. Introduction • Downstream water quality problems • Non point sources of pollution • Sediment and nutrient export • Significant non point sources • Poor agriculture activities • Bare land • Intensive livestock activities • Expanding urban • Coastal Catchment Initiative (CCI) • Identify the location of pollution • Implement best management practice • Reduce the pollutants to river

  3. Catchment Modelling - AnnAGNPS feedlots & point sources overland flow irrigation & cropping impoundments BASE PROCESSES: • drainage area • elevation • land cover/uses • soil types subsurface flows Model and Methodology AnnAGNPS(ANNualized AGricultural NonPoint Source Model) Image: S. Claus

  4. Agricultural catchments Subdivide catchment into smaller land areas (‘cells’) Disadvantages Developed for catchments in the US Extensive input parameters • AnnAGNPS • Advantages • shape of the cells based on overland flows (i.e. small subcatchments) • cells represent a single landuse and soil type • ability to select cell size • HOW TO SELECT CELL SIZE? • Best management practice • GIS interface

  5. Model Major Input • They represent the variability in the catchment • Landuse and Soil

  6. How AnnAGNPS defines the catchment

  7. Optimising the model by choosing the right cell sizeCell size is defined by two independent parameters • Critical Source Area (CSA): Amount of land which creates runoff • Source Channel Length (SCL): • Length of river that receives the runoff

  8. Optimise cell sizes using various scenarios A) CSA B) SCL

  9. CSA15 and SCL100 CSA35SCL100 CSA100SCL100Cells and Reaches with Various CSA

  10. Sediment Yield Water Yield 4 CSA 200 Criteria for choosing optimum cell size • Minimise spatial variability • within a cell • e.g. one landuse & one soil • Model outputs match the measured data

  11. SCL100 CSA35 Testing Results with Various CSA and SCL

  12. Distribution of cell number with different landuse and soil types • Most cells have 1 or 2 landuses and soil types • Expect single landuse and soil types • Compromise between model accuracy and model effort

  13. Wang Wauk C35S100 Area=184 km2 Bulahdelah C100S100 Area=363 km2 Different catchments with varying CSA and SCL • Represent the hydrological characteristics • Represent spatial variability of the landscape in catchment

  14. Conclusions • Strong dependency between model outputs and cell size • Optimizations of cell size are dependent on the landscape heterogeneity and the total catchment area • Optimizations are achieved through i) fitting model outputs to measured data ii) consideration of computational expenses and additional input data preparation • This is the first study to have examined the effect of cell size on AnnAGNPS outputs Thank You

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