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 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

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

Agricultural catchments AnnAGNPS Advantages Agricultural catchments Subdivide catchment into smaller land areas (‘cells’) 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 Disadvantages Developed for catchments in the US Extensive input parameters

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

How AnnAGNPS defines the catchment

Amount of land which creates runoff Optimising the model by choosing the right cell size Cell 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

Optimise cell sizes using various scenarios CSA(ha) SCL(m) 4 100 15 20 25 30 35 40 50 150 200 CSA(ha) SCL(m) 35 50 75 100 125 150 200 300 400 800 B) SCL A) CSA

CSA15 and SCL100 CSA35SCL100 CSA100SCL100 CSA15 and SCL100 CSA35SCL100 CSA100SCL100 Cells and Reaches with Various CSA

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

Testing Results with Various CSA and SCL

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

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

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