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

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
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

4. 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

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

6. How AnnAGNPS defines the catchment

7. 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

9. 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

10. CSA15 and SCL100 CSA35SCL100 CSA100SCL100
CSA15 and SCL CSA35SCL CSA100SCL Cells and Reaches with Various CSA

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

12. Testing Results with Various CSA and SCL

13. 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

14. 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

15. 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