Model (UKCEH)
SAGIS (Source Apportionment GIS)
SAGIS was developed for UKWIR, with support from the Environment Agency, SEPA and Natural England, to analyse sources of chemicals at the river basin scale and identify measures to improve river, lake and estuary water quality. National data on the source of chemicals from a range of point and diffuse sectors are processed to create inputs to the water quality model SIMCAT, and the outputs used to create databases and visualisations of predicted apportioned water quality.
- Version
- Version 2015
- Contact
- , UK Water Industry Research, 8th Floor, 50 Broadway, London, SW1H 0RG https://www.ukwir.org/
- Keywords
- License
- Commercial license required from UKWIR to use SAGIS, also requires a licence for LowFlows Enterprise from Wallingford Hydro Solutions to operate
- Operating Requirements
- Runs on any standard computer. Software: MS-DOS, ArcGIS, 10.0, 10.1, 10.2, Microsoft Access, SIMCAT version 14.8 Hard drive > 10GBytes RAM - 3.5 Gbytes
- Application Type
- ArcGIS un-compiled VBA SIMCAT executable
- User Interface
- ArcGIS
- Support Available
- SAGIS user forum supported by UKWIR
- Application Scale
- national
- Geographical Restrictions
- None although existing models only cover England, Wales and Scotland
- Temporal Resolution
- Monthly and annual statistics related to the data input period (typically 3 years)
- Spatial Resolution
- Data inputs are based on point locations and a 1km grid for diffuse inputs, outputs at user defined intervals along the river line – typically 1km
- Primary Purpose
- Generates source apportioned river and lake concentrations and loads to identify sources of chemicals. Provides a comparison with water quality standards to assess compliance and scenario testing to assess the impact of point and diffuse measures on compliance
- Key Output Variables
- Source apportioned concentrations and loads for a wide nutrient (N & P), metals and over 20 organic chemicals.
- Key Input Variables
- Diffuse flows (derived from Lowflows 2000). Nutrient inputs derived from PSYCHIC and NEAP N. Wastewater treatment work effluent flows and concentrations. Observed river and lake water quality data. Environmental standards.
- Calibration Required
- Optional. Uses calibration tools developed for UWKIR and by the Environment Agency.
- Model Structure
- Monte Carlo based statistical model (SIMCAT) within GIS framework. Wide range of GIS processing tools..
- Model Parameterisation
- Relatively few adjustable parameters based partly on literature and partly derived from observed data (river travel times, first order decay rates)
- Input Data Available on CaMMP Catalogue
- some
- Documentation
- https://www.ukwir.org/tools/sagis
Key References
- S.D.W.Comber, R.Smith, P.W.G.Daldorph, M.J. Gardner, C.Constantino, and B.Ellor (2013) Development of a Chemical Source Apportionment Decision Support Framework for Catchment Management. Environ. Sci. Technol., 2013, 47 (17), pp 9824–9832
Input Data
- Diffuse inflow (derived from Lowflows 2000) at waterbody scale
- Diffuse inputs (outputs from PSYCHIC, NEAP N, HAWRAT, soils data) and monthly or annual average statistics
- Observed Effluent flow and water quality (continuous discharges and CSOs) as annual average and standard deviation statistics or non parametric files
- Observed river water quality and flow
- GIS layers for the river network, lakes, estuaries, waterbodies and location of point features (wastewater treatment works, abstractions, flow gauges, mines, intermittent discharges etc.)
Output Data
- Spatial data on simulated river flow annual mean and percentile statistics
- Spatial data on simulated water quality as annual mean and percentile concentrations and loads statistics separated into sectors (wastewater treatment works, industrial discharges, mines, intermittent discharges, arable, livestock, natural background, urban runoff, on site wastewater treatment works, highways, atmospheric)
- Spatial probability of compliance statistics
- Spatial information on lake volume, lake outflow, water quality concentrations and annual mean and percentile statistics
Quality Assurance
- Developer Testing
- Yes
- Internal Peer Review
- Yes
- External Peer Review
- Yes
- Use of Version Control
- Yes
- Internal Model Audit
- Yes
- External Model Audit
- Yes
- Quality Assurance Guidelines and Checklists
- Unknown
- Governance
- Yes
- Transparency
- Yes
- Periodic Review
- Yes