SCIMAP - Diffuse Pollution Risk Mapping

SCIMAP in EdenDTC

Sim Reaney — Fri, 20 Apr 2012 16:52:26 +0000

SCIMAP is being used in the Defra funded River Eden Demonstration Test Catchment (EdenDTC) project. This project is investigating the dynamics of water quality from agricultural land using state of the art, real time, water quality monitoring to gain new insights. This new insight will then be used to design mitigation measures to reduce the impact of agricultural activity on in-stream water quality and ecology whilst maintain agricultural production.

SCIMAP is being used as part of the catchment characterisation work and has been applied to the three focus catchments (Dacre Beck, Morland Beck and Pow Beck) in Cumbria. An example output set is below for Morland Beck in KML format (compatible with GoogleEarth):

mor_landerosTrans – Morland potential pattern of surface erosion. Red is the highest relative risk, blue is the lowest
mor_connect – Morland potential hydrological connectivity. Blue represents the highest potential connectivity and red the lowest.
MorlandSCIMAP-inchannel-risk-concn – Morland in-stream relative risk. Red and yellow represent higher risk and blue represents lower risk.

This information is being used to comceptually understand how the catchment may be working and to target the important areas for the installation of mitigation features to reduce the amount of diffuse pollution reaching the streams and rivers.

Durham Wildlife Trust using SCIMAP

Sim Reaney — Mon, 23 Jan 2012 13:52:50 +0000

Durham Wildlife Trust is using SCIMAP to identify areas with high fine sediment pollution risk within the River Wear catchment. The trust exists to protect wildlife and is therefore committed to tackling the problems that excess fine sediment cause in aquatic ecosystems; problems such as increased turbidity, reduced visibility and the smothering of the channel bed, all of which degrade the habitat quality for aquatic species. In order to tackle these problems, the trust needs to know where to target its resources. SCIMAP identifies the highest risk sub-catchments, tributaries and fields, allowing the trust to prioritise work in areas that need it most. The results show the source areas and pathways of eroded material, as well as identifying channel stretches that are likely to have higher sediment concentrations, thus allowing practical management interventions to be proposed at strategic locations. These could include buffer and wetland creation, fencing of vulnerable land and even cultivation changes. The trust will also use ecological, water quality and flood data from the Environment Agency to further prioritise areas based on correlation of high sediment risk with other pressures. Interventions can then be designed to tackle multiple problems and consequently lead to a greater positive outcome. With the SCIMAP results as a foundation, the trust aims to work with landowners to make the intervention proposals a reality, thus reducing fine sediment delivery rates and enhancing aquatic ecosystems.

Elizabeth Willows, Durham Wildlife Trust

AGU2011: Understanding nutrient connectivity at the landscape scale

Sim Reaney — Mon, 05 Dec 2011 20:14:27 +0000

Understanding nutrient connectivity at the landscape scale: The use of the SCIMAP approach in the UK and Ireland.

Sim M Reaney¹, David Milledge¹, Stuart N Lane², Louise, Heathwaite³, Mairead Shore⁴, Alice Melland⁴, Phil Jordan⁴

1. Department of Geography, Durham University, Durham, United Kingdom.
2. Faculté des géosciences et de l’environnement, Université de Lausanne, Lausanne, Switzerland.
3. Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom.
4. Agricultural Catchments Programme, Teagasc, Wexford, Ireland.
Many approaches to understanding diffuse pollution risk at the landscape scale have focused on its ‘sources’ and ‘mobilisation’ with a basic representation of the effect of connectivity between the landscape the receiving waters. Connectivity will determine whether source areas become critical source areas and create problems in the receiving waters. It is the landscape position of a source, both in terms of its upslope contributing area and its downslope flow path, that determine the likelihood of a connection being made. The SCIMAP approach, developed at Durham and Lancaster Universities with the Environment Agency, has taken a strongly connectivity driven approach, set within a risk based framework. SCIMAP aims to predict the location in the catchment that is most likely to be the source of an in stream water quality problem derived from diffuse pollution. The predictions are generated at a 5m-pixel level, to give within field estimates of risk and connectivity, and applied to whole landscapes (from 1 to 1000 km2 +) to give a broad overview of the issues. Recent work has shown that there is significant value in adding a detailed connectivity treatment when predicting measured patterns of water quality.
The SCIMAP approach to diffuse pollution risk mapping has been applied by: the Environment Agency under the Catchment Sensitive Farming program; the Teagasc ‘Agricultural Catchments’ program; the Defra funded ‘River Eden Demonstration Test Catchment’; and various river and wildlife trusts in the UK. This poster shows an overview of the SCIMAP approach and the results from both the Teagasc ‘Agricultural Catchments’ and the EdenDTC projects.
http://www.scimap.org.uk
http://www.edendtc.org.uk

The full poster is on the AGU ePoster website.

SCIMAP 2011 Download

Sim Reaney — Mon, 05 Dec 2011 20:08:19 +0000

You can download the 2011 version of SCIMAP for non-commercial use under a Creative Commons license. This version has the SCIMAP-Forward based calculations for fine sediment predictions, a method to buffer your catchment to prevent no-data leaking into you area of interest and the network index landscape scale connectivity calculations.

If you want to use SCIMAP for commercial use, then please contact us.

SCIMAP is bundled with SAGA-GIS as the user interface and the GIS framework. It is based on SAGA 2.0 for Microsoft Windows Xp – Vista – 7 (32 bit). Since SCIMAP is not a currently funded project, it is not possible to update the software to the latest version of SAGA-GIS or create the 64-bit version. The software is provided ‘as is’, without any warranty, support beyond the training and information on this website.

There is extensive help on using SAGA-GIS user in the user forum at:

http://sourceforge.net/projects/saga-gis/forums/forum/790705

Download SCIMAP 2011

SCIMAP by Sim Reaney, Durham University is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
Based on a work at www.scimap.org.uk.

Overview Demonstration of Creating a SCIMAP Fine Sediment Risk Map

Sim Reaney — Thu, 03 Nov 2011 10:02:10 +0000

Creating a SCIMAP fine sediment risk map with SAGA GIS and ESRI ArcMap.

Presentation authors: Dr. Sim Reaney and Dr. David Milledge, Durham University

If the above video does not work, please use this link.

Is SCIMAP always based on the Topographic Wetness Index?

Sim Reaney — Thu, 21 Apr 2011 08:43:25 +0000

Within SCIMAP, the hydrological connectivity is based on the ‘Network Index’ (Lane et al. 2004). This index calculates the level of landscape wetness for each point to both generate runoff and for there to be a continuous flowpath to the river or lake. In many applications of SCIMAP, the topographic wetness index is used to give predictions of relative wetness within the catchment. However, within the SCIMAP framework the wetness index can be substituted for other predictions of relative wetness. In the paper by Lane, Reaney and Heathwatite published in 2010, they used soil moisture pattern predictions from a distributed hydrological model (CRUM3) to assess the hydrological connectivity. In short, any spatial pattern of soil moisture can be used to calculate the Network Index representing the hydrological connectivity. As a user, you are therefore able to adjust the SCIMAP Framework to your local conditions and to better capture your understanding.

EGU2011: Inverse modelling of the risk of diffuse pollution in agricultural catchments

DaveMilledge — Tue, 19 Apr 2011 13:23:27 +0000

Dave Milledge (1,2), Stuart Lane (1,2), Louise Heathwaite (3), Sim Reaney (1,2)

The hydrological and biogeochemical processes that operate in catchments influence the ecological quality of freshwater systems through delivery of fine sediment, solutes and organic matter. Most models that seek to characterise the delivery of diffuse pollutants from land to water are reductionist. The multitude of processes that may be parameterised in a model to ensure generic applicability results in the development of complex models that require input data that are rarely available. Here we outline an alternative based on ‘inverse modelling’ where the focus is upon using extant measured data in Bayesian approach to learn the kind of model representation that is required to explain those data. We invert SCIMAP, a simple risk based model with an explicit treatment of hydrological connectivity, and use a Bayesian approach to determine the risk that must be assigned to different land uses in a catchment order to explain the spatial patterns of measured instream solute concentrations. We apply the model to identify the key sources of nitrogen (N) and phosphorus (P) risk in eleven UK catchments across a range of landscape characteristics focussing on the Hampshire Avon, Eden and Wensum catchments. The model results show that: 1) some catchment land use generates a consistently high or low risk of diffuse nutrient pollution; but 2) the risks associated with different land uses vary both between catchments and between N and P delivery; and 3) that the dominant sources of N and P risk in the catchment are often a function of the spatial configuration of land uses. Our results suggest that ‘inverse modelling’ may be used to learn what our model assumptions should be, in ways that, on a case by case basis, can be used to inform the focus of interventions to reduce diffuse pollution risk for freshwater ecosystems.

(1) Department of Geography, Durham University, Durham, United Kingdom (d.g.milledge@durham.ac.uk), (2) Institute of
Hazard Risk and Resilience, Durham University, Durham, United Kingdom, (3) Centre for Sustainable Water Management,
Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom

Geophysical Research Abstracts
Vol. 13, EGU2011-8622, 2011
EGU General Assembly 2011
© Author(s) 2011

EGU2011: Can a static metric for hydrologic connectivity improve predictions of instream water quantity and quality?

DaveMilledge — Tue, 19 Apr 2011 13:21:41 +0000

Dave Milledge (1,2), Stuart Lane (1,2), Sim Reaney (1,2), and Louise Heathwaite (3)

The concept of connectivity is increasingly being applied in hydrology as researchers attempt to move beyond the traditional partial or variable source area models for runoff generation and consequent material transport to recognize that the flux of water and the materials it transports is spatially and temporally discontinuous. The network index is a topographically defined description of the spatial arrangement of catchment wetness that we suggest is a metric for landscape hydrological connectivity in temperate catchments. This static metric has been compared with the space-time patterns of connectivity from a physically based distributed hydrological model. The results demonstrate that it can generalize a significant proportion of the time-averaged spatial variability in connectivity, in terms of both the propensity to and duration of connection. Here we go on to assess its ability to improve instream water quality and peak river flow predictions. We compare the performance of 1) instream Nitrate and Phosphate risk predictions from the SCIMAP risk-based water quality model; and 2) high flow predictions from TOPMODEL, with and without our connectivity treatment. Our results suggest that even a simple, static, topographically based connectivity metric, like the network index, can considerably improve predictions of both water quality and peak flows. A primary reason for this is that the spatial distribution of time-averaged (i.e. static) connectivity implicitly contains a temporal component as locations in a catchment that are more difficult to connect in space are also, by implication, connected for shorter durations. This is an important property, and demonstration of its value is an important finding, because it opens up an alternative to complex continuous simulation models of hydrological and water quality response.

Geophysical Research Abstracts
Vol. 13, EGU2011-8538, 2011
EGU General Assembly 2011
© Author(s) 2011

AGU2010: Inverse modelling of diffuse pollution risks in agricultural catchments

DaveMilledge — Thu, 07 Apr 2011 10:45:51 +0000

H51D-0936 Poster
Inverse modelling of diffuse pollution risks in agricultural catchments
Milledge, D, Lane, S N, Heathwaite, L and Reaney, S
Watershed scale processes can influence instream aquatic ecosystems, through delivery of fine sediment, solutes and organic matter. Certain areas in a watershed are critical sources, where the ability both to entrain material and to connect it to the drainage network controls the level of delivery. The models that have been developed to identify these areas tend to be reductionist and, given the multitude of processes that could be included in a model to guarantee that it can work in many situations, results in the development of ever more complex models. Here we outline a new approach based on ‘inverse modelling’. We invert SCIMAP, a simple risk based model with an explicit treatment of hydrological connectivity, and use a Bayesian approach to determine the risk that must be assigned to different land uses in order to explain spatial patterns of measured instream solute concentrations. We apply the model to identify the key sources of Nitrate and Phosphate risk in 11 UK catchments across a range of hydrological and agricultural conditions but focussing on Hampshire Avon, Eden and Wensum catchments. The model results show that: 1) certain landuses are consistently high or low risk; but 2) the risks associated with different land uses vary both between catchments and between nutrients; and 3) that the dominant sources in the catchment are often a function of the spatial configuration.

Rainfall data for SCIMAP?

DaveMilledge — Thu, 17 Mar 2011 19:21:27 +0000

Questioner:

How can I get hold of the rainfall grid that you refer to in the SCIMAP manual?

Help:

The data is freely available from the Met Office it is UKCP09 data but you can’t transfer the data from person to person, you have to apply for it. You can either:

1) register to use the data. You register at:

http://www.metoffice.gov.uk/climatechange/science/monitoring/ukcp09/gds_form.html

A reasonable statement of purpose for SCIMAP would be:

“monthly average rainfall data will be aggregated to an annual average to use as a weighting factor within SCIMAP, a diffuse pollution risk profiling tool”

Once you have registered to use the data and that has been approved by the Met Office either you can generate the annual average rainfall intensity grid by taking the average of the monthly average rainfall intensity grids (“Total precipitation” in http://www.metoffice.gov.uk/climatechange/science/monitoring/ukcp09/available/monthly.html)

2) still run SCIMAP but assuming the average annual rainfall is spatially uniform over your catchment. Depending on where you are working this may or may not be a reasonable assumption. However, it will allow you to continue testing SCIMAP. To do this you need to take the following steps in SAGA:

a. Ignore earlier mentions of the rainfall grid then just before you get to the step where you run SCIMAP you should go to the “Modules” tab then “Grid – Tools” then “create Constant Grid”.

b. Choose the “Grid system” that has the DEM and landcover map that you want to use in SCIMAP

c. Choose the DEM that you want to use in SCIMAP as your “>>Base Grid”

d. Leave “Value” set to 1 & hit “Okay”

e. A new grid will be generated with the same extent as the DEM but made up entirely of 1s.

f. Now run SCIMAP but in the “>> Rainfall Pattern box” choose the new grid of 1s.