Download Risk-based assessment of the changing nature of soil erosion as a

FORGOTTEN
FIELDS:
Risk-based assessment of the changing
nature of soil erosion as a function of landuse change in the Lower Rother valley
1 PROBLEM STATEMENT
• SCIMAP requires that all input datasets are in raster format and of uniform resolution; vector
LCM2007 and tithe data were therefore rasterized and all input data resampled to 10m cell size
Increased pressure on agricultural land systems has emerged from land-use policy, climate change
and external market forces since the late 20th century.
• Depressions (sinks) are then removed from the DEM to establish flow direction for every cell,
ensuring connectivity even via flat, ‘consuming’ areas
Enhanced soil erosion associated with pasture conversion to more intensive arable land-uses has
received increasing recognition in the lower Rother catchment (Fig. 1) by a range of stakeholders (i.e.
local authorities, utilities, farmers, property-owners and researchers).
SCIMAP applies default land-use risk weightings, derived during model development, to each landcover type8
•
•
Increasing demand for integrated methods at the catchment scale by authorities (i.e. SDNPA)
has developed in parallel with higher resolution datasets for use in GIS
SCIMAP’s risk-based framework highlights the relative likelihood of fine sediment pollution (i.e.
erosion) across a landscape based on soil erodibility, and degree of hydrological connectivity
4 DISCUSSION
Three distinct ‘foresight’ scenarios representing c.2080 will be generated (Fig. 4) based upon;
 Observations of historical land-use change in the catchment and;
 Climate projections developed by the Met Office10; ; &
 Parameterisation observations derived from model sensitivity analyses.
SCENARIO 1
‘Environmental
Enhancement’
Following dataset pre-processing, these were input to SCIMAP and outputs were derived for both the current and historic periods.
Figure 1. Location and aerial view of the catchment (red), demonstrating its agricultural character12,13
OUTPUT 1
Aim: Adopt and assess the utility of a broad-scale GIS-based approach for revealing the
changing nature of soil erosion risk in the lower Rother catchment
ENERGY
AVAILABLE FOR
EROSION
INPUT 1
INPUT 3
INPUT 2
Parameter sets developed for each scenario will be input into SCIMAP, generating
further accumulated in–channel erosion risk map outputs (i.e. as in Fig. 3, shown
over OS data12 in Fig. 5).
These results will identify channels likely to require restoration under a range of
future land management and climatic options.
IN-SITU EROSION
RISK
The SCIMAP framework estimates in relative terms the risk of fine sediment
pollution across a catchment by combining the following datasets;
FINAL OUTPUT:
1) Catchment topography;
2) Annual average rainfall; &
3) Land-cover (Fig. 2).
ACCUMULATED IN-CHANNEL RISK
These data were acquired from the Environment Agency6 (EA), Met Office10 and Centre for
Ecology and Hydrology2 (CEH) respectively.
INPUT 3 – LAND COVER
Figure 2. The required inputs for SCIMAP, including DEM (1), Average-annual rainfall (2) and Land-cover (LCM2007) data (3)
• To explore erosion risk change in relation to land-use, an historical land-cover database was
reconstructed from Tithe Surveys and standardised to the SCIMAP classification11,
representing the c.1840 agricultural landscape by proxy
Catherine
Hudson
[email protected]
Department of Geography | University of Portsmouth
Department of Geography | University of Portsmouth
Buckingham
Building
| Lion
Terrace
| Portsmouth
Buckingham Building
| Lion Terrace
| Portsmouth
| Hampshire
| PO1 3HE | Hampshire | PO1 3HE
These reflect feasible future options by manipulation of land-cover and climatic (i.e.
rainfall) inputs from the present-day level
Figure 5. Provisional in-channel accumulated risk output example, overlaid on OS 1:25,000 Raster data12
2 DATA & METHODS
Catherine
Contact
Details:Hudson
++ RAIN
HYDROLOGICAL
CONNECTIVITY
SOIL ERODIBILITY
OUTPUT 4
INPUT 2 – RAINFALL
+ RAIN
‘Environmental
Degradation’
OUTPUT 3
OUTPUT 2
Objectives include:
• Simulating present and historic soil erosion risk and variation between
• Developing and applying potential future scenarios to SCIMAP
• Assessing the utility value of the model outputs for authorities
INPUT 1 - DEM
‘Business as Usual’
Figure 4. Foresight scenarios developed for model re-application, simulating potential futures under variable land
management and rainfall inputs
3 ANTICIPATED RESULTS
INPUT 2
SCENARIO 3
SCENARIO 2
+/- RAIN
INPUT 1
+ ARABLE/- WOOD
+/- ARABLE & WOOD
- ARABLE/+ WOOD
Some significant assumptions should be noted prior to use14 including:
 Geology and soil type uniformity;
 No seasonal variation in land-use; &
 Empirical land-cover risk weightings utilised.
Figure 3. Simplified standard SCIMAP framework, including primary model inputs, intermediate outputs and how these contribute to
final in-channel accumulated erosion risk output (high-risk red, low-risk blue)
•
Fig. 3 demonstrates the basic SCIMAP framework, simplified from a more detailed structure
diagram which can be found on the SCIMAP website4
•
Comparison of the accumulated risk outputs will quantify change between the Tithe and
LCM2007 model runs within the specified interest areas previously identified as high risk
Following suggestions from previous research1,3,8 and considering SCIMAP parameterisation and
associated assumptions, greatest erosional change is anticipated in areas which therefore host a
combination of:
1) Greater land-cover erodibility (i.e. arable, improved grassland); &
2) Higher degree/longer duration of hydrological connectivity.
References
REFERENCES
et al. J. Environ. Manage., 2009, 90
•
2 CEH. Land Cover Map 2007 [online]. 2011
• Durham Thesis, Durham Uni. [online] 2013
3 Dixon.
4 Durham
•
Uni. SCIMAP model diagram [online] (n.d.)
1 Boardman
The potential translation of in-channel accumulated risk to a spatial accumulated
risk map could highlight the host cells of high-risk points as problematic fields in
need of management change.
5 RESEARCH IMPACT
Application of a risk-based model such as SCIMAP allows identification of likely erosion ‘hotspots’
within a landscape, thus enhancing the quality and efficiency of targeted mitigation measures.
The framework “replaces direct observation with remote observation, bridging the separation that
exists between researchers/policy-makers and land managers”8.
The historic database and future ‘foresight’ modelling will inform various stakeholders, including:
 Authorities (i.e. SDNPA, WSCC, EA);
 Utilities (i.e. Southern Water);
 Unions and Trusts (i.e. NFU, ARRT);
 Residents (i.e. farmers, land-owners); &
 Scientific community.
10 Met
6 EA.
LiDAR 2m DTM data [online]. 2015
7 Google. Google Earth imagery [online]. 2015
8 Lane et al. J. Agric. Econ., 2006, 57, 2
9 Met Office. UKCP09: 5km rain data. [online] 2009
Office. UKCP09. Climate projections [online] 2009
Milledge et al. Science of Tot. Env. 2012, 433, 434
12 OS. 1:25 000 Scale Colour Raster. [online] 2016
13 OS. Boundary-Line. [online] 2016
14 Reaney et al. Ecol. Model. 2011, 222, 1016
11
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