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© University of Reading 2008
Modelling, GIS andremote sensingPart 2 –Opportunities from remote sensing05 November 2008Andrew Wade ([email protected])The University of Reading, UK(with slides from Colin Neal, CEH Wallingford, UK)
Introduction• Need for scenario projections• Reminder of uncertainties in models• SCIMAP• New opportunities
– Digimap– Digital elevation data (LIDAR)– Land cover– Soil moisture– High frequency water quality data
• New approaches based on new data– New process understanding– New calibration methods
• Summary
Upper Kennet Catchment
Input from livestock
Kennet and Lambourn
Sewage Treatment Works
Equifinality in model structure and parameters
Wade et al., 2006. Journal of Hydrology, 330(12):185203
Temperature changes 19602100 for A2 and B2 scenariosfor 3 GCMs Hadley, CSIRO and CGCM2
A2 Emissions Scenario
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1960 1980 2000 2020 2040 2060 2080 2100
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B2 Emissions Scenario
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1960 1980 2000 2020 2040 2060 2080 2100
Annu
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A2 Emissions Scenario
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1960 1980 2000 2020 2040 2060 2080 2100
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B2 Emissions Scenario
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1960 1980 2000 2020 2040 2060 2080 2100
Sum
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Wilby et al., 2006, Journal of Hydrology, 330(12): 204220
Percentage Change in Flows for 20s,50s and 80s for A2 and B2 Scenariosfor 3 GCMs: Hadley, CSIRO and CGCM2.
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A2 B2
Jackson et al. 2007, Ecol. Modelling, 209: 4152
Climate scenario A2; CGCM2
Jackson et al. 2007, Ecol. Modelling, 209: 4152
Red = observation
Blue = calibration to observation
Green = P removal to 1 mg P/Lin final effluent
UncertaintyGCMs
ScenariosPrecipitationTemperature
Hydrochemicalmodels
StructureParameters
Other Input Data
Land managementPoint sources
Observations
Grab samplesAnalytical error
? Oreskes et al 1994.
UncertaintyGCMs
ScenariosPrecipitationTemperature
Hydrochemicalmodels
StructureParameters
Other Input Data
Land managementPoint sources
Observations
Grab samplesAnalytical error
?
Monte Carlo +Sensitivity/Uncertainty
Analysis
Dealing with uncertainty• GISbased risk approach
– The Sensitive Catchment Integrated Modelling AnalysisPlatform (SCIMAP)
• Remotelysensed data– Land cover
– Digital Elevation Model
– Soil moisture
– Water chemistry
Upper Kennet Catchment
Input from livestock
Phosphorus
Sewage Treatment Works
SCIMAP• The Sensitive Catchment Integrated Modelling Analysis Platform
(SCIMAP)
• Riskbased assessment technique• Sediment and nutrient loss• Uses GIS data
– DTM– Land cover
• Lane, S. 2006. Journal of Agricultural Economics, 57(2): 239257• www.scimap.org.uk• Universities of Durham and Lancaster
Lane, S. 2006. Journal of Agricultural Economics, 57(2): 239257
SCIMAP –data requirement
Land cover
Digital elevation data
SCIMAP –data requirement
Saturation Index
Risk of sediment or nutrient loss
Maize grown in the River Kennetcatchment
Surface ponding in the Kennetcatchment
SCIMAP –limitations
• Uncertainty in identification of Critical Source Areas– Land cover changes– Local barriers to flow movement– Groundwaterdominated catchments– Large rivercatchments
• Instream processes• Multiple source areas• History of P in sediment
– Farming techniques
GIS and remotely sensed data:state of the art
• Easy to access information in GIS Digimap
• Centre for Ecology and HydrologyLand Cover Survey 2007
• LIDAR
• SMOS
• Water chemistry
Digimap
• Ordnance Survey digital map collection
• Webbased, GIS based, data resource
• EDINA (National Academic Data Centre at U. Edinburgh)
• 10 m Digital Elevation Model
• 1:10 000 field boundaries
• Longterm changes in land cover –1931
• Agcensus
• OS Mastermap
River Lambourn at LambournImage of 1:10 000 mapping
River Lambourn at LambournImage of 1:50 000 mapping
Not to scale: both images have been resized
CEH Land Cover Survey 2000• LANDSAT data• 1km cell, 25 m available gridded from land parcels• New release 2007
– Linked to OS MasterMap– Better structural quality
LIDAR• Light Detection and Ranging• Airbourne mapping technique• LASER based, measures distance to ground from
aircraft• Very high resolution mapping• 2m DTM• Height differences in centimetres• Used for flood inundation estimation by UK
Environment Agency
• Example
SMOS
• ESA Soil Moisture and Ocean Salinity• 2D interferometric radiometer• Measure microwave radiation emitted from the Earth’s
surface at Lband (1.4 GHz)• Moisture effects emissivity• 50 km cell
Artist impression of SMOS
High frequency water quality data
• Total phosphorus, Northern Ireland
• New data from Plynlimon, Wales
Monitoring Total Phosphorus
Jordan et al. 2005. Journal of Hydrology 304: 2034
Monitoring Total Phosphorus• Oona Water (96 km2)• Northern Ireland
Continuous TP analyserPhil Jordan, University of Ulster
Acoustic Doppler Channel ProfilerPhil Jordan, University of Ulster
Monitoring Total Phosphorus
Jordan et al. 2007. Hydrology and Earth System Sciences 11(1): 372381
Model comparison
• Nasr et al., 2007. Water Research 41: 10651073• Oona Water (96 km2)• Clarianna (23 km2)• Dripsey (15 km2)• SWAT• HSPF• SHETRAN/Grid Orientated Phosphorus Component
Nasr et al., 2007. Water Research 41: 10651073
Plynlimon, Wales
Prof. Colin NealCentre for Ecology and Hydrology
10 20 30 40 50 60 70Day number (day 1 = 01/03/2007)
0
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Cl (
mg/
l)
Rainfall Upper Hafren Lower Hafren
Chloride
10 20 30 40 50 60 70Day number (day 1 = 01/03/2007)
0
0.05
0.1
0.15
0.2
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0.3N
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O3
N/l)
Rainfall Upper Hafren Lower Hafren
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Rainfall Upper Hafren Lower Hafren
Sulphate
10 20 30 40 50 60 70Day number (day 1 = 01/03/2007)
0
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6D
OC
(mgC
/l)
Rainfall Upper Hafren Lower Hafren
DOC
Hafren stream
0.0001
0.001
0.01
0.1
1
10
0.01 0.1 1 10
Spe
ctra
l pow
er
3 years of daily data14 years of weekly data
Wavelength (years)
Cl concentration spectra
Rain
1/f 0.97
Kirchner, Feng and Neal, 2000. Nature 403:524526
0.001
0.01
0.1
1
10
100
1000
1 10 100 1000 10000
log(Period) Days
log(
Pow
er)
ObservedModelledObserved (m=1.1)Modelled (m=1.5)
Courtesy of Martyn Futter, Macaulay Institute
Simulated and observed DOC concentrationpower spectra Dickie Stream, Canada
High frequency dataWhere will it lead us?
• Better load estimates• Better understanding of diurnal cycles
– Biology– Evaporation– Catchment functioning; timing of inputs
• Better model calibration or model rejection• New models
Summary
• Integrated catchment nutrient models– Needed for future projections– Data intensive
• Uncertainty• Opportunities from remotely sensed data in a GIS framework
– Land cover– Digital elevation data– Soil moisture– High frequency chemistry
SCIMAP –link with Mastermap
Ordnance Survey data from EDINA Digimap
10 20 30 40 50 60 70Day number (day 1 = 01/03/2007)
0
0.01
0.02
0.03
Br
(mg/
l)
Rainfall Upper Hafren Lower Hafren
Bromide
Management• Model output
– Learn about possible catchment response– Models with estimates of uncertainty
• Other environmental factors– Water quality– Residence time– Solar radiation
• SocioEconomics and Politics
Catchment management decisions made by politicians notscientists