Transcript Document

Predicting the Effects of Climate Change and Water
Resources and Food Production in the Kennet Catchment
Potential Application to China ?
Richard Skeffington, Aquatic Environments Research Centre
Phillip Jones and Richard Tranter, Centre for Agricultural Strategy
University of Reading
Integrated Project to evaluate
the Impacts of Global Change
on European Freshwater Ecosystems
The Kennet Catchment
1137 km2
Geology: chalk with clays at the East end
Maximum altitude 297m above sea level
Mean annual rainfall (1961-90): 759 mm
Mean annual runoff (1961-90): 299 mm
Theale
Kennet Agriculture
Largely arable
Kennet Agriculture 2
Largely
arable
and
livestock
production
Kennet Land Use
It is probably not very like China!
There are some urban areas (this is Reading)
Problems on the Kennet
1. A low flow problem – the upper reaches can almost
dry up in a dry summer
2. A (potential) nitrate problem – increasing concentrations
Photo: Helen Jarvie
Modelling Agricultural Change
CLIMATE CHANGE
Change in river flows
and composition
Change in agriculture
in catchment
Changes in world agriculture
Is it possible to model
these outcomes?
Changes in crop prices and demand
…with any credibility?
SOCIOECONOMIC CHANGE: population, global trade policies etc
Predicting the effect of climate change on water resources
and food
production
Modelling land use impacts
07 July 2015
© University of Reading 2008
www.reading.ac.uk
Overview
Global Climate Change &
CO2 Scenarios (HadCM3)
The socio-economic change
scenarios
IPCC SRES futures
UKCIP refinements for UK
BLS world food trade
model
Crop Models
Changes in Crop Yields
Over a Global Network of
Sites
Global Socio-economic
Futures (SRES)
Input to BLS
Aggregation &
Extrapolation to region,
Counties & Commodities
Regional Climate Change
& CO2 Scenarios
Changes Simulated by
World Food Trade Model,
in Production Potential &
Prices
Output From BLS
The climate change scenarios
HadCM3 projections
The economic/land use model
(CLUAM)
Changes in Regional Crop
Yields
CEH Land Classification
System
CLUAM
Changes in Regional Land
Use Allocations
Sensitivity Tests
The SRES storylines
Autonomy
National
Enterprise
Local
Stewardship
Consumerism
Community
Conventional
Development
World
Markets
Global
Sustainability
Interdependence
• Scenarios selected were:
– A2 – low globalisation/market based solutions
– B2 – low globalisation/sustainability led
Climate change scenarios
• AOGCM HadCM3
(UK Hadley Centre’s1 third generation coupled AtmosphereOcean Global Circulation Model)
– This used with the A2 & B2 SRES scenarios to project to 2100
– Our modelling scenarios sample 2020 and 2050
1
Hadley Centre for Climate Prediction and Research (part of UK Meteorological Office)
Basic Linked System (BLS)
-1-
• International Institute for Applied Systems Analysis
(IIASA)
• Framework for analysing the world food trade system
• The BLS is an applied general equilibrium (AGE)
model system
– All economic activities are represented
• 34 national and/or regional geographical components
– 18 eighteen single-country national models
– 2 region model
– 14 country groupings
Basic Linked System (BLS)
-2-
• Market clearance (production and uses must balance)
• The model is recursively dynamic, ie, working in
annual steps
– For given prices calculate Global net exports and imports
– Check market clearance for each commodity
– Revise prices. When markets are balanced, accept prices as
world market solution for year and proceed to next year
• This process is repeated until the world markets are
simultaneously cleared in all commodities
BLS outputs
• Production levels
(volumes)
Global Climate Change &
CO2 Scenarios (HadCM3)
Crop Models
Changes in Crop Yields
Over a Global Network of
Sites
Global Socio-economic
Futures (SRES)
• Market prices
Input to BLS
Aggregation &
Extrapolation to region,
Counties & Commodities
• Technology change
(yields)
Regional Climate Change
& CO2 Scenarios
Changes Simulated by
World Food Trade Model,
in Production Potential &
Prices
Output From BLS
LUAM also requires
climate-driven yield
changes
Changes in Regional Crop
Yields
CEH Land Classification
System
CLUAM
Changes in Regional Land
Use Allocations
Sensitivity Tests
Climate induced yield changes
• Two stage process:
– Meta analysis of
existing data on UKspecific crop yield
changes due to
climate change
– Decisions on where
crops would not grow
due to climate limit
The CLUAM
• An LP model of England
& Wales agriculture
Value of national inputs and
outputs to the agricultural
sector (DNIC)
Actual Land Use
(MAFF June Census)
• Range of major land
using agricultural
enterprises included
Input / Output Coefficients
(production relationships from
farm management type data)
Specification and Calibration
of the model
– Outputs (revenue)
– Inputs (incur costs)
• Land base partitioned by
CEH Land Classification
system
Experimental demand,
yield and supply
data:
Experimental
environmental Data:
CLUAM
Livestock
Livestock Numbers,
Numbers, Crop
Cropand
and
Grass
GrassAreas
Areas and
and Yields
Yields
Demand Change
Price Change
• Model objective maximize
gross margin,
– Subject to various
constraints
Projection of Changes in
Land Use and Production
ITE : LCS
Climate Change
Yield Change
Results – Agricultural Change
Kennet land cover areas (upland & lowland combined)
No climate change A2/B2
700 00
ha
600 00
Idle-Ro ugh
500 00
Idle-Perm
400 00
Idle-Ley
Idle-Arable
300 00
Ro ugh
200 00
Perm
Ley
100 00
Other arable
0
REF 1990s
2020 A2
2020 B2
2050 A2
2050 B2
Ce reals+oil
Kennet land cover areas (upland & lowland combined)
Climate change A2/B2
70000
60000
Idle-Rough
ha
50000
Idle-Perm
Idle-Ley
40000
Idle-Arable
30000
Rough
Perm
20000
Ley
10000
Other arable
Cereals+oil
0
REF 1990s
CC 2020 A2
CC 2020 B2
CC 2050 A2
CC 2050 B2
Kennet livestock numbers (upland & low land com bined)
No clim ate change A2 /B2
500 00
Livestock Numbers
LSU
400 00
300 00
S hee p LSU
200 00
B eef L SU
100 00
Dairy L SU
0
REF 199 0s
20 20 A 2
202 0 B2
2 050 A 2
20 50 B2
She ep LS U
182 43
1 899 1
634 6
6
2 205 8
Bee f LS U
6 608
23 17
22 000
6 037
711
Dair y LSU
212 97
2 474 5
950 2
246 04
1 896 2
Kennet livestock numbers (upland & low land com bined)
Clima te change A2/B2
5 000 0
LS U
4 000 0
3 000 0
Sh eep L SU
2 000 0
Be ef L SU
1 000 0
Da iry LSU
0
REF 199 0s
20 20 A 2
202 0 B2
2 050 A 2
20 50 B2
She ep LSU
182 43
1 209 6
21 428
4
348
Bee f LSU
6 608
0
11 047
8 133
980
Dair y LSU
212 97
2 409 9
913 4
207 99
2 425 4
Modelling Agricultural Change
CLIMATE CHANGE
Change in river flows
and composition
Change in agriculture
in catchment
Changes in world agriculture
Changes in crop prices and demand
SOCIOECONOMIC CHANGE: population, global trade policies etc
Downscaling in Space and Time
The INCA-N model for predicting nitrate and flow
works on a daily time step & requires daily
temperature, rainfall and evapotranspiration.
This work has used the UK Climate Impacts Programme
(UKCIP02) Scenarios, derived as follows.
SRES Scenarios (4 future climates, including A2 and B2)
“Experiments” run by the Hadley Centre
HadCM3
c.300 km grid
HadAM3H
c.120 km grid
Global Models
HadRM3
c.50 km grid
European Model
Monthly Time Step
More Downscaling
HadRM3
c.50 km grid
Monthly
EARWIG
Kennet Catchment
5 km grid, Daily
Environment Agency Rainfall and Weather Impacts Generator
Stochastic “weather generator” giving daily values for:
• Rainfall
• Potential evapotranspiration (Penman –MORECS or FAO)
• Min and Max temperatures (and others)
Actual evapotranspiration estimated by a simple spreadsheet
model constrained by soil water deficit.
EARWIG: Mean Monthly
Temperatures
Temperature (C)
Mean Daily Temperature
20
18
16
14
12
10
8
6
4
2
0
Base
2020
2050B2
2050A2
1
2
3
4
5
6
7
8
9
10
Month
Annual means: Base (1961-90)
2020
2050 B2
2050 A2
9.2 C
10.2 C
11.0 C
11.3 C
11
12
EARWIG: Mean Monthly Rainfall
Rainfall
Rainfall (mm)
100
80
60
Base
40
2020
2050B2
20
2050A2
0
1
2
3
4
5
6
7
8
9
Month
Annual Totals:
Base 759 mm
2020s 787 mm
2050s 757 mm
10
11
12
Each sub-catchment has 6 land uses:
Urban;
Forest;
Arable + Oilseeds;
Grassland;
.
Unfertilised;
Not covered
by CLUAM.
Catchment divided
into sub-catchments
How does INCA work?
.
Land Cell: Hydrological Model
Hydrological Model
P
AET
Quick
Quick
flow
flow
Soil
Quick
Throughflow
Abstraction
(e.g. for water supply)
Groundwater
Groundwater
flow
Ammonium +
Nitrate fertiliser
Ammonium +
Nitrate deposition
Nitrogen
Fixation
Urban waste
to River
Nitrate
Addition
Plant Ammonium
uptake
Addition
denitrification
Plant
uptake
Net
mineralisation
nitrification
NO3
NH4
Organic N
Reactive Soil Zone
Leaching
to river
Leaching
to river
NO3
NH4
Groundwater Zone
INCA-N Soil
Processes
Land Uses and Fertiliser Inputs
Each land use parameterised separately for all the above
N Fertiliser in kg N ha-1yr-1
Scenario
Arable
Grass
Not in
Urban,
CLUAM Forest
Unfert.
1990
180
261
5
0
0
Socio-
2020 A2
180
263
5
0
0
Econ.
2020 B2
180
249
5
0
0
2050 A2
162
276
5
0
0
2050 B2
180
259
5
0
0
Socio-
2020 A2
180
269
5
0
0
Econ. +
2020 B2
180
248
5
0
0
Climate
2050 A2
162
272
5
0
0
change
2050 B2
180
283
5
0
0
IN-STREAM
PROCESSES
in INCA
Annual Hydrology
900
Rainfall
PET
800
AET
HER
700
mm/yr
600
500
400
300
200
100
0
Base
2020s A2/B2
2050s
2050s A2
Low summer rainfall protects the river from extra evaporation –
to some extent
Period of River Recharge Shortens
100
90
80
4 months
5 months
6 months
8 months
Percentage of years
70
60
50
40
30
20
10
0
1961-90
2020s
2050s B2
2050s A2
Consecutive months without hydrologically-effective rainfall
What Happens to Nitrate?
8
7
Nitrate (mgN/L)
6
5
4
3
2
EU Drinking water standard: 11.3 mg/L
1
0
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
22000
Day
60-year realisation of nitrate in the R. Kennet: baseline climate
Mean Nitrate Concentrations
6
Baseline
2020 A2
2020 B2
5
2050 A2
2050 B2
Nitrate mg N/L
4
3
2
1
0
Base
Crops in reference
state (1990)
SE
Crop changes due
to socio-economic
factors only
CCSE
Crop changes due
to socio-economic
& climate change
Variation in Nitrate: 2050 A2
Annual mean nitrate (mg N/L)
8
Baseline
CCSE 2050 A2
SE 2050 A2
Base 2050 A2
6
4
2
0
0
5
10
15
20
25
30
35
40
45
Year
Socio-economic change makes a difference
– adding climate change has no effect
50
55
60
Variation in Nitrate: 2050 B2
Annual mean nitrate (mg N/L)
8
Baseline
CCSE 2050 B2
SE 2050 B2
Base 2050 B2
6
4
2
0
0
5
10
15
20
25
30
35
40
45
Year
Socio-economic change makes small difference
– adding climate change increases it
50
55
60
Other Modelling Work
Same river, same climate scenario
Different downscaling method, INCA parameterisation
Nitrate increases in response to climate change!
Uncertainty
Conclusions
• It is possible to predict the effects of climate change on river flows and
water quality, but a long chain of models and assumptions is required;
• Different assumptions can lead to radically different outcomes;
• These start at the top of the model chain – some GCMs give a substantial
increase in rainfall by 2050 when downscaled to this catchment;
• The SRES Scenarios are looking a bit dated – need an “Energy Security”
scenario?
• Better confidence on the hydrological predictions than the water quality –
need to understand the effects of temperature and hydrological change
on nitrogen cycle processes much better than we do;
• The work shows that potentially, changes in the world agricultural system
can affect water quality at the catchment scale, but it is hard to predict
what that influence might be in individual cases;
• Might have more predictive power at a more aggregated scale
Implications for China
The methodology would be transferable, but the
results of course are not;
Technological and economic change is likely to be greater
in China than the UK (?) and thus even more important
as a driver of change;
With current understanding, only worth doing at a highly
aggregated scale
May be more valuable in generating a set of plausible
scenarios than in making predictions.
THANK YOU