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Predicting the Effects of Climate Change and Water

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Predicting the Effects of Climate Change and Water Resources and Food Production in the Kennet Catchment Potential Application to China? Richard Skeffington, Aquatic ... – PowerPoint PPT presentation

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Title: Predicting the Effects of Climate Change and Water


1
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
2
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
3
Kennet Agriculture
Largely arable
4
Kennet Agriculture 2
Largely arable
and livestock production
5
Kennet Land Use
It is probably not very like China!
There are some urban areas (this is Reading)
6
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
7
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
8
Predicting the effect of climate change on water
resources and food production
  • Modelling land use impacts

9
Overview
  • The socio-economic change scenarios IPCC SRES
    futures UKCIP refinements for UK BLS world food
    trade model
  • The climate change scenarios HadCM3 projections
  • The economic/land use model (CLUAM)

10
The SRES storylines
  • Scenarios selected were
  • A2 low globalisation/market based solutions
  • B2 low globalisation/sustainability led

11
Climate change scenarios
  • AOGCM HadCM3
  • (UK Hadley Centres1 third generation coupled
    Atmosphere-Ocean 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)

12
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

13
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

14
BLS outputs
  • Production levels (volumes)
  • Market prices
  • Technology change (yields)
  • LUAM also requires climate-driven yield changes

15
Climate induced yield changes
  • Two stage process
  • Meta analysis of existing data on UK-specific
    crop yield changes due to climate change
  • Decisions on where crops would not grow due to
    climate limit

16
The CLUAM
  • An LP model of England Wales agriculture
  • Range of major land using agricultural
    enterprises included
  • Outputs (revenue)
  • Inputs (incur costs)
  • Land base partitioned by CEH Land Classification
    system
  • Model objective maximize gross margin,
  • Subject to various constraints

17
Results Agricultural Change
18
Livestock Numbers
19
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
20
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
HadRM3 c.50 km grid
European Model Monthly Time Step
Global Models
21
More Downscaling
HadRM3 c.50 km grid Monthly
Kennet Catchment 5 km grid, Daily
EARWIG
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.
22
EARWIG Mean Monthly Temperatures
Annual means Base (1961-90) 9.2 ?C
2020 10.2 ?C 2050 B2 11.0 ?C 2050
A2 11.3 ?C
23
EARWIG Mean Monthly Rainfall
Annual Totals Base 759 mm 2020s 787
mm 2050s 757 mm
24
How does INCA work?
.
Each sub-catchment has 6 land uses
Urban Forest Arable Oilseeds Grassland Unfer
tilised Not covered by CLUAM.
Catchment divided into sub-catchments
.
25
Hydrological Model
Land Cell Hydrological Model
Quick flow
Abstraction (e.g. for water supply)
26
INCA-N Soil Processes
27
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 CLUAM Urban, 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
28
IN-STREAM PROCESSES in INCA
29
Annual Hydrology
Low summer rainfall protects the river from extra
evaporation to some extent
30
Period of River Recharge Shortens
Consecutive months without hydrologically-effectiv
e rainfall
31
What Happens to Nitrate?
EU Drinking water standard 11.3 mg/L
60-year realisation of nitrate in the R. Kennet
baseline climate
32
Mean Nitrate Concentrations
Crop changes due to socio-economic factors only
Crop changes due to socio-economic climate
change
Crops in reference state (1990)
33
Variation in Nitrate 2050 A2
Socio-economic change makes a difference
adding climate change has no effect
34
Variation in Nitrate 2050 B2
Socio-economic change makes small difference
adding climate change increases it
35
Other Modelling Work
Same river, same climate scenario
Different downscaling method, INCA
parameterisation
Nitrate increases in response to climate change!
36
Uncertainty
37
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

38
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
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