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 Environments Research Centre Phillip Jones and Richard Tranter, Centre for Agricultural Strategy. University of Reading. - PowerPoint PPT PresentationTRANSCRIPT
Predicting the Effects of Climate Change and WaterResources and Food Production in the Kennet Catchment
Richard Skeffington, Aquatic Environments Research Centre
Phillip Jones and Richard Tranter, Centre for Agricultural Strategy
Potential Application to China ?
Integrated Project to evaluatethe Impacts of Global Changeon European Freshwater Ecosystems
University of Reading
The Kennet Catchment1137 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 arableand livestock production
Kennet Land Use
There are some urban areas (this is Reading)
It is probably not very like China!
Problems on the Kennet1. A low flow problem – the upper reaches can almost
dry up in a dry summer2. A (potential) nitrate problem – increasing concentrations
Photo: Helen Jarvie
Modelling Agricultural ChangeCLIMATE 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
Is it possible to model these outcomes?
…with any credibility?
22 April 2023 © University of Reading 2008 www.reading.ac.uk
Predicting the effect of climate change on water resources and foodproductionModelling land use impacts
OverviewThe socio-economic change
scenariosIPCC SRES futuresUKCIP refinements for UKBLS world food trade model
The climate change scenariosHadCM3 projections
The economic/land use model (CLUAM)
Input to BLS
Output From BLS
Global Climate Change & CO2 Scenarios (HadCM3)
Crop Models Sensitivity Tests
Changes in Crop Yields Over a Global Network of
Sites
Aggregation & Extrapolation to region,
Counties & Commodities
Changes Simulated by World Food Trade Model, in Production Potential &
PricesRegional Climate Change & CO2 Scenarios
Changes in Regional Crop Yields
CEH Land Classification System
CLUAM
Changes in Regional Land Use Allocations
Global Socio-economic Futures (SRES)
Input to BLS
Output From BLS
Global Climate Change & CO2 Scenarios (HadCM3)
Crop Models Sensitivity Tests
Changes in Crop Yields Over a Global Network of
Sites
Aggregation & Extrapolation to region,
Counties & Commodities
Changes Simulated by World Food Trade Model, in Production Potential &
PricesRegional Climate Change & CO2 Scenarios
Changes in Regional Crop Yields
CEH Land Classification System
CLUAM
Changes in Regional Land Use Allocations
Global Socio-economic Futures (SRES)
The SRES storylines
• Scenarios selected were:– A2 – low globalisation/market based solutions– B2 – low globalisation/sustainability led
LocalStewardship
ConventionalDevelopment
Autonomy
Community
Interdependence
Consumerism
NationalEnterprise
WorldMarkets
GlobalSustainability
Climate change scenarios• AOGCM HadCM3
(UK Hadley Centre’s1 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)
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)• Market prices • Technology
change (yields)
LUAM also requires climate-driven yield changes
Input to BLS
Output From BLS
Global Climate Change & CO2 Scenarios (HadCM3)
Crop Models Sensitivity Tests
Changes in Crop Yields Over a Global Network of
Sites
Aggregation & Extrapolation to region,
Counties & Commodities
Changes Simulated by World Food Trade Model, in Production Potential &
PricesRegional Climate Change & CO2 Scenarios
Changes in Regional Crop Yields
CEH Land Classification System
CLUAM
Changes in Regional Land Use Allocations
Global Socio-economic Futures (SRES)
Input to BLS
Output From BLS
Global Climate Change & CO2 Scenarios (HadCM3)
Crop Models Sensitivity Tests
Changes in Crop Yields Over a Global Network of
Sites
Aggregation & Extrapolation to region,
Counties & Commodities
Changes Simulated by World Food Trade Model, in Production Potential &
PricesRegional Climate Change & CO2 Scenarios
Changes in Regional Crop Yields
CEH Land Classification System
CLUAM
Changes in Regional Land Use Allocations
Global Socio-economic Futures (SRES)
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
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
Livestock Numbers, Crop andGrass Areas and Yields
Livestock Numbers, Crop andGrass Areas and YieldsCLUAM ITE : LCS
Experimental demand,yield and supply
data:
Demand ChangePrice Change
Experimentalenvironmental Data:
Climate ChangeYield Change
Specification and Calibrationof the model
Projection of Changes inLand Use and Production
Actual Land Use(MAFF June Census)
Input / Output Coefficients(production relationships fromfarm management type data)
Value of national inputs andoutputs to the agricultural
sector (DNIC)
Results – Agricultural ChangeKennet land cover areas (upland & lowland combined) No climate change A2/B2
0
10000
20000
30000
40000
50000
60000
70000
REF 1990s 2020 A2 2020 B2 2050 A2 2050 B2
haIdle-Rough
Idle-PermIdle-Ley
Idle-Arable
RoughPerm
LeyOther arable
Cereals+oil
Kennet land cover areas (upland & lowland combined) Climate change A2/B2
0
10000
20000
30000
40000
50000
60000
70000
REF 1990s CC 2020 A2 CC 2020 B2 CC 2050 A2 CC 2050 B2
ha
Idle-RoughIdle-PermIdle-LeyIdle-ArableRoughPermLeyOther arableCereals+oil
Livestock NumbersKennet livestock numbers (upland & lowland combined)
No climate change A2/B2
0
10000
20000
30000
40000
50000
LSU Sheep LSU
Beef LSU
Dairy LSU
Sheep LSU 18243 18991 6346 6 22058
Beef LSU 6608 2317 22000 6037 711
Dairy LSU 21297 24745 9502 24604 18962
REF 1990s 2020 A2 2020 B2 2050 A 2 2050 B2
Kennet livestock numbers (upland & lowland combined)
Climate change A2/B2
0
10000
20000
30000
40000
50000
LSU Sheep LSU
Beef LSU
Dairy LSU
Sheep LSU 18243 12096 21428 4 348
Beef LSU 6608 0 11047 8133 980
Dairy LSU 21297 24099 9134 20799 24254
REF 1990s 2020 A2 2020 B2 2050 A2 2050 B2
Modelling Agricultural ChangeCLIMATE 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
This work has used the UK Climate Impacts Programme (UKCIP02) Scenarios, derived as follows.
The INCA-N model for predicting nitrate and flow works on a daily time step & requires daily temperature, rainfall and evapotranspiration.
HadCM3c.300 km grid
HadAM3Hc.120 km grid
HadRM3c.50 km grid
SRES Scenarios (4 future climates, including A2 and B2)
“Experiments” run by the Hadley Centre
Global Models European ModelMonthly Time Step
More DownscalingHadRM3c.50 km gridMonthly
Kennet Catchment5 km grid, DailyEARWIG
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
Mean Daily Temperature
02468
101214161820
1 2 3 4 5 6 7 8 9 10 11 12
Month
Tem
pera
ture
(C)
Base20202050B22050A2
Annual means: Base (1961-90) 9.2 C 2020 10.2 C 2050 B2 11.0 C 2050 A2 11.3 C
EARWIG: Mean Monthly RainfallRainfall
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12
Month
Rai
nfal
l (m
m)
Base20202050B22050A2
Annual Totals: Base 759 mm2020s 787 mm2050s 757 mm
How does INCA work?
.
.
Each sub-catchment has 6 land uses:Urban;Forest;Arable + Oilseeds;Grassland;Unfertilised;Not covered by CLUAM.
Catchment divided into sub-catchments
Land Cell: Hydrological Model
Quick flow
QuickSoil
Groundwater
Quick flow
Throughflow
Groundwater flow
P AET
Hydrological Model
Abstraction (e.g. for water supply)
NO3
Groundwater Zone
NH4
Urban wasteto River
NitrogenFixation
Ammonium +Nitrate deposition
Ammonium +Nitrate fertiliser
NO3 NH4
nitrification
Organic N
Netmineralisation
NitrateAddition
Plantuptake
AmmoniumAddition
Plantuptake
Reactive Soil Zone
denitrification
Leachingto river
Leachingto river
NO3
Groundwater Zone
NH4
Urban wasteto River
NitrogenFixation
Ammonium +Nitrate deposition
Ammonium +Nitrate fertiliser
Urban wasteto River
NitrogenFixation
Ammonium +Nitrate deposition
Ammonium +Nitrate fertiliser
NO3 NH4
nitrification
Organic N
Netmineralisation
NitrateAddition
Plantuptake
AmmoniumAddition
Plantuptake
Reactive Soil Zone
denitrification
Leachingto river
Leachingto river
INCA-N Soil Processes
Land Uses and Fertiliser InputsEach land use parameterised separately for all the above
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
N Fertiliser in kg N ha-1yr-1
IN-STREAM PROCESSES
in INCA
Annual Hydrology
0
100
200
300
400
500
600
700
800
900
Base 2020s A2/B2 2050s 2050s A2
mm
/yr
RainfallPET AETHER
Low summer rainfall protects the river from extra evaporation –to some extent
Period of River Recharge Shortens
0
10
20
30
40
50
60
70
80
90
100
1961-90 2020s 2050s B2 2050s A2
Per
cent
age
of y
ears
4 months5 months6 months8 months
Consecutive months without hydrologically-effective rainfall
What Happens to Nitrate?
0
1
2
3
4
5
6
7
8
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000
Day
Nitr
ate
(mgN
/L)
60-year realisation of nitrate in the R. Kennet: baseline climate
EU Drinking water standard: 11.3 mg/L
Mean Nitrate Concentrations
0
1
2
3
4
5
6
Base SE CCSE
Nitr
ate
mg
N/L
Baseline2020 A22020 B22050 A22050 B2
Crops in reference state (1990)
Crop changes due to socio-economicfactors only
Crop changes due to socio-economic & climate change
Variation in Nitrate: 2050 A2
0
2
4
6
8
0 5 10 15 20 25 30 35 40 45 50 55 60
Year
Ann
ual m
ean
nitr
ate
(mg
N/L
)
BaselineCCSE 2050 A2SE 2050 A2Base 2050 A2
Socio-economic change makes a difference – adding climate change has no effect
Variation in Nitrate: 2050 B2
0
2
4
6
8
0 5 10 15 20 25 30 35 40 45 50 55 60
Year
Ann
ual m
ean
nitr
ate
(mg
N/L)
BaselineCCSE 2050 B2SE 2050 B2Base 2050 B2
Socio-economic change makes small difference – adding climate change increases it
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 ChinaThe 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