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 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

Is it possible to model these outcomes?

…with any credibility?

20 April 2023 © University of Reading 2008 www.reading.ac.uk

Predicting the effect of climate change on water resources and foodproduction

Modelling land use impacts

Overview

The socio-economic change scenarios

IPCC SRES futuresUKCIP refinements for

UKBLS world food trade

model

The climate change scenarios

HadCM3 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

ha

Idle-Rough

Idle-Perm

Idle-Ley

Idle-Arable

Rough

Perm

Ley

Other 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-Rough

Idle-Perm

Idle-Ley

Idle-Arable

Rough

Perm

Ley

Other arable

Cereals+oil

Livestock NumbersKennet livestock numbers (upland & lowland combined)

No climate change A2/B2

0

10000

20000

30000

40000

50000

LS

U 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 A2 2050 B2

Kennet livestock numbers (upland & lowland combined)

Climate change A2/B2

0

10000

20000

30000

40000

50000

LS

U 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 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

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 Downscaling

HadRM3c.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

Te

mp

era

ture

(C

)

Base

2020

2050B2

2050A2

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 Rainfall

Rainfall

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12

Month

Rai

nfa

ll (m

m)

Base

2020

2050B2

2050A2

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 Inputs

Each 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

Rainfall

PET

AET

HER

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

Pe

rce

nta

ge

of y

ea

rs

4 months

5 months

6 months

8 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

Nit

rate

(m

gN

/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

Nit

rate

mg

N/L

Baseline

2020 A2

2020 B2

2050 A2

2050 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

An

nu

al

me

an

nit

rate

(m

g N

/L)

Baseline

CCSE 2050 A2

SE 2050 A2

Base 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

An

nu

al m

ean

nit

rate

(m

g 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