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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 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 24 May 2017 © 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 LS U 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 LS U 0 REF 199 0s 20 20 A 2 202 0 B2 2 050 A 2 20 50 B2 She ep LS U 182 43 1 209 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. 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