Regional impact assessment modelling

September 2011

Regional climate change impact assessment modelling

What do you need?

• A good reason for doing it

• Decisions on the type, resolution, scope of the study

• Weather data, soils data, land use data, …

• Agricultural systems information (crops, varieties,

livestock species, management, …)

• A software system to run the agricultural impact model(s)

• A software system to analyse the outputs

• Healthy scepticism as to the results

Type, spatial resolution, scope of study

Driven by:

• the question you are trying to answer

• data availability

• processing power available

• skills and time available

Type of study:

• by “representation”: pick representative points and extrapolate to

broader land units (quicker, less spatial variation, needs

characterisation work, may need fewer data)

• by pixels: simulate responses on all viable land units (slower, more

spatial variation, data intensive)

Representation: aggregation of modelled response

a

b

a AreaProduction

Value

County/ProvinceStateCountryAgro-ecological zone

E.g. yield predictionsextrapolated from specific (sentinel) sites (a, b, c)

- Run model based on the distributions of regional

data and sensitivity tests

- Multiple factors

- Identify data that are important for

given agricultural system/region

- Many simulations

- Aggregate sentinel site yields into regional

production using agro-ecological zones and

remotely-sensed information

cultivar

%

Soil

%

Temperature

%

Aggregation of modelled response

Aggregation of modelled response: complete pixel coverage

AreaProduction

Value

County/ProvinceStateCountryAEZ

Generate yield predictions for every pixel / land unit

Pixels: how big (spatial resolution), how many (masking)?

Availability of data at specific scales

Processing power available (1 km versus 18 km)

Assume land units are independent of one another:

• Model crop growth in one pixel, move onto the next

• Good for cluster or parallel processing

• No good if you want to do landscape hydrology modellingOmit non-agricultural soils?

Urban areas?Places with a very short growing season?

Daily weather data

• Current conditions:

• Specific sites, historical data (can also use for model

calibration)

• Gridded data – for example:

• WorldClim, long-term climate normals (need a weather

generator)

• CRU TS3.0 half-degree, historical monthly data (1901-

2006) (need a weather generator to get daily data)

• Indian Met Department Daily 1-degree gridded datasets

of rainfall (1951-2004) and temperatures (1969-2004)

Daily weather data

• Future conditions: which GCMs, which emissions scenarios,

which time slices, which variables, how to process

• A few options, including

a version of MarkSimGCM

that runs in batch or

script mode

Soils data: one option, FAO soils map of the world

For each mapping unit:

Fo 50% - Grade 2Af 20% - Grade 2Ao 20% - Grade 2I 10% - Non Agric.

Multiple “representative” DSSAT soil profiles for each of the ~83 FAO soil types (WISE databases)

Soils: another option, HC27 Generic Soil Profile Data

Dimes & Koo, HarvestChoice

Each FAO soil type classified into 27 meta-soil types, defined by

• soil organic carbon content• soil rooting depth, a proxy for available water content• major constituent

high medium low

deep medium shallow

sand loam clay

One of the 27 soil profiles in DSSAT format

http://labs.harvestchoice.org/2010/08/hc27-genericprototypical-soil-profiles/

Agricultural systems information: e.g. crops

Need decisions on:

• which crops, which varieties (yield gap analysis, common

practice, changing durations, …)

• planting densities (potential yields, common practice, …)

• initial conditions of the soil (to reproduce current yields,

improved yields, different soil management options, …)

• planting dates, cropping calendars (current practice,

adaptation options, maximise yields/minimise risk of crop

failure, …)

Can use start and length of growing period for each pixel:

• Estimate soil water holding capacity from soils data

• Calculate a daily water balance via available soil water, runoff, water deficiency and the actual to potential evapo-transpiration ratio (Ea/Et)

• Count a growing day if average temperature > 6 °C and Ea/Et > 0.35

• Growing period starts after five consecutive growing days have occurred. Season ends after 12 consecutive nongrowing

days

Estimating planting dates regionally

Average LGP (current conditions), days per year

Average start of the primary growing season, day of year

Estimating planting dates regionally

Total crop area from national statistics (2000) compared with land cover products for Africa

“… ideally … a hybrid product that combines the best of the … products, depending upon the region and country”

IIASA leading an effort to try to create such a hybrid

Fritz et al. (2010)

Spatial crop informationhttp://mapspam.info

Regional agricultural system information

• Multiple seasons in a year (rainfed or irrigated)

• Intercropping, multiple cropping

• Interactions between different enterprises (crops, livestock, …)

• What do farmers actually do in a place, in terms of management?

Global databases on these things do not yet exist

A software system to run the simulations and analyse the results?

Many options (including within DSSAT v4.5)

Customised software

Do it yourself, or …

Issues of speed, cost,

ease-of-use, …

An example: Agriculture and food systems in sub-Saharan Africa in a four-plus degree world

To try to answer the question, “what will a +5°C agriculture look like in sub-Saharan Africa?”

Specifically, what may happen to indicator crop yields in SSA as a result of such warming?

IPCC Fourth Assessment models and data:• 14 GCMs• 3 emissions scenarios (SRES B1, A1B, A2)• Monthly data for the 2090s: rainfall, tmax, tmin• Scaled to +5°C (global temp)

Generated characteristic daily weather data using MarkSim as a GCM downscaler (difference interpolation + stochastic downscaling + weather typing)

Estimated growing days and growing seasons using daily weather data and the simple water balance model

Analysis

GCM data from Mark New & Gil Lizcano, University of Oxford

Ensemble mean of LGP change estimates to the 2090sSubstantial losses away from equator, some small gains in parts of E Africa

Ensemble CV (%) of LGP change estimates to the 2090sThree zones – background small variation (<20), then higher in cropland (dark blue), thengreen and brown in arid-semiarid rangelands

• We looked at• Maize (a C4 crop)• Phaseolus bean (a C3 crop)• Brachiaria decumbens (an indicator pasture species)

• Used the crop models in the DSSAT v4 (ICASA, 2007)

• Used a 10-arc-minute pixel triage based on cropland and pastureland as defined by Ramankutty et al. (2006)

Crop modelling

Simulated yields (30 reps) in SSA under current conditions and in the 2090s

Simulated yields (30 reps) in SSA under current conditions and in the 2090s

High CVs of yield changes elsewhere: results depend on choice of GCM & emissions scenario

Simulated yields (30 reps) in SSA under current conditions and in the 2090s

Low CVs of yield changes in E Africa: quite a robust result

• Losses in length of growing season translate directly into crop yield decreases

• Even in the parts of E Africa that may get wetter, while growing seasons may expand, this will not necessarily translate into higher yields: increases in rainfall may be more than offset by increases in crop evapo-transpiration due to higher temperatures

• The details of yield changes depend on the climate model and emissions scenario used: but apparently not for East Africa, where this is reasonable consensus

What do the modelling results mean?

Thornton, Jones, Ericksen, Challinor (2011)

CCAFS-commissioned reports coming soon on AR4 GCM evaluation on the three target regions (IGP, Wsat Africa, East Africa)

ccafs.cgiar.org

• Some recent work challenges the AR4 climate model results for a wetter East Africa in the future

• If East Africa gets drier (matching recent trends), then growing periods will contract, and yields will decrease even more

What will a +5°C agriculture look like in SSA?

• In many places, much higher probabilities of crop failures• Massive increases in intensive cropping in the highlands will

be needed (“sustainable intensification”)• Huge expansion of the marginal areas (highly uncertain

cropping)• Radical livelihood transitions (croppers to livestock keepers,

abandonment of agriculture, …)

• Not included: water, human health, crop/livestock disease,

weeds & pests, other ecosystem and coastal impacts, …

• But human adaptive capacity?

What will a +5°C agriculture look like in SSA?

A linear approach to “cascading uncertainty”

Challinor (2009)

Alternatively: a decision-centred approach to support good decision-making, where climate change risk is recognised as only one driver

Willows and Connell (2003)

Limitations & uncertainties remain with the impact models …

Unit, Level Processes Modelled

Science Maturity

Examples Key Gaps, Challenges

Animal Maintenance, growth, lactation, reproduction

Mature Requirement systems: NRC, AFRC, INRA, ...

Herd Births, growth, deaths

Mature ILCA, Lesnoff, ...

Plants Growth, reproduction, competition (ish)

Mature DSSAT, APSIM, CropSyst, ...

Species competition CO2, ozone impacts on growth,

competition

Plant-animal interactions

Feed intake, animal impacts on pasture

Fairly mature

Ruminant, Cornell system, ...

Diet selection Impacts of anti-nutritional compounds CO2, ozone

Ecosystems (100s km2)

Growth, development, reproduction of browse, pastures, animal herds

Fairly mature

SAVANNA (CSU), SimSAGS (Ed), ...

Parameterisation, calibration, validation Few impact studies done for tropical grasslands / rangelands (AR4) CO2 impacts on plant productivity;

ability to resolve changes in intra-annual precipitation patterns (Tietjen & Jeltsch 2007)

What is currently happening on the ground, for translating into data layers for input to models

Enormous system characterisation uncertainties

Options

• Use sophisticated crop / livestock / ecosystem models regionally, globally

• Use much simpler relationships or even “rules of thumb” (e.g. RUE, 1 kg of consumable DM per ha per mm of rainfall)

• Develop and use “simplified” complex models (a combined crop-climate model such as GLAM)

• Use other alternative lines of enquiry to complement what comes out of such studies

Regional / global impact models(Millions of km2)