Regional impact assessment modelling


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CCAFS workshop titled "Using Climate Scenarios and Analogues for Designing Adaptation Strategies in Agriculture," 19-23 September in Kathmandu, Nepal.

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  • Outline: impacts in the large (aggregate) Impacts in the small (localised) What is being done about it, and what can be done in the future And something on the outlook for Africa with regard to CC Focus is on agriculture, and ag research for development – CC affects many other sectors too. Also acknowledge inputs of various people at ILRI and elsewhere – particular partners-in-crime being Peter Jones (ex-CIAT, now at large) and Robin Reid, Russ Kruska, Mario Herrero, An Notenbaert and Tom Owiyo in Nairobi.
  • Regional impact assessment modelling

    1. 1. Regional impact assessment modelling September 2011
    2. 2. Regional climate change impact assessment modelling <ul><li>What do you need? </li></ul><ul><li>A good reason for doing it </li></ul><ul><li>Decisions on the type, resolution, scope of the study </li></ul><ul><li>Weather data, soils data, land use data, … </li></ul><ul><li>Agricultural systems information (crops, varieties, livestock species, management, …) </li></ul><ul><li>A software system to run the agricultural impact model(s) </li></ul><ul><li>A software system to analyse the outputs </li></ul><ul><li>Healthy scepticism as to the results </li></ul>
    3. 3. Type, spatial resolution, scope of study <ul><li>Driven by: </li></ul><ul><ul><li>the question you are trying to answer </li></ul></ul><ul><ul><li>data availability </li></ul></ul><ul><ul><li>processing power available </li></ul></ul><ul><ul><li>skills and time available </li></ul></ul><ul><li>Type of study: </li></ul><ul><ul><li>by “representation”: pick representative points and extrapolate to broader land units (quicker, less spatial variation, needs characterisation work, may need fewer data) </li></ul></ul><ul><ul><li>by pixels: simulate responses on all viable land units (slower, more spatial variation, data intensive) </li></ul></ul>
    4. 4. Representation: aggregation of modelled response Area Production Value County/Province State Country Agro-ecological zone E.g. yield predictions extrapolated from specific (sentinel) sites (a, b, c) a b a
    5. 5. - 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
    6. 6. Aggregation of modelled response: complete pixel coverage Area Production Value County/Province State Country AEZ Generate yield predictions for every pixel / land unit
    7. 7. Pixels: how big (spatial resolution), how many (masking)? <ul><li>Availability of data at specific scales </li></ul><ul><li>Processing power available (1 km versus 18 km) </li></ul><ul><li>Assume land units are independent of one another: </li></ul><ul><ul><li>Model crop growth in one pixel, move onto the next </li></ul></ul><ul><ul><li>Good for cluster or parallel processing </li></ul></ul><ul><ul><li>No good if you want to do landscape hydrology modelling </li></ul></ul>Omit non-agricultural soils? Urban areas? Places with a very short growing season?
    8. 8. Daily weather data <ul><li>Current conditions: </li></ul><ul><ul><li>Specific sites, historical data (can also use for model calibration) </li></ul></ul><ul><ul><li>Gridded data – for example: </li></ul></ul><ul><ul><ul><li>WorldClim, long-term climate normals (need a weather generator) </li></ul></ul></ul><ul><ul><ul><li>CRU TS3.0 half-degree, historical monthly data (1901-2006) (need a weather generator to get daily data) </li></ul></ul></ul><ul><ul><ul><li>Indian Met Department Daily 1-degree gridded datasets of rainfall (1951-2004) and temperatures (1969-2004) </li></ul></ul></ul>
    9. 9. Daily weather data <ul><li>Future conditions: which GCMs, which emissions scenarios, which time slices, which variables, how to process </li></ul><ul><li>A few options, including a version of MarkSimGCM that runs in batch or script mode </li></ul>
    10. 10. Soils data: one option, FAO soils map of the world For each mapping unit: Fo 50% - Grade 2 Af 20% - Grade 2 Ao 20% - Grade 2 I 10% - Non Agric. Multiple “representative” DSSAT soil profiles for each of the ~83 FAO soil types (WISE databases)
    11. 11. Soils: another option, HC27 Generic Soil Profile Data Dimes & Koo, HarvestChoice <ul><li>Each FAO soil type classified into 27 meta-soil types, defined by </li></ul><ul><ul><li>soil organic carbon content </li></ul></ul><ul><ul><li>soil rooting depth, a proxy for available water content </li></ul></ul><ul><ul><li>major constituent </li></ul></ul>high medium low deep medium shallow sand loam clay One of the 27 soil profiles in DSSAT format
    12. 12.
    13. 13. Agricultural systems information: e.g. crops <ul><li>Need decisions on: </li></ul><ul><ul><li>which crops, which varieties (yield gap analysis, common practice, changing durations, …) </li></ul></ul><ul><ul><li>planting densities (potential yields, common practice, …) </li></ul></ul><ul><ul><li>initial conditions of the soil (to reproduce current yields, improved yields, different soil management options, …) </li></ul></ul><ul><ul><li>planting dates, cropping calendars (current practice, adaptation options, maximise yields/minimise risk of crop failure, …) </li></ul></ul>
    14. 14. <ul><li>Can use start and length of growing period for each pixel: </li></ul><ul><ul><li>Estimate soil water holding capacity from soils data </li></ul></ul><ul><ul><li>Calculate a daily water balance via available soil water, runoff, water deficiency and the actual to potential evapo-transpiration ratio (Ea/Et) </li></ul></ul><ul><ul><li>Count a growing day if average temperature > 6 °C and Ea/Et > 0.35 </li></ul></ul><ul><ul><li>Growing period starts after five consecutive growing days have occurred. Season ends after 12 consecutive nongrowing </li></ul></ul><ul><ul><li>days </li></ul></ul>Estimating planting dates regionally
    15. 15. Average LGP (current conditions), days per year
    16. 16. Average start of the primary growing season, day of year
    17. 17. Estimating planting dates regionally
    18. 18. 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)
    19. 19. Spatial crop information
    20. 20. Regional agricultural system information <ul><li>Multiple seasons in a year (rainfed or irrigated) </li></ul><ul><li>Intercropping, multiple cropping </li></ul><ul><li>Interactions between different enterprises (crops, livestock, …) </li></ul><ul><li>What do farmers actually do in a place, in terms of management? </li></ul><ul><li>Global databases on these things do not yet exist </li></ul>
    21. 21. 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, …
    22. 22. 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?
    23. 23. <ul><li>IPCC Fourth Assessment models and data: </li></ul><ul><ul><li>14 GCMs </li></ul></ul><ul><ul><li>3 emissions scenarios (SRES B1, A1B, A2) </li></ul></ul><ul><ul><li>Monthly data for the 2090s: rainfall, tmax, tmin </li></ul></ul><ul><ul><li>Scaled to +5 ° C (global temp) </li></ul></ul><ul><li>Generated characteristic daily weather data using MarkSim as a GCM downscaler (difference interpolation + stochastic downscaling + weather typing) </li></ul><ul><li>Estimated growing days and growing seasons using daily weather data and the simple water balance model </li></ul>Analysis GCM data from Mark New & Gil Lizcano, University of Oxford
    24. 24. Ensemble mean of LGP change estimates to the 2090s Substantial losses away from equator, some small gains in parts of E Africa
    25. 25. Ensemble CV (%) of LGP change estimates to the 2090s Three zones – background small variation (<20), then higher in cropland (dark blue), then green and brown in arid-semiarid rangelands
    26. 26. <ul><li>We looked at </li></ul><ul><ul><li>Maize (a C 4 crop) </li></ul></ul><ul><ul><li>Phaseolus bean (a C 3 crop) </li></ul></ul><ul><ul><li>Brachiaria decumbens (an indicator pasture species) </li></ul></ul><ul><li>Used the crop models in the DSSAT v4 (ICASA, 2007) </li></ul><ul><li>Used a 10-arc-minute pixel triage based on cropland and pastureland as defined by Ramankutty et al. (2006) </li></ul>Crop modelling
    27. 27. Simulated yields (30 reps) in SSA under current conditions and in the 2090s
    28. 28. 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
    29. 29. 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
    30. 30. <ul><li>Losses in length of growing season translate directly into crop yield decreases </li></ul><ul><li>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 </li></ul><ul><li>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 </li></ul>What do the modelling results mean? Thornton, Jones, Ericksen, Challinor (2011)
    31. 31. CCAFS-commissioned reports coming soon on AR4 GCM evaluation on the three target regions (IGP, Wsat Africa, East Africa)
    32. 32. <ul><li>Some recent work challenges the AR4 climate model results for a wetter East Africa in the future </li></ul><ul><li>If East Africa gets drier (matching recent trends), then growing periods will contract, and yields will decrease even more </li></ul>
    33. 33. What will a +5 °C agriculture look like in SSA ? <ul><li>In many places, much higher probabilities of crop failures </li></ul><ul><li>Massive increases in intensive cropping in the highlands will be needed (“sustainable intensification”) </li></ul><ul><li>Huge expansion of the marginal areas (highly uncertain cropping) </li></ul><ul><li>Radical livelihood transitions (croppers to livestock keepers, abandonment of agriculture, …) </li></ul><ul><li>Not included: water, human health, crop/livestock disease, weeds & pests, other ecosystem and coastal impacts, … </li></ul><ul><li>But human adaptive capacity? </li></ul>
    34. 34. What will a +5 °C agriculture look like in SSA ?
    35. 35. A linear approach to “cascading uncertainty” Challinor (2009)
    36. 36. Alternatively: a decision-centred approach to support good decision-making, where climate change risk is recognised as only one driver Willows and Connell (2003)
    37. 37. Limitations & uncertainties remain with the impact models … <ul><li>Parameterisation, calibration, validation </li></ul><ul><li>Few impact studies done for tropical grasslands / rangelands (AR4) </li></ul><ul><li>CO 2 impacts on plant productivity; ability to resolve changes in intra-annual precipitation patterns (Tietjen & Jeltsch 2007) </li></ul>SAVANNA (CSU), SimSAGS (Ed), ... Fairly mature Growth, development, reproduction of browse, pastures, animal herds Ecosystems (100s km 2 ) <ul><li>Diet selection </li></ul><ul><li>Impacts of anti-nutritional compounds </li></ul><ul><li>CO 2 , ozone </li></ul>Ruminant, Cornell system, ... Fairly mature Feed intake, animal impacts on pasture Plant-animal interactions <ul><li>Species competition </li></ul><ul><li>CO 2 , ozone impacts on growth, competition </li></ul>DSSAT, APSIM, CropSyst, ... Mature Growth, reproduction, competition (ish) Plants ILCA, Lesnoff, ... Mature Births, growth, deaths Herd Requirement systems: NRC, AFRC, INRA, ... Mature Maintenance, growth, lactation, reproduction Animal Key Gaps, Challenges Examples Science Maturity Processes Modelled Unit, Level
    38. 38. What is currently happening on the ground, for translating into data layers for input to models Enormous system characterisation uncertainties
    39. 39. <ul><li>Options </li></ul><ul><li>Use sophisticated crop / livestock / ecosystem models regionally, globally </li></ul><ul><li>Use much simpler relationships or even “rules of thumb” (e.g. RUE, 1 kg of consumable DM per ha per mm of rainfall) </li></ul><ul><li>Develop and use “simplified” complex models (a combined crop-climate model such as GLAM) </li></ul><ul><li>Use other alternative lines of enquiry to complement what comes out of such studies </li></ul>Regional / global impact models (Millions of km 2 )
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