Science and tools for fire adaptation and mitigation
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This presentation given in cooperation by CIFOR and the Earth Institute focuses on fires in tropical regions: what's influencing them, what can help reduce them and which monitoring methods can be applied.
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Science and tools for fire adaptation and mitigation
- 1. Science and tools for fire adaptation and mitigation CIFOR-EARTH INSTITUTE PARTNERSHIP Miguel Pinedo-Vasquez, Victor Gutierrez-Velez, Katia Fernandes, Christine Padoch, Maria Uriarte, Walter Baethgen and Ruth DeFries
- 2. Outline 1. Many tropical areas are experiencing profound and rapid landscape change. 2. Some of these changes are resulting in an increased risk of agricultural fires escaping. 3. Drivers of increased fire incidence are complex and interrelated, often in non-linear ways 4. We are developing science and tools that may help communities both mitigate and adapt to climate change and fire risk in tropical landscapes.
- 4. Fires in humid tropical regions • Fire is not a natural phenomenon but its use can be dated to 8,000 yrs BP. (Bush et al. 2007) • It is the cheapest tool for land preparation, pasture and plantation management (Fernandes et al. 2011) • Where climate change leads to decreased precipitation (Malhi, 2008), fire uses need to adapt
- 5. Multiple dimensions of change influencing fire risk Land cover dimensions Demographic Shifts Absentee Landlords FIRE RISK Land use/cover changes Urbanization Mobility Large pastures plantations Droughts Social/demographic dimensions Variability Seasonality Dry spells Climatic dimensions Vegetation change
- 6. Fires are increasing in areas with declining rural Prop. change in population in Western Amazonia rural population More people Fewer people Uriarte et al. (2012).
- 7. Gutierrez-Velez et al (submitted) x SPI SPI*** x SPI SPI*** SPI*** x SPI SPI*** SPI*** SPI*** SPI*** SPI*** SPI*** SPI*** SPI*** SPI*** palm*** SPI*** SPI*** c. palm alm*** SPI*** 0.0 More fire 0.5 0.0 0.0 palm*** c. palm palm*** 1.0 0.5 0.5 dary*** alm*** c. palm 1.5 1.0 1.0 owth*** dary*** palm*** 2.0 1.5 1.5 rass*** owth*** dary*** 2.0 2.0 hort*** rass*** owth*** hort*** rass*** hort*** Standardized parameter estimate Secondary forests and adult oil palm plantations can help OccurNoRdNoYoungerX OccurNoRdNoYoungerX OccurNoRdNoYoungerX reduce fire occurrence. -0.5 -0.5 -0.5 -1.0 -1.0 -1.0
- 8. Gutierrez-Velez et al (submitted) SPI*** SPI*** x SPI SPI*** SPI*** SPI*** SPI*** SPI*** SPI*** SPI*** SPI*** SPI*** palm*** SPI*** SPI*** c. palm palm*** SPI*** 8 x SPI SPI*** x SPI 0.0 More fire 0.5 0.0 0.0 palm*** c. palm palm*** 1.0 0.5 0.5 dary*** palm*** c. palm 1.5 1.0 1.0 owth*** dary*** palm*** 2.0 1.5 1.5 rass*** owth*** dary*** 2.0 2.0 hort*** rass*** owth*** hort*** rass*** hort*** Standardized parameter estimate Drought severity reduces the ability of secondary forests but not of OccurNoRdNoYoungerX OccurNoRdNoYoungerX OccurNoRdNoYoungerX adult oil palm plantations to reduce fire occurrence -0.5 -0.5 -0.5 -1.0 -1.0 -1.0
- 9. Fire Early Warning System in Western Amazonia J/A/S Western Amazonia fire anomalies can be forecast from retrospective Sea Surface Temperature. 3.00 Precip-JAS 2.00 1.00 0.00 -1.00 -2.00 Fernandes, K., et al. (2011). Fires-JAS
- 10. Fires Early Warning System in Western Amazonia JAS 2010 Fire-Anomalies predicted since (a) April, (b) May and (c) June with 95% confidence. (a) Fernandes, K., et al. (2011). Fernandes, K., et al. (2011). (b) (c)
- 11. We are developing science and tools for local adaptation to fire risk as well as for mitigation: 1. Designing early warning systems for adaptation to climate variability. 2. Providing institutional support for fire prevention and intervention systems. 3. Mapping vulnerability to fire based on sociodemographic dynamics and landscape change. 4. Designing land cover management strategies to mitigate climate and fire risk in transitional landscapes
- 12. Thank you
Editor's Notes
- In many areas, smallholder landscapes that involved a finely-grained mosaic of forests, fallows, fields, settlements, and other features is being changed. Mosaics are being replaced with far larger patcheswhether they be larger pastures, plantations of commodity crops, or protected areas.
- These changes also reflect a number of changes in
- Censuses from 1987, 1993, 2003, 2007
- We developed satellite products and incorporated them into a Bayesian statistical model to assess the effect of land cover changes and drought severity on fire activity in the study area. The parameter estimates for each variable indicate the sign and strength of the correlation between predictors and fire occurrence (the probability of each pixel to burn). Positive values mean more fire. Land covers representing different stages of regrowth portray an U shape trend with pastures and falllow promoting fire and degraded pastures and secondary forests inhibiting it. Oil palm plantations exhibit a negative linear trend with age class. Young plantations (0-5 yr) increase fire probability, adolescent (5-10 yr) have an non-significant effect and adult plantations (>10 yr) reduce it.
- Drought is the variable with the highest correlation. Parameter estimates for multiplicative interactions between land covers and drought exhibit a positive trend. LCs representing more advanced stages of forest regrowth increase fire probability as it becomes dryer. In contrast adult plantations do not promote fire even during anomalously dry years. Results show that land cover management can help reducing fire occurrence.
- Climate change mitigation in the context of droughts and rainforests can work through fire prevention. Early warning systems (EWS) for droughts and fires are available at IRI, for both Western Amazon and in Kalimantan-Indonesia. Dry season (July-August-September) fire and precipitation anomalies averaged over the Western Amazon domain vary close together (top plot). JAS precipitation is also driven by North Tropical Atlantic SST anomalies (bottom plot), so if the climate (SST) is the main driver of fire variability, we should be able to predict anomalies in upcoming fire seasons using seasonal forecast.Fire anomalies prediction for western Amazon is shown for JAS 2010. The colorbar shows standardized anomalies (no unit), so if you really need to mention what the map is showing is: “standardized anomalies of fire count at each pixel”. The fire data used is “Active Fires from MODIS”.The warm colors indicate prediction of an active fire season made in April, May and June. The dots basically show the pixels where we got it right. White areas in the map represent, for most part, areas where fires are very rare and are not included in the prediction.The seasonal fire prediction can be used, a few months prior to the beginning of the dry season, to identify regions where concentrated effort to prevent fires should be directed.
- Fire anomalies prediction for western Amazon is shown for JAS 2010. The colorbar shows standardized anomalies (no unit), so if you really need to mention what the map is showing is: “standardized anomalies of fire count at each pixel”. The fire data used is “Active Fires from MODIS”.The warm colors indicate prediction of an active fire season made in April, May and June. The dots basically show the pixels where we got it right. White areas in the map represent, for most part, areas where fires are very rare and are not included in the prediction.The seasonal fire prediction can be used, a few months prior to the beginning of the dry season, to identify regions where concentrated effort to prevent fires should be directed.
- The link for our product is too long, so I am copying it here instead of having it in the slide. You can email it if necessary.http://iridl.ldeo.columbia.edu/home/.katia/.FirePrediction/.Jul/.JAS_SST_Forecast/figviewer.html?my.help=&map.T.plotvalue=2005.0&map.lat.units=degree_north&map.lat.plotlast=5.025N&map.url=lon+lat+fig-+colors+thin+states+thinnish+countries+-fig&map.domain=+%7B+%2FJAS_SST_Forecast+-2+2+plotrange+%2FT+2005.0+plotvalue+lat+-20.075001+5.0250001+plotrange+%7D&map.domainparam=+%2Fplotaxislength+432+psdef+%2Fplotborder+72+psdef+%2FXOVY+null+psdef&map.zoom=Zoom&redraw.x=20&redraw.y=14&map.lat.plotfirst=20.075S&map.lon.plotfirst=81.55W&map.lon.units=degree_east&map.lon.modulus=360&map.lon.plotlast=65.45W&map.JAS_SST_Forecast.plotfirst=-3&map.JAS_SST_Forecast.units=Standard+Deviation&map.JAS_SST_Forecast.plotlast=3&map.newurl.grid0=lon&map.newurl.grid1=lat&map.newurl.land=countries&map.newurl.plot=colors&map.plotaxislength=432&map.plotborder=72&map.fnt=Helvetica&map.fntsze=12&map.color_smoothing=1&map.XOVY=auto&map.iftime=25&map.mftime=25&map.fftime=200