early warning system: monitoring aspect
DESCRIPTION
Early Warning System: monitoring aspect. Celso von Randow Earth System Science Center - INPE. [email protected]. EWS framework. Remote sensing monitoring system. Modeling System. In situ monitoring stations. Monitoring system Modeling System Analysis Tools - PowerPoint PPT PresentationTRANSCRIPT
Early Warning System: monitoring aspect
Celso von RandowEarth System Science Center - INPE
EWS framework
In situ monitoring stations
Modeling SystemRemote sensing monitoring system
Monitoring, analysis and prediction
Communicating alerts
Policy responses
EARLY WARNING SYSTEM
• Monitoring system• Modeling System• Analysis Tools• Communications division
Early Warning System
• Which ecosystem services and other properties of the Amazon would be important to monitor and prevent from tipping into a degraded state ?
• What to warn about?– Degradation of ecosystem services in what time
scales (years – decades) ? – Not only critical transitions, but also gradual
change
Critical indicators
• The basis of such a system is long-term monitoring of critical indicators
• These indicators should be quantities that are relatively accessible, and easy to monitor at high temporal and/or spatial resolution.
• should represent the variability of the Amazon ecosystem services and other important tipping phenomena
=> their behaviour near critical transitions should reliably point to imminent change in the state of that particular ecosystem service.
System beingforced past abifurcation point
yt+1 = ayt + sht
a = exp(- kDt) k→0 and a→1 at bifurcation
Tipping point early warning signals
Alternative stable states?
Treeless state:T < 5%
Savanna state:5% ≤ T < 60%
Forest state:T ≥ 60%
Frequency of Tree Cover (Global)
Hirota et al., Science, 2011
Tree cover X MAP (global):
Hirota et al., Science, 2011
Scheffer et al., TREE, 2003
Tree cover X MAP (global):
Hirota et al., Science, 2011
Scheffer et al., TREE, 2003
Statistical procedure (Livina
et al., 2010)confirmed 3 classes:
Analysis tools• Generic Early Warning indicators – detection
on basis of change in variability
Analysis tools• Generic Early Warning indicators – detection
on basis of change in variability• Detection on basis of exceedance of critical
thresholds - analysis of trends and changing trends
Analysis tools• Generic Early Warning indicators – detection
on basis of change in variability• Detection on basis of exceedance of critical
thresholds - analysis of trends and changing trends
• Identification of outliers from analysis of PDFs – (given a range of conditions that sustain a particular forest, look into predictions of extremes)
List of possible variables to monitor
• Sea Surface Temperature (SST) - indicator of global-scale change
• Precipitation (patterns, quantity, dry season length…) primary driver as well as an ecosystem service that can be affected
• Climate modes (ENSO, Atlantic Oscillations, etc) - often correlated indicators of high-impact changes or episodes in Amazonia
• River flow and discharge • Evapotranspiration - prime driver of recycling
List of possible variables to monitor
• overall vegetation productivity changes – [CO2] over the tropical belt + anthropogenic emissions
• Biomass - remote sensing (eg S-band Radar) and well-referenced growth bands in forest plots across the basin
• Water use efficiency from tree-ring & gas exchange monitoring
• Remote sensing indices (NDVI , EVI)
List of possible variables to monitor
• Fires (remote sensing and in-situ observations) – not simply occurrence or area, but also fire effects (e.g. type of vegetation affected and recovery of previously burned areas
• Economic indicators, such as the GDP of the region, transport, trade and migration patterns
• Exposure and Vulnerability (?)
COSMOS (COsmic-ray Soil Moisture Observing System)
Could it be used to monitor ‘flammability’ of the forest? Monte-Carlo Simulation of Neutron Density
Monte-Carlo Simulation of Neutron Density
In moister soil,less neutrons escape
In drier soil,more neutrons escape
COSMOS probes detect neutrons at two energies, but
use “fast” neutrons for soil moisture detection because
calibration is less sensitive to the chemistry of the soil
(thermal neutrons give information on above-ground
water, e.g. snow cover)Thermal Neutron
Detector
Fast Neutron Detector
This is largely a soil-dependent “shift”,
SO ONLY ONE FIELD CALIBRATE NEEDED
Example COSMOS Data for the San Pedro Basin
Soil moisture from cosmic-ray neutron data compared with gravimetric samples
Gravimetric samples are in red, with sampling error
Day in July 2007
7 8 9 10 11 12 13 14 15 16 17 18 19 20
Gra
vim
etr
ic w
ate
r co
nte
nt
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Diurnal Cycles
(moisture redistribution)
How many point measurements are needed to get a similar
(2%) precision in area-average
soil moisture?
For the (single) calibration of a COSMOS probe
(made at installation), soil will be sampled
at 3 depths, 8 directions, and 3 radii
around the probe(i.e., 72 samples).
COSMOS (COsmic-ray Soil Moisture Observing System)
Institutional and practical embedding
• From stakeholders consultation: – EWS: MMA, MAPA, MME, MDA– ‘Users’ / Policy makers: MMA, SAE (Secret.
Assuntos Estratégicos), CENSIPAM (Centro Gestor e Operacional do Sistema de Proteção da Amazônia), Órgãos Estaduais de Meio Ambiente (OEMAs)
Communication
• Communication of complex issues to non-science audience is a major challenge
• Design should be (as much as possible) stakeholder-driven (e.g. focus on critical transitions in environmental conditions or direct impacts in ecosystem services) ?
• Reduce risk of false positives
ConclusionsDesign of an Early Warning System for critical transitions in the
Amazon region:
• Requires a multi-disciplinary approach and involvement of relevant stakeholders
• Based on long-term monitoring of critical indicators that should be relatively accessible to monitor and should represent the variability of relevant ecosystem services
• Their behaviour near critical transitions should reliably point to imminent change in the state of that particular service.
• Through model analysis and analysing data sets, the most efficient monitoring and analysis tools need to be designed
• Communication is a major challenge for effective policy actions
WP2 runs (Moore protocol)A B C D
LPJml OK OK
INLAND Ongoing OK Ongoing
JULES OK OK
ORCHIDEE OK ongoing
Simulation A (Potential vegetation A): natural disturbances + no land-use change + changing climate (recycling the SHEF driver) + changing CO2.
Simulation B (Potential vegetation B): natural disturbances by excluding fire + no land-use change + changing climate (recycling the SHEF driver) + changing CO2. Simulation C ( Changing climate ): This simulation need to be achieved by two steps: 1) natural disturbances + no land-use change + changing climate (recycling the SHEF driver) + changing CO2 from 1715 to 1970; 2) natural disturbances + no land-use change + changing climate + constant CO2 (=325.713 ppm) from 1970 to 2008.Simulation D (Full changes): natural disturbances + land-use change + changing climate + changing CO2.
WP3 runs
How to achieve a sustainable future?
High Social Development“In 2050, Brazil is one of the main economies of the world. Social indicators also place Brazil among the most equitable and socially fair countries in the world. Society as a whole has access to high quality education, health services, economic opportunities, supported by strong institutions. ”
Low Social Development“In 2050, Brazil is one of the main economies of the world, but structural inequalities in society persist. Land in rural areas is highly concentrated, urban areas remain violent, segregated, with bad quality services in poor neighborhoods.”
Low Environmental Development
“Badly managed natural resources, few natural vegetation areas remaining, and high greenhouse emissions.”
“Well managed natural resources, ecosystem services provision and low greenhouse emissions”
High Environmental Development
A
Vision A:High, HighSustainable
Vision CLow, High
Vision BLow, High
Vision DLow, Low
Social
Environmental EconomicEconomicEnvironmental
Social
Economic
Economic
National Storylines(based on Nobre et al., forthcoming)
LUCC spatially explicit models
adapted from Verburg et al., 2006
LuccME / BrAmazoniamodel summary
Selection of relevant policies
PoliciesInternational and national non-Amazonian: • UNFCCC: Decisions taken during COP 17 change the accounting rules applying
to the land-use sector and to wood converted to products. These new rules are, however, unlikely to increase pressure to import wood from non-EU nations to an important extent.
• Nationally appropriate mitigation actions (NAMAs). These are voluntary actions by development countries and countries in transition to reduce GHG emissions, aiming at seeking and matching international financial, technology, and capacity-building support for proposed actions and at recognizing individual actions which may be implemented without international support. NAMA registry is not yet operational and given the vague definition and the wide range of support options, they can be expected to strongly overlap or to be combined with instruments such as credit generation for the carbon markets.
• Reducing emissions from deforestation and forest degradation (REDD) - Multilateral initiatives: UN-REDD programme, Forest Carbon Partnership Facility (FCPF), Forest Investment Program (FIP), and REDD+ partnership; bilateral agreements; and the voluntary carbon market.
• Standards and certification
Policies
Brazilian Forest Code (recent modifications, debate ongoing) Action Plan for Prevention and Control of the Legal Amazon Deforestation
(PPCDAM) (significantly reduced deforestation rates since 1994)
Credit and subsidies program – National Environment Program, Green Aid, Protected Areas Fund, Climate Fund, and agricultural policies.
Soy moratorium Land titling Land zoning Food purchase program Payment for environmental services Infrastructure for transportation and energy Climate change plans, including REDD+ in each Amazonian state
Policies The Brazilian Forest Code: rationale and current status - Created almost 50 years ago, intended to be a tool for
soil/water resources management and for environment protection. In 1996, the government decided to increase the protected area to 80% of any property in the Amazon. However, compliance to the Forest Code was not always observed, with implications to forest conservation and agriculture expansion. In an attempt to minimize the problem, the Congress recently approved many modifications on the Forest Code. To date, the debate has continued.
Credit and subsidies program - This includes a National Environment Program, Green Aid, Protected Areas Fund, a Climate Fund, and agricultural policies.
Soy moratorium - Anticipating the possibility that trade barriers could be built against Brazilian exports, ABIOVE (Brazilian Vegetable Oil Industry Association) and ANEC (Brazilian Grain Exporters Association) decided not to purchase this grain originated from areas of the Amazon Biome deforested after July 2006.
Land titling - In 2009, the government initiated program with the main objective to promote legal land use by legitimating previous occupations.
Land zoning Food purchase program Payment for environmental services Infrastructure for transportation and energy - Several main roads traversing the Amazon are in the process of
being paved and increasing accessibility. Climate change plans, including REDD+ in each Amazonian state Program for the Acceleration of Development PAC Action Plan for Prevention and Control of the Legal Amazon Deforestation (PPCDAM) - PPCDAM is an attempt of
Brazil to reduce deforestation of the Brazilian Amazon Forest. Implemented in 2004, it significantly contributed to the decrease of deforestation rates, discouraging illegal deforestation in Amazon Forest.
Primary forest clear-cut deforestation
rates
Secondary vegetation dynamics
Roads Protected areas (or Public Forests - UC,
TI, PAE, PDS)
Forest code enforcement
Scenario A: Sustainability
Zero deforestation after 2020
21 to 40% of deforested area;
regeneration after 2020.
No new federal or State roads; only
BR163 paved in 2015
2010 network maintained
After 2014, partial - 50%
Scenario B: Middle of the
Road
2020 deforestation reduction targets, low
after that
21% of deforested area, 5 years half life
No new federal or State roads; all
planned roads paved in 2015
2010 network maintained
Not enforced - 20% of forest area preserved
Scenario C: Historic
occupation pattern
Repeating ups and downs of the past 40
years
21% of deforested area, 5 years half life
All paving and planned roads (Federal and
State) built
After 2020, return to 2004 area
Not enforced - 20% of forest area preserved
Summary of LuccME/BRAmazonia Scenarios – v1 – March, 2013
Deforestation rates
20002002
20042006
20082010
20122014
20162018
20202022
20242026
20282030
20322034
20362038
20402042
20442046
20482050
0
5000
10000
15000
20000
25000
30000
35000
Future deforestation rates in each scenario
A (HS/HE) B (LS/HE) C (HS/LE) D (LS/LE)
Def
ores
tati
on (k
m2y
r-1)
C and D:Mirroring past curve
B: Voluntary targets until 2020
A: Zero deforestation after 2020
Quantifiable phenomena that affect ecosystem services
• Precipitation - crucial to maintain both natural vegetation and agriculture, replenish rivers and maintain evapotranspiration
• Recycling of moisture and evapotranspiration (moisture transport)
• River discharge – navigability/communications and habitability of river margin people communities in the region, as well as fisheries and the vitality of floodplain ecosystems (varzeas)
• Biomass and productivity of vegetation (forests) - carbon stored and sequestered by the region; large economic value in terms of timber.
Quantifiable phenomena that affect ecosystem services
• Agricultural productivity - mainly grass for cattle, soy beans, a range of newly developed, sustainably produced cash crops (Açai, Guarana, etc), palm oil and various regional products
• People migration and economy. Migration can enhance deforestation but also be a consequence of a degrading environment
• Land-use change itself affects most of the variables, as well as being associated with fire and air quality (smoke and nitrogen emissions).