Early Warning System: monitoring aspect

Celso von RandowEarth System Science Center - INPE

celso.vonrandow@inpe.br

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).