SE CSC Science in the US Caribbean Adam Terando, USGS SECSC - - PowerPoint PPT Presentation

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SE CSC Science in the US Caribbean Adam Terando, USGS SECSC - - PowerPoint PPT Presentation

Oct 22, 2022 338 likes •1.1k views

SE CSC Science in the US Caribbean Adam Terando, USGS SECSC Climate models, frog calls, and the path towards long-term adap6ve species management With special thanks to: Jaime Collazo, NC Coop Fish and Wildlife Research Unit Jared Bowden,

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SE CSC Science in the US Caribbean

Adam Terando, USGS – SECSC Climate models, frog calls, and the path towards long-term adap6ve species management

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With special thanks to: Jaime Collazo, NC Coop Fish and Wildlife Research Unit Jared Bowden, NCSU, Applied Ecology

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Guajataca Dam, Quebradillas, PR. Source: The Atlan6c

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Utuado, PR. Source: NY Times

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Corozal, PR. Source: The Atlan6c

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Yabucoa, PR. Source: The Atlan6c

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San Juan, PR. Source: The Atlan6c

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Toa Alta, PR. Source: The Atlan6c

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Toa Baja, PR. Source: The Atlan6c

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Naranjito, PR. Source: The Atlan6c

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Puerto Rican Parrot (Amazona vi*ata)

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MOTIVATION

Chadwick, R. 2016. Sub-tropical drying explained. Nat. Clim. Change.

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Amphibians in Puerto Rico

25 species Endangered PR Crested Toad 17 Eleutherodactylus

  • 2 endangered
  • 14 at risk
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El Yunque Rainforest

How will subtropical drying affect amphibians on the island?

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How will subtropical drying affect amphibians on the island?

Guánica Dry Forest

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How will subtropical drying affect amphibians on the island?

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Wise et al. (2014) Global Environmental Change.

BROADER CONCEPTUALIZATION

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How wide is this space?

What is it’s trajectory?

VULNERABILITY FORCING

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Ul6mately, trying to evaluate candidate strategies for adap6ve management

  • Passive management in marginal habitats
  • Translocate Popula6ons
  • Habitat acquisi6on
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Khalyani et al. (2016)

18°N

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Risk of Extinction Time between rainfall events

Time

Guanica (dry forest) Maricao (wet forest)

Present Exposure 2060 Exposure

Exposure/Response Func6ons

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Risk of Extinction Egg Development/Hatch Rates Guanica

Risks Rates Present Exposure 2060 Exposure

Exposure/Response Func6ons

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climate-response func:on

Cloud-based height Ground heat flux April Rainfall > 9mm/day Soil moisture

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CLIMATE MODELING FIELD ECOLOGY

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Expect Sub-tropical Drying in This Region

Chadwick, R. 2016. Sub-tropical drying explained. Nat. Clim. Change.

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Global Climate Models are s6ll very coarse

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Risk of Extinction Time between rainfall events

Time

Guanica (dry forest) Maricao (wet forest)

Present Exposure 2060 Exposure

Exposure/Response Func6ons

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Risk of Extinction Time between rainfall events

Time

Guanica (dry forest) Maricao (wet forest)

Present Exposure 2060 Exposure

Insights from Downscaling

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1) Projec6ons that reflect reality given constraints of GCMs and oceanic context. 2) Simulate precipita6on and

  • ther covariates response to

the anthropogenic forcings across Puerto Rico.

**Elicit expert knowledge to select relevant climate variables.

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To 30-km To 10-km To 2-km

Chose to use dynamical downscaling

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To 30-km To 10-km To 2-km

OUR GOAL: 2-KM Horizontal ResoluWon With Hourly Output Using mulWple RCM-GCM combinaWons

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RSM NHM

Weather Research and ForecasWng Model (WRF) Regional Spectral Model (RSM) and the Non-HydrostaWc Model (NHM)

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RSM NHM

Weather Research and ForecasWng Model (WRF) Regional Spectral Model (RSM) and the Non-HydrostaWc Model (NHM)

Collabora6on with Vasu Misra at FSU

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Select Global Climate Models to Downscale Scenario RCP8.5 (High GHG Emissions)

Historical (1986-2005) and Future (2041-2060) * indicates completed CNRM-CM5 CCSM4 GFDL-CM5

WRF RSM-NHM WRF-CCSM4* RSM-NHM-CCSM4* WRF-CNRM-CM5* RSM-NHM-GFDL-CM5

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Experimental Design for Regional Climate Modeling

  • THREE GCMs

– CCSM4, CNRM5, GFDL-CM3

  • TWO RCMs

– WRF, NHM-RSM

  • TWO 20 year periods

– 1986-2005 (past) – 2040-2060 (future) – RCP 8.5 – high fossil fuel emissions scenario

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Many More Physical Variables Available

(and relaWonships between variables are maintained)

  • Surface

– Rainfall, Temperature, Humidity, winds, soil moisture/ temperature, runoff, evapotranspira6on, pressure

  • Above canopy

– As above, plus others – Mixing height, ver6cal winds

  • Radia6on

– Incoming, outgoing, diffuse, net, cloud frac6on

  • Diagnos6c Variables

– Height of cloud base, – Sta6s6cal : Heat Wave dura6on, extremes, percen6les, etc.

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Many More Physical Variables Available

  • Surface

– Rainfall, Temperature, Humidity, winds, soil moisture/ temperature, runoff, evapotranspira6on, pressure

  • Above canopy

– As above, plus others – Mixing height, ver6cal winds

  • Radia6on

– Incoming, outgoing, diffuse, net, cloud frac6on

  • Diagnos6c Variables

– Height of cloud base, – Sta6s6cal : Heat Wave dura6on, extremes, percen6les, etc.

Time, Storage, and Processing Constraints => Cannot Retain All Variables at All Time Steps

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2-Day Stakeholder workshop hosted by CLCC in San Juan to refine climate model output

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IDEA IS TO HAVE CLIMATE PROJECTIONS THAT ARE SPECIFIC TO THE DECISION, BUT ALSO RELEVANT TO OTHER SCIENTIFIC/ ECOLOGICAL QUESTIONS

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How could climate change affect shade coffee producWon? Providing public goods

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Follow-up workshop in August 2016 to discuss available modeling outputs

Providing public goods

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Rank climate variables based on ecological significance

Used this dialogue to help retain necessary climate model data

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Downscaled Climate Variables

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We reduced ~1 Petabyte of model output to < 20TB with the knowledge of climate variables to retain from prior workshop Exceeded 1 million CPU hours to accomplish the downscaling for just one of the regional climate models.

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Maximum 2-m Temperature Change annual average

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PrecipitaCon Change percent change for the annual total

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ECOREGION ANALYSIS (Subtropical wet forest - wet season) > 1”/hr Hourly rainfall bin % difference

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Projected Changes Soil Moisture

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Low-level Cloud FracWon

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SLIDE 57 p(Occupancy | Temperature) Temperature (°C) Density 20 22 24 26 28 30 0.00 0.02 0.04 0.06 0.08 0.10 p(Occupancy | Precipitation) Annual Precip (mm/yr) Density 500 1000 1500 2000 0e+00 1e−04 2e−04 3e−04 4e−04 5e−04 p(Occupancy | Dry Seas Soil Moisture) Soil Moisture (%) Density 20 40 60 80 100 0.000 0.002 0.004 0.006 0.008 0.010 p(Occupancy | Temperature) Temperature (°C) Density 20 25 30 0.00 0.05 0.10 0.15 p(Occupancy | Precipitation) Annual Precip (mm/yr) Density −500 500 1000 1500 2000 2500 0.0000 0.0002 0.0004 0.0006 0.0008 0.0010 0.0012 p(Occupancy | Dry Seas Soil Moisture) Soil Moisture (%) Density 0.2 0.4 0.6 0.8 1.0 0.0 0.5 1.0 1.5 2.0 2.5

Temperature Precipita6on Soil Moisture

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 100 200 300 400 500 600 700 800 900 1000

Local Occupancy Probability (Psi)

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 100 200 300 400 500 600 700 800 900 1000

Local Occupancy Proability (Psi) ElevaWon (m), PrecipitaWon

E.wightmanae E.brifoni

What are the environmental limits of these species?

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Use acous6c recorders to es6mate occupancy of three species across environmental gradients

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EsWmate occupancy based on recorded calls

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How could these gradients change with climate change?

Precipita6on La6tude Longitude 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 100 200 300 400 500 600 700 800 900 1000

Local Occupancy Probability (Psi)

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 100 200 300 400 500 600 700 800 900 1000

Local Occupancy Proability (Psi) ElevaWon (m), PrecipitaWon

E.wightmanae E.brifoni

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NEXT STEPS

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Next steps: Explore resilience of windward slopes

El Yunque Caribbean Na6onal Rainforest

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PotenCal to couple to WRF-Hydro Model

El Yunque Caribbean Na6onal Rainforest

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Hybrid downscaling

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Select Global Climate Models to Downscale Scenario RCP8.5 (High GHG Emissions)

Historical (1986-2005) and Future (2041-2060) * indicates completed CNRM-CM5 CCSM4 GFDL-CM5

WRF RSM-NHM WRF-CCSM4* RSM-NHM-CCSM4* WRF-CNRM-CM5* RSM-NHM-GFDL-CM5

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Global Climate Models to Downscale Scenario RCP8.5 (High GHG Emissions)

Historical (1986-2005) and Future (2041-2060) CNRM-CM5 CCSM4 GFDL-CM5

WRF RSM-NHM WRF-CCSM4 RSM-NHM-CCSM4 WRF-CNRM-CM5 RSM-NHM-GFDL-CM5 ARRM-WRF-CNRM-CM5 ARRM-WRF-CCSM4

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Combining sta6s6cal and dynamical downscaling approaches

CCSM4 (GCM) OBS

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Combining sta6s6cal and dynamical downscaling approaches

RCM OBS

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Combining sta6s6cal and dynamical downscaling approaches

Sta6s6cal Model OBS

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Combining sta6s6cal and dynamical downscaling approaches

Hybrid OBS

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Taking occupancy modeling a step further. Is reproduc6on occurring at occupied sites? Are sites being occupied by a few individuals or by “many”? Plus gene6c work to establish popula6on structure.

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Augment field work with terraria experiments to test eco- physiological limits (w/colleagues at Univ. Puerto Rico)

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Geo Data Portal (GDP)

Web-based access to and processing of global change data to address climate and landscape change

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THANKS! QUESTIONS?