Climate Change Impacts: watersheds of Mesoamerica and the watersheds of Mesoamerica and the

CaribbeanWater quality and availabilityWater quality and availability

Flood potentialFlood potentialFlood potentialFlood potential

ContextContext

• CATHALAC & SERVIRCATHALAC & SERVIR• Climate Change and Biodiversity

I f d d• Increasing frequency and intensity in disasters• Sediment and erosion modeling• Land use change

Deforestation α soil loss, water retentionDeforestation α soil loss, water retentionUrban and agricultural expansion α water needs

CATHALACCATHALAC

— MissionMissionPromote integratedwatershed manage-ment in Latin Am-

Vision —A prosperous andsustainable envi-

erica and the Carib-bean, by means ofapplied investigation,

ronment in theLatin America and

Caribbean region

education, and technology transfer

CATHALACCATHALAC

• Integrated Watershed Resource ManagementIntegrated Watershed Resource Management• Climate Change

E l M d l d A l• Environmental Modeling and Analysis• Risk Management

Our International Education Programs are derived from these programsare derived from these programs

www.servir.net

the Regional Visualization & Monitoring System (SERVIR)Terra

AquaMesoamerica’s Earth Observation

& F ti Pl tfq & Forecasting Platform

LandSat MODIS

Fire

Red Tides

Land Cover / Use Change

Earth Observing SystemData and Operations System

LandSat MODISSRTM AMSR-EIKONOS ASTER

Land Cover / Use Change

Mesoamerican & CaribbeanGovernment agencies

Users

ImpactsTest-bed at

NASA MSFCWeb Interfacewww.servir.net

ThematicAreas

Government agenciesNGOs, ResearchersEducators, etc.

Impacts

Emergency ResponsePolicy ChangesCorridor PreservationSpecies PreservationSustained DevelopmentEnvironmentalEnvironmental

5

SERVIR is a SERVIR is a completely opencompletely open--SERVIR is a SERVIR is a completely opencompletely open--

AgricultureBiodiversityClimateEcosystemsEnergy

Areas Sustained DevelopmentImproved livelihoods Monitoring & Decision

Support ProductsMonitoring & Decision

Support Products

access system with access system with products in range of products in range of formatsformats

access system with access system with products in range of products in range of formatsformats

Operational Nodeat CATHALAC

Panama

gyDisastersHealthWaterWeather

Potential Impacts of Climate Change on Biodiversity in Central America, Mexico, and the Dominican Republic, CATHALAC 2008.Dominican Republic, CATHALAC 2008.

Data derived from: NatureServe InfoNatura Species Distribution Grids.WorldClim Climate Grids: Current and Future Conditions.IUCN World Commission on Protected Areas, 2007.

Data derived from: NatureServe InfoNatura Species Distribution Grids.WorldClim Climate Grids: Current and Future Conditions.IUCN World Commission on Protected Areas, 2007.

Trend analysisTrend analysis

• Climate change is expected to increase the Climate change is expected to increase the frequency and intensity of tropical systems

1908 1918 1928 1938 1948 1958 1968 1978 1988 1998 2007

Case study: Panama Canal Watershed (RUSLE)Case study: Panama Canal Watershed (RUSLE)

Local water qualityLocal water quality

• Sedimentation Sedimentation to Runoff ratio

Historic– Historic– Future

projectionsprojections– Identification of

critical areascritical areas

Water availabilityWater availability

• Current demandCurrent demand• Future demand

L d h • Land use change scenarios:– Urban & agricultural growth scenario– Conservation scenario

• Put changing demands in the context of g gchanging precipitation trends

Data - terrainData - terrain

• SRTM DEM 90mSRTM DEM, 90m• Synthetic rivers and watersheds: D-8 method

(Fairfield and Laymarie 1991)(Fairfield and Laymarie 1991)– Why synthetic?

R i l d d l h b d • Regional data do not always show transboundary watersheds very well… and they’re the important ones!

• Support hydrological modelingpp y g g

– Validate with national datasets

Data - climateData - climate

• Worldclim baseline (1950s-1990s): 1km interpolated ( ) pprecipitation (Hijmans et al. 2005)

• 18 Worldclim climate change scenarios

2020s

20 0A2

Hadley Centre Coupled Model, version 3 (HadCM3)

2050s

2080sB2

x xCanadian Centre for Climate Modelling and Analysis, Coupled Global Climate Model (CGCM3T47)

Commonwealth Scientific and Industrial

• SERVIR RCM (Hernandez et al. 2006)

Commonwealth Scientific and Industrial Research Organization coupled model, Australia (CSIRO Mk3)

– Jan, Feb, June, July, August, September

Flood potentialFlood potential

• Runoff before and afterRunoff before and after• Hurricane or storm scenarios (MUSLE)

• Related to regional flood risk mapL ele ati n c astal nes– Low elevation coastal zones

– National flood risk maps• Highlight currently flood prone areas that are • Highlight currently flood prone areas that are

expected to undergo change; highlight new prone areas that weren’t beforeprone areas that weren t before

Other dataOther data

• MOD44 tree cover / regional land coverMOD44 tree cover / regional land cover– Cover factor

H i d ti l il d t• Harmonized national soil data– Erodibility (k factor) and USGS hydrological group

ToolsTools

• Spatial AnalystSpatial Analyst– Hydrology tools

Surface tools– Surface tools– Others (e.g., map algebra, raster calculator)

• N-SPECT: Nonpoint Source Pollution and Erosion Comparison Tool (NOAA)

Preliminary results, per watershedPreliminary results, per watershed

• 12 baseline months + annual accumulation12 baseline months + annual accumulation• (12 projected months + annual accum.) x 18

scenarios = 234scenarios = 234• Anomaly per watershed, for the above

• Zonal Statistics• Map Algebra

Monthly accumulated precipitation per watershed: baseline Monthly accumulated precipitation per watershed: baseline

Light Heavy

Monthly accumulated precipitation per watershed: HadCM3 A2 2020sMonthly accumulated precipitation per watershed: HadCM3 A2 2020s

Light Heavy

Monthly anomaly per watershed:[HadCM3 A2 2020s] – baseline Monthly anomaly per watershed:[HadCM3 A2 2020s] – baseline [ ][ ]

Drier Wetter

Monthly anomaly per watershed:scenario – baseline Monthly anomaly per watershed:scenario – baseline

• For each month consider:For each month, consider:– HadCM3, CGCM3T47, CSIRO Mk3

A2 & B2 scenarios– A2 & B2 scenarios– 2020s, 2050s, 2080s

18 x 12 = 216 monthly precipitation anomaly per watershed gridsper watershed grids

Monthly anomalies: a snapshot of capital cities in the regionMonthly anomalies: a snapshot of capital cities in the regiongg

Regional water qualityRegional water quality

• Utilize N-SPECT to derive runoff and Utilize N-SPECT to derive runoff and sedimentation estimates under baseline conditions and climate change scenariosconditions and climate change scenarios

C h f d ff • Compare the ratio of sedimentation to runoff to discover rivers that are projected to

d h l experience more sedimentation than normal, given the change in precipitation patterns

Regional water availabilityRegional water availability

• TEST: Add into the mix land cover change TEST: Add into the mix land cover change scenarios, based on population growth and agricultural expansion trendsagricultural expansion trends

• CONTROL: Leaving land cover the same

• We are already familiar with land cover change cases– El Valle de Anton, Panamá

Land use change and erosion Land use change and erosion

2001 2008

La Indiana Dormida,

Intensification of Agriculture

La Indiana Dormida,

Intensification of AgricultureIntensification of AgricultureIntensification of Agriculture

Land use change and erosion Land use change and erosion

20082001

Cerro El Gaital

Soil and Forest Conservation Soil and Forest Conservation

Expected results: regional water availabilityExpected results: regional water availability

• Observed trend of drier rainy season in many Observed trend of drier rainy season in many parts of the region

Further stresses on water resources– Further stresses on water resources

T b d t h d• Transboundary watersheds– Where the rain falls doesn’t exactly show us the

i t d impacted areas– Downstream analyses sometimes cross borders

Flood riskFlood risk

• Utilizing the event-based feature in N-SPECT Utilizing the event-based feature in N-SPECT, a normal week can be compared to a very rainy weekrainy week

• Application of similar downstream analyses

ContactContact

• For more complete results please visit For more complete results, please visit http://www.servir.net or contact servir@cathalac orgservir@cathalac.org