19 October 2011, Mexico City, Mexico

Hydrological Modeling and Impact of Climate changes in the Caribbean Islands of Dominican

Republic, Puerto Rico and Jamaica

Shimelis G Setegn, Ph.D.Postdoctoral Research Scientist

Florida International University, Dep. of Earth and Environment

Project Personnel'sAssefa Melesse (PI)

Francisco NunezDale WebberJorge Ortiz

Felipe Vicioso

The presentation consists ofThe presentation consists of CCS - CCS - Core Science Objectives Study area Study area Modeling toolsModeling tools Modeling ResultsModeling Results Climate change projectionsClimate change projections Impact of climate change on water Impact of climate change on water

resourcesresources

Caribbean Coastal Scenarios

Core Science Objectives Determine spatial and temporal variability in

climate across the region.

Determine geographic & demographic characteristics of catchments – topography, land cover, geology, soil, land

management techniques, population, roads and infrastructure, urban systems, etc.

Consider present & future trends in the nature & distribution of dynamic characteristics – e.g. land cover, management techniques, population,

infrastructure, urban systems.

Caribbean Coastal ScenariosCore Science Objectives (cont.) Simulate seasonal and inter-annual

fluxes of fresh water, sediments, and dissolved loads to coastal zones as a function of climate and catchment characteristics.

Montego Bay

Caribbean Costal RegionsCaribbean Costal Regions

Puerto RicoPuerto Rico Manate and Plata BasinsManate and Plata Basins

Dominican RepublicDominican Republic Haina and Yuna watershedsHaina and Yuna watersheds

JamaicaJamaica Great River and Re CobreGreat River and Re Cobre

STUDY AREA

Islands of interestIslands of interest

Watershed ModelingWatershed Modeling

Many hydrological models are developed to Many hydrological models are developed to describe the hydrology, erosion and describe the hydrology, erosion and sedimentation processes. sedimentation processes.

They describe the physical processes controlling They describe the physical processes controlling the transformation of precipitation to runoff and the transformation of precipitation to runoff and detachment and transport of sediments.detachment and transport of sediments.

Overview of Watershed modelling

Watershed models are used to implement Watershed models are used to implement alternative management strategies in the alternative management strategies in the areas ofareas of

– water resources allocationwater resources allocation– flood controlflood control– impact of land use changeimpact of land use change– impact of climate changeimpact of climate change– environmental pollution controlenvironmental pollution control

SWAT (Soil water Assessment SWAT (Soil water Assessment Tool)Tool)

SWAT is a river basin scale developed to predict the impact of land management practices on water, sediment and agricultural chemical yields

It is a public domain model actively supported by the USDA Agricultural Research Service at the Grassland, Soil and Water Research Laboratory in Temple, Texas, USA.

The SWAT system (ArcSWAT), embedded within geographic information system (GIS),

can integrate various spatial environmental data, including soil, land cover, climate, and topographic features.

SWAT cont.SWAT cont.

The model is physically based The model is physically based i.e., it requires specific information i.e., it requires specific information

It is computationally efficientIt is computationally efficientSimulation of very large basinsSimulation of very large basins

SWAT enables to study long-term SWAT enables to study long-term impactsimpacts

Phases of hydrologic cycle simulated by Phases of hydrologic cycle simulated by SWATSWAT

Land phase

Water phase

Courtesy: SWAT Manual

Model InputModel Input

GIS input files needed for the SWAT GIS input files needed for the SWAT model includemodel include

the digital elevation model (DEM),the digital elevation model (DEM), land cover, and land cover, and soil layerssoil layers

The DEM can be utilized by ArcSWAT to The DEM can be utilized by ArcSWAT to delineate basin and subbasin delineate basin and subbasin boundaries, calculate subbasin boundaries, calculate subbasin average slopes and delineate the average slopes and delineate the stream network.stream network.

The The land use, soil and Slope land use, soil and Slope layers layers are used to creat and define are used to creat and define Hydrological response units (HRU’s).Hydrological response units (HRU’s).

Metrological DataMetrological Data

The weather variables for driving the The weather variables for driving the hydrological balance arehydrological balance are – precipitation, precipitation, – air temperature, air temperature, – solar radiation, solar radiation, – wind speed and wind speed and – relative humidity. relative humidity.

Model Input Cont.Model Input Cont.

Hydrological dataHydrological data

River Discharge and Suspended sediment loadRiver Discharge and Suspended sediment load

Land ManagementLand Management Management input files include planting, harvest, tillage Management input files include planting, harvest, tillage

operations, and pesticide and fertilizer application.operations, and pesticide and fertilizer application.

Model Input Cont.Model Input Cont.

Model Calibration and EvaluationModel Calibration and Evaluation

The ability of a watershed model is evaluated The ability of a watershed model is evaluated through sensitivity analysis, model calibration, and through sensitivity analysis, model calibration, and model validation.model validation.

For model evaluation we used the goodness of For model evaluation we used the goodness of measures such as NSE, Rmeasures such as NSE, R22, ,

MODELING MODELING RESULTSRESULTS

Puerto Rico, Rio Manati

Time serious graph for calibration period – Rio Manati

Water balance Component Annual Average (mm)

Precipitation 1620

Surface runoff 86

Lateral soil flow 386

Groundwater flow (shallow aquifer) 3

Revap (shallow aquifer => soil/plants) 102

Deep aquifer recharge 5

Total aquifer recharge 94

Total water yield 474

Percolation out of soil 89

Actual evapotranspiration 1067

Potential evapotranspiration 1838

Annual average water balance of the Rio De Manati watershed

MONTHS

RAIN, (mm)

SURF Q, (mm)

LAT Q

Water Yield, (mm)

ET, (mm)

PET, (mm)

1 108.76 4.17 32.9 38.29 67.41 101.332 88.83 5.01 26.28 32.13 76.37 121.583 101.83 4.81 22.5 27.64 118.21 184.684 151.33 7.36 23.39 30.89 116.1 172.32

5 118.01 3.19 26.49 29.68 118.83 188.356 61.9 0.93 19.67 20.61 98.55 203.887 76.59 0.97 14.98 15.94 76.01 204.73

8 145.36 2.99 20.23 23.2 73.26 172.569 187.47 7.18 32.94 40.08 87.54 148.13

10 272.15 29.28 56.65 85.78 87.8 129.211 178.87 13.44 61.68 75 79.9 117.3812 131.44 6.57 48.81 55.29 69.08 97.17

Average Monthly Basin Values of Manati watershed

Area (%)0.0034.141

28.3871.2950.2250.612

51.0780.107

13.0150.0111.127

Land use: Plata Watershed, PR

Puerto Rico – Plata

Time serious graph for calibration period – Rio Plata

Area1.279

46.92217.80010.244

0.0274.9160.0120.296

17.5680.936

Land use: Haina Watershed, DR

Dominican Republic - Rio Haina

Time serious graph for calibration period – Haina Watershed

Water balance Component Annual Average (mm)

Precipitation 2101

Surface runoff 927,63

Lateral soil flow 21

Groundwater flow (shallow aquifer) 215

Revap (shallow aquifer => soil/plants) 17

Deep aquifer recharge 12.33

Total aquifer recharge 246.64

Total water yield 1161.63

Percolation out of soil 250.31

Actual evapotranspiration 890.6

Potential evapotranspiration 1702

Annual average water balance of the Haina watershed

Jamaica, Great River Basin

Time series of observed and simulated monthly flow for calibration (top) and validation (bottom) period at

Lethe station of Great River

Jamaica, Rio Cobre Watershed

The time-series comparison between measured and simulated monthly flow at Rio Cobre Watershed

Water balance Component Annual Average (mm)

Precipitation 1953.0

Surface runoff 102.8

Lateral soil flow 427.7

Groundwater flow (shallow aquifer) 368.8

Revap (shallow aquifer => soil/plants) 9.0

Deep aquifer recharge 19.9

Total aquifer recharge 397.6

Total water yield 899.0

Percolation out of soil 393.5

Actual evapotranspiration 1028.3

Potential evapotranspiration 1579.8

Annual average water balance of the Rio Cobre watershed (1997-2008).

Seasons/months Rainfall

, mm

Surface

runoff,

mm

Lateral

flow, mm

Water

Yield,

mm

AET,

mm

PET,

mm

Average (1997-2008) 154.44 21.68 38.10 79.73 71.50 180.42

Dry (Jan-Mar) 57.72 4.20 11.67 28.24 68.12 180.33

Wet (Aug-Oct) 267.09 52.20 72.15 151.99 77.49 179.79

Monthly mean and seasonal water balance components for the Rio Cobre watershed

Spatial distribution of actual evapotranspiration in the Rio Cobre Watershed, Jamaica.

Spatial distribution of water yield in the Rio Cobre Watershed, Jamaica.

Climate ChangeClimate Change

30 August 2010, Gran Melia, Puerto Rico, photo by Shimelis S 30 August 2010, Gran Melia, Puerto Rico, photo by Shimelis S

• GCM’s are numerical coupled models that represent various earth systems including the atmosphere, oceans, land surface and sea-ice and offer considerable potential for the study of climate change and variability.

Climate Change Impact on Water Resources Variability

Climate change scenarios• Scenarios are images of the future, or alternative futures. They are

neither predictions nor forecasts.

• The Special Report on Emissions Scenarios (SRES) are grouped into four scenario families (A1, A2, B1 and B2) that explore alternative development pathways, covering a wide range of demographic, economic and technological driving forces and resulting GHG emissions.

Center Model Atmospheric resolution (approx)

NASA Goddard Institute for Space Studies (NASA/GISS), USA,AOM 4x3 4 x 3

Goddard Institute for Space Studies (GISS), NASA, USA GISS_ModelE-H 4 x 5

Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model (CGCM3)

Hadley Centre for Climate Prediction and Research, Met Office United Kingdom

Hadley Centre Global Environmental Model, version 1 (HadGEM1)

1.25 x 1.875

Bjerknes Centre for Climate Research Norway (BCCR) Bergen Climate Model (BCM2.0) 2.8×2.8

Canadian Center for Climate Modelling and Analysis Canada (CCCMA) Coupled Global Climate Model (CGCM3) 3.75× 3.7

Centre National de Recherches Meteorologiques France(CNRM) CNRM-CM3 2.8× 2.8

Australia's Commonwealth Scientific and Industrial Research Organisation Australia (CSIRO)

CSIRO Mark 3.0 1.9× 1.9

Australia's Commonwealth Scientific and Industrial Research Organisation Australia (CSIRO)

CSIRO Mark 3.5 1.9× 1.9

Max-Planck-Institut for Meteorology Germany (MPI-M) ECHAM5/MPI-OM 1.9× 1.9

Meteorological Institute of the University of Bonn (Germany), (MIUB) ECHO-G 3.75× 3.7

Geophysical Fluid Dynamics Laboratory USA ( GFDL) CM2.0 - AOGCM 2.5× 2.0

Geophysical Fluid Dynamics Laboratory USA (GFDL) CM2.1 - AOGCM 2.5× 2.0 Institute for Numerical Mathematics Russia (INM) INMCM3.0 5.0× 4.0 Institut Pierre Simon Laplace France (IPSL) IPSL-CM4 3.75× 2.5 Meteorological Research Institute Japan (MRI) MRI-CGCM2.3.2 2.8× 2.8 National Centre for Atmospheric Research USA (NCAR) Parallel Climate Model (PCM) 2.8× 2.8

National Centre for Atmospheric Research USA(NCAR) Community Climate System Model, version 3.0 (CCSM3)

1.4× 1.4

Hadley Centre for Climate Prediction and Research, Met Office, United Kingdom - UK Met. Office UK (UKMO)

HadCM3 3.75× 2.5

Trends in Climate ChanTrends in Climate Change - Temperaturege - Temperature

Trends in Climate ChanTrends in Climate Change - Rainfallge - Rainfall

Projected Seasonal changes in Rainfall

Changes in stream flow due to changes in precipitation and air temperature for the period 2046-2065 and 2080-2100

Changes in potential and actual evapotranspiration (PET and AET) for the 2046-2065

Annual changes in potential and actual evapotranspiration (PET and AET) for the 2080-2100

Annual changes in soil water storage for 2046-2065 and 2080-2100 period

Changes in surface and ground water for 2046-2065 and 2080-2100 periods

Changes in surface and ground water for 2046-2065 and 2080-2100 periods

Uncertainties in GCM model outputsUncertainties in GCM model outputs

Thank You!Thank You!

30 August 2010, Puerto Rico 30 August 2010, Puerto Rico