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Rodrigo C. D. Paiva [email protected] Phd student IPH – Institute of Hydraulic Research UFRGS – Federal University of Rio Grande do Sul Porto Alegre / Brazil. Advisors : Walter Collischonn IPH – Institute of Hydraulic Research

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  • Rodrigo C. D. Paiva

  • [email protected]

  • Phd student

    • IPH – Institute of Hydraulic Research

    • UFRGS – Federal University of Rio Grande do Sul

    • Porto Alegre / Brazil

  • Advisors:

  • Walter Collischonn

  • IPH – Institute of Hydraulic Research

  • UFRGS – Federal University of Rio Grande do Sul

  • Brazil

  • Marie Paule Bonnet

  • Institut de recherche pour Le développement (IRD)

  • Laboratoire des Mécanismes et Transferts en Géologie (LMTG)

  • University of Toulouse III (UT3 Paul Sabatier)


  • Research interests:

    • Hydrological processes

    • Amazon River basin hydrology

    • Hydrological modelling

    • Forecast systems

    • Hydrological data assimilation

    • Remote sensing for hydrology


HYDROLOGICAL AND HYDRODYNAMIC MODELING IN THE AMAZON RIVER BASIN

  • Interesting Challenge

  • size of the basin (7,000,000 km2);

  • limited data;

  • particular hydrological features:

    • climate diversity

    • backwater effects

    • large wetlands

  • Importance in global climate and biogeochemical cycles

  • Hydrological extremes

    • Floods and droughts

  • Context:

  • Integrated Project of Amazon Cooperation and Modernization of Hydrological Monitoring


  • Main topics of the PhD studies: BASIN

    • Development of a hydrological – hydrodynamic model for the Amazon River basin

    • Studying Amazon hydrological processes using modelling results and remote sensing

      • The role of floodplains

    • Data assimilation of remote sensing data into hydrological models

      • Forecast systems

      • Retrospective analyses of extreme events (floods, droughts)


HYDROLOGICAL MODEL BASIN

MGB - IPH (Collischonn, 2001; Paiva, 2009)

Modelo de Grandes Bacias- Instituto de Pesquisas Hidráulicas

  • Physical based model to simulate land hydrological processes

  • Daily or shorter time step

  • Distributed

Amazon River basin

Catchment discretization

~ 6,900 catchments


MGB-IPH HYDROLOGICAL MODEL BASIN

Water and Energy balance

Catchment i

Downstream

catchment


River cross sections

Floodplain units

MGB-IPH HYDROLOGICAL MODEL

Hydrodynamic Model (Paiva et al 2010)

  • Hydrodynamic 1D model

  • Full Saint Venant equations solved with finite difference method

  • Improved Skyline algorithm for river network solution


River cross sections

Floodplain units

MGB-IPH HYDROLOGICAL MODEL

Hydrodynamic Model

(Paiva et al 2010)

- Flood inundation model:

  • Simple Storage model

  • v = 0

  • floodplains act only as storage areas

  • horizontal water level

  • river – floodplain lateral exchange:


Terrain processing for model parameters BASIN

Digital Elevation Model

  • HydroSHEDS - Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (500 m resolution)


DATA BASIN

Precipitation and Meteorological Data

  • Remote sensed estimates from Tropical Rainfall Measurement Mission

    • Daily rainfall data from TRMM 3B42 algorithm

    • Spatial resolution of 0.25o×0.25o

  • Climatic Research Unit – CRU for surface air temperature, atmospheric pressure, solar radiation, moisture and wind speed


172 stream gauges BASIN

MOCOM-UA optimization algorithm (Yapo et al., 1998)

“Multi-objective complex evolution ”

MODEL CALIBRATION


Acre River at Rio Branco city BASIN

  • Rapid floods

  • Good model performance

Lower Purus

  • Delay and attenuation

  • Good model performance

Discharge results – Purus River


Discharge results – Solimões River BASIN

Solimões river at Peru

  • Tamshiyaco

  • Delay and attenuation OK

  • Volume error ~ -12%

Lower Solimões / Manacapuru

  • Delay and attenuation OK

  • Volume error ~ -11%


Water level results – Solimões River BASIN

Solimões at S.P. Olivença

  • Peru/Brazil border

  • Phase OK

  • Amplitude OK

  • Good model performance

Solimões river

  • Phase OK

  • Amplitude OK

  • Good model performance


Flood inundation results BASIN

09-oct-2001

08-dec-2001

06-feb-2002

07-apr-2002

06-jun-2002

16-jul-2002


Flood inundation results BASIN

Central Amazon – Minimum water depth from the 2001/2002 year


Flood inundation results BASIN

Central Amazon – Maximum water depth from the 2001/2002 year


Previously flood inundation model validation BASIN

Validation in Solimões river basin (Paiva, 2009)

Simulated water depth

High water

may/jun 1996

Model Validation with

remote sensing estimates

from HESS et al (2003)

using JERS-1 data


The role of floodplains and backwater effects BASIN

Floodwave is 45 days in advance

Simple model

Model results fits observations

Water storage in floodplains and backwater effects are very important for flood wave travel times and attenuation

Full Model


Small tributaries of large rivers BASIN

- Complete simulation:

Small tributaries are controlled by large rivers and backwater effects

- Simulation without floodplains:

Small tributaries controlled by upstream floods


Hydrological data assimilation BASIN

  • Retrospective analyses

  • Forecast systems

t2

t1

y

x2

t3

t4

t5

t1

t2

t3

t4

t5

t

x1

  • Data:

  • Ground stream gauges

  • Remote sensing:

    • Altimetry

    • Gravimetry

    • Soil moisture

    • Energy fluxes and ET

  • Methods:

  • Kalman Filters

  • Variational methods

  • Particle filters

True trajetory

Forecast

Correction

Observation


Example BASIN

  • São Francisco River

  • Simple flow routing algorithm

  • Discharge from stream gauge stations

  • Ensemble Kalman Filter (Evensen, 2003)


OBRIGADO BASIN


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