slide1
Download
Skip this Video
Download Presentation
Rodrigo C. D. Paiva [email protected] Phd student

Loading in 2 Seconds...

play fullscreen
1 / 23

Rodrigo C. D. Paiva [email protected] Phd student - PowerPoint PPT Presentation


  • 99 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Rodrigo C. D. Paiva [email protected] Phd student' - robbin


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

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)
slide2

Research interests:

    • Hydrological processes
    • Amazon River basin hydrology
    • Hydrological modelling
    • Forecast systems
    • Hydrological data assimilation
    • Remote sensing for hydrology
slide3

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
slide4

Main topics of the PhD studies:

    • 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)
slide5

HYDROLOGICAL MODEL

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

slide6

MGB-IPH HYDROLOGICAL MODEL

Water and Energy balance

Catchment i

Downstream

catchment

slide7

Model discretization:

    • Catchments
    • River reaches

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
slide8

Model discretization:

    • Catchments
    • River reaches

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:
slide9

Terrain processing for model parameters

Digital Elevation Model

  • HydroSHEDS - Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (500 m resolution)
slide10

DATA

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
slide11

172 stream gauges

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

“Multi-objective complex evolution ”

MODEL CALIBRATION

slide12

Acre River at Rio Branco city

  • Rapid floods
  • Good model performance

Lower Purus

  • Delay and attenuation
  • Good model performance

Discharge results – Purus River

slide13

Discharge results – Solimões River

Solimões river at Peru

  • Tamshiyaco
  • Delay and attenuation OK
  • Volume error ~ -12%

Lower Solimões / Manacapuru

  • Delay and attenuation OK
  • Volume error ~ -11%
slide14

Water level results – Solimões River

Solimões at S.P. Olivença

  • Peru/Brazil border
  • Phase OK
  • Amplitude OK
  • Good model performance

Solimões river

  • Phase OK
  • Amplitude OK
  • Good model performance
slide15

Flood inundation results

09-oct-2001

08-dec-2001

06-feb-2002

07-apr-2002

06-jun-2002

16-jul-2002

slide16

Flood inundation results

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

slide17

Flood inundation results

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

slide18

Previously flood inundation model validation

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

slide19

The role of floodplains and backwater effects

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

slide20

Small tributaries of large rivers

- Complete simulation:

Small tributaries are controlled by large rivers and backwater effects

- Simulation without floodplains:

Small tributaries controlled by upstream floods

slide21

Hydrological data assimilation

  • 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

slide22

Example

  • São Francisco River
  • Simple flow routing algorithm
  • Discharge from stream gauge stations
  • Ensemble Kalman Filter (Evensen, 2003)
ad