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Rodrigo C. D. Paiva Walter Collischonn Marie Paule Bonnet Phd student

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Presentation on theme: "Rodrigo C. D. Paiva Walter Collischonn Marie Paule Bonnet Phd student"— Presentation transcript:

1 Rodrigo C. D. Paiva Walter Collischonn Marie Paule Bonnet Phd student
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)

2 Research interests: Hydrological processes
Amazon River basin hydrology Hydrological modelling Forecast systems Hydrological data assimilation Remote sensing for hydrology

3 Interesting Challenge
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

4 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)

5 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

6 MGB-IPH HYDROLOGICAL MODEL
Water and Energy balance Catchment i Downstream catchment

7 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 Model discretization: Catchments River reaches River cross sections Floodplain units

8 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: Model discretization: Catchments River reaches River cross sections Floodplain units 8

9 Terrain processing for model parameters
Digital Elevation Model HydroSHEDS - Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (500 m resolution)

10 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

11 MODEL CALIBRATION 172 stream gauges
MOCOM-UA optimization algorithm (Yapo et al., 1998) “Multi-objective complex evolution ”

12 Discharge results – Purus River
Acre River at Rio Branco city Rapid floods Good model performance Lower Purus Delay and attenuation Good model performance

13 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%

14 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

15 Flood inundation results
09-oct-2001 08-dec-2001 06-feb-2002 07-apr-2002 06-jun-2002 16-jul-2002

16 Central Amazon – Minimum water depth from the 2001/2002 year
Flood inundation results Central Amazon – Minimum water depth from the 2001/2002 year

17 Central Amazon – Maximum water depth from the 2001/2002 year
Flood inundation results Central Amazon – Maximum water depth from the 2001/2002 year

18 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

19 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

20 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

21 Hydrological data assimilation
Retrospective analyses Forecast systems t2 t1 x2 y t3 t4 t5 x1 t1 t2 t3 t4 t5 t Methods: Kalman Filters Variational methods Particle filters Data: Ground stream gauges Remote sensing: Altimetry Gravimetry Soil moisture Energy fluxes and ET True trajetory Forecast Correction Observation

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

23 OBRIGADO


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