Understanding the hydrology of the large wetlands of the Bolivian Amazon using remote sensing and hydrological modeling
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1 Understanding the hydrology of the large wetlands of the Bolivian Amazon using remote sensing and hydrological modeling Preliminary results 6 th SO HYBAM Scientific Meeting Cusco, Octubre 2015 Alex Ovando (CCST INPE) Javier tomasella, Celso vonrandaw (CCST INPE) Jean-Michel Martinez (IRD)
2 Why the wetlands of the Bolivian Amazon are so relevant? Extension Biogeochemical processes Water ecosystem services Connectivity with the Andes
3 There is an urgent need to develop tools to understand wetlands
4 Understanding wetlands Remote sensing Hydrologic-hydrodynamic modeling Paz, 2010 Contributions wetland vegetation, water properties, fluvial dynamics, flood extension, water stage, etc. Represent the whole catchment and all the processes within
5 Understanding wetlands Remote Sensing Hydrologic-hydrodynamic modeling Paz, 2010 X Constraints X Spatial and temporal coverage, Atmosphere conditions, cost Input data (soils, meteo, topography) Inaccurate channel floodplain exchanges
6 Understanding wetlands Remote Sensing Hydrologic-hydrodynamic modeling Paz, 2010 Multitemporal flood maps & Water stage & Distributed model
7 Multi-temporal flood mapping Optical Radar The MODIS sensor aboard Aqua and Terra satellites, Collection 5 (M*D09A1), 7 spectral bands in the 400nm to 2500nm, 500m resolution. 8 day product images The (PALSAR) aboard the Land Observation Satellite (ALOS), L-band sensor, 100m resolution, 46 days revisit 6 mosaics covering lowlands Mar 07 Mar09
8 Understanding wetlands Flood mapping Segmentation over ALOS PALSAR stack
9 Multi-temporal flood mapping Multitemporal Radar backscatter behavior at L band (ALOS PALSAR), for different landscapes in the Llanos de Moxos floodplain
10 Multi-temporal flood mapping PALSAR Classification flowchart
11 Multi-temporal flood mapping Comparison between Potential Maximum Flood Extension Map from this study and the Amazon Wetland Extent and Vegetation Type and Dual-season Flooding State Map of Hess et al. (2015) Hess, L.L. et al., LBA-ECO LC-07 Wetland Extent, Vegetation, and Inundation: Lowland Amazon Basin. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI:
12 Multi-temporal flood mapping MIR reflectance of a Savanna unit in the Mamore floodplain
13 MODIS Classification flowchart Multi-temporal flood mapping
14 Multi-temporal flood mapping Comparison between MODIS and PALSAR flood estimations showing the total floodable area captured by both sensors.
15 Multi-temporal flood mapping Time-variation of the flooded area in the study area during period The horizontal red line indicates the mean of the flood peaks; the yellow solid line indicates the minimum open water extension.
16 Multi-temporal flood mapping Extreme flood events in the Bolivian Amazon wetlands A. Ovando 1, J. Tomasella 1, D.A. Rodriguez 1, J.M. Martinez 2, J.L. Siqueira-Junior 1, G. L. N. Pinto 1, P. Passy 3, P. Vauchel 2, L. Noriega 4, and C. von Randow 1
17 Multi-temporal flood mapping Time-variation of the flooded area during the 2007, 2008 and 2014 flood events
18 Multi-temporal flood mapping Relationship between flooded area and water stage at Puerto Siles during the three floods. Arrows indicate the time evolution of the floods
![Extreme floods - hydrometeorological context Discharges [m 3 s -1 ] 14000 12000 10000 8000 6000 4000 2013-2014 Oct Nov Jan Mar May Jul Sep Puerto Siles Príncipe da Beira Guajará-Mirim Cachuela](/docs-images/93/112651530/images/19-0.jpg "Esperanza Abunã 2000 0 60000 out nov jan mar mai jul set 50000 Discharges [m 3 s -1 ] 40000 30000 20000 10000 0 Oct Nov Jan Mar May Jul Sep Major flood events in the upper Madeira are characterized")
19 Extreme floods - hydrometeorological context Discharges [m 3 s -1 ] Oct Nov Jan Mar May Jul Sep Puerto Siles Príncipe da Beira Guajará-Mirim Cachuela Esperanza Abunã out nov jan mar mai jul set Discharges [m 3 s -1 ] Oct Nov Jan Mar May Jul Sep Major flood events in the upper Madeira are characterized by the superposition of flood waves
20 Extreme floods - hydrometeorological context October January November February December Flood dynamics are strongly dependent on the timing and spatial distribution of positive rainfall anomalies and location of intense rainfall hotspots (endogenous) March
21 Hydrologic-hydrodynamic modeling MHD INPE model
22 Understanding wetlands MHD INPE model Hydrologic model: MHD-INPE Distributed Physically based Daily time step + floodplain processes Paz, 2010
23 Hydrologic-hydrodynamic modeling MHD INPE model MODIS flood maps Adjustment of parameters related to channelflodplain and floodplain fluxes
24 Satellital altimetry
25 Satellital altimetry Satellite altimetry ENVISAT/SARAL Virtual stations within model cells in the floodplains
26 Satellital altimetry Flooded fraction (MODIS) as a function of water stage (ENVISAT) (Top); Cell flooded fraction according to a MHD model routines based on SRTM DEM (Bottom)
27 Satellital altimetry Comparison between water levels in the river channel and water in the floodplain
28 Perspectives / Aplications The Parapeti Izozo project
29 Perspectives / Aplications Flooded area in the Parapeti Izozo floodplain ALOS PALSAR The Parapeti Izozo project
30 Perspectives / Aplications Flooded area in the Parapeti Izozo floodplain MODIS The Parapeti Izozo project
31 Perspectives / Applications Flooded area in the Parapeti Izozo floodplain MODIS The Parapeti Izozo project
32 Perspectives / Applications Multitemporal flood maps may be used for further and deeper analysis SST Connectivity - ecology
33 Perspectives / Applications Model applications Land cover / climate change impacts on wetlands hydrology Information for better understanding biosphere atmosphere processess
34 Gracias
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