 It is not representative of the whole water flow  High costs of installation and maintenance  It is not uniformly distributed in the world  Inaccessibility.

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Presentation transcript:

 It is not representative of the whole water flow  High costs of installation and maintenance  It is not uniformly distributed in the world  Inaccessibility of many remote areas  Data sharing among developing countries  Growing reduction of hydrometric monitoring networks (Mishra and Coulibaly, 2009), Reviews of Geophysics

ERS-2/ENVISAT: -River discharge estimation by using altimetry data and simplified flood routing modeling (Tarpanelli et al., 2013, Remote Sensing) -Coupling MODIS and radar altimetry data for discharge estimation in poorly gauged river basin (Tarpanelli et al., 2014, JSTARS) -Exploring the potential of radar altimetry and SRTM Topography to Support Flood Propagation Modeling: the Danube Case Study (Yan et al., 2014 Journal of Hydrologic Engineering) -The use of remote sensing-derived water surface data for hydraulic model calibration (Domeneghetti et al., 2014, Remote Sensing of Environment) -Investigating the uncertainty of satellite altimetry products for hydrodynamic modelling (Domeneghetti et al., 2015, Hydrological Processes) SARAL/Altika 1)River discharge estimation by coupling radar altimetry measurements and MODIS images (PO RIVER) 2) The use of altimetry data for supporting the hydrological model (CONGO RIVER)

Flow velocity River Discharge Flow Area derived by MODIS (Tarpanelli et al., Remote Sensing of Environment) A=f(water level, geometry) unknown (Moramarco et al., 2013 – Journal of Hydrology) derived by radar altimetry observation Known (Topographic survey)

(Brakenridge et al., 2005; 2007) C= Land pixel M=Water pixel 1 C = land pixel (located near the river in an area free of surface water even during high floods) M = water pixel (located within the river with permanent presence of water) wet dry wet 123 flood signal C/M increases with the presence of water and, hence, of discharge

2. Exclusion of pixels affected by cloud cover and/or snow by using a simple threshold and a visual inspection 3. Choice of the M and C pixels and calculation of the ratio C/M 4. Application of the smoothing exponential filter (C/M*) 1. Selection of a box centered on the investigated gauged station from each MODIS image PROCEDURE

2. Exclusion of pixels affected by cloud cover and/or snow by using a simple threshold and a visual inspection 3. Choice of the M and C pixels and calculation of the ratio C/M 4. Application of the smoothing exponential filter (C/M*) PROCEDURE 1. Selection of a box centered on the investigated gauged station from each MODIS image

PROCEDURE 2. Exclusion of pixels affected by cloud cover and/or snow by using a simple threshold and a visual inspection 3. Choice of the M and C pixels and calculation of the ratio C/M 4. Application of the smoothing exponential filter (C/M*) 1. Selection of a box centered on the investigated gauged station from each MODIS image

PROCEDURE 2. Exclusion of pixels affected by cloud cover and/or snow by using a simple threshold and a visual inspection 3. Choice of the M and C pixels and calculation of the ratio C/M 4. Application of the smoothing exponential filter (C/M*) 1. Selection of a box centered on the investigated gauged station from each MODIS image

PROCEDURE (Wagner et al., 1999) 2. Exclusion of pixels affected by cloud cover and/or snow by using a simple threshold and a visual inspection 3. Choice of the M and C pixels and calculation of the ratio C/M 4. Application of the smoothing exponential filter (C/M*) 1. Selection of a box centered on the investigated gauged station from each MODIS image

RMSE (C/M*-v) R (C/M*-v) PIACENZA CREMONA BORGOFORTE PONTELAGOSCURO REGIONAL RELATIONSHIPS

R=0.73 RMSE=0.35 ms -1 Boretto

WATER LEVEL measurements from April 2013 to October 2014 (track 588) R=0.98 RMSE=0.64 m RRMSE=3.03%

Boretto R=0.97 RMSE=258 m 3 s -1 RRMSE=15 % R=0.91 RMSE=423 m 3 s -1 RRMSE=36 % ENVISAT SATELLITE SARAL SATELLITE Np=12 Np=52

A b = 3’717’698 km 2

DEVELOPEMENT OF DISTRIBUTED RAINFALL-RUNOFF MODELLING COUPLED WITH HYDRAULIC MODEL FOR THE ASSIMILATION OF WATER LEVEL RETRIEVED BY ALTIMETRY ykyk

MISDc model (Brocca et al., 2011) INPUT DATA: RAINFALL AIR TEMPERATURE OUTPUT DATA: AVERAGE SATURATION DEGREE DISCHARGE 7 PARAMETERS TO BE ESTIMATED Italy, Spain, France, Luxembourg, US Applied in Italy, Spain, France, Luxembourg, US for flood modelling (e.g., Brocca et al., 2013a; 2013b; Massari et al., 2014a) … also assimilating satellite soil moisture data (Brocca et al., 2010; 2012) - RAINFALL (Global Precipitation Climatology Project database: 1° daily) - TEMPERATURE (ERA-Interim database: 0.75° daily) - DISCHARGE (ORE-HYBAM website: daily) - RAINFALL (Global Precipitation Climatology Project database: 1° daily) - TEMPERATURE (ERA-Interim database: 0.75° daily) - DISCHARGE (ORE-HYBAM website: daily) FREELY AVAILABLE !!!

A b = 75’419 km 2 A time [year]

A b = 732’130 km 2 B time [year]

A B A b = 75’419 km 2 A b = 732’130 km 2

A b =3’717’698 km 2 R 2 =0.59

1) The good results obtained by coupling two different satellite sensors (spectroradiometer and altimeter) demonstrate that they can be used for river discharge estimation also in ungauged sites. 2) The preliminary results on the Congo basin show that the water level retrieved by altimetry is consistent with the modelled discharge for two different locations. In the near future, a distributed hydrologic-hydraulic model will be developed to assess the potential benefit of using altimetry, through data assimilation, in scarcely gauged basins.