EGU 2013, Vienna, 07-12 April 2013 A. Tarpanelli, L. Brocca, S. Barbetta, T. Moramarco National Research Council, Research Institute for Geo-Hydrological.

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Presentation transcript:

EGU 2013, Vienna, April 2013 A. Tarpanelli, L. Brocca, S. Barbetta, T. Moramarco National Research Council, Research Institute for Geo-Hydrological Protection Via Madonna Alta 126, Perugia, Italy European Geosciences Union General Assembly 2013 Vienna, Austria, 07 – 12 April

Over the last decade, the possibility to obtain river discharge estimates from satellite sensors data has come to be of considerable interest. Recent advances in radar altimetry technology have improved the accuracy in the monitoring of the water surface level of large rivers and lakes located in ungauged or poorly gauged inland regions. Radar altimetry technology has been applied to large rivers, such as the Amazon River ( Koblinsky et al., 1993; Birkett, 1998; Campos et al., 2001; Frappart et al., 2006; Leon et al., 2006; Zakharova et al., 2006; da Silva et al., 2010 ). Few studies devoted to an in-depth assessment of radar altimetry over other rivers, such as Ob, Mekong, Negro, Gange and Brahmaputra have been published ( Kouraev et al., 2004; Frappart et al., 2005; Getirana et al., 2009; Pereira Cardenal et al., 2011; Biancamaria et al., 2011; Birkinshaw et al., 2010; 2012 ). EGU 2013, Vienna, April 2013

Additionally, although not specifically dedicated, sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) also have the potential to provide river discharge estimates. A preliminary example, in which the discharge is estimated by MODIS considering a very short time series, is reported in Brakenridge and Anderson (2006). The study by Tarpanelli et al. (accepted on RSE) shows that MODIS can give good estimates of discharge and mean flow velocity for medium sized basins (70000 km 2 ) characterized by high temporal variability of discharge and it can be used also for ungauged river sites.

EGU 2013, Vienna, April 2013 To evaluate the potential of two sensors, radar altimetry and MODIS, for the estimation of river discharge in a medium sized basin (70000 km 2 ) in Northern Italy (Po river).

EGU 2013, Vienna, April channels at 250 m 5 channels at 500 m 29 channels at 1 km 1-2 images a day SPATIALRESOLUTION TEMPORALRESOLUTION MODIS MODerate resolution Imaging Spectroradiometer RADAR ALTIMETER 70 km inter-track spacing at the Equator beam footprint width about 3.5 km 35 days SATELLITE TERRA (1999-today) AQUA (2002-today) ERS-2 ( ) ENVISAT ( )

EGU 2013, Vienna, April 2013 VS3 VS2 ALTIMETRY DATA – RLH (River & Lake hydrology) VS2  ERS-2 ( ) VS3  ERS-2 ( ) + ENVISAT ( )

EGU 2013, Vienna, April 2013 H VS2 - H in situ H VS3 - H in situ Comparison from 1995 to 2011 r r

EGU 2013, Vienna, April 2013 Q = c 1 ∙ W a ∙Y b ∙ S d Bjerklie, D.M., Dingman, S.L., Vorosmarty, C.J., Bolster, C.H., Congalton, R.G., Evaluating the potential for measuring river discharge from space. Journal of Hydrology, 278, Discharge Surface top width Depth of the equivalent rectangular section Average water surface slope Q = 7.22 ∙ W 1.02 ∙Y 1.74 ∙ S 0.35 Parameters calibrated and validated using 1012 discharge measurements in 102 rivers in the United States and New Zealand D Y

EGU 2013, Vienna, April 2013 C= Land pixel M=Water pixel dry wet 123 dry wet 123 flood signal Brakenridge, Nghiem, Anderson, Mic, “Orbital microwave measurement of river discharge and ice status”, Water Resources Research, 2007 Brakenridge, Anderson, “MODIS-based flood detection, mapping and measurement: the potential for operational hydrological applications”, Transboundary floods: reducing risks through flood management, C = land pixel (pixel located near the river in an area free of surface water even during high floods) M = water pixel (pixel located within the river with permanent presence of water) C/M increases with the presence of water and, hence, of discharge

EGU 2013, Vienna, April 2013 PROCEDURE: 1.Selection of a box centered on the investigated gauged station from each MODIS image 2. Exclusion of pixels affected by cloud cover and/or snow by using a simple threshold and a visual inspection 3.Calculation of the ratio C/M 4.Application of the smoothing exponential filter (C/M*) 5.Identification of local or regional relationship between C/M* and velocity, v

EGU 2013, Vienna, April 2013 AQUA- MODIS Reflectance value of Channel 2 (10-Feb :10) 1. Selection of a box centered on the investigated gauged station from each MODIS image

EGU 2013, Vienna, April Exclusion of pixels affected by cloud cover and/or snow by using a simple threshold and a visual inspection AQUA- MODIS Reflectance value of Channel 2 (06-Jan :20)

EGU 2013, Vienna, April Calculation of the ratio C/M

EGU 2013, Vienna, April Application of the smoothing exponential filter (C/M*)

EGU 2013, Vienna, April Identification of a local relationships between C/M* and v Piacenza Cremona Borgoforte Pontelagoscuro r=0.67 r=0.77 r=0.68 r=0.74

EGU 2013, Vienna, April Identification of a regional relationship between C/M* and v Q = v ∙ A A = f(h)

EGU 2013, Vienna, April 2013 RADAR ALTIMETER r

EGU 2013, Vienna, April 2013 RADAR ALTIMETER r r

EGU 2013, Vienna, April 2013 MODIS MODerate resolution Imaging Spectroradiometer RADAR ALTIMETER r r r

EGU 2013, Vienna, April 2013 MODIS MODerate resolution Imaging Spectroradiometer RADAR ALTIMETER r r r r

EGU 2013, Vienna, April 2013 MODIS MODerate resolution Imaging Spectroradiometer h from radar altimeter (VS3) v from MODIS image (VS3) RADAR ALTIMETER r

EGU 2013, Vienna, April 2013

The analysis showed the potential of satellite data for estimation of the discharge in river sites where only the survey of the cross section is needed. The capability of MODIS to estimate mean flow velocity can be efficiently employed together with other satellite sensors (altimeter). It is worth noting, however, that for regionalization a velocity calibration took place. These aspects may be of particular interest in view of the next satellite mission SWOT for which significant improvements are expected in terms of vertical accuracy and spatial and temporal resolution.

EGU 2013, Vienna, April 2013 Entropy Model ( Moramarco et al. submitted to Journal of Hydrology ) Depth from altimetry Maximum velocity by MODIS

EGU 2013, Vienna, April 2013 Biancamaria, S., Hossain, F., Lettenmaier, D.P., Forecasting transboundary river water elevations from space. Geophysical Research Letters, Vol 38, L11401, DOI: /2011GL Birkett, C.M., Contribution of the Topex NASA radar altimeter to the global monitoring of large rivers and wetlands. Water Resources Research 34 (5), 1223–1239. Birkinshaw, S.J., O’Donnell, G.M., Moore, P., Kilsby, C.G., Fowler, H.J., Berry, P.A.M. (2010). “Using satellite altimetry data to augment flow estimation techniques on the Mekong River.”, Hydrological Processes, 24, 3811–3825. Birkinshaw, S.J., Moore, P., Kilsby, C.G., O’Donnell, G.M., Hardy, A.J., Berry, P.A.M. (2012). “Daily discharge estimation at ungauged river sites using remote sensing”, Hydrological Processes, DOI: /hyp.9647 Brakenridge, G. R., & Anderson, E., MODIS-based flood detection, mapping and measurement: the potential for operational hydrological applications, Proceedings of the NATO on Transboundary floods: reducing risk through flood management, Eds. Marsalek J., Stancalie G., Balint G., Vol. 72, pp Brakenridge, Nghiem, Anderson, Mic, “Orbital microwave measurement of river discharge and ice status”, Water Resources Research, 2007 Tarpanelli, A., Brocca L., Lacava T., Melone F., Moramarco T., Faruolo M., Pergola N., Tramutoli V., Towards the estimation of river discharge variations using MODIS data in ungauged basins. Remote Sensing of Environment (under review). Bjerklie, D.M., Dingman, S.L., Vorosmarty, C.J., Bolster, C.H., Congalton, R.G., Evaluating the potential for measuring river discharge from space. Journal of Hydrology, 278, Campos, I.O., Mercier, F., Maheu, C., Cochonneau, G., Kosuth, P., Blitzkow, D., Cazenave, A., Temporal variations of river basin waters from Topex/ Poseidon satellite altimetry. Application to the Amazon basin. Comptes Rendus de l’Académie des Sciences – Series IIA – Earth and Planetary Science 333 (10), 633–643. da Silva J. S., Calmant S, Seyler F., Otto Corrêa Rotunno Filho, Cochonneau G., Webe João Mansur.“Water levels in the Amazon basin derived from the ERS 2 and ENVISAT radar altimetry missions”. Remote Sensing of Environment, 114, pp doi: /j.rse Frappart, F., Seyler, F., Martinez, J.-M., et al. Floodplain water storage in the Negro River basin estimated from microwave remote sensing of inundation area and water levels. Remote Sensing of Environment 99, 387–399, Frappart, F., Calmant, S., Cauhope, M., Seyler, F., and Cazenave, A.: Preliminary results of ENVISAT RA-2-derived water levels validation over the Amazon basin, Remote Sensing of Environment, 100, 252–264, Getirana, A. C. V., Bonnet, M. P., Calmant, S., Roux, E., Rotunno, O. C., and Mansur, W. J.: Hydrological monitoring of poorly gauged basins based on rainfall-runoff modeling and spatial altimetry, Journal of Hydrology, 379, 205–219, Koblinsky, C. J., Clarke, R. T., Brenner, A. C., and Frey, H.: Measurement of River Level variations with Satellite Altimetry, Water Resources Research, 29, 1839–1848, Kouraev, A. V., Zakharova, E. A., Samain, O., Mognard, N. M., and Cazenave, A.: Ob’ river discharge from TOPEX/Poseidon satellite altimetry (1992–2002), Remote Sensing of Environment, 93, 238– 245, Leon, J. G., Calmant, S., Seyler, F., Bonnet, M. P., Cauhope, M., Frappart, F., Filizola, N., and Fraizy, P.: Rating curves and estimation of average water depth at the upper Negro River based on satellite altimeter data and modeled discharges Journal of Hydrology, 328, 481–496, Pereira-Cardenal SJ, Riegels ND, Berry PAM, Smith RG, Yakovlev A, Siegfried TU, Bauer-Gottwein P Real-time remote sensing driven river basin modeling using radar altimetry. Hydrology and Earth System Sciences 15: 241–254. Zakharova EA, Kouraev AV, Cazenave A, Seyler F Amazon River discharge estimated from TOPEX/Poseidon altimetry. Comptes Rendus Geoscience 338: 188–196.