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Upstream Satellite-derived Flow Signals for River Discharge Prediction. Tom Hopson 1 F. A. Hirpa 2 ; T. De Groeve 3 ; G. R. Brakenridge 4 ; M. Gebremichael 2 ; P. J. Restrepo 5. 1. 2 Uconn; 3 Joint Res Counc ; 4 U. of CO; 5 Office Hydro Devel. Outline. - PowerPoint PPT Presentation
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Upstream Satellite-derived Flow Signals for River Discharge Prediction
Tom Hopson1
F. A. Hirpa2; T. De Groeve3; G. R. Brakenridge4;M. Gebremichael2; P. J. Restrepo5
1
2Uconn; 3Joint Res Counc; 4U. of CO; 5Office Hydro Devel
OutlineI. Monitoring river flows using Satellite-based
Passive-Microwave data II. Case study: Bangladesh’s vulnerability to
floodinga) Estimating flood wave celerityb) River discharge estimation “nowcasting”c) Discharge forecasting
III. Future Work
-- Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) & NASA TRMM(Future: Global Precipitation Measurement System) -- Utilizing 36-37Ghz (unaffected by cloud)-- pixel size ~20km-- ~2day complete global coverage (night-time brightness temperatures)-- data range: 1997 to present
Objective Monitoring of River Stage and Flow:Satellite-based Passive Microwave Radiometer
Other Approaches: satellite altimeter-derived water level (and discharge derived through rating curve):e.g. Birkett, 1998; Alsdorf et al. 2000; Jung et al. 2010, Papa et al. 2010, Alsdorf et al. 2011, Biancamaria et al. 2011
One day of data collection(high latitudes revisited most frequently)
=> On average, global coverage 1-2 days
Example: Wabash River, Indiana, USA
Black square shows measurement pixel. White square is calibration pixel.
1000120014001600180020002200240026002800
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Brakenridge, et al, 1998, 2005, 2007
GDACS, JRC Gridded Approach
De Groeve, et al, 2009, 2010
=> Use “M/C” ratio
s =MC
=Tb,measurement
Tb,calibration
(World Food Program)
Damaging Floods:large peak or extended durationAffect agriculture: early floods in May, late floods in September
Recent severe flooding: 1974, 1987, 1988, 1997, 1998, 2000, 2004, and 20071998: 60% of country inundated for 3 months, 1000 killed, 40 million homeless, 10-20% total food production2004: Brahmaputra floods killed 500 people, displaced 30 million, 40% of capitol city Dhaka under water2007: Brahmaputra floods displaced over 20 million
Case Study: Bangladesh River Flooding
Hardinge Bridge gauge(Ganges)
Gauging data reference: Hopson 2005; Hopson and Webster 2010
Bahadurabad gauge(Brahmaputra)
Ganges
Brahmaputra
Correlation vs. lag time and distance between Gage Q & satellite signals
Ganges Brahmaputra
Estimating the kinematic wave celerity
Assumptions (large): hydraulic parameters homogenous; rainfall predominantly upper-catchment; wave speed variation with depth lower-order; dynamic wave effects lower-order (confirmed through rating curve analysis – not shown), etc.
Independent analysis: Kleitz-Seddon Law for kinematic wave celerity c: W channel top-width, Q discharge, y the river stage.Brahmaputra at Bahadurabad: 4m/s < c < 8m/s; Ganges at Hardinge Bridge: 2m/s < c < 6m/s.
c =1W
dQdy
Generation of Nowcasts and Forecasts
Regressor and model selection procedure:
i) Correlate all variables with gauged observations
ii) Sort in decreasing level
iii) Select using step-wise forward-selection using K-fold cross-validation and RMSE cost function
iv) Repeat ii) – iii) for all lead-times
Discharge estimation “Nowcasting”
Ganges 2003 Brahmaputra 2007
1-, 5-, 15-day Forecasts
Skill Scores
Nash-Sutcliffe efficiency(fraction of variance explained):
-- 80% (1-day) to 55% (15-day) explained
Now, generate a forecast including “persistence”:
-- satellite information improves forecasts by 10-20% at all lead-times
SS =Aforc −Aref
Aperf −Aref
Future WorkBlend with ECMWF-derived forecasts
(Hopson and Webster 2010; Operational in 2003)
2007 Brahmaputra Ensemble Forecasts and Danger Level Probabilities
7-10 day Ensemble Forecasts 7-10 day Danger Levels
7 day 8 day
9 day 10 day
7 day 8 day
9 day 10 day
Summary
Satellite-based passive microwave imagery useful tool for monitoring river conditions for limited-gauged basins
Useful for estimating:-- flood wave celerity-- discharge estimates (nowcasts) including filling in missing data
Combined with other methodologies, a potentially useful tool for flood forecasting of large river basins
Further questions: [email protected]
MODIS sequence of 2006 Winter Flooding
2/24/2006 C/M: 1.004 3/15/2006 C/M: 1.029 3/22/2006 C/M: 1.095
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Site 98, Wabash River at New Harmony, Indiana, USA