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RIVER WATER LEVEL PREDICTION USING PASSIVE MICROWAVE SIGNATURES

Workshop CeTeM-AIT 2012 Bari, 4-5 dicembre 2012. RIVER WATER LEVEL PREDICTION USING PASSIVE MICROWAVE SIGNATURES. C. Vittucci 1 , L. Guerriero 1 , P. Ferrazzoli 1 , R. Rahmoune 1 , V. Barraza 2 , F. Grings 2. Tor Vergata University, DICII, Rome, Italy

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RIVER WATER LEVEL PREDICTION USING PASSIVE MICROWAVE SIGNATURES

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  1. Workshop CeTeM-AIT 2012 Bari, 4-5 dicembre 2012 RIVER WATER LEVEL PREDICTION USING PASSIVE MICROWAVE SIGNATURES C. Vittucci1, L. Guerriero 1, P. Ferrazzoli1, R. Rahmoune1, V. Barraza2, F. Grings 2 Tor Vergata University, DICII, Rome, Italy Institutode Astronomíay Física del Espacio, IAFE, Buenos Aires, Argentina CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012

  2. Summary • Objective: • To investigate the exploitation of satellite acquisitions of Brightness Temperature (TB) for the prediction of river water level. • To develop a useful forecast model using ground and satellite observations. • Hypothesis: • Passive sensors sensitivity to short term variations of TB after rainfall or floodingalso in presence of vegetation during different seasons. • soilsurface antecedentconditions • Relationship betweenflooding and: infiltration capacity • local and upper basin rainfalls • Tools: • AMSR-E(at C, X, Ka Bands) and SMOS (L Bands) + hydrometric and rainfall ground measurements. • Temporal Range: • 2010-2011observations datasets. • Case Study: • LowerBermejo Basin, northen Argentina, seasonally affected by severe flooding events. CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012

  3. The Bermejo Basin Area: [-22 ; -27 S] Lat and [-58; -66 W] Lon, about123,000km2 Climate: Continental, subtropical characteristics Lower basin vegetation: Rain forest, humid valley, gallery forest moderately dense Study Area: Humid Chaco, dominated by a typical tree species Schinopsisbalansaein the North,grasslandin the South. ElSauzalito Hydrometric Station Rainfall Station El Colorado Puerto Bermejo Laguna Limpia GralVedia Mapped area CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012

  4. AMSR-E C Band emissivitymaps SMOS –L Band emissivity maps AMSR-E X Band emissivitymaps [-27 -25 Lat S; -60 -58 Lon W] epf= TBpf/Ts* epf= TBpf/Ts AMSR-E emissivity: AMSR-E Ts: SMOS emissivity: Ts= 0,94 TBv(ka) + 30,8 *Ts extracted from ECMWF auxiliary products. (a) NormalCondition (b) RainCondition (c) FloodingCondition CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012

  5. HydrometricStationsInvolved 2010 – 2011 ElSauzalitodailyobservationsofriver water level 2010 – 2011 El Colorado dailyobservationsofriver water level Rainy Season Rainy Season Dry Season Dry Season CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012

  6. Emissivity at C Band and Rain Trend Emissivity at L Band and Rain Trend Emissivity at X Band and Rain Trend Rainy Season Rainy Season Rainy Season Rainy Season Rainy Season Rainy Season Dry Season Dry Season Dry Season Dry Season Dry Season Dry Season CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012

  7. Adaptive Filter theory Daily Satellite + ground data asINPUTof LINEAR ADAPTIVE FILTER* Weightschange with time to minimizethe errorhere between the model output and ground truth. L= Lag time, forecasthorizon y(t+L) = ŵ(t)x(t) y(t): filter output, i.e., predicted Water Level WL at El Colorado Station w(t): weight vector x(t) : input signal * Haykin, “AdaptiveFilterTheory” , 2001 CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012

  8. FloodForecastingInputs Legend Pn(i)= Precipitation at t time occurred in the nthstation (n=1, 2,3,4) WL ElS(i) = ElSauzalito Water Level at t time e Bpf(i) = Emissivity values for both polarizations at C, X, Ka, L bands, averaged over (0.5 x 0.5 deg) area B = number of days backward with respect to the actual day t. (In our study B=7 days before t time) i = t - B+1, t P1(i), GralVedia P2 (i), Puerto Bermejo P3 (i), El Colorado P4 (i),Laguna Limpia WL ElS(i), ElSauzalito AQUA AMSR-E or SMOS MIRAS e BvC(i), eBhC(i), e BvX(i), e BhX(i), eBvL(i),e BhL(i) eBvKa(i), e BhKa(i), GROUND INPUT X(t) SATELLITE INPUT CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012

  9. Water LevelForecastingAlgorithm L = lag time, heretested for L=3; L=5; L=7 days Y(t+L) = W(t)TX(t) X(t)is updated with the new incoming data and contains the information acquired from day t-B+1tot. R(t): Residual error R(t) = WL(t) – Y(t)Y(t):water level prediction at time t WL(t): WL observed at El Colorado Station Residual is computed at each step to adjust the vector of weights applying the following formula: W(t+1)= W(t) + µ X(t-L) R(t) to minimise residualerror. µ= step size parameter. CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012

  10. Results : Observed and PredictedTrendsof Water Level L = 3 L = 7 CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012

  11. Results: Predicted vs. Observed L=3 L=5 L=7 CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012

  12. Inputs : Ground measurements NO Microwaveradiometric data as INPUTS ALGORITHM TEST To prove the effectivenessof satellite information L=3 L=7 CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012

  13. FloodForecastingStatistics Tab 2. RMSE and R2 for each Lead time for both sensors (e case) CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012

  14. Conclusions SMOS or AMSR-E + Rainfall and upstream water level Successfull InputsTogether Adaptivealgorithm Water LevelPrediction Sensitivity to surface conditions Accurate Prediction (best for L=3) • Realapplications • FloodingRisk Management • Agriculture • Electricity Production ForecastHorizons: L=3; L=5; L=7 Simultaneouslyapplied • Over Target • No assumptions • No costraints CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012

  15. Workshop CeTeM-AIT 2012 Bari, 4-5 dicembre 2012 THANKS FOR YOUR ATTENTION CONTACT: vittucci@disp.uniroma2.it CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012

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