river water level prediction using passive microwave signatures

22/04/2023 CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012 1

RIVER WATER LEVEL PREDICTION USING PASSIVE MICROWAVE SIGNATURES

(1) Tor Vergata University, DICII, Rome, Italy

(2) Instituto de Astronomía y Física del Espacio, IAFE, Buenos Aires, Argentina

C. Vittucci 1, L. Guerriero 1, P. Ferrazzoli 1, R. Rahmoune 1,

V. Barraza 2, F. Grings 2

Workshop CeTeM-AIT 2012Bari, 4-5 dicembre 2012


SummaryObjective:

• 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 flooding

also in presence of vegetation during different seasons. soil surface antecedent conditions• Relationship between flooding 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:• 2010-2011 observations datasets.

Case Study:• Lower Bermejo Basin, northen Argentina, seasonally affected by severe flooding

events.


The Bermejo Basin

El Sauzalito Hydrometric StationRainfall Station

Laguna Limpia

Puerto BermejoEl Colorado

Gral Vedia

Mapped area

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 Schinopsis balansae in the North, grassland in the South.

Area: [-22 ; -27 S] Lat and [-58; -66 W] Lon, about 123,000 km2

AMSR-E X Band emissivity maps


(a) Normal Condition (b) Rain Condition (c) Flooding Condition

[-27 -25 Lat S; -60 -58 Lon W]

AMSR-E C Band emissivity mapsSMOS –L Band emissivity maps

epf = TBpf / Ts Ts = 0,94 TBv(ka) + 30,8AMSR-E Ts: AMSR-E emissivity: epf = TBpf / Ts* SMOS emissivity: *Ts extracted from ECMWF auxiliary products.


Hydrometric Stations Involved2010 – 2011 El Sauzalito daily observations of river water level2010 – 2011 El Colorado daily observations of river water level

Rainy Season Rainy SeasonDry Season Dry Season

Emissivity at X Band and Rain Trend


Rainy Season Rainy SeasonDry Season Dry SeasonRainy Season Rainy SeasonDry Season Dry Season

Emissivity at C Band and Rain Trend

Rainy Season Rainy SeasonDry Season Dry Season

Emissivity at L Band and Rain Trend


Adaptive Filter theory

LINEAR ADAPTIVE FILTER*

* Haykin, “Adaptive Filter Theory” , 2001

Daily Satellite + ground data as INPUT of

Weights change with time to minimize the error here between the model output and ground truth.

y(t): filter output, i.e., predicted Water Level WL at El Colorado Stationw(t): weight vectorx(t) : input signal

y(t+L) = ŵ(t)x(t)L= Lag time,

forecast horizon

22/04/2023 8

Flood Forecasting Inputs

P1(i), Gral Vedia

P2 (i), Puerto Bermejo

P3 (i), El Colorado

P4 (i), Laguna Limpia

WL ElS (i), El Sauzalito

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),

CeTeM-AIT 2012, Bari, 4-5 Dicembre 2012

i = t - B+1, tG

ROU

ND

INPU

TSA

TELL

ITE

INPU

T Legend

Pn(i) = Precipitation at t time occurred in the nth station (n=1, 2,3,4)

WL ElS (i) = El Sauzalito 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)

X(t)


Water Level Forecasting Algorithm

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+1 to t. 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 residual error.

µ= step size parameter.

L = lag time, here tested for L=3; L=5; L=7 days


Results : Observed and Predicted Trendsof Water Level

L = 3

L = 7


Results: Predicted vs. Observed

L=3 L=5 L=7


Inputs : Ground measurements

NO Microwave radiometric data as INPUTSALGORITHM TEST

To prove the effectiveness of satellite information

L=3 L=7


Flood Forecasting Statistics

L = 3 B = 7 L = 5 B = 7 L = 7 B = 7

WL < 5 m WL > 5 m WL < 5 m WL > 5 m WL < 5 m WL > 5 m

RMSE R2 RMSE R2 RMSE R2 RMSE R2 RMSE R2 RMSE R2

AMSR-E Data

0.38 m 0.97 0.79 m 0.77 0.47 m 0.96 1.02 m 0.73 0.59 m 0.93 1.24 m 0.72

SMOS Data

0.36 m 0.98 0.90 m 0.84 0.52 m 0.96 0.99 m 0.77 0.62 m 0.95 1.42 m 0.64

Tab 2. RMSE and R2 for each Lead time for both sensors (e case)


Conclusions

Adaptive algorithmWater Level Prediction

SMOS or AMSR-E+

Rainfall and upstream water level

Sensitivity to surface conditions

Over Target

• No assumptions • No costraints

SuccessfullInputs Together

Forecast Horizons:L=3; L=5; L=7

Simultaneously applied

Accurate Prediction(best for L=3)

Real applications

• Flooding Risk Management• Agriculture

• Electricity Production


THANKS FOR YOUR ATTENTION

CONTACT: [email protected]

Workshop CeTeM-AIT 2012Bari, 4-5 dicembre 2012

river water level prediction using passive microwave signatures

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