1. June 3, 2010temimi@ce.ccny.cuny.edu Eastern Region Flash Flood Conference June 2 - 4, 2010 Wilkes-Barre, Pennsylvania Flood and discharge monitoring during the 2008 Iowa flood using AMSR-E data Authors: Marouane Temimi *1, Teodosio Lacava 2, Tarendra Lakhankar 1, Valerio Tramutoli 4, Hosni Ghedira 3, Reza Khanbilvardi 1 1 NOAA-CREST, City University of New York, 160 Convent Avenue, New York, NY, 10031, USA 2 Institute of Methodologies for Environmental Analysis (IMAA) - National Research Council (CNR), C.da Santa Loja, 85050 Tito Scalo (PZ) - Italy 3 American University in Dubai, Dubai, UAE 4 Department of Engineering and Physics of Environment (DIFA) - University of Basilicata – via dell’Ateneo Lucano, 10, 85100 Potenza - Italy

2. June 3, 2010temimi@ce.ccny.cuny.edu The flood event in Iowa Several regions particularly in Iowa were affected by a 500 year flood which was classified as the worst in the history of the region. Major damages and large inundated areas related to these floods have been recorded. http://www.flood2008.iowa.govhttp://www.flood2008.iowa.gov & http://www.iowaflood.com http://www.iowaflood.com http://www.flood2008.iowa.govhttp://www.iowaflood.com

3. June 3, 2010temimi@ce.ccny.cuny.edu Study area delineation and objective The objective of this work is to demonstrate the potential of using passive microwave data in monitoring flood and discharge conditions

4. June 3, 2010temimi@ce.ccny.cuny.edu PR used for flood monitoring in Canada This study is an expansion of a previous work in which passive microwave data have shown an interesting potential for flood monitoring The water surface fraction derived from visible images does not include the effect of soil wetness, and provides only an estimate of the waterbody’s extent. On the other hand, passive microwave is a combination of waterbodies, flooded area and soil moisture responses WSF(MODIS) = ow + f WSF(AMSR-E) = ow + f + sm

5. June 3, 2010temimi@ce.ccny.cuny.edu Basin Wetness Index WSF(MODIS) WSF(AMSR-E) a) WSF(MODIS) WSF(AMSR-E) c) WSF(MODIS) WSF(AMSR-E) b) WSF(MODIS) WSF(AMSR-E) d) Different configurations of inundated and wet soils as sensed by MODIS and AMSR-E; a) non flooded area and wet soil; b) large flooded area and wet soils; c) non flooded area and large wet soil extent; d) limited wet soil extent and large flooded area

6. June 3, 2010temimi@ce.ccny.cuny.edu We propose, therefore, to define a Basin Wetness Index (BWI) based on the difference between the passive microwave and visible responses. BWI varies between 0 and 1. It represents the fraction of wetlands within the non-flooded area. WSF(MODIS) ?? Definition of a new BWI Temimi, M., R. Leconte, F. Brissette, N. Chaouch. (2007). Flood and Soil Wetness Monitoring Over the Mackenzie River Basin Using AMSR-E 37 GHz Brightness Temperature. Journal of Hydrology. Vol. 333 (2). p. 317. Leconte et al. 2001

7. June 3, 2010temimi@ce.ccny.cuny.edu Polarization Ratio Variation Index (PRVI) Where, μi and σi are average and standard deviation of the PR=(Tbv- Tbh)/(Tbv+Tbh) respectively for a given month i. Average and standard deviation were estimated on a monthly basis to account for changes in surface conditions (i.e soil roughness and vegetation density) which might affect the microwave signal. PRVI Measures PR anomalies and minimizes surface conditions effects Marouane Temimi, Teodosio Lacava, Tarendra Lakhankar, Hosni Ghdira, Reza Khanbilvardi, Trumatoli, V. Flood and discharge monitoring during the 2008 flood In Iowa using AMSR-E data. Hydrological Processes. (Under Review)

8. June 3, 2010temimi@ce.ccny.cuny.edu PRVI vs PR Observed total precipitation at 8 sites in May, June and July 2008 and their corresponding Thiessen Polygons. Calculated PR and PRVI compared to total precipitation recorded across the study area PRVI shows a higher sensitivity to soil moisture variation with respect to the PR

9. June 3, 2010temimi@ce.ccny.cuny.edu PRVI vs water level a)b) c) PRVI values obtained on June 9th, 2008 in a) compared to AMSR-E soil moisture product (g/cm3) in b) and observed water levels above flood stage as provided by the USGS (black triangle) in c) (http://water.usgs.gov/osw/)http://water.usgs.gov/osw/

10. June 3, 2010temimi@ce.ccny.cuny.edu

11. June 3, 2010temimi@ce.ccny.cuny.edu Observed discharge

12. June 3, 2010temimi@ce.ccny.cuny.edu PRVI vs observed discharge downstream A consistent time lag has been observed

13. June 3, 2010temimi@ce.ccny.cuny.edu Rating Curve  The lag term introduction Q (t) = a PRVI b (t) Q (t) = a PRVI b (t-d.Δt) The lag term ``d`` will maximize the crosscorrelation function between discharge observations and FA vectors WSF Q Q Q d<0 d>0d=0

14. June 3, 2010temimi@ce.ccny.cuny.edu  Use of the Kalman filter Log (Q(t)) = log(a) + b log ( PRVI(t+ d.Δt)) Y = A X + B Y = A X + B Yt = Ht. At where Yt = Y At+1 = Φt At +Wt Yt = Ht At+ Vt (State equation) (Observation equation) With the Kalman filter, the dynamic rating curve model continuously readjusts its parameters to satisfy the non-stationary behavior of hydrological processes. The model is thus sufficiently flexible and adapted to various conditions. A t = A B H t = X 1

15. June 3, 2010temimi@ce.ccny.cuny.edu Correlation vs lag term The best cross- correlation value is obtained when the time lag is 21 days Note that lag values have been varying through the summer (before, during and after the flood) as discharge magnitude and land surface conditions vary

16. June 3, 2010temimi@ce.ccny.cuny.edu Discharge vs lag term The time lag increases in absolute value as the discharge increases A higher discharge means larger inundated area and therefore a longer drainage time The time lag is however limited by the time of concentration

17. June 3, 2010temimi@ce.ccny.cuny.edu Temporal variation of the b coefficient in the rating curve formula In hydrology, this parameter when used in a relationship between discharge and effective width (Smith et al. 2008) is an indication of the geomorphology of the river and the characteristics of its cross- section. The coefficient ‘’b’’ converges towards a constant value. This conclusion corroborates results by Smith et al. 2008 which demonstrated that ‘’b’’ when estimated over several cross-sections converge towards a constant value. In this study, the temporal variation, like the variation in space studied by Smith et al. 2008, reveals that ‘’b’’ is constant too. Smith and Pavelsky, 2008

18. June 3, 2010temimi@ce.ccny.cuny.edu Estimated vs Observed discharge Comparison between estimated and observed discharge at Saint Louis station

19. June 3, 2010temimi@ce.ccny.cuny.eduConclusions… PRVI detects anomalies in soil moisture and can therefore be used in flood and discharge monitoringPRVI detects anomalies in soil moisture and can therefore be used in flood and discharge monitoring A time lag term must be introduced in the rating curve formulaA time lag term must be introduced in the rating curve formula The lag between flooded area extent and discharge downstream seems to be sensitive to discharge magnitude and surface conditionsThe lag between flooded area extent and discharge downstream seems to be sensitive to discharge magnitude and surface conditions The time lag seems to be compatible with the time of concentration of the study areaThe time lag seems to be compatible with the time of concentration of the study area The ‘’b’’ coefficient of the rating curve model when readjusted in time converges towards a constant valueThe ‘’b’’ coefficient of the rating curve model when readjusted in time converges towards a constant value