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
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Eastern Region Flash Flood Conference
June 2 - 4, 2010
Flood and discharge monitoring during the 2008 Iowa flood using AMSR-E data
Marouane Temimi*1, Teodosio Lacava2, Tarendra Lakhankar1, Valerio Tramutoli4, Hosni Ghedira3, Reza Khanbilvardi1
1NOAA-CREST, City University of New York, 160 Convent Avenue, New York, NY, 10031, USA
2Institute of Methodologies for Environmental Analysis (IMAA) - National Research Council (CNR), C.da Santa Loja, 85050 Tito Scalo (PZ) - Italy
3American University in Dubai, Dubai, UAE
4Department of Engineering and Physics of Environment (DIFA) - University of Basilicata – via dell’Ateneo Lucano, 10, 85100 Potenza - Italy
The flood event in Iowa
http://www.flood2008.iowa.gov & http://www.iowaflood.com
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.
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
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.
WSF(MODIS) = ow + f
On the other hand, passive microwave is a combination of waterbodies, flooded area and soil moisture responses
WSF(AMSR-E) = ow + f + sm
Basin Wetness Index
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
Definition of a new BWI
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.
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
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)
PRVI vs PR
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
Observed total precipitation at 8 sites in May, June and July 2008 and their corresponding Thiessen Polygons.
PRVI vs water level
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/)
PRVI vs observed discharge downstream
A consistent time lag has been observed
Q(t)= a PRVIb(t)
Q(t)= a PRVIb(t-d.Δt)
The lag term ``d`` will maximize the crosscorrelation function between discharge observations and FA vectors
Log (Q(t)) = log(a) + b log ( PRVI(t+ d.Δt))
Y = A X + B
Yt = Ht. At where Yt = Y
At+1 = Φt At +Wt
Yt = Ht At+ Vt
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.
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
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
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.
Smith and Pavelsky, 2008
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.
Estimated vs Observed discharge
Comparison between estimated and observed discharge at Saint Louis station