Analysis of floods and droughts events in the Bermejo River Basin: Contribution of microwave

remote sensing in monitoring and prediction

V. Barraza Bernadas F. Grinds, A. Carbajo, H. Karszenbaum

FUNCEME - COSPAR TRAINING AND CAPACITY BUILDING COURSE ON Earth Observation Understanding of the Water Cycle (Over Land and Ocean)

01 to November 12, 2010

Floods and droughts are the most important hydrological disturbances in intermittent

streams.

The concept of hydrological disturbance is strongly related to the ecosystem dynamics.

The hydrologic mechanisms underlying the climate-soil-vegetation dynamics control the most

basic ecologic patterns and processes.

Introduction

Eco-hydrology is the science that studies the mutual interaction between the

hydrological cycle and the ecosystems.

Remote sensing provides an useful approach toward measuring important

elements of the hydrological balance and vegetation monitoring.

Map the hydrological behavior of the Bermejo watershed using microwave

systems (AMSR-E, SMOS and SAC-D Aquarius) and optical systems (MODIS), with

special emphasis in the analysis of multitemporal series (2002-on).

Use optical images to obtain patterns of NDVI and enhanced vegetation index (EVI)

at regional level, from TERRA-AQUA MODIS time series that accounts for

ecosystem dynamics related to flood and drought events.

Thesis Objectives

Study Area

Bermejo River Basin

The Bermejo River is one of the main

tributaries of the Paraguay-Paraná River

system, and flows from south-west Bolivia to

north-east Argentina through a variety of

habitats from the Andean Chain to the plains

of the lower Chaco.

It is the only river that crosses completely the huge Chaco Plains, behaving as a corridor for the

connection of biotic elements of both the Andean mountains and the Chaco Plains.

It is one of the rivers plains with the largest solid discharge of the world.

Hydrological variables

Microwave passive systems have advantages over the optical ones related to

hydrological variables.

Frequency (1-30GHz) passive microwave studies can reveal large scale

inundation patterns even in the presence of cloud cover and dense vegetation.

Provides low resolution brightness temperature (Tb) data (~ hundreds of Km2),

characterized by a high temporal resolution (~ 2-3 days).

The measured brightness temperature of the terrain is related to soil dielectric

constant and ultimately, to its flood condition.

Introduction

AMSR-E

data from 2002-on.

microwave radiometer operating at six frequency bands: 6.925 GHz (C), 10.65 GHz (X),

18.7 GHz (Ku), 23.8 GHz, 36.5 GHz (Ka), and 89.0 GHz, and two polarizations (H y V).

The IFOV is dependent on frequency, going from 43 x 75 km at C Band to 8 X 14 km at Ka

band.

In order to compare among different frequencies, we are using products with the same

resolution (AMSR-E Res-1).

We have adopted a repetition time of 16 days, which corresponds to the exact repeat

orbit.

SAC-D Aquarius and SMOS will be providing data for L band (1.3 GHz).

Available data

Work in progress

Processing AMSR-E data of all frequencies from 2002-on

Estimating flooded area and mean water level

Basic model [Hamilton, 2002]

The model has three end-members, that represent the contributions of water, non-flooded

land, and inundated floodplain to the total observed polarization difference ΔTobs,

where ΔTobs is the ΔT observed by the radiometer, fw, fnf and ff are the fractional areas of open

water (rivers and lakes without emergent vegetation), non-flooded land, and seasonally

flooded land, respectively, and ΔTw, ΔTnf, and ΔTf are the ΔT values for open water, non-

flooded land, and seasonally flooded land.

Simultaneous solution of these equations yields the following equation for the fraction of

inundated floodplain (ff):

ffnfnfwwobs TfTfTfT

fnfw fff 1

nff

nfwnfwwobsf TT

TfTTfTf

fTwT

nfT

Vegetation dynamic.

Vegetation Index analysis is one of the techniques most frequently used in remote sensing

to estimate the type/quality/biomass of the vegetation.

There is huge experimental evidence that shows that the temporal variation of NDVI can

be used to evaluate vegetation phenological patterns and to monitor landuse/landcover

change.

Normalized Difference Vegetation Index

NDVI= (NIR – RED)/(NIR + RED)

NDVI is based on the reflection properties of green vegetation and is determined by the

ratio of the amount of absorption by chlorophyll in the red wavelength (600–700 nm) to

the reflectance of the near infrared (720–1300 nm) radiation.

Introduction

NDVI data from MODIS sensor, with 250 m spatial resolution and a revisit period of once

each 16 days from 2002-on.

Moderate temporal evolution of NDVI

Vegetation dynamics

2JDJDNDVI

The main advantage of this type of model is

that it is sufficiently complex to provide a

good fit, and simple enough to have an

ecological interpretation for each parameter.

2JDJDNDVI

Parameter Definition Biological meaning

α [NDVI] Initial value of NDVI pattern

amount of green biomass at the beginning of the observation period

β [NDVI/day] Linear slope of NDVI pattern

Initial growth rate of vegetation or biomass increased green.

γ [NDVI/day2] Concavity of NDVI pattern.

Largest absolute values mean shorter vegetation growing seasons.

Vegetation dynamics

F. Grings, V. Douna, V. Barraza, M. Salvia, H. Karszenbaum, N. Gasparri, P. Ferrazzoli y R.

Rahmoune. C band radiometric response to rainfall events in the subtropical Chaco forest.

(submitted).

M. Salvia, F. Grings, P. Ferrazzoli, V. Barraza, V. Douna, P. Perna y H. Karszenbaum. Estimating

flooded area and mean water level using active and passive microwaves: The example of

Paraná River Delta floodplain (submitted).

Project

La Plata Basin floods and droughts: Contribution of microwave remote sensing in monitoring

and prediction. SAC-D Aquarius AO- CONAE-NASA. PI: Haydee Karszenbaum

Publications

Thanks…