TITLE:
On the Use of Landsat-5 TM Satellite for Assimilating Water Temperature Observations in 3D Hydrodynamic Model of Small Inland Reservoir in Midwestern US
AUTHORS:
Meghna Babbar-Sebens, Lin Li, Kaishan Song, Shuangshuang Xie
KEYWORDS:
Data Assimilation; Reservoir Hydrodynamics; Numerical Models; Temperature; Landsat
JOURNAL NAME:
Advances in Remote Sensing,
Vol.2 No.3,
August
20,
2013
ABSTRACT: Accuracy of hydrodynamic and water quality
numerical models developed for a specific site is dependent on multiple model
parameters and variables whose values are attained via calibration processes
and/or expert knowledge. Real time variations in the actual aquatic system at a
site necessitate continuous monitoring of the system so that model parameters
and variables are regularly updated to reflect accurate conditions. Multiple
sources of observations can help adjust the model better by providing benefits
of individual monitoring technology within the model updating process. For example,
remote sensing data provide a spatially dense dataset of model variables at the
surface of a water body, while in-situ monitoring technologies can provide data at multiple depths and at more
frequent time intervals than remote sensing technologies. This research aims to
present an overview of an integrated modeling and data assimilation framework
that combines three-dimensional numerical model with multiple sources of
observations to simulate water column temperature in a eutrophic reservoir in
central Indiana.
A variational data assimilation approach is investigated for incorporating
spatially continuous remote sensing temperature observations and spatially
discrete in-situ observations to
change initial conditions of the numerical model. The results demonstrate the
challenges in improving the model performance by incorporating water
temperature from multi-spectral remote sensing analysis versus in-situ measurements. For example, at a
eutrophic reservoir in Central Indiana where four images of multi-spectral
remote sensing data were assimilated in the numerical model, the overall error
for the four images reduced from 20.9% (before assimilation) to 15.9% (best
alternative after the assimilation). Additionally, best improvements in errors
were observed on days closer to the starting time of model’s assimilation time
window. However, when the original and updated model results for the water
column temperature were compared to the in-situ measurements during the data assimilation period, the error was
found to have actually increased from 1.8℃ (before assimilation) to 2.7℃ (after assimilation). Sampling depth differences between remote sensing
observations and in-situ measurements, and spatial and temporal sampling of remote sensing observations
are considered as possible reasons for this contrary behavior in model
performance. The authors recommend that additional research is needed to
further examine this behavior.