Environmental Engineering Reference
In-Depth Information
7.6
Figure 2.12
Estimated water-budget
components for six historical scenarios,
1926 to 2004, central and west Maui,
Hawaii (Engott and Vana,
2007
).
Fog
Water inflows
Irrigation
5.7
Rain
3.8
1.9
0
1.9
3.8
Drainage
Runoff
Evapotranspiration
5.7
Water outflows
7.6
1926-79 1980-84 1985-89 1990-94 1995-99 2000-04
Scenario
useful for developing recharge estimates. The
abundance of satellite-based data, the continual
improvement of satellite-mounted sensors, and
the large research effort invested in analysis
of remotely sensed data provide promise for
future utility (Schmugge
et al
.,
2002
; Entekhabi
and Moghaddam,
2007
). In addition, in underde-
veloped parts of the world, remote sensing may
provide the only approach for estimating water-
budget components (Brunner
et al
.,
2007
).
Data on energy emissions and reflections
from Earth have been collected for decades by
Earth-orbiting satellites. Translation of these
data, which are collected in a variety of wave-
length intervals, into reliable estimates of
water and energy fluxes has evolved slowly over
the years. Estimation of precipitation, evapo-
transpiration, and water-storage changes over
large areas can benefit from remote-sensing
techniques. Remote sensing is not commonly
used to estimate streamflow (although satellite-
based data have been used to delineate flooded
areas). Satellite remote sensing is attractive
because of the broad spatial coverages that can
be obtained. The frequency with which images
are obtained may be a concern. Some sen-
sors repeat measurements hourly or daily; for
others, the interval between images can be as
long as 26 days. Each image provides an instant-
aneous set of data, whereas what is needed for
recharge studies, and water-budget studies in
general, are data that are integrated over the
time between images.
Models are used to relate sensor readings to
actual water fluxes. These models include land
surface models (LSMs), such as NOAH (Ek
et al
.,
2003
) and Interaction Soil Biosphere Atmosphere
(ISBA; Noilhan and Planton,
1989
), and Soil-
Vegetation-Atmosphere Transfer (SVAT) models,
such as the Surface Energy Balance Algorithm
for Land (SEBAL), the Two Source Energy Budget
model (TSEB) (Timmermans
et al
.,
2007
), and
the Community Land Model (CLM) (Bonan and
Levis,
2006
). These models vary in complex-
ity, but they are all water-budget and energy-
budget models of one type or another, and they
are largely based on equations presented in this
chapter. They relate surface energy state and
temperature to water and energy exchanges
among the soil surface, subsurface, vegetation
canopy, and atmosphere. In addition to their
use in analyzing remotely sensed data, these
models are important components in atmos-
pheric general circulation models. More details
on remote sensing and water budgets can be
found in Schmugge
et al
. (
2002
) and Krajewski
et al
. (
2006
).
In addition to estimating water-budget
components, remote sensing provides useful
information on surface characteristics, such
