2
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
activities. Water science is becoming increasingly recognized as an important element of global environmental research.
Hydrologists often find themselves in a need to understanding the anthropogenic controls that influence hydrologic processes across heterogeneous landscapes.
Within this context, how water exchanges between and within the Earth system’s
components over a wide range of space and time scales has received wide attention
(Krajewski et al. 2004). Consequently, when tackling those complexities, existing
water management systems face a reduced solution space of feasible options that
are constrained by competing and conflicting stakeholders’ interests. These interests include, but are not limited to, irrigation demand, drinking water production,
recreation, flood control, disaster management, nonrenewable energy demand via
hydropower production, and environmental flow requirements in terrestrial freshwater systems. Such a reduced solution space further increases societal vulnerability
as global changes continue to progress.
Hydrologic sciences have been making a concerted effort to raise the visibility
of interdisciplinary water resources research under global climate change impacts
(Intergovernmental Panel on Climate Change 2010). Great efforts have been directed
to extend the range and scale of observations by employing new sensor and networking technologies to estimate hydrologic surrogates for multiscale processes.
This can help improve our understanding of model predictions. Over the last few
decades, satellite remote sensing, unmatched by surface-based systems, has become
an invaluable tool for providing estimates of spatially and temporally continuous
hydrologic variables and processes for an emerging global hydrology era. To address
such impacts, research areas of interest may focus on using remote sensing technologies to observe hydrologic and environmental responses to changing climate
and land use patterns at different scales. This requires linking hydrologic theories and field observations to monitor the flux of water, heat, sediment, and solutes
through varying pathways across scales. The need to develop more comprehensive
and predictive capabilities now requires intercomparing observations across in situ
field sites and remote sensing platforms, as well as cohesively integrating multiscale
hydrologic observations to regional and global extent (Consortium of Universities for
the Advancement of Hydrologic Science, Inc. 2011). Such an integrated hydrologic
observatory that merges surface-based, airborne, and spaceborne data with predictive capability indicates promise to revolutionize the study of global water dynamics. This may especially be true if remote sensing technologies are deployed in a
coordinated manner and the synergistic data are further assimilated into appropriate
predictive models.
1.2 CURRENT CHALLENGES
Under the assumption of stationary water resource systems, the challenges of hy -
drologic predictability have been historically categorized as (1) model structure,
(2) input data including initial and boundary values, and (3) parameter optimization problems. Kumar (2011) discussed two additional types of challenges
that arose from changing hydrologic systems. These obstacles were the changes
in spatial complexity driven by evolving connectivity patterns and cross-scale
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
activities. Water science is becoming increasingly recognized as an important element of global environmental research.
Hydrologists often find themselves in a need to understanding the anthropogenic controls that influence hydrologic processes across heterogeneous landscapes.
Within this context, how water exchanges between and within the Earth system’s
components over a wide range of space and time scales has received wide attention
(Krajewski et al. 2004). Consequently, when tackling those complexities, existing
water management systems face a reduced solution space of feasible options that
are constrained by competing and conflicting stakeholders’ interests. These interests include, but are not limited to, irrigation demand, drinking water production,
recreation, flood control, disaster management, nonrenewable energy demand via
hydropower production, and environmental flow requirements in terrestrial freshwater systems. Such a reduced solution space further increases societal vulnerability
as global changes continue to progress.
Hydrologic sciences have been making a concerted effort to raise the visibility
of interdisciplinary water resources research under global climate change impacts
(Intergovernmental Panel on Climate Change 2010). Great efforts have been directed
to extend the range and scale of observations by employing new sensor and networking technologies to estimate hydrologic surrogates for multiscale processes.
This can help improve our understanding of model predictions. Over the last few
decades, satellite remote sensing, unmatched by surface-based systems, has become
an invaluable tool for providing estimates of spatially and temporally continuous
hydrologic variables and processes for an emerging global hydrology era. To address
such impacts, research areas of interest may focus on using remote sensing technologies to observe hydrologic and environmental responses to changing climate
and land use patterns at different scales. This requires linking hydrologic theories and field observations to monitor the flux of water, heat, sediment, and solutes
through varying pathways across scales. The need to develop more comprehensive
and predictive capabilities now requires intercomparing observations across in situ
field sites and remote sensing platforms, as well as cohesively integrating multiscale
hydrologic observations to regional and global extent (Consortium of Universities for
the Advancement of Hydrologic Science, Inc. 2011). Such an integrated hydrologic
observatory that merges surface-based, airborne, and spaceborne data with predictive capability indicates promise to revolutionize the study of global water dynamics. This may especially be true if remote sensing technologies are deployed in a
coordinated manner and the synergistic data are further assimilated into appropriate
predictive models.
1.2 CURRENT CHALLENGES
Under the assumption of stationary water resource systems, the challenges of hy -
drologic predictability have been historically categorized as (1) model structure,
(2) input data including initial and boundary values, and (3) parameter optimization problems. Kumar (2011) discussed two additional types of challenges
that arose from changing hydrologic systems. These obstacles were the changes
in spatial complexity driven by evolving connectivity patterns and cross-scale