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In the history of hydrologic science, many of the significant advances have resulted from new measurements (NRC, 1991). Whereas there are often field experiments with specific research objectives, the hydrologic community has not placed a high priority on design and operation of observing systems that meet both operational and research needs. In addition, rarely has high priority been given to the establishment of long-term observing systems. Indeed, over the past decade, only modest progress has been made in hydrologic measurements, and data needs are much the same as delimited in Opportunities in the Hydrologic Sciences (NRC, 1991).
The science challenges identified in Chapter 2 have in common the need to characterize better the hydrologic cycle. The challenges fall into three categories. First, information is needed about the fluxes and storage of water as it moves through the hydrologic cycle and about associated fluxes of solutes, sediments, and energy. Second, hydrologic data are needed to monitor and understand changes in the quantity and quality of water. Third, in the traditional scientific sense, hydrologic data are needed to test hypotheses and models and to formulate new hypotheses. Data are needed at a variety of scales. However, the spatial and temporal scales of available data limit our ability to investigate hydrologic variability and to detect changes. For most fluxes, a fundamental impediment to progress is how to relate point measurements to fluctuations in hydrologic variables at large spatial scales, in heterogeneous terrain, and over long periods of time.
Existing data networks should be viewed as critical elements within the USGCRP that are needed to answer its identified research challenges. These networks should be viewed as opportunities upon which to build the data sets needed in the next century to address questions regarding global change. Resolution of the questions in Chapter 2 requires new data, with measurement location, frequency, duration, and accuracy characteristics that go beyond the capabilities of existing networks. Allocation of new resources for data collection and data archiving must seek a compromise between scientific and operational needs, between ground-based networks and remotely sensed measurements, and between information on water quantity and quality. In establishing new systems or revamping old ones, the needs of operational water management as well as those of research and long-term monitoring should be considered carefully. Such foresight can obviate costly retrofitting or attempts to analyze data trends from operational systems in which the data noise may be greater than the signal. The establishment of any data system must take into account cost-effectiveness, but should do so with a long-range view.
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3—
Measurement and Data Strategies
In the history of hydrologic science, many of the significant
advances have resulted from new measurements (NRC, 1991). Whereas
there are often field experiments with specific research
objectives, the hydrologic community has not placed a high priority
on design and operation of observing systems that meet both
operational and research needs. In addition, rarely has high
priority been given to the establishment of long-term observing
systems. Indeed, over the past decade, only modest progress has
been made in hydrologic measurements, and data needs are much the
same as delimited in Opportunities in the Hydrologic
Sciences (NRC, 1991).
The science challenges identified in Chapter 2 have in common
the need to characterize better the hydrologic cycle. The
challenges fall into three categories. First, information is needed
about the fluxes and storage of water as it moves through the
hydrologic cycle and about associated fluxes of solutes, sediments,
and energy. Second, hydrologic data are needed to monitor and
understand changes in the quantity and quality of water. Third, in
the traditional scientific sense, hydrologic data are needed to
test hypotheses and models and to formulate new hypotheses. Data
are needed at a variety of scales. However, the spatial and
temporal scales of available data limit our ability to investigate
hydrologic variability and to detect changes. For most fluxes, a
fundamental impediment to progress is how to relate point
measurements to fluctuations in hydrologic variables at large
spatial scales, in heterogeneous terrain, and over long periods of
time.
Existing data networks should be viewed as critical elements
within the USGCRP that are needed to answer its identified research
challenges. These networks should be viewed as opportunities upon
which to build the data sets needed in the next century to address
questions regarding global change. Resolution of the questions in
Chapter 2 requires new data, with measurement location, frequency,
duration, and accuracy characteristics that go beyond the
capabilities of existing networks. Allocation of new resources for
data collection and data archiving must seek a compromise between
scientific and operational needs, between ground-based networks and
remotely sensed measurements, and between information on water
quantity and quality. In establishing new systems or revamping old
ones, the needs of operational water management as well as those of
research and long-term monitoring should be considered carefully.
Such foresight can obviate costly retrofitting or attempts to
analyze data trends from operational systems in which the data
noise may be greater than the signal. The establishment of any data
system must take into account cost-effectiveness, but should do so
with a long-range view.
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Maintaining and Upgrading Ground-Based
Networks
Research in hydrologic science relies heavily on data from
operational ground-based networks, which need to be upgraded to
meet evolving needs. In some cases adding relatively low-cost
sensors or expanding the coverage of existing networks could
provide the measurements to help meet the scientific challenges
identified in Chapter 2. For example, thermistors to measure soil
and snow temperature, devices to estimate soil water content, and
instruments to sense snow properties are readily available but are
deployed only to a limited extent as part of operational networks.
In other cases, even large financial investments for incremental
expansion of instrument networks will leave important questions
about hydrologic fluxes and reservoirs unanswered. Thus, technical
and analytical innovations are necessary to overcome the paucity of
useful hydrologic data being collected and collated.
The need for long-term measurements is becoming increasingly
clear in investigations of environmental change, including changes
to the hydrologic cycle. Despite the increasingly recognized
importance of data records of long duration, only a few dedicated
organizations have successfully maintained high-quality
data-collection efforts over periods of 50–200 years.
Research organizations have experienced difficulties in committing
limited monies year after year to monitoring a task often not
viewed as research. There are also problems associated with
obtaining continuous and homogeneous observations from operational
networks. These networks often designed for purposes other than
long-term research tend to change in response to new technology, to
changing operational needs, and to budget pressures. Detecting
hydrologic change requires data sets of greater quality and
reliability than are often needed for research on processes or for
operational forecasting. Federal agencies that make up the USGCRP
must demonstrate their commitment to the collection, archiving, and
dissemination of data suitable for the hydrologic research
challenges identified in the FY2000 Our Changing Planet. In
particular, USGCRP and its member agencies should reevaluate
existing ground-based networks and consider future networks in
light of the critical need to detect hydrologic change in future
decades.
Most hydrologic data have been collected with local-, regional-,
or national-scale questions in mind. Some of the science issues in
Chapter 2 are local or regional, but others are continental or
global. Because it will not be possible with known technology and
present-level resources to monitor hydrologic phenomena over the
entire Earth, there is a need to actively promote international
cooperation to implement networks that yield the maximum
return.
Under the current budgets, there is a decreasing number of
measurement points that federal, state, and local agencies can
maintain, and there is a limit to the number of measurements that
they can produce. For example, the national streamgaging network,
maintained by the U.S. Geological Survey (USGS), is a unique and
irreplaceable source of primary data supporting hydrologic
research, planning, and management. It is of critical national
importance that this source of long-term, consistent, and reliable
data be maintained and upgraded to support the measurement of both
streamflow and stream stage, especially in extreme conditions such
as floods (USGS, 1998; NRC, 1999a). Federal agencies must implement
effective measures to reverse the trend in river flow monitoring
network losses. This can be achieved only through both inter- and
intra-agency commitments to making long-term measurements in
support of work on recognized national priorities in water
resources planning, water quality management, aquatic ecosystem
protection and restoration, hazard mitigation, and global change
research. Implementation plans should address stable funding
partnerships to maintain the networks.
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Similarly, a national network of groundwater monitoring wells is
essential for groundwater recharge characterization, an important
scientific challenge presented in Chapter 2. These data are also
central to the identification of long-term trends related to
pumping, drought, and climate and land-use change. Although the
cost of making water-level measurements is often relatively low
(measurements are sometimes made by volunteers), the costs of
coordination, data management, and well maintenance are relatively
high. Current funding is insufficient to maintain the present
network, with the result that some states have been forced to drop
wells from their networks (e.g., 43 wells—25 percent of the
total were dropped—from the network in Wisconsin owing to
decreased funding). New approaches should be explored to ensure
that a long-term network of monitoring wells for both water level
and water chemistry is maintained, such as the establishment of new
partnerships between the federal and state agencies that manage the
network. Furthermore, funding sources should be stabilized to
ensure that the network is maintained.
Integration of Remote Sensing with
Ground-Based Measurements
Over the past decade the ability of remote sensing to provide
spatial data on rainfall, soil moisture, snow cover, snow water
equivalence, and other key hydrologic variables has been
demonstrated. As recognized in FY 2000 Our Changing Planet,
the capabilities of remote sensing are powerful, in part because
remote sensing has the unique capability of providing consistent,
global coverage. Remote sensing has the potential to improve our
understanding of terrestrial hydrology. However, much of this
capability remains underused in terrestrial hydrologic research and
applications because of a lack of investment in research and
technology by the USGCRP agencies. Important technological
questions remain to be answered before data with sufficient
accuracy and reliability can be collected and systematically
applied for measuring and detecting changes in land surface
hydrologic properties. Moreover, hydrologic science has yet to
articulate, or in some cases even recognize, the necessary synergy
between remotely sensed and ground-based measurements. Remote
sensing will. not preclude the need for ground-based measurements.
However, it is not likely that existing ground-based networks are
adequately configured to take maximum advantage of remote sensing
measurements. Further research is required to determine the mix of
remote sensing and ground-based networks to address several of the
questions posed in Chapter 2.
The four areas that offer the greatest promise for satellite
remote sensing to further hydrologic research and science are
rainfall, soil moisture, snowpack properties, and surface-water
level monitoring. Significant investment in remote sensing
technology and hydrologic research is needed to prepare adequately
for future missions in these areas. Two additional areas that have
been initiated by other disciplines, but in which hydrologic
science has an obvious interest, are vegetation properties and ice
sheet mass balance.
Following the success of the Tropical Rainfall Monitoring
Mission (TRMM) in measuring rainfall using a combination of active
and passive microwave observations, the National Aeronautics and
Space Administration (NASA) initiated planning for a global
precipitation mission. Rainfall is associated with weather systems
that display considerable spatial and temporal variability,
requiring a sampling frequency of multiple times per day. The
proposed global precipitation mission provides measurements that
are central to the understanding of the predictability and
variability of regional and global water cycles (Chapter 2). It
also directly addresses USGCRP priorities in the global water
cycle, particularly weather prediction and weather-climate
relationships.
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Space-based remote sensing offers the potential of measuring
soil moisture, one of the main hydrologic variables that is not
measured in any large spatial array of ground-based instruments.
Soil moisture is the most significant indicator of the state of the
terrestrial hydrologic system, and it is the governing parameter
for partitioning rainwater among evaporation, infiltration, and
runoff. It also provides a critical link between the physical
climate and biogeochemical cycles. No existing or planned
instrument meets these needs; thus, NASA has initiated studies for
an exploratory mission. Soil moisture measurement is clearly a high
priority within the global water cycle, and planning should be
initiated for a measurement system that can effectively blend
ground-based and satellite measurements.
With the launch of Terra, the hydrologic community will have the
possibility of developing more accurate snow-covered-area products
using the moderate resolution spectrometer (MODIS) instrument. The
gain in accuracy comes from the improved spatial and spectral
resolution of MODIS over the NOAA's Advanced Very High Resolution
Radiometers (AVHRR) instruments. However, as with AVHRR, MODIS's
surface observations will be hampered by clouds. An instrument with
at least the resolution of MODIS should be made available to
provide snow cover data for the foreseeable future. Research has
shown that further advances in measuring snowpack properties from
space will come only with a multispectral, multifrequency microwave
sensor, either through active synthetic aperture radar (SAR) or
through a high-resolution radiometer. Both systems offer the
promise of measuring snow water equivalent, with a lower-frequency,
higher-resolution SAR being preferred for measuring deep snow
properties in mountainous regions, and with higher-frequency
passive sensors being preferred for thinner snow packs on the Great
Plains. A SAR sensor for snow water equivalence thus helps address
perhaps the most significant water resources management challenge
in the western United States—that of seasonal water supply
forecasting. Over 85 percent of the Colorado River streamflow
derives from snowmelt from mountainous regions, and late-spring
water-supply forecasts based on estimates of how much snow is on
the ground still differ from measured seasonal runoff by 20 percent
to 50 percent in the various subbasins. SAR data have also been
shown to be useful in estimating ice sheet dynamics. Better snow
observations in the plains can help forecast floods like the record
1997 flood on the Red River of the North, and they can help in
predicting moisture availability in agricultural areas on the Great
Plains.
A fourth exploratory area that shows great potential is river
discharge and changes in inland lake levels. Although river stage
can be conveniently observed in situ for small to moderate rivers,
discharge information for many of the world's major rivers is not
available. Monitoring river discharge and lake storage would allow
for the closure of the terrestrial water balance at continental
scales and would provide important data on the linkage between the
terrestrial and oceanic hydrospheres. Such monitoring would
potentially yield an important indicator of global change.
Precision altimetric sensors developed for oceanographic and
ice-sheet measurements are potentially capable of detecting changes
of a few centimeters in inland water surfaces.
In addition to these areas, continued work in quantifying and
characterizing land cover changes is of critical interest to the
hydrologic science community, as well as to the fields of
ecological and biogeochemical research (NRC, 1999a). Terrestrial
vegetation is an important determinant of hydrologic conditions
through its effects on the surface radiation balance, on
precipitation-runoff partitioning, and on evaporation. Remotely
sensed data provide the only means by which variables such as
growth form (e.g., woody vs. herbaceous vegetation), leaf type, and
growing season onset and termination can be estimated at high
resolution. Research and applications—currently using AVHRR,
Landsat, and SPOT (Satellite Pour l'Observation de la Terre), and
by extention MODIS—need to be extended to include data from
the Vegetation Canopy
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Lidar (VCL) mission. The use of VCL data for biological and
hydrologic monitoring, process studies, and model parameter
estimation needs to be expanded, since this mission will provide
unique data on vegetation structure that may identify vegetation
stress related to climatic factors and landscape (or land use)
change. As discussed subsequently, multivariate model assimilation
schemes might be used to incorporate these and other in situ
ecosystem observations with other in situ and remotely sensed
hydrometeorological data sources into a consistent framework. Such
assimilations would facilitate subsequent hydrologic (and
ecological) process studies, as well as provide a check on the
underlying physics and parameterizations of the coupled
ecological-hydrologic models used in the assimilation process.
Earth satellite missions, data management, and science to
support development and use of data from these missions form the
centerpiece of the USGCRP (e.g., NASA's Earth Science Enterprise).
Potentially, this arrangement can serve hydrology well. However,
for hydrologic science and applications to get the maximum benefit
from the missions, a much closer link with ground-based network
design, data management, and science support will be needed.
Further, the ready availability of remote sensing information poses
several specific challenges to the hydrologic community: (1)
classical data-poor hydrologic models designed to function (and
calibrate) with sparse in situ observations of precipitation and
stream discharge need to be augmented and possibly replaced with
new, process-resolving distributed hydrologic models that are
forced with multispectral remotely sensed (and ground-based)
observations, (2) basin-scale and regional validation databases
consisting of coordinated in situ and remote-sensing data
collection programs need to be established, and (3) hydrologists
must adjust from being passive recipients of limited remote-sensing
observations to acting as a unified scientific community engaged in
supporting the definition, design, and implementation of satellite
remote sensing missions. The USGCRP agencies with research and
applications program in hydrologic science must be proactively
supportive of these goals.
Data Interpretation: Synergy in
Modeling and Measurement
Advances in data interpretation capabilities are required to use
the measurements effectively in the context of science questions
and societal needs. Data assimilation (merging of models and data),
solution techniques for inverse problems (inferring system
properties from sparse data with the aid of models of the system),
nested regional atmospheric models (and hydrologic models), and
expanded analysis capabilities of geographical information systems
(GIS) are opening new possibilities for the interpretation and use
of data.
Data assimilation, an approach routinely used in meteorology and
oceanography, is a particularly promising aid in the integration of
large volumes of multisource and multiscale data as well as in the
interpretation of measurements. It is an extension of standard
model calibration. However, data assimilation is data-driven
whereas calibration is model- and data-driven. Thus, one of the
primary purposes of data assimilation is to produce data products
that are directly useful for hydrologic analyses. For example,
sequential remotely sensed data of ground temperature and microwave
emissivity for the top few centimeters of soil may be used in
conjunction with a soil column model to infer profiles of soil
temperature and moisture down to tens of centimeters. In the
context of groundwater, tracer and water level measurements in a
sparse network of monitoring wells may be assimilated into a
numerical groundwater model to infer soil hydraulic properties
throughout the aquifer. Another use of data assimilation is to
demonstrate the impact of under used and nontraditional data types
(e.g., remote sensing, groundwater levels, and new in situ
networks) on
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hydrologic predictions. The framework may be used to analyze the
value of various observing systems through so-called ''data-denial"
experiments. In these experiments the value of a particular data
source can be quantified in terms of the effect it has on estimates
of a particular hydrologic variable. The design of new,
cost-effective observing networks may be similarly done in the
framework of data assimilation.
Nested regional climate models provide a bridge between the
spatial scales of atmospheric and land surface processes.
Understanding predictability in hydrologic systems and tapping that
predictability for planning, assessments, and forecasts clearly
require bridging tools that can make use of detailed spatial data
to estimate spatial responses. In the western United States,
capturing precipitation and runoff patterns requires topographic
information of detail considerably greater than that found in even
the most detailed general circulation models.
Advanced GIS tools make spatial modeling and interpretation
possible. For example, assessing the impact of land-use changes on
runoff or recharge requires information on the time evolution of
land use as inputs to hydrologic models. GIS tools are likely to
become a central part of hydrologic modeling.
Supporting Long-Term Experimental
Sites
Long-term experimental sites for characterizing the water
balance, flow pathways, and reactive transport provide the data
essential for developing models and methods to scale hydrological
variables, characterize basin-scale variability, and understand the
limits of predictability. In the future they should also provide
the data essential to understanding the coupling between hydrologic
systems and ecosystems, and they should provide measurements for
the calibration and validation of remote sensing data. The goal is
to operate dozens of such basins worldwide over decades and across
a range of bioclimatic zones, geological settings, and land-use
characteristics. Existing program in the United States (e.g., the
National Science Foundation's Long-Term Ecological Research sites;
the U.S. Department of Agriculture's experimental watersheds; the
Water, Energy, and Biogeochemical Budget research watersheds of the
USGS; and the AmeriFlux CO2 sites
partially supported by the U.S. Department of Energy and NASA)
provide a useful starting point; some were designed as hydrological
study sites, bit others do not have a significant hydrological
component. Agencies that support these sites should encourage
multidisciplinary observations and studies. International and
interagency coordination activities need to be launched to augment
these sites for hydrologic research in a cost-effective manner. At
larger scales, activities such as the USGS's National Water Quality
Assessment (NAWQA) program (Box D) are needed to monitor and to
understand the trends in the quality (and quantity) of the nation's
surface and subsurface water resources for both science and policy
purposes. Furthermore, more sites are needed in the United States
and in selected climatic zones around the world in order to
characterize hydrologic processes across the Rill range of
environmental conditions.
Data Accessibility and Quality
Control
Advances in hydrologic science will depend largely on how well
investigators can integrate reliable, large-scale, long-term data
sets. Inevitably, many scientists from a variety of disciplines and
backgrounds will be involved in data collection and analysis over a
significant period of time. Creating effective data
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