Part 2 of the Water Cycle Science Plan
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CHAPTER 3 -- TO WHAT EXTENT CAN VARIATIONS IN THE
GLOBAL AND REGIONAL WATER CYCLE BE PREDICTED?
1.0 SYNOPSIS
Societal Need: Quantitative predictions of hydrologic variability and water related
hazards (e.g. droughts and floods) along with estimates of the confidence in
these predictions are critical for water resources and ecosystem management
and hazard mitigation.
What is missing scientifically?
• A complete description of the spatial (catchment, regional and continental)
and temporal (daily, seasonal-to-interannual, and decade-to-century) regimes
within which accurate prediction of hydrologic variables relevant to forecasting
floods and droughts is possible.
• Understanding and quantification of fluxes between key hydrologic reservoirs
including evapotranspiration, recharge, and surface water/groundwater
interactions which can enhance prediction accuracy and reliability.
• Methods to effectively apply knowledge from physical climate and hydrologic
models to water resources management strategies.
What do we Propose?
• Identification of the predictable components of the water cycle at daily,
seasonal-to-interannual, and decadal-to-centennial time scales and all spatial
scales, assessing the limits of predictability of less predictable components,
and quantifying prediction uncertainty through a program of monitoring,
process studies and model development.
• Development and implementation of instruments, methods, networks, and
assimilation techniques to estimate the two presently unobserved fluxes that
link surface and subsurface reservoirs over land (recharge/discharge) and
ocean-land-biosphere with the atmosphere (evaporation).
• An interdisciplinary initiative that uses the systems modeling framework that
links climate, hydrologic and socioeconomic systems to break the linear
pathway from observations to theory to predictions to users by integrating
users’ requirements into the design and implementation of observing
systems, model-based prediction and forecast verification.
2.0 Background
Both from the scientific and practical points of view, the question of predictability of the
components of the Earth’s water cycle is central to improving our understanding of
climate variability and change. As discussed in Chapter 2, the variability and
interactions of processes of the water cycle (the world’s oceans and the global
atmosphere, the land surface biosphere and surface and groundwater zones as well as
the fluxes within and among these reservoirs) vary over a wide range of spatial and
temporal scales (see Figure 3.1). Some of these processes interact and manifest
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themselves at long time scales, such as processes in the deep ocean and in
groundwater, while others, such as atmospheric water vapor and surface moisture, are
dominated by relatively fast fluctuations. As noted in Chapter 2, land surface processes
act to link these slow and fast processes. A scientific challenge is to understand the
scales at which interactions and feedbacks of these processes occur and isolate the
slower modes of variability from the faster ones for enhanced predictive ability at a wide
range of scales.
Prediction of the components of the water cycle is among the most difficult problems
facing both science and society. The elements of the physical climate system that are
most difficult to model – water vapor, clouds, rain and snow, and groundwater – are
coupled to the terrestrial biosphere and are precisely the quantities that are of critical
importance to ecosystems and water resources usage by humans. Critical decision
making for ecosystem and water resource management, near-term policy matters and
long term regulation and legislation are all becoming more and more dependent upon
quantitative predictions of water cycle variability and change. Because the water cycle is
intimately related to living organisms, the transport of chemicals and ecosystem
dynamics, the problem of modeling these complex and nonlinear interactions necessarily
involves meteorology, hydrology, climatology, geomorphology, chemistry, ecology,
biology, forestry, water resources management, and socioeconomics.
Water cycle variations naturally fall into three time scales and different processes limit
predictability in these distinct regimes. At the shortest time scale of days to several
weeks, relatively fast fluctuations associated with day-to-day weather are dominant. Box
3.1 provides an example of where improved weather forecasts on this time scale could
improve the mitigation of damage to public infrastructure from a large-scale flood.
Variability at a given location depends upon both the propagation of disturbances from
elsewhere (e.g., an atmospheric front or flood crest) and the development of
disturbances due to instabilities (e.g., thunderstorms). The predictability of such
fluctuations is largely determined by the state of the system just prior to the passage or
development. The extent to which the future state of a dynamical system can be
estimated, based on complete knowledge of the initial state, is a measure of the
system’s predictability. Predictability is an inherent attribute of a system - it is not to be
confused with our current ability to make forecasts which depends upon the degree to
which the initial state can be completely and accurately characterized and the fidelity of
the model being used to make predictions (see Figure 3.2). There is enough evidence to
suggest that atmospheric circulation is a chaotic system and is therefore sensitively
dependent on the initial conditions, i.e. small errors in the initial conditions will amplify as
the prediction evolves, as illustrated in Figure 3.3.
On seasonal-to-interannual time scales, there is evidence that the slow, global
phenomena (such as El Niño and the Southern Oscillation - ENSO) are connected to
regional precipitation and temperature fluctuations on seasonal time scales (e.g., Cayan
et al., 1999 and Masutani and Leetmaa, 1999). In order to predict such phenomena, it is
necessary to have a complete description of the recent evolution of the Earth’s nearsurface climate – upper ocean heat distribution, soil wetness, snow, etc. In other words,
seasonal-to-interannual predictions depend on the nature of the lower boundary
conditions of the atmosphere. Seasonal predictions with multi-seasonal lead times can
provide a means of narrowing the uncertainty in hydrologic predictions that impact water
resources management, natural hazard mitigation, decision-making and policy guidance.
There is evidence that the low frequency predictable components of the water cycle
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variability (e.g. seasonal variations) that are superposed on higher frequency chaotic
fluctuations (e.g. daily variations) can be isolated and predicted (Shukla 1998).
A recent example of how seasonal predictions have had an impact is the successful
prediction of the El Niño event of 1997-1998. In that case, the convergence of
improvements in the global observing system (primarily the deployment of moored buoys
in the tropical Pacific Ocean), the understanding of the coupled ocean-atmosphere
dynamics that are responsible for ENSO and the advancement of global climate models
led several research groups around the world to issue predictions of the tropical Pacific
sea surface temperature. The predictions were used in several sectors throughout the
world for the first time in such a way as to provide a socioeconomic benefit (Box 3.2).
On longer time scales, variations in global and regional climate are associated with
changes in very slowly varying components of the Earth system such as the deep
oceans, glaciers, ice sheets and sea ice, land cover and land use, and atmospheric
composition. These may be manifested in decadal variations such as the North Pacific
Oscillation (Graham 1994 and Mantua et al., 1997) or may appear to be secular trends
such as have recently been observed (Karl et al., 1999). Such variations represent nonstationary behavior with respect to the period for which we have direct observations of
the Earth’s climate, and so the challenge for predicting these fluctuations depends on
our ability to understand and model fundamental processes that affect secular changes
in climate.
Successful modeling of hydrologic processes and the global water cycle on all three time
scales requires a detailed representation of the physical processes that control water
and energy fluxes, and it requires higher spatial resolution by one to two orders of
magnitude than is currently used in dynamical weather forecasting models and climate
change simulation models. Because the predictability of the components of the global
water cycle is typically quantified within the context of models that embody both our
theoretical understanding of the relevant processes and a representation of all available
observations, formulating an effective modeling strategy to embrace the diversity of
temporal and spatial scales is a high priority.
Global and regional climate models encapsulate the fluid dynamic and physical
processes that cause large-scale (100 to 10,000 km scales) fluctuations in the
atmosphere and oceans. They are typically expressed as numerical approximations to
the continuous equations of motion that describe atmospheric and oceanic flows
mathematically. Such models were first devised to predict the weather and later applied
to simulate the general circulation of the atmosphere and oceans that is driven by the
radiative forcing at the top of the atmosphere and the rotation of the Earth. Water is a
critically important component of the general circulation insofar as its vapor phase is the
most active absorber/emitter of radiation, thus warming the Earth’s surface by the socalled greenhouse effect. In the liquid and solid phases, water forms clouds in the
atmosphere and snow and ice at the Earth’s surface that are the principal determinants
of the planetary albedo. Finally, water vapor that is transported by atmospheric winds
and liquid and solid water flows over the surface influence the distribution of vegetation
on the land surface and, by weathering, the slow changes in the nature and structure of
the land surface. General circulation models and regional climate models include
approximations to each of the components of the water cycle with the general
characteristic that, the smaller the scale of the natural variability of the phenomenon, the
cruder the representation of the relevant phenomenon in the model. Climate models
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solve the equations governing fluid dynamics and thermodynamics of the atmosphere
and oceans by stepping the numerical approximations to those equations forward in time
in small increments. Thus, climate model output can in principle include all the state
variables and fluxes relevant to the water cycle with fairly high temporal resolution (steps
of tens of minutes) at the grid resolution of the model.
Hydrologic models are generally formulated on the catchment scale. This spatial scale is
appropriate for water resources management and natural hazard mitigation (see Figure
3.4); however, uncertainties in describing key processes at this scale remain to be
resolved. Much of the hydrologic research in the past three decades has focused on
water movement within the hydrologic reservoirs with minimal attention to interactions
between these reservoirs, such as fresh water flux between the atmosphere and oceans,
groundwater recharge and evapotranspiration between the land and the atmosphere and
stream-groundwater interactions near the land surface. As a resource, water must be
considered in a continuum encompassing all its reservoirs and it needs to be evaluated
and managed with this in mind. A major gap in our knowledge of the water cycle is the
quantification and modeling of fluxes between reservoirs. Some of these fluxes, (e.g.
evaporation and transpiration) provide critical feedback to the climate models. This
knowledge gap results in large uncertainties in predictions.
In the context of water resources, regional sustainability, an important function that is
currently lacking is an effective closure of the loop of activities linking measurement,
understanding (through synthesis and modeling), education/outreach, decision making,
and intervention (Figure 3.5).
Societal interests in the water cycle are focused on issues related to policy for water
allocation, environmental hazard mitigation including floods and droughts, and long-term
variations in water availability. In resource planning, facility design and management,
time horizons extend 30 to 100 years depending on the enterprise. Interstate and
international water conflicts centered around long-term water allocation to the different
entities, as well as river flow maintenance for ecological and conservation needs, are
increasingly coming to the fore. The limitations of the historical records for policy
analyses related to extreme floods are illustrated by the scientific and political debates
as to the adequacy of methods and data for several recent events, e.g., the 1927 and
1993 Mississippi River floods, 1986 and 1997 American River floods, the 1982-87 Great
Salt Lake flood, and the 1992-date Devils Lake flood. Each such event appears to be
dramatic in the context of the local historical record. However, connections of such
basin-scale events to the larger climate picture are now emerging. Both the Devils Lake
and the Great Salt Lake variations have been tied to persistent, hemispheric scale,
atmospheric and oceanic circulation features. The longer (1847-date) Great Salt Lake
record allows for a considerably better characterization of the probabilities of different
climate regimes and their ability to forecast the 1980's extreme, than the 1910-date
record of Devils Lake. For both lakes, anecdotal evidence of 19th century lake levels
and prior sediment records provided qualitative inputs into flood control decisions.
Similar climate connections were established for the American and Mississippi River
floods. Significant advances in constructing paleo-flood bounds for a number of rivers
have also been demonstrated. Consequently, the time is ripe to move from such
demonstrations to a comprehensive synthesis of the joint space-time-flood-climate state
properties, across continental North America.
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Droughts also constitute natural hazards that can have large economic impacts,
particularly on agriculture. In the semiarid southwest, water availability in times of
drought is an important driver of water resources management. Drought
characterization and forecasting across North America is limited by the historical data.
Droughts are often multi-year events that make the short historical data base even more
limited in its usefulness for risk assessment. Examples are the 1930s and 1950s multiyear droughts that devastated the northern and southern Great Plains, respectively.
Considerable progress has been made in recent years in using proxy indices of past
drought, especially those derived from tree rings, for improving estimates of the return
time of such extreme events. This work focused on climatic drought indices, and hence
is not directly relevant to river system management. Yet, with the proper choice of
paleoclimatic records, it is clearly possible to develop useful estimates of important
hydrologic variables such as low-flow and seasonal hydrographs. Streamflow
reconstruction of the Upper Colorado River flow from tree rings has had an impact on
the renegotiation of the Colorado River Compact. As with floods, there is an opportunity
to develop spatially consistent low flow and climate reconstructions for drought using
paleoclimatic and historical data sources that could significantly impact water resources
management strategies. At a minimum, future water availability could be assessed on
the basis of projected future demands calculated from population growth and repetition
of past drought conditions.
Traditionally surface water and groundwater have been managed separately. However,
interactions between these two reservoirs are forcing integrated management of surface
water and groundwater. Increasing groundwater production generally results in reduced
baseflow to streams in areas of effluent streams and may ultimately result in capture of
streamflow, which would have very harmful effects on ecosystems. Surface water and
groundwater should be managed as a single resource to ensure sustainable
development.
The physical models and the processes in place for managing water resources and
ecosystems described above have been developed in each case to answer a particular
question about the physical environment or about the human systems that make use of
or control elements of the environment. The models are quite different from each other in
design and function, and typically, the output from the physical models is used as
information input to the decision-making models. This view that users of scientific
information exist outside the process of creating that information leads to difficulties in
the effective use and exchange of information. By considering the physical models and
the socioeconomic models in a single systems modeling framework, it is possible to
break the linear pathway from models to users as illustrated in Figure 3.6. The systems
modeling framework works effectively on daily, seasonal to interannual and decadal to
centennial time scales. For example, seasonal and longer lead predictions of the tropical
sea surface temperature and its effects on climate variability in other parts of the globe
have been provided to the public. However, different sectors have made use of this
information with varying degrees of effectiveness. By incorporating the needs of users
into the way in which the predictions are made, the information can be made significantly
more useful, even in regimes for which predictability is quite modest (e.g., Barnston et
al., 2000).
Recognizing the wide range of scales at which ocean, atmosphere, land and
groundwater processes interact and provide feedback to each other (e.g., Figure 3.1),
seasonal and interannual predictions at the regional or catchment scale (needed for
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water management decisions) must necessarily take into account larger scale
anomalies. This creates the need to couple global, climate and hydrologic and land
surface models and a conceptual scheme for achieving that is shown in Figure 3.7. In
this scheme, prognostic state variables of the physical processes such as precipitation,
air temperature and surface and groundwater storages, are passed from one scale to
another to estimate the basin response, e.g., the flood hydrograph needed for water
management decision. Global, climate and hydrologic models must also include
uncertainty propagation from the climate models to the hydrologic models and the
additional uncertainty generated from the hydrologic models (due to uncertainty in the
model parameterizations and input data) to the "end users" (e.g. decision makers,
resource users, interest groups, economic models, policy scenario analysts, etc.).
Predictions from the climate-hydrologic models are more useful when accompanied by
uncertainty estimates as these uncertainties provide information for decision-making and
enable informed management of risk. Therefore the information produced by models (as
shown in Figure 3.7) and transferred to users (as shown in Figure 3.5) should not be
limited to the "mean states" but to measures of uncertainty around these states. These
uncertainty estimates are crucial given that vulnerability to climate change varies from
region to region and sector to sector and depends to a large degree on the lead times at
which decisions must be made and the time scales over which decisions have an effect.
Presently, the quantities that numerical weather and climate models predict are not
necessarily the same quantities that are needed by decision-makers managing water
resources or mitigating natural hazards. The resultant information transfer gap
represents a potential barrier to effective use of predictions of water cycle variables and
indicates a need to develop a framework within which model predictions can be related
to the requirements of decision-making in the context of complex interactions among
multiple parties, uses and values.
3.0 Goals
3.1 Goal 1: Demonstrate the degree of predictability of variations in the
water cycle on three distinct time scales: (a) daily, (b) seasonal to
interannual and (c) decadal to centennial for which the limits of
predictability are phenomenologically distinct.
Why: It is necessary to concentrate efforts on the predictable components of the water
cycle because progress in this area will be the most cost effective and rapid. At the
same time, less predictable or inherently unpredictable processes must be understood
and their limits of predictability assessed. Since predictability at the daily, seasonal to
interannual and decadal to centennial time scales is limited by different processes, the
predictability of water cycle components must be assessed for each.
How: This will be accomplished by identifying predictable components of the water cycle
at the daily, seasonal to interannual and decadal to centennial time scales and all spatial
scales, assessing limits of predictability of less predictable components, and quantifying
prediction uncertainty through a program of monitoring, process studies and model
development.
3.2 Goal 2: Improve predictions of water resources by quantifying fluxes
between key hydrologic reservoirs using observations, process
understanding and numerical modeling.
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Why: Quantifying fluxes between hydrologic reservoirs such as recharge and
evapotranspiration in terrestrial systems is critical for predicting water resources.
Climate predictions may also be affected because of feedback caused by
evapotranspiration. Increasing demands on water as a result of population growth make
societies more vulnerable to the effects of potential future droughts and floods requiring
accurate estimates of recharge and discharge fluxes. As individual components of the
system become increasingly stressed (e.g. surface water and groundwater) it will be
imperative to understand and quantify interactions between these components to
effectively manage water resources.
How: Fluxes between reservoirs will be quantified through a comprehensive program
that includes observations, process experiments, and numerical modeling. Advances in
observing systems allow remote, automated monitoring of parameters such as
groundwater levels, water temperature, soil moisture, evapotranspiration, snowpack
storage, and vegetation parameters that can be used to estimate recharge and
evapotranspiration. Ground-based networks provide ground truth for remote sensing as
well as accurate point data; the two approaches complement each other to provide
optimal data coverage. Detailed process studies will allow in depth evaluation of
controls on recharge and evapotranspiration and can be used to develop conceptual and
numerical models of these fluxes. Faster computers and availability of topographic, soil,
vegetation, and snow properties at increasing resolution allow distributed numerical
models to be readily developed that simulate spatial and temporal variability in fluxes.
Such models can be used to test our understanding of these processes and can be
validated with ground based and remote sensing data.
3.3 Goal 3: Establish a systems modeling framework for making
predictions and estimates of uncertainty that are useful for water-resources
management, natural hazard mitigation, decision-making, and policy
guidance.
Why: Our current water management regime, which minimizes further expansion of
surface water reservoirs, reduces our buffer to climate variability and makes us more
reliant on accurate forecasts with long lead-time. In addition, efforts to balance
competing water uses and to provide for effective ecosystem preservation and
restoration are hampered by a limited ability to predict the responses of hydrologic
systems to management actions and climatic fluctuations. Meanwhile, water resources
management decisions have the potential to alter the climate system in ways that can
affect predictability of water cycle components at all three time scales.
How: This will be accomplished through process studies that quantify fluxes between
hydrologic reservoirs, through model improvements and by effective transfer of
knowledge among scientists using climate, hydrologic, socio-economic and ecosystem
management models.
4.0 Program Elements
4.1 Program Element 1: Identify inherent predictability and limits of
prediction (Goal 1)
Fundamental research must be devoted to isolate the potentially predictable
components of water cycle variability at each of the three fundamental time scales. For
daily and seasonal-to-interannual fluctuations, this amounts to separating the low
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frequency or persistent components of the variability in the water cycle from the more
unpredictable high frequency components. Low frequency variations often provide the
forcing for the fastest varying processes and thus are, in a sense, initial conditions for
the dynamics of these processes. Being able to characterize and reduce the size and
structure of errors in the initial conditions (here, in the low frequency variations)
contributes to the ability to assess the limits of predictability of the fastest varying
processes.
Although, in theory, the limits of predictability of a deterministic dynamical system can be
assessed analytically, the water cycle dynamics at all scales are too complex and thus
must be represented using numerical models. As a result, predictability assessment
requires tools to quantify how well a model performs compared to the “truth” (which here
is limited to sparse observations). A plethora of scientific questions arise. These include:
(a) The practical need to discretize the continuum of space-time scales of water
cycle variability when numerically solving the equations of its dynamic evolution.
This is necessary for computational feasibility and one way of achieving it is with
nested modeling. Issues that require serious research in this approach are
discussed below.
(b) Because the atmospheric state can never be measured exactly, no single
solution, but a spectrum of possibilities exist, even with a perfect model. This
gives rise to the need for probabilistic forecasting and to the need to evaluate
predictions within their uncertainty.
(c) The scales of the natural variability of the processes involved, the scales at which
measurements are available and the scales at which models are run are never
the same. These mismatches create the need to develop methodologies within
which processes can be compared (e.g., observed vs. modeled) for verification
or for data assimilation purposes.
These and other related questions are discussed in more detail in the following.
4.1.1 Nested models
The use of nested models to overcome the problem of passing the information from
larger scales down to smaller scales in a computationally efficient manner raises many
concerns. Fundamental problems may result because of inconsistent representation of
processes within each nested domain and at their interfaces. There is evidence that the
resolution of the outer domain considerably influences the predictions in the inner
domain since small changes in the forcing (outputs of the outer domain and initial
conditions for the inner domain) can result in large differences as the system evolves in
time. Efforts are needed to investigate what scales of nesting are more appropriate in
reducing the uncertainty propagation across the spatial scales and optimizing the
exchange of information at the interfaces. One-way vs. two-way nesting, the effects of
model resolution of each nested domain, and the effect of including subgrid-scale
parameterizations implicitly by parameterizations or explicitly by on-line dynamic
downscaling of key variables, are all issues that need to be carefully investigated. Also,
the question of whether a nested model framework vs. a single model of high resolution
within the whole domain (which might soon be computationally feasible) provides more
accurate predictions at all scales, should be explored.
4.1.2 Probabilistic modeling framework
Due to uncertainty in model inputs (measurement errors, natural variability of
atmospheric variables, inadequacy of space-time resolution of these variables) and
model formulation (simplifications, parameterizations, and incomplete understanding of
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all the interactions and nonlinear feedbacks in atmospheric dynamics), the output of any
numerical weather prediction or climate simulation model has to be viewed in a
probabilistic rather than a deterministic framework. This gives rise to the need to
produce ensembles of forecasts that aim to provide reliable estimates of the probability
distribution of the atmospheric state. Ensemble forecasting also provides information of
forecast uncertainty from the dispersion of ensemble members. How this ensemble
would best (i.e., most economically) be generated in view of the chaotic character of the
atmosphere and the errors in the observables needs continuous research at all scales:
global, regional and local. In particular, the need exists to understand how to design
effective initial perturbation fields that can characterize the forecast uncertainty with a
minimum number of ensemble members and as a function of scale, i.e., in global,
regional and storm scales and for short-term and long-term prediction. In addition, there
is a need to explore alternative ensemble strategies, such as perturbation of the model
physical parameterizations and multi-model (as opposed to single model) ensembles.
Effective management and operation of water resource systems requires accurate
forecasts of streamflow over a range of lead times from hours to days (e.g. for flood
protection) to a season or longer for water supply. Improved ability to predict weather, for
lead times up to several days, and climate for lead times of months or longer, should
have great potential for improving the efficiency of water management. Success in
forecasting the 1997-98 El Niño event received widespread attention in the popular
press, and the water resources community, especially in the western U.S., made use of
this information, albeit often in an ad hoc way. Evolving ensemble weather and climate
forecast methods now in routine use in the global forecast community have yet to be
adopted by the water management community, however, at either the weather or climate
time scales. One reason for the slow acceptance of these methods is that their accuracy
has not been well quantified in terms that are usable by water managers. Experimental
studies that have attempted to apply these methods to water management have
generally found that forecasted surface variables (especially precipitation) by both
weather and climate forecast models are biased to the extent that forecast products are
unusable without some kind of post-processing to remove the bias. Although some
progress has been made in this area, much more work needs to be done. On the other
hand, the use of dynamic coupled land-atmosphere-ocean models to produce ensemble
forecasts of reservoir system inflow is attractive because it avoids the inevitable
limitations of record length suffered by traditional methods that “train” forecast models to
historical data. Furthermore, the common assumption that time series of historic
observations (e.g., of streamflow and precipitation) are statistically stationary is not
required by ensemble forecast methods. And, ensemble forecasts are readily updated
as model improvements become available (notwithstanding that model assessment is
complicated by continuing revisions of model physics). Therefore, there is a strong
argument for encouraging the use of ensemble forecast methods in water management
applications. There is, though, a coincident need for understanding the required
characteristics of forecast products for water management applications by the climate
(and weather) modeling communities, and of the nature and limitations of forecast
models by the water management community. Furthermore, although there is a demand
for technology transfer, this is not a one way street, as the nature of forecast products
required for water management applications likely constitute a higher standard than has
previously been experienced by the climate and weather forecast communities. Use of
their forecast products in water management applications may well provide diagnostic
information that will help to improve the forecast models.
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4.1.3 Scaling models and observing systems
Observations of atmospheric, hydrologic and land-surface variables are often available
at more than one scale, e.g., point measurements of precipitation from rain gauges and
areal averages from radar and satellites. Moreover, the scales of these observations are
not typically the same as the scale (resolution) of the numerical prediction models. In
order to be able to validate a model and also to incorporate the knowledge of
observations into its dynamics (using data assimilation), it is necessary to have a
framework by which observations at different scales can be optimally merged to produce
the best conditional estimates of the process and their uncertainty at a scale of interest,
say, the model resolution. Such a framework should explicitly acknowledge and account
for the scale dependency of variability and uncertainty in both observations and model
predictions. Developing such a formalism would also allow questions of sampling
strategies (in terms of sampling frequency or spacing vs. areal extent) to be addressed
in order to reduce prediction errors. Mathematical advancements in multiscale filtering
and conditional simulation, which have been mostly developed for efficient image
processing and transmission, have a lot to offer in this respect (e.g., see Kumar, 1999),
and fundamental research to explore these methodologies and their applicability to the
prediction problem should be fostered.
Hence, this program element will require initiatives that (1) perform basic research on
questions of predictability, (2) decide on effective strategies of model nesting for
improving predictions at all scales, (3) improve model parameterizations and methods
for quantitative assessment of model performance, and (4) improve monitoring systems
of the state variables (e.g. temperature and precipitation) to be incorporated into
innovative data assimilation and initialization methods.
4.2 Program Element 2: Quantify Fluxes between Water Reservoirs (Goal 2)
Improved predictability of the water cycle fluctuations relies on improving our
understanding of the physical processes involved and of their nonlinear interactions at all
scales. Fluxes between hydrologic reservoirs (including evaporation, transpiration,
recharge and surface-groundwater exchanges) are not well understood let alone
parameterized in atmosphere-land-groundwater coupled models.
4.2.1 Basin-scale recharge
Accurate estimates of fluxes between surface and subsurface systems are central to
evaluating water availability for human consumption and for ecosystem maintenance.
These water fluxes are of paramount importance in determining the low frequency
component for many hydrologic systems (e.g. groundwater flow). To evaluate basin
scale recharge, a combination of several approaches that have evolved independently
must be merged. Techniques for estimating recharge include catchment scale water
budgets, inversion of groundwater models, and chemical and isotopic tracers in the
unsaturated and saturated zones. Numerical modeling of surface and subsurface
systems have benefited greatly from faster computers, improved analysis capabilities of
geographical information systems, digital elevation models, and detailed information on
aspect, soil types, vegetation, and land use. These data sources allow distributed
models to be developed of surface and subsurface systems. Advances in inverse
modeling also allow estimation of parameters that cannot readily be measured. In
addition, recent applications of environmental tracers such as chlorofluorocarbons and
tritium/helium allow accurate dating of groundwater systems which can be used to
quantify recharge. Environmental tracers in groundwater generally constitute long-term,
large-scale, in situ tracer experiments that can average many large scale geologic
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heterogeneities. Environmental tracers in the subsurface may also provide estimates of
recharge that average short and long term variations in climate, vegetation, and
geomorphology. Natural tracers are very powerful tools that can provide valuable
information on recharge and discharge processes. Although costs of tracer analysis
may be considered high, sampling of tracers is generally only required for a limited
number of times (once or twice) because of the long times represented by many tracers.
Therefore analysis costs are offset by the limited number of analyses required. Tracer
studies can complement long term monitoring programs and provide valuable
information that can be used to validate numerical models.
4.2.2 Stream-aquifer interaction
Increasing emphasis is being placed on stream-aquifer interaction as water resource
managers begin to realize how development of surface water impacts groundwater and
vice versa. Previous groundwater management strategies have emphasized the
importance of safe yield and restricting groundwater pumping to recharge. Such a
concept ignores the fact that many streams rely on groundwater discharge to maintain
baseflow. With increasing importance being placed on ecosystem maintenance, there
has been a shift toward managing surface water and groundwater as a single resource
and recognition of the importance of maintaining minimum in stream flows and spring
flows for ecosystems. Information on all aspects of stream-aquifer interactions is limited
including observations of interactions between streams and adjacent aquifers and
understanding of flow processes. The disparity in time scales between stream flow
(minutes to hours) and groundwater flow (months to years) has hindered development of
numerical models that include both systems. In addition, systematic methods to directly
measure fluxes between surface water and groundwater have not been undertaken (Box
3.3).
4.2.3 Evaporation
Evaporation from land surfaces substantially influences the energy and precipitation
partitioning and serves as the primary feedback mechanism that links land cover to the
water vapor and energy budgets within large-scale climate models. To understand
basin-scale evaporation and transpiration requires vegetation observations at such a
scale (using remote sensing), models of vegetation that include growth and evolution,
photosynthesis and biochemical assimilation of nutrients, and soil moisture limitations
(using theories of hydraulic limitations within plants, xylem, cavitation etc.). Remote
sensing data should be complemented with point estimates of evapotranspiration
measured with eddy correlation systems or Bowen ratio systems. Recent advances in
these ground-based measuring systems have greatly increased their reliability and
reduced their costs. The ability of catchment-scale models to predict spatial and
temporal variability in recharge depends on accurate partitioning of residual water (after
runoff is calculated) between evapotranspiration and recharge. As water resources
become more and more limited particularly in the semi-arid southwest, there may be
increased emphasis on reducing evapotranspiration and enhancing recharge by
replacing deep rooted non-native vegetation such as creosote and mesquite with
shallow-rooted grasses.
4.2.4 Spatial inhomogeneity
One important difficulty is the pronounced spatial inhomogeneity of most variables
involved, e.g., precipitation, soil moisture, topography, land surface characteristics, and
the need to derive upscaling relationships for modeling purposes. Since the interactions
of these processes are nonlinear, subgrid-scale variability of one variable can
11
significantly affect the grid-average estimate of another variable. Thus, fluxes between
water reservoirs must be studied at a range of space-time scales and the relative roles
of these processes and scales in improving water cycle predictions must be assessed.
4.3 Program Element 3: Transfer information from physical to socioeconomic models (Goal 3)
The hypothetical transfer of information model is shown in Figures 3.5 and 3.7 in which
forcing functions and state variable information cascades from climate models to socioeconomic models involving multiple decision makers using the intermediate basin-scale
hydrologic models. Development of disaggregation strategies for downscaling largescale climate model predictions for input to hydrologic models is required. Precipitation,
temperature, and radiation are generally provided at 100 km2 scale from climate models.
These quantities must then be disaggregrated to provide forcing functions for hydrologic
models as illustrated in Figure 3.7. Uncertainties in these parameters must also be
propagated to the hydrologic models. When hydrologic models are used to forecast
droughts, floods, and other hazards, such predictions are also uncertain because of the
uncertainties in forcings generated by the climate models and inherent uncertainties in
the hydrologic models. Output from hydrologic models will provide probabilistic input to
various decision-makers whose interactions determine the allocation of water use across
various sectors (Figure 3.8). This information can then be used to assess the
vulnerability of various sectors to climate variability. Information from this process may
be valuable to current efforts to improve coordination among independent decisionmaking entities. For example, public agencies and other parties who are engaged in
local watershed management initiatives could use this information to develop alternative
management strategies to cope with climate variability. Quantitative information on
water resources and associated uncertainties is critical for planning and development of
a policy framework for responding to and mitigating extreme hydrologic events. One of
the challenges to successfully implement a climate-hydrologic-economic model
framework is clear communication between the scientific requirements of the water
management decision process and the predictive ability of the hydrologic models.
5.0 Initiatives
5.1: Develop a comprehensive data, modeling and knowledge-transfer
framework at each of the three fundamental time scales that links
observability, predictability, and controllability as well as linking detailed
physical processes with large-scale dynamics.
Efforts to predict components of the water cycle are particularly useful for forecasts of
extremes (e.g., droughts and floods) that may respond to large-scale climate variability
and local atmosphere-land-groundwater coupling differently than near-normal
hydrological conditions. For this reason, this initiative is directed toward improving the
capabilities of physical (numerical) climate, hydrology and water quality models. It is
recognized, however, that improvements in these physical models must be guided by
the requirements of the end users, so the modeling framework must include a
knowledge transfer element.
5.1.1 Determine optimal modeling strategies to cope with issues of multiple sources of
uncertainty and multiple scales of natural variability.
Uncertainty in predictions of variations in the water cycle is introduced both from the
inherently chaotic character of the atmosphere and from a number of elements involved
in the process of making predictions, including undersampling, short record length,
12
measurement error, analysis error, parameter value identification, and systematic errors
in numerical models. These uncertainties arise at different spatial scales and at different
lead times in a given prediction. A fundamental question in seasonal and longer lead
prediction of water fluxes involves the choice of a modeling strategy that best minimizes
the propagation of uncertainty among components of a predictive model. For example,
predictability of regional hydrologic systems is limited by the predictability of large-scale
climate fluctuations in situations in which the large-scale climate forces regional
hydrology. If there is two-way land-atmosphere coupling or modulation of climate by
local hydrologic processes, then there is potentially greater predictability that can be
exploited through coupled modeling.
Given the scales of relevance in prediction of floods and droughts - global, continental,
regional, and catchment scales - there are two methodologies that may be employed in
modeling the relevant processes: nested models and high resolution global models. The
nested model approach that is most often employed predicts each phenomenon with the
model most relevant to that scale and uses output of larger scale models to drive smaller
scale models. For example, the large-scale atmospheric seasonal mean atmospheric
circulation that is in balance with the globally distributed sea surface temperature
anomalies is most appropriately modeled with a global atmospheric general circulation
model. A regional model for a particular region where seasonal predictability is high can
be nested in the global model to obtain predictions of the regional details of the
distribution of precipitation and temperature anomalies that are consistent with the largescale moisture and energy fluxes in the global model. Nested regional climate models
can provide a means of bridging the spatial scales of atmospheric, land-surface and
subsurface processes. A great deal of weather and climate simulation research has
been done recently with nested regional models, and several operational weather
forecasting products are generated using regional models. Further nesting of surface
and subsurface hydrology models to simulate the runoff and streamflow that results from
the precipitation predicted by the regional model is needed in many catchments where
the basin response is sensitive to small-scale inhomogeneities. While the suitability of
the nested model approach has been demonstrated, it has well known limitations, such
as the fact that the models are not formulated to obey integral constraints associated
with mass and energy conservation laws, which can limit their accuracy for climate
simulation (see also discussion under 3.1.1). Such limitations could, in principle, be
removed by using a high-resolution global model strategy. The initiative being proposed
herein is a systematic test of the two approaches for optimal prediction of seasonal
water fluxes.
Integrated system models typically link atmospheric, land-surface, stream, and
groundwater component models into a single modeling framework. These models are
inherently nonlinear, because they include feedbacks among components that are
themselves nonlinear. Further, these component systems exhibit variability at many time
and space scales. The challenge to link global coupled ocean-atmosphere models to
integrated system models that can produce predictions and associated estimates of
uncertainty that are suitable for guiding decision-making in water resources
management requires high spatial and temporal resolution that places huge demands on
computational resources and observational data for initializing and verifying the models.
A systematic approach to model design and development is therefore needed that will
permit us to determine, based on the outcome of basic research in this area, the scales
at which predictive information should be exchanged within a nested modeling approach
13
or if a global integrated system model is required. The research must answer questions
such as what is the appropriate way to nest models (e.g., one-way coupling, two-way
coupling with “fuzzy” matching at the boundaries, perturbation expansion, adaptive mesh
techniques, etc.) and what is the appropriate way to develop ensembles of model
integrations to obtain robust measures of forecast uncertainty (e.g., ensembling over
possible initial states, over model parameter estimates, etc.).
This research in modeling strategy will be heavily computational in nature and will
require enhancements to the available computing capabilities of the nation. In particular,
a virtual distributed modeling software engineering infrastructure must be developed to
facilitate model comparison, and interchange of modeling components and data sets
among models, and adequate computing facilities distributed to the participating
modeling centers must be made available.
Additionally, the expanded analysis capabilities of geographical information systems and
the availability on the web of information on input parameters - such as elevation,
vegetation type, soil type, and land use land cover, river reaches, and hydrologic unit
boundaries - at finer spatial and temporal scales, are opening new possibilities for
developing distributed hydrologic models (Figure 3.9). Improvements in visualization
enhance our ability to understand hydrologic systems and allow researchers to transfer
model results more readily to the user community.
5.1.2 Determine the parameter, accuracy, and sampling requirements through observing
system simulation experiments at all spatial scales.
A powerful method for determining the gaps in our measurements that can most
significantly improve our understanding of predictability is the so-called observing
system simulation (OSS) methodology. In an OSS experiment, a model that faithfully
represents some natural process is used to generate a long time series of states that
serve as a proxy for Nature. The model can then be “sampled” in the same way as a
prospective observing system would sample Nature in order to determine if the sampling
strategy is adequate to capture the relevant modes of variability in the natural process.
The OSS methodology provides information of relevance to predictability studies
because it involves the use of a predictive model, a set of measurements that are either
already operational or are prospective, and a data assimilation system that optimally
combines observations and model output.
Systematically combining observational data and models will require a systematic
expansion of four dimensional data assimilation research and observing system
simulation experiments. At present, assimilation of atmospheric observations has been
advanced to some degree, but assimilation of land surface observations has only
recently begun emerging. Assimilation of observations in hydrologic (surface and
groundwater) models has yet to be attempted in a systematic way. Therefore, to
adequately assess the impact of new observing systems on our understanding of
predictability and our ability to predict at seasonal and longer leads, it will be necessary
to develop an OSS methodology to enable effective combination of hydrology models
and observations. This approach can then be used to make estimates of the appropriate
scales at which to couple or nest component models. It is also important to consider the
impact of new observing systems on the ability of predictive models to make more
accurate forecasts.
14
In addition to OSS experiments for providing direction on future observing systems,
companion observing system experiments (OS) are needed to assess prediction
impacts, sampling and deployment strategy, and improved utilization of already existing
observing systems. Both OSS and OS strategies have been formally defined by the
North American Observing System Program (NAOS), established in 1995 to develop
scientifically based procedures, centered around four dimensional data assimilation, to
determine optimal atmospheric observing system strategies, including impacts on
prediction model accuracy.
Methods for optimally merging data from heterogeneous observing systems, e.g.,
weather radar, satellite and surface and upper air stations, will be required for these
observing system experiments to be possible. This initiative is described in Chapter 2
and also in Section 3.1.3 of this chapter.
5.1.3 Make fundamental advancements in the mathematical theories of predictability as
derived from principles in probability, stochastic processes, nonlinear dynamics, and
numerical methods.
The mathematical theories are now being developed in many other physical sciences
but their emergence and systematic application to the components of the water cycle
have been limited. What is needed is a new program in the science and mathematics of
water cycle predictability that will guide the applications of atmospheric and hydrologic
theories over a broad range of space and time scales. It is envisaged that this program
will forge collaboration and links between mathematicians and statisticians with
hydrologists and climate modelers to guide and advance the science of predictability in
hydrology. Such a program can take advantage of the proposed long-term monitoring
network (described below) as well as the planned remote sensing measurements and
climate model output for wealth of measurements and model results on hydrologic
processes.
Until recently, climate models and hydrologic models were used for deterministic
predictions. This means that initial conditions and boundary conditions were applied to a
single realization of the weather or climate system, and a single value was predicted. It
is well known, however, that deterministic predictions of the instantaneous state of the
atmosphere, for example, cannot be made with lead times longer than a few days due to
chaotic fluctuations (Figure 3.3). Therefore, climate predictions on seasonal and longer
time scales must be made within a probabilistic framework that takes into account the
uncertainty of the initial and boundary conditions as well as the inherent characteristics
of the distribution of possible states that may ensue from the given initial state. This is
currently done by making an ensemble of model predictions each with slightly different
initial or boundary conditions that are indistinguishable from the actual state of the
environment given the available measurements. Research is required to place this ad
hoc methodology on firmer theoretical basis.
5.1.4 Promote knowledge transfer between scientists and end users.
Earth system models may be able to predict variations in the global water cycle with
some skill. For these predictions to be useful to, say, water resources managers,
however, they must be put into the right form. Coarse resolution predictions, for
example, may need to be converted to the local watershed scale using empirical
disaggregation techniques or through the additional application of a higher resolution
nested model. Uncertainties associated with the model predictions must be provided to
the end users in an understandable and quantitatively useful way. The key to achieving
15
this knowledge transfer, of course, is communication and coordination between the
scientists and the societal end users early in the development of prediction systems.
Modelers should design their end-to-end systems with the users in mind. Users, in turn,
must be fully versed in the limitations of the prediction systems and in the proper
interpretation of the model-generated data.
Toward this end, it will be necessary to develop a prediction system framework that
identifies and quantifies the characteristics (parameter, spatial and temporal resolution,
lead time and accuracy) of what can be observed, what can be predicted, and what can
be controlled or managed. This implies that users of water cycle prediction information
define what aspects of water resources and ecosystems can be managed and establish
the requirements for predictive information. The modelers must use this information in
the design and implementation of predictability experiments and operational prediction
systems. The two processes of information exchange must be iterated as new
developments in observing systems, predictive models and water resources and
ecosystems management experience are achieved. The effective transfer of knowledge
gained through this process must be enabled and optimized.
5.2 Initiate process research for the three time scales to characterize
heterogeneity, guide the development of models, reduce knowledge
uncertainty and enhance predictions of state variables.
Large-scale and basin-scale experiments are needed that help provide an effective
means for improving models in terms of representing the relevant processes, estimating
model parameters, and validating model simulations and predictions, and that evaluate
fluxes between hydrologic reservoirs such as evapotranspiration, recharge and surface
water groundwater interactions in watersheds with different types of land cover or major
anthropogenic effects. The relevant processes whose representation must be greatly
improved in the large-scale models include cloud dynamics and the microphysics of
precipitation, the interaction of cloud liquid water, water vapor and radiation, the
transport of water vapor in the planetary boundary layer, and the
aggregation/disaggregation of surface hydrologic processes over complex landscapes.
In the land surface models, there are several processes that are poorly represented. The
issues of model parameter estimation and model validation require the integration of
observations, process-resolving models and large-scale (parameterized) models through
data assimilation and adjoint techniques.
The biospheric responses are inadequately represented for both short term and longterm processes. For example, the response of vegetation in different biomes around the
world to increasing concentrations of CO2 in the atmosphere is poorly known as only a
limited number of plant species have been studied. Further, the geographic distribution
of different biomes is strongly controlled by climate, especially the precipitation and
temperature regimes, and the manner in which the boundaries between major biomes
may change is unknown and certainly has not been quantitatively represented in
models. The potential for such long-term change is apparent in a comparison of
representative climate diagrams for temperate grasslands, temperate forests, and boreal
forests. The southern boundaries of boreal forests are either temperate grasslands or
temperate forests, and warmer conditions, which shorten the winter period and promote
forest fires, could allow northward expansion of temperate grasslands and forests.
These transitions in biome distribution may limit the predictability of biospheric
responses to atmospheric processes over longer time scales, and should be considered
16
as observations are integrated with process-resolving models and large-scale
parameterized models.
The experiments for evaluating fluxes should be designed to develop techniques for
quantifying fluxes between reservoirs at the catchment scale. Such fluxes include
evapotranspiration and recharge. The experiments should incorporate both remote
sensing and in situ measurements. A major effort should be made to downscale remote
sensing data and to upscale point measurements of fluxes to a common scale where
process comparison can be made. Water balance modeling may be used to provide
initial estimates of evapotranspiration, recharge, and surface water groundwater
interactions. Remote sensing information on vegetation dynamics can be used as input
to these models. Environmental tracers can provide integrated measures of recharge
over large spatial and temporal scales and may also provide valuable insights into flow
mechanisms. Existing numerical models that incorporate surface water groundwater
interactions are extremely limited and new models need to be developed based on
quantitative understanding of the interactions between these reservoirs from detailed
field experiments.
5.3 Develop and implement instruments, methods, networks, and
assimilation techniques to estimate the two presently unobserved fluxes
that link surface and subsurface reservoirs over land (discharge/recharge)
and ocean-land-biosphere with the atmosphere (evaporation).
It is critical to develop a monitoring network that can provide initial states for model
predictions, verifications of model forecasts and validations of model components.
Advances in measurement techniques now allow land surface properties (e.g.,
topography, vegetation type and state, and snow depth and moisture content) to be
determined at significantly finer scales than was possible only a few years ago.
Exploiting these advances to verify and validate predictive models as well as to
determine the scales at which measurements are required to realistically and accurately
simulate hydrologic variability is a high priority. In addition to the development of new
monitoring sites, the proposed monitoring program should take full advantage of existing
sites such as the NSF Long-Term Ecological Research sites, USDA experimental
watersheds, USGS Water, Energy and Biogeochemical Budget research watersheds,
and the AmeriFlux network, by augmenting with an intensive array of soil moisture
sensors, precipitation and interception gages, a network of ground water monitoring
wells, and frequent remote sensing measurements, particularly using NASA's new
canopy lidar. The addition of soil moisture instruments to the existing eddy-covariance
instruments will permit quantification of soil moisture content controls on transpiration
models and its feedback to climate models. Long-term experimental sites for
characterizing the water balance and flow pathways provide the essential data for
developing models and methods to scale hydrologic variables, characterize basin-scale
variability and understand the limits of predictability. Elements of this monitoring program
include several components.
5.3.1 Precipitation
Precipitation over the entire globe at sufficiently high resolution to capture its diurnal
variability and spatial inhomogeneities will improve our understanding of water cycle
exchanges and improve predictions at all scales. The Global Precipitation Mission,
which, with its currently planned specifications, promises to provide three-hourly, global
four-kilometer precipitation coverage, could be the cornerstone of the needed effort. .
17
Accurate snow-cover mapping by an integration of satellite and ground-based networks
is also a high priority.
5.3.2 Ocean fluxes
A global ocean surface flux monitoring program that will provide, for the first time, an
estimate of the fresh water flux from atmosphere to ocean (precipitation) and from the
oceans to the atmosphere (evaporation) is needed. This component of the global water
cycle remains a major source of uncertainty in our ability to characterize the fluxes of
water between reservoirs and to predict fluctuations in these fluxes at seasonal time
scales. Changes in the Earth’s two major ice sheets represent the largest source of
water for interannual-to-millennium-scale changes in ocean circulation. Accurate
measurement programs to estimate both interannual variability and long-term changes in
their mass are needed.
5.3.3 Fluxes among reservoirs
Monitoring hydrologic components that permit quantification of fluxes among
atmospheric, surface and subsurface reservoirs is needed. Monitoring efforts should
include a spectrum of land cover variability, with time ranging from stable to rapidly
changing conditions using in situ instrumentation and remote sensing. Soil moisture is
clearly a parameter of great importance in characterizing the variations in the water
cycle. Monitoring parameters should include surface water fluxes (stream gauges), water
content, pressure, and temperature in the unsaturated zone, and water levels and
temperatures in the saturated zone. Geophysical measurements using electromagnetic
induction or ground-penetrating radar should be used to interpolate and extrapolate
information between monitoring locations at a site. Data on environmental tracers such
as chloride, tritium, tritium/helium and chlorofluorocarbons should be measured in the
unsaturated or saturated zones to date the water for recharge estimation and to evaluate
flow mechanisms (piston vs. preferential flow). A national network of groundwater
monitoring wells for both water level and water chemistry is essential for groundwater
recharge characterization and for the identification of long-term trends due to pumping,
drought, and land-use change.
5.4 Establish an interdisciplinary forum that uses systems modeling
frameworks to break the linear pathway from observations to theory to
predictions to users by integrating users’ requirements into the design and
implementation of observing systems, model-based prediction and
forecast validation.
A current barrier to effectively integrating the output from large-scale climate models in
hydrology and water resource management is the lack of active collaboration between
scientists in these various disciplines and stakeholders. An intellectual forum and
creative funding arrangements are needed that would provide opportunities for crossdisciplinary collaboration on important water resources and environmental management
issues that are sensitive to climate variability. One essential task of this intellectual
forum would be to identify basins that are particularly vulnerable to climate variability and
to develop climate and hydrologic data bases including environmental and
socioeconomic variables, that would be useful in assessing vulnerability and in making
decisions. In order to understand how uncertainties propagate through these systems
we need to link uncertainties in climate variability along with uncertainties in human
impacts and evaluate the feedback between these elements. Output from these types of
analyses can be used to evaluate policy options and assess their implications for
18
mitigating the potential adverse impacts from climate variability and anthropogenic
disturbances. Basins in which local watershed forums have been established may
provide a useful focus for such efforts because these communities may be both
vulnerable to climate variability and especially receptive to the use of improved forecast
information.
Practical useof a particular forecast is a more involved problem than simply assessing
the quality or accuracy of the forecast. The forecast and its uncertainty must be
integrated with site and case-specific information (e.g., flood stage vs. damage curves,
shear stress vs. aircraft safety curves, etc.) to compute a “risk of failure” which can guide
decisionmaking. Methods for translating an uncertain forecast (and by forecast is meant
a suite of multidimensional variables) to products useful for decision-making are in their
infancy and much research is needed to advance them. It is emphasized that this area
has to be developed in parallel with the efforts of improving forecast quality, as even
forecasts with large uncertainty can provide useful guidance for water management
decisions.
Another current barrier to effective use of hydro-meteorological information in water
resources decision making is the difficulty of achieving coordinated, rational water use
and management decisions in the presence of divided authority. In most basins, a large
number of individuals and public agencies, at different levels of government, exercise
independent authority over some aspect of the water resource. The resulting myriad
lines of communication, overlapping jurisdictional boundaries, differing legal constraints,
and multiple decisions being made or influenced by parties with strongly divergent
interests can result in sub-optimal resource allocation and ineffective overall risk
management. Efforts to overcome such problems are have gathered momentum over
the past decade, for example, with the recent proliferation of local watershed
management initiatives. These efforts, which are aimed at finding pragmatic solutions
to local problems, are likely to be able to benefit from improved access to information on
the implications of water cycle variability for local resource management concerns.
5.5 Knowledge transfer.
As described in section 4.1.2, the rationale for introducing ensemble forecast methods to
the water resources management community is a strong one. The use of ensemble
forecasts of either weather or climate variations in the components of the water cycle,
despite its clear advantages, has not penetrated the water resources management
community. The applications initiative should be designed to assist water managers in
using ensemble forecast products for operation of water resource systems. The primary
focus should be on reservoir systems (or, in some cases, free-flowing rivers), but the
implications for ground water in systems that conjunctively use surface and groundwater
should be recognized. The necessary technology transfer might be accomplished
through a cooperative applications program parallel to that proposed in Chapter 2 for
development of improved design protocols that incorporate climate information. As in the
cooperative applications program called for in Chapter 2, the applications initiative would
be funded and managed by one or more of the science agencies. This agency would
solicit proposals for demonstration applications of ensemble forecast methods to water
management problems. These solicitations would include a mechanism for funding of
dedicated personnel who would work in an operational setting, but would also require
evidence of participation from a credible science-based organization. This might be
accomplished by a fellowship system that assigned personnel on a part-time rotating
basis to a government research laboratory or university, and part-time to a government
19
agency or university. Applications should go well beyond the water resources
management community, and embrace other regional economic development and
resource-management decisions where climate and hydrologic information can play an
important role.
20
Figure 3.1: Schematic of selected atmospheric, surface and subsurface hydrologic
processes and their temporal and spatial scales of occurrence. [Modified from a figure
in Bloschl and Sivalapan, 1995.]
Figure 3.2: Relative contributions of observations and models in resolving the structure
of an atmospheric or hydrologic phenomenon as a function of the forecast lead time.
The upper dashed curve represents the (unknown) theoretical limit to predictability, while
the three lower curves depict hypothetical contributions of observations and models to
the total predictability of the system. At the initial time (t=0), observations provide most
of the information on the phenomenon, aided by models that may contribute some
information by either providing a first-guess field from a previous forecast or by
assimilating limited observations to provide a consistent analysis of the variables of
interest. In the early part of the forecast period, the observations dominate the models in
providing information on the phenomenon; however, this contribution decays rapidly.
With time, the information provided by the models may actually increase as dynamic
adjustments occur and the appropriate balance is achieved.
(Diagram adopted from Anthes, 1984, American Inst. of Physics).
100
Theoretical Limit
to Predictability
80
% Accuracy
Total
60
40
Models
Observations
20
0
Lead Time
1
Figure 3.3: Example of error growth in a chaotic dynamical system.
A series of weather maps showing the distribution of geopotential height (analogous to
pressure) at 500 hPa (a mid-tropospheric level in the atmosphere) can be used to
illustrate the growth of errors in a chaotic system. The panels on the left represent the
initial state (top) and subsequent forecasts at three and six days lead time made using
the National Weather Service Medium Range Forecast model. The panels on the right
are identical to the panels on the left except that a small perturbation was added to the
initial state before the forecast model was run. The initial states are nearly
indistinguishable in this presentation. After three days, the forecasts have diverged
somewhat, but the weather pattern over the continental U.S. is quite similar. By day six,
the forecasts have diverged to a degree that quite different weather forecasts would be
issued based on the model guidance from one or the other of these two runs. The two
forecasts illustrate both the growth rate and the amplitude to which small errors can
grow in a chaotic fluid such as the Earth’s atmosphere.
2
3
Figure 3.4: Schematic of the temporal scales of water cycle variations and associated
water resource problems impacted by these variations.
We distinguish three major scales: (1) weather scale (a few hours to a few days), (2)
seasonal to interannual scale (several days to several years), and (3) climate scale
(decadal to longer time scale). Different processes are dominant in each of these
scales, but the same systems modeling framework can be used to quantify predictive
power of models and the practical value of predictions. The systems modeling
framework is presented in Figure 3.6.
Real Time
Control
Design and
Long-term Planning
Management
Water Use
Irrigation and
Water Supply
Reservoirs
Hydropower
Optimization
Land Use and Climate Change
Environmental Impact Assessment
Flood Protection
Urban
Drainage
Culverts
Detention
Basins
Minor Dams
Flood
Warning
1 hr
Major Dams
1d
1 mo
Seasonal to
Interannual
Scale
Weather
Scale
4
1 yr
100 yrs
Climate
Scale
Figure 3.5: Decision making for water resources and ecosystems management is a
complex, multi-faceted process involving many stakeholders.
The process of making decisions in managing water resources and ecosystems is a
complex one that involves the economics of supply and demand, the regulatory actions
of government authorities at various levels, and the litigation of disputes among
stakeholders. This process is iterative and dynamic – multiple contractual, legislative and
judicial actions change the priorities and rights of the individuals and groups involved as
well as the water resources and ecosystems themselves. The historical record of water
cycle variations and predictions of future water cycle changes can be used in this
process to provide quantitative information about the physical system. Some information
is provided in a probabilistic format, so it is necessary for the various stakeholders to be
able to make effective use of such information. Finally, it is important to note that the
decisions that are made result in actions whose consequences – both intended and
unintended – result in changes to the water cycle in both direct and indirect ways.
Model-based
Suppliers &
Utilities
Extreme Events
Droughts, Floods
Water Availability,
Quality, Temperature
Evaluation of Risks,
Impacts on Various
Interests
Water Rights
Owners
Organized User
Groups
Sediment Transport
Probabilistic
Forecasts
Supply &
Demand
Water and Ecosystem
Management Decisions
Federal Laws
Current State
Historical
Record
Water ResourceMgt. &
Hazard Mitigation
Actions&
Consequences
Public Agencies
State Laws
Regulation&
Remediation
Observations
5
Courts
Figure 3.6: Systems modeling framework for prediction and transfer of information to
end users.
Rather than viewing users of scientific information and environmental predictions as
outside the process, a systems modeling framework can explicitly provide for feedback
by stating societal needs in terms already identified by users through national and
regional assessments and by including a social science perspective on the value of
information. In this way, the value of information that can be extracted from predictions
of water cycle variations can be quantitatively evaluated. In the systems modeling
framework, all elements of the system, including the methods for observing and
monitoring the natural environment, the models used to make predictions, and the risks
and values associated with the predictive information can all be evaluated within a
common framework. Notice that this systems modeling framework is generic and
applies to all three time scales: daily, seasonal-to-interannual and decadal-to-centennial.
Typically, the users require probabilistic predictions for risk-based decision making. For
that, the model must run in an ensemble mode where the initial conditions or model
physics are perturbed to produce a suite of predictions and characterize their
uncertainty.
Initial
Conditions
Model
Prediction
Users
Boundary
Conditions
Monitoring
Verification
Assimilation
6
Figure 3.7: Nesting climate and hydrologic models.
A coupled ocean-atmosphere model is used to compute the global sea temperature,
which is transferred as input, along with the land-surface state, to a global atmospheric
model. The global model output drives a large-scale atmospheric circulation, whose
output serves as an input to a regional atmospheric dynamics model. This regional
model computes surface temperature, precipitation, and other radiation components
which, when spatially disaggregated, and upon specifying the land state again at a basin
resolution, provide the input for a basin-scale hydrologic model. The hydrologic model
predictions along with risk statistics can provide input to a complex decision making
process.
Coupled
OceanAtmos.
Processes
Global
SST
Global
Atmos.
Dynamics
Model
Largescale
Atmos.
Circulation
Regional
Atmos.
Dynamics
Model
Regional
Surface
Temp. &
Precipitation
Coupled
Hydrology &
Ecosystem
Models
Land
Surface
State
Floods and
Droughts
Forecast
Sediment
Transport
Water
Quality &
Temp.
7
Figure 3.8: Schematic of how a probabilistic model forecast can be used for risk-based
decision-making.
Depending on the user-specified and application-dependent tolerance level and the
sensitivity of decisions to that level and prediction uncertainty, valuable feedback might
result for: (a) model verification (i.e., no need to demand from the model more than what
the user can make use of), (b) model improvements (i.e., when the model predictions
are not reliable enough to support a decision), and (c) observational requirements (i.e.,
collection or assimilation of more observations to improve the reliability of predictions).
Model
P1
P2
P3
.
.
.
.
.
.
PN
f(P)
T
Pcr
Compare T
with Tcr
Take
appropriate
Action
P
P1, P2 … PN = predicted variables of interest; for example precipitation amount over a basin, flood stage
at a point, ecosystem stress, etc.
Pcr = user specified “critical value” of P which if exceeded, an action or decision must be taken.
T = chance of critical value being exceeded.
Tcr = user specified tolerance level (might involve societal, monetary or environmental considerations).
Action = depends on tolerance level Tcr and sensitivity of decision to uncertainty in the prediction.
8
Figure 3.9: Example of available data that can be used to aid in the development of
distributed hydrologic models.
A wide variety of information is readily available from the web for distributed hydrologic
modeling such as vegetation type (US Forest Service), soil type from the US Department
of Agriculture, and land use/land cover (US Geological Survey).
9
Box 3.1: In late April and early May of 1997, the Red River of the North
experienced a catastrophic flood, far exceeding for that river the century's previous
record flood in 1950. The Red River flows north through Fargo and Grand Forks,
North Dakota, and then on into Manitoba, Canada. Flood damages in the U.S. part
of the river basin totaled four billion dollars.
In 1997 the water table was still high from the heavy precipitation during the previous
year and the soil throughout the region was frozen, minimizing infiltration of new
precipitation and snow melt. During the period of March through April, a series of heavy
blizzards moved through the region resulting in deep snowpack. The subsequent warm
thaw was rapid and augmented by additional precipitation. The flood crest in Grand
Forks in late April was several feet above the forecast peak flood stage (and four feet
higher than the 1950 flood). Severe damage resulted in Grand Forks partly because
insufficient forecast quality and lead time failed to spur adequate reinforcement of flood
control structures such as dikes. Post flood assessments have indicated that improved
quality and application of 1-4 week forecasts of precipitation, temperature, snowpack
depth, and snowmelt volume could have significantly reduced prediction errors of peak
flood stage. (International Red River Basin Task Force: Final Report of the International
Joint Commission, April 2000.)
Box 3.2: The importance of predicting the effects of El Nino on North America – the
example of the 1997-98 ENSO event.
Among the largest El Nino events of this century, the winter of 1997-98 witnessed nearly
unprecedented rainfall in several parts of the southwestern and southeastern U.S. that were
attributed directly to the effects of the extremely warm sea surface temperature in the tropical
Pacific. Unlike other such events, however, the 1997-98 event was relatively well predicted, both
in terms of the SST anomaly in the Pacific and in terms of its remote effects, especially in the
U.S. The figure shows the precipitation prediction for January through March 1998 made by the
U.S. Climate Prediction Center (top) three months in advance of the winter season as well as the
observed precipitation and the historical expectation based solely on the presence of El Nino
conditions in the tropical Pacific. As is evident from the figure, the CPC forecast was based on
the expectation that El Nino would have a major effect on the winter precipitation, and the
prediction was quite accurate for many regions of the country. Individuals and organizations in
climate-sensitive situations across the country made use of the forecast information, and took
steps to mitigate the potential costs of the effects of El Nino, thereby substantially reducing the
actual costs realized.
Box 3.3: Example of resource management decision constrained by inadequate
understanding of fluxes between surface and groundwater systems
California’s San Joaquin Valley exemplifies the tensions that now exist between irrigated
agriculture and a growing social interest in ecosystem preservation and restoration. In the
summer of 1999, after ten years of litigation by environmental groups, the Bureau of
Reclamation and the Valley’s agricultural community agreed to release 35,000 acre-feet
of water from Friant Dam. The release was part of an experiment to test the feasibility of
restoring a frequently dry part of the San Joaquin River between the reservoir and
Mendota Pool. Many observers predicted that a large fraction of the released water would
quickly be lost to infiltration as it passed over the normally dry section of the river bed.
However, most of it flowed through that section directly to Mendota Pool, where it
became available for use by irrigators in the lower San Joaquin Valley. The success of the
$2.5 million experiment was a surprise. Was it simply a fortuitous result of high
groundwater levels, resulting from a sequence of years with normal to above-normal
precipitation, or can such success be sustained under California’s frequent excursions into
drought conditions? Without a better understanding of the fluxes of water between the
Valley’s surface and groundwater systems, we cannot reliably answer that question.
CHAPTER 4 -- HOW WILL VARIABILITY AND CHANGES
IN THE CYCLING OF WATER THROUGH TERRESTRIAL
AND FRESHWATER ECOSYSTEMS BE LINKED TO
VARIABILITY AND CHANGES IN CYCLING OF CARBON,
NITROGEN AND OTHER NUTRIENTS AT REGIONAL
AND GLOBAL SCALES?
1.0 SYNOPSIS
Societal Need.
Changes in fluxes of carbon (C) and nitrogen (N) are connected to changes in
the water cycle, land use patterns, agricultural practices, urban development, and
vegetation. The changes in C-N cycles in turn induce further changes in the
water cycle itself, which can lead to adverse impacts on terrestrial and aquatic
resources. Carbon dioxide levels in the atmosphere affect water use efficiency of
plants through stomatal response and total leaf area, thereby altering local and
regional water cycles. Atmospheric nitrogen deposition can have a fertilizer
effect, inducing changes in both the water and carbon cycles. Emissions of
nitrous oxide, a strong greenhouse gas, have increased greatly due to
anthropogenic alterations of the nitrogen cycle. Increased worldwide use of
nitrogen fertilizer has also directly affected surface-water and groundwater
chemistry. Eutrophication in coastal waters is strongly linked to delivery of
nitrogen from the continents in major river systems. Hydrologic extremes, be they
floods or droughts, have significant impacts on water quality (sediment and
salinity as well as carbon and nitrogen). Anticipating or avoiding these changes
and impacts requires a fundamental understanding of the linkages between the
C-N-water cycles in terrestrial and inland aquatic systems. In addition, it is
important to understand the factors driving human activities that impact
vegetation distribution and water quality and to evaluate the sensitivity of these
activities to a range of policy alternatives. Quantitative models that capture these
linkages and feedbacks are essential to improve our ability to evaluate potential
changes in ecosystems, to predict climate variations, and to manage water
resources effectively.
What is missing scientifically?
• Observations of C and N reservoirs, fluxes, and all other parameters
relevant to their coupling to water fluxes at representative spatial and
temporal scales.
• Observations of water use and of institutional controls on water availability
and use
• Quantitative understanding of the linkages between changes in land use
and changes in water and nutrient cycling.
• Adequate models of transport and transformation of C and N from the land
surface to the atmosphere, to river networks, and to coastal oceans,
1
•
•
especially as linked to the water cycle
Fully coupled biosphere-climate models that resolve all important
feedbacks over a broad spectrum of time scales, ranging from hours to
decades and longer.
Coupled models of water demand and use, agricultural practices, land
use, and water quantity and quality
What do we propose?
• Integrated remote and ground-based observation programs, where the
observations are conducted at a hierarchy of spatial and temporal scales
and recorded in a sustainable data archive and retrieval system.
• Field studies to establish quantitative description of processes relevant to
the coupled C-N-water cycling
• Merger of observations and models to understand and quantify slower
feedback mechanisms of vegetation structural dynamics on the coupled
C-N-water cycling.
• Knowledge-transfer program for collaboration and communication among
researchers, decision-makers and stakeholders.
2.0 Background
Water, carbon, and nitrogen cycles all are critical for humans and ecosystems and have
strong links to climate. The cycles have been perturbed by human activity throughout
human history, in ways that have accelerated in the past five decades or so with
concomitant significant regional and global changes. There are important feedbacks
among the water-C-N cycles. These feedbacks need to be understood within the context
of the proposed water cycle initiative in order 1) to address the questions of causes of
variability and predictability of the water cycle and 2) to anticipate or avoid adverse
impacts of climate variation on water resources and aquatic and terrestrial ecosystems.
Human activities have had profound effects on hydrologic processes (e.g., Potter 1991)
and nitrogen cycling (e.g., Galloway et al. 1995) in terrestrial and freshwater aquatic
ecosystems. Wetlands have been drained for flood control and to allow for agricultural
development. Estimates of wetland areas altered in the U.S. alone are very large, with
the loss of over 50% of the Everglades in the last century being a prominent example.
Agricultural practices have markedly altered the availability of nitrogen for transport to
aquatic ecosystems. For example, annual applications of nitrogen fertilizers on U.S.
farmlands increased approximately 5 fold between 1960 and 1980 (Alexander and
Smith, 1990), and more than 233 million U.S. acres were treated with nitrogen fertilizer
in 1997 (USDA, 1997). The construction of dams on major rivers throughout the world
has affected flow regimes and has also changed fluxes of carbon and nitrogen in concert
with ecosystem dynamics. Changes in erosion and sedimentation alter channel and
floodplain morphology and have important feedback to the water, carbon, and nitrogen
cycles. Furthermore, sediment from erosion episodes can have long-lasting influence on
river hydrology (e.g., Meade 1982). Land use changes affect hydrological processes and
these interact with carbon and nutrients in a variety of ways.
2
The rate at which water moves through segments of terrestrial and aquatic ecosystems
often has a major impact on processes and on rates at which dissolved and particulate
materials are leached from the land to water courses. Land-use and land management
affect the hydrological response of a system and thus the fluxes of nutrients. Conversely,
changes in nutrient regimes (e.g., through fertilization) change plant growth rates and
sometimes community structure, thereby changing evapotranspiration, surficial soil
characteristics, and hydrological response. An improved understanding of the
interactions and feedback mechanisms among water, C, and N cycles and on their
linkage with ecosystems is essential for simulating variability and change in these linked
processes. Important interactions occur at time scales ranging across weather and
hydrologic events to seasonal to decadal and longer.
Increasing nitrogen concentrations in groundwaters, rivers, lakes and reservoirs, and
coastal oceans lead to potential adverse effects on both human health and ecosystem
functioning. High concentrations of nitrate are associated with methemoglobinemia
(blue-baby syndrome). Nitrate concentrations in groundwater in some rural areas exceed
the EPA drinking water standard leading to decreased usefulness of this water supply.
Concerns about nitrogen contamination in drinking water are even more pervasive in
other parts of the world. Phytoplankton and aquatic plants in coastal oceans and
estuaries commonly are nitrogen limited in which case increasing inflow of nitrogen in
rivers leads to eutrophication. Algal blooms are a cause of concern in many estuaries in
the U.S. as they are worldwide. In addition, hypoxia caused as an aftereffect of excess
phytoplankton growth has caused major disruption of ecosystems in the Mississippi delta
region and in Chesapeake Bay, among other areas. (See Box 4.1.)
Understanding and quantifying the effects of the carbon cycle on climate and climate
change are central to the Carbon Cycle Science Plan (1999). Similarly, understanding
and quantifying effects of the water cycle are central to this water cycle initiative.
Nitrogen cycling also plays a role in a diverse array of global changes, many of which
link to an even broader array of effects through interactions with carbon and water. The
mechanisms through which ecosystems regulate the uptake and emission of nitrogen
are incompletely understood, but it is known that agricultural practices can have a large
impact on fluxes as can land use.
The program elements of the proposed water cycle initiative discussed below will
address the most pressing issues related to the impacts from changes and variability in
the coupled water-C-N cycles. There are two fairly distinct aspects of the work. The first
is research related to land-atmosphere transfers. For this aspect of the work, the water,
C, and N cycles are linked directly by virtue of the tight coupling of cycles through
terrestrial vegetation. In these terrestrial systems, the feedbacks among cycles occur at
all time scales. Short time scale fluxes from, to, and within plant canopies are linked in
part because stomata act to regulate transfers to and from leaves but also respond to
environmental conditions (e.g., Jarvis and McNaughton 1986). Precipitation is affected
by vegetation feedbacks on time scales of individual storms, but also across growing
seasons (e.g., Pielke et al. 1999). Finally, climate changes can lead to shifts in largescale vegetation patterns (e.g., Shugart 1998), which, in turn, can accentuate or mitigate
climate changes through feedbacks among the water, nitrogen, carbon, and energy
cycles. There is great uncertainty about the fate of nitrogen added to the landscape by
atmospheric deposition or fertilizer application – only a fraction of nitrogen applied runs
off with drainage water. One aim of the proposed work is to account for the components
3
and coupled exchanges of the water-C-N cycles fully and simultaneously (Aber 1999) at
all important time scales through modeling, observational, and experimental projects.
The second aspect of the program elements focuses on aquatic systems, which are very
strongly impacted by changes in water and nutrient cycling but have lesser direct,
coupled interactions with the global water cycle than do terrestrial systems. (There are
important feedbacks between aquatic systems and climate through the nitrogen cycle.
Human activities have doubled emissions of N2O from coastal areas and these are a
significant fraction of total emissions (Kroeze and Seitzinger, 1998)). At least as
important as linkages with tight coupling are those that lead to impacts on aquatic
ecosystems. Fluxes of nitrogen and carbon, in addition to fluxes of sediments, other
nutrients, and contaminants, from upland source areas to freshwater and coastal
ecosystems have profound effects on aquatic ecosystems and on water resources (e.g.,
Naiman et al. 1995). A critical scientific need exists to quantify these fluxes and to gain a
better understanding of how human activities affect them.
3.0 Goals
3.1 Goal 1: Develop observations and experiments that characterize the
coupling and feedbacks of water, C and N cycles
Why? Quantification of the critical, dominant couplings between biological and
hydrological processes requires measurements of mass fluxes. Current measurement
programs are insufficient to the task. Programs at most terrestrial sites focus on
measuring concentrations of various constituents in air, but pay little attention to
measurement of corresponding hydrological variables at the same time scales. Such
measurements are required to estimate mass fluxes and to establish the important
linkages among processes. Current monitoring approaches in aquatic systems were
designed for purposes such as local water quality rather than for quantifying C and N
flux coupling to the hydrological cycle. There is a major deficiency in the form of a
mismatch in the temporal and spatial scales of sampling with respect to the processes
that need to be quantified. Without monitoring information during major hydrologic
events, a full accounting of nutrients and pesticides transported by streams is
incomplete, and a full understanding of the effects of these contaminants on the health
and living resources of receiving waters, such as the Chesapeake Bay, is restricted
(Fuhrer et al. 1999).
How? Will be accomplished by implementing an appropriate program of observation
using new technologies.
3.2 Goal 2: Develop a quantitative predictive framework for water, C and N
fluxes coupled to ecosystem responses.
Why? Understanding of both aquatic and terrestrial processes requires that ongoing
observations be linked to the continued development and testing of models. Developing
terrestrial C-N-water models that include state-of-the-art representations for all of the
component processes on multiple temporal and spatial scales requires new
understanding of the processes to be described. The same is true in freshwater and
coastal aquatic systems.
4
How? Improved process models, linked to observational and experimental data, will be
developed and tested.
3.3 Goal 3: Distinguish between human-induced and natural variations in
the coupling of water, carbon, and nitrogen cycles.
Why? Human activities have effects on water and nutrient cycles that are both intensive
and extensive. Avoiding or mitigating unwanted consequences of human activities on
water resources requires distinguishing human-induced changes from natural variability.
Future management of water resources will require a strong scientific base, including
work that integrates social, economic, and ecological elements (NRC 1999b).
How? Observations will be made on a variety of natural and disturbed systems.
Paleoclimate records will be employed to deduce pre-industrial natural variability.
Models will be improved to incorporate information about human and natural
disturbance. In particular, information will be developed on economic, biological and
physical factors driving human disturbances, such as nitrogen loadings from agricultural
operations and shifts in vegetation cover due to changes in land use. Analysis and
modeling efforts will be conducted in sufficient detail to develop predictive capabilities,
allowing researchers to examine the potential consequences of policies designed to
mitigate adverse impacts. A fully integrated knowledge-transfer program will be
established, which will interact with national and state water quality management efforts
as well as with watershed-scale land-use planning activities.
4.0 Program Elements
4.1. Program Element 1: Observations and Integrated Database (Goal 1)
This program element addresses the need for new methods of acquiring and managing
data to improve understanding of ecosystem processes that link the water, C and N
cycles. These new methods are needed for quantifying critical components of these
cycles. The need for better data on water use and on institutions also is addressed.
4.1.1 Fluxes of water, carbon and nitrogen at vegetated surfaces
Considerable effort is being dedicated to measuring long-term CO2 fluxes between
vegetated systems and the atmosphere and notable successes have been realized (e.g.,
Wofsy et al., 1993). Currently, 36 sites are operational in the U.S. to monitor Net
Ecosystem Exchange (NEE) of carbon as part of the AmeriFlux initiative, 15 sites have
already published long-term NEE in Europe as part of the EuroFlux initiative (Valentini et
al. 2000), and 27 other sites distributed in Japan, India, Australia, Brazil, and South
Africa have been initiated. These monitoring sites are now part of the international
FluxNet (Kaiser, 1998), a long-term measurement network of carbon dioxide exchange
integrated into a consistent, quality assured, documented dataset (http://wwweosdis.ornl.gov/FLUXNET/index.html). This initiative requires further expansion in scope
for understanding the coupled water-C-N cycles. The network itself lacks 1) systematic
measurements of N and detailed water cycle components, 2) a clear methodology to
integrate its findings to regional and global scales, and 3) measurements of vegetation
attributes and their dynamic physiological properties. Hence, this program element
proposes a coordinated expansion of the FluxNet to identify and monitor components of
the coupled water-C-N cycle. The program element takes advantage of on-going efforts
towards understanding the global carbon cycle. We propose expansion in two stages:
the first stage requires U.S. flux monitoring sites to be expanded in both number of sites
5
and scope of measurements (e.g. water and N-monitoring); and, the second stage
strives for a full expansion of the international FluxNet.
New instrumentation for measuring concentrations of gaseous and liquid N species is
required, along with expanded soil water measurements. Recent advances in laser
diode technology permits gaseous N species concentration to be measured at 20 Hz, a
sampling rate ample for conducting eddy-covariance flux measurements between the
land and atmosphere. Presently, the cost of such instrumentation is prohibitive for
routine flux monitoring of N species. Similar barriers exist for liquid N deposition and
transport. Most AmeriFlux sites measure and resolve temporal variation in water vapor
fluxes between the land and atmosphere via eddy-covariance methods and components
of the land surface energy balance. A critical augmentation is measurements of the
dynamics of the water table, soil moisture, soil water tension, soil hydraulic properties,
interception and throughfall, and root properties. These measurements, along with
subsurface N-species measurements, will support analysis of nitrogen transformations
(e.g., nitrification-denitrification in root-soil) induced by atmospheric N deposition. These
N transformations produce a wide range of N-species in the liquid and gaseous phase
(e.g., soluble NH3, NO2, NO3), within the soil-vegetation-atmosphere continuum (plant
NO2, NO3, NH3, organic N, gaseous N). Few of these species can be routinely
measured at a temporal resolution needed to capture their transport and transformation
at the desired time scales. These data will provide the basis for exploring the coupled
impacts of C and N on water cycling through changing terrestrial ecosystems in the face
of changing atmospheric forcing (e.g. Farquahar et al., 1980; Collatz et al., 1991)
An integration of network fluxes to regional and global scales is needed Satellite remote
sensing data are potentially useful for integrating the network results beyond landscape
scales. EOS-MODIS data are being employed in support of carbon studies (Running et
al., 1999). New sensor platforms, such as NASA's Terra and Aqua, offer opportunities
for spatially integrating water and N cycle component models with remotely sensed
terrestrial and atmospheric data to generalize (or scale) network results to regional and
global scales. The addition of new monitoring flux sites can also be designed to balance
the need to sample diverse biomes with the need to sample at a hierarchy of spatial
scales to identify and test techniques for integrating from landscape to regional and
global scales. One major complication to implementing these scaled results in a global
modeling framework is subgrid heterogeneity, which impacts the description of land
surface fluxes (Avissar, 1993). Vegetation classification using new nonlinear
hierarchical tree-like scaling (Hanson et al., 1996) offers promise for describing subgrid
variability. Finally, the development of dielectric (Crow et al., 2000) and laser vegetation
imaging sensors (Weishampel et al., 2000) offers unique opportunities to measure and
resolve the spatial variation and dynamics of the three-dimensional leaf area distribution
and near-surface soil moisture fields.
4.1.2 Fluxes of water, carbon, nitrogen and sediment in rivers
Despite considerable efforts over the past several decades, mass fluxes of dissolved
and suspended constituents in river systems are poorly known. In large part, this
deficiency has arisen because water quality sampling has been intended to assess
concentrations, not to understand processes or to estimate fluxes. Thus, most data on
stream chemical composition are not linked to measurements of discharge. Even when
discharge measurements are available, water-quality samples typically are sparse in
time relative to discharge. Given that concentrations can change significantly with river
discharge, estimates of fluxes are highly suspect under these conditions. These values
6
are critical to calculate global biogeochemical budgets (e.g., Seitzinger and Kroeze,
1998) and also "as one of the best integrated measures of the success of clean water
efforts, free from biases caused by changes in total discharge from year to year" (NRC
1999b, p 129).
We have only a general conceptual understanding of how in-stream processes modify
fluxes of nutrients in rivers and how hydrologic variability influences those processes.
Recent studies of individual streams (e.g., Mulholland and Hill 1997, Burns 1998) or
regional analyses of river basins (Alexander et al. 2000) have indicated the existence of
important in-stream sinks for nutrients, particularly in low-order streams. In-stream sinks
can be important controls on riverine fluxes of nutrients across the landscape.
To accumulate the necessary data, coordination of measurements of water fluxes with
other measurements will be critical. For example, the NSF currently is designing a new
program, NEON (National Ecological Observation Network). A strong hydrological
component in NEON could address the critical question of linked variations among the
water and nutrient cycles. The enhancement of ongoing monitoring programs to include
data across disciplines, and on appropriate geographic scales will provide major
improvements necessary for global biogeochemistry and for water resources
management. For example, the USDA reports data on U.S. fertilizer use for major crops,
but the data are not organized on the basis of hydrologic units. The LUCC (Land Use
and Cover Change) program / IGBP is making observations of land use dynamics and
conducting process studies, but these studies have not been linked to hydrologic effects.
Addressing these linkages will be important for understanding how land management
decisions and land use change will affect water quality.
As urged by a recent NRC committee, stream gauging and monitoring network design
should emphasize adequate temporal resolution, sampling of storm events,
measurement of appropriate ancillary hydrological and biogeochemical data, and should
use the highest possible quality of sampling and analysis (NRC 1999b). Nevertheless,
even with major programs such as NEON, the EPA Environmental Monitoring and
Assessment Program (EMAP), the USGS National Water Quality Assessment
(NAWQA), and others, limitations on flux estimates will remain because resource
constraints limit sampling frequency. The data from standard sampling programs must
be augmented through the use of instruments capable of near continuous measurement
and through judicious use of remote sensing. Program design should also include the
many volunteer and cooperative water quality monitoring networks in the U.S. and
worldwide.
4.1.3 In situ sensors
In situ measurement using new micro- and nano-technologies has the potential to
overcome constraints on our ability to monitor variations in C and N associated with
variations in hydrologic processes. In-situ measurement of conductivity and temperature
is already included at some stream-gauging stations. Other sensors are being developed
to monitor dissolved oxygen and carbon dioxide reliably; these sensors would allow
continuous estimates of respiration and photosynthesis, processes relevant to carbon
storage and transport. Similar instruments should, in principle, allow for in situ
measurement of many other constituents, including C and N species.
Unfortunately, transfer of in situ monitoring technology into the field of water chemical
analysis has been limited because the large investment required for development. This
7
program element would support the necessary development and testing to make such in
situ instruments available for deployment at a reasonable cost. Development and testing
could involve a partnership of federal agencies and the private sector.
The types of detectors that are best suited for the instruments of interest are
photometers, spectrophotometers, and fluorometers. Photometers can be used directly
to measure turbidity, which is an indicator of particulate matter that generally contains
significant amounts of C and N. Measurement of dissolved N species requires the
addition of one or more reagents. Thus, instruments would be designed to store
reagents and to release them in prescribed amounts. Reactions with reagents will
generate colored species that can then be detected with a spectrophotometer.
Fluorometers can detect both chlorophyll in particulate matter and dissolved organic
material. Testing and implementation of monitoring using these detectors would require
a period of overlap with the traditional sample collection at sites that are part of
established monitoring networks.
4.1.4 Remote Sensing
Existing and enhanced remote sensing tools provide significant opportunities to enhance
the temporal and spatial resolution of available data. Some remotely operated devices
can be installed permanently at sites, others can be deployed periodically using aircraft,
and still others make use of satellite systems. As part of this program element, we
envision a balance of ground-based and remote data collection.
Spectral radiometers mounted at sites would be useful to detect changes in periphyton
on the streambed. Periphyton concentrations could be used to estimate N retention as
well as fluxes of organic N. Radiometers would also be used for monitoring riparian
wetlands.
We now have the capability to detect algal blooms in the open ocean using aircraft and
satellite instruments such as MODIS and SeaWIFS. These tools have not yet been
applied to monitor similar processes in large rivers, lakes, and reservoirs. Remote
sensing can detect transparency/turbidity, pigments (e.g. chlorophyll) and color or
fluorescence (for DOC), which are three critical measurements associated with C and N
flux. Important issues involved in applying satellite remote sensing are spatial resolution,
georeferencing, image analysis, and frequency of cloud-free images relative to
hydrological and ecological events such as snowmelt and algal blooms. The satellite and
aircraft measurements will be calibrated with ground-based observations at particular
locations. In this manner, hydrologic events that we expect to be critical to the C and N
flux can be examined as natural experiments with the appropriate level of resolution to
evaluate quantitative models.
4.1.5 Integrated Database Development
To make the best use of hydrologic and water quality data collected in ongoing and past
monitoring programs, it is essential that the data be archived in sustainable, accessible
data and information systems. As noted by the NRC Committee on Hydrologic Science,
the most comprehensive national system for water quality data, the EPA-maintained
STORET, has many limitations as a water quality archive (NRC, 1999). Furthermore,
this system is limited to measurements made in the U.S. and does not include hydrologic
data that are necessary to examine linkages between water quality parameters and
water fluxes. Data and information systems that can address questions of linked global
cycles of water, C and N must incorporate both hydrologic and water quality data from
8
throughout the world and store the data in readily accessible format for comparison,
synthesis, and testing of conceptual models.
There have been substantial advances in the past decade in the design of relational
databases that can integrate data collected at a wide range of spatial and temporal
scales. These databases are organized through a spatial framework, and have been
developed to handle very large quantities of data. These databases can be linked to a
GIS through definition of the location of the data collection site. Although software for
both databases and GIS will continually evolve, the existing systems are sufficiently
mature that future upgrades to commercial software should be able to carry forward data
organized in a relational manner. Thus, the usefulness of the data that are incorporated
into the initial database can be safeguarded for the future.
Development of the proposed database will require a large up-front investment in
software, hardware, and personnel, but the rewards will benefit many scientists in the
community as the data are probed to test new predictions from coupling of hydrologic
and ecological and biogeochemical processes. Types of continuous or discrete
monitoring data that could be integrated in this database include the following:
climate-precipitation
air temperature/water temperature
light, wind speed, and humidity
landscape-soil moisture
leaf area index
snowpack
lake/stream/river- streamflow
lake level
water temperature
conductivity/salinity
pH
suspended sediment
chlorophyll a
color
DIC, DOC, POC,
PON, inorganic N species
and possibly other nutrients
agricultural practices data (e.g. cropped acres, animal units, nitrogen applications)
land use data (e.g. percent impervious surface)
4.1.6 Human influence on the water cycle and links to nutrient cycles
Although some water uses are metered, much water use governed by riparian rights and
involving extraction from privately owned wells is unmetered and unmeasured. This gap
in observation makers it difficult to estimate net fluxes between surface and ground
waters and to model the processes affecting the chemical composition of freshwater
(e.g., salinization), and therefore to estimate the effects of human activities on water
supply and quality. Other observations are also missing. For example, it is important to
classify and catalogue the institutions (organizations and sets of rules) governing water
use in vulnerable regions.
In addition, spatially disaggregated observations on land uses and agricultural practices
that affect water quality will be important for managing non-point source contamination of
9
water bodies. For example, watershed-scale monitoring of the quantity and timing of
fertilizer applications, tillage practices, and land use change are needed to understand
how variations in these factors affect water quality and runoff characteristics in response
to hydrologic events.
4.2. Program Element 2: Field studies (Goals 1, 2, and 3)
Experimental studies on the coupling of water, C and N cycles will be used to test
quantitative formulations for controlling processes, and to explore the behavior of key
forcings outside the range of current ambient conditions. Experiments can also reveal
important responses of C and N cycles to forcing from simultaneous changes in multiple
hydrologic and ecological factors, such as climate and land use. In watersheds, the
processes controlling the water and nutrient cycles operate over a range of spatial and
temporal scales, from responses of stream communities to rainfall events to changes in
vegetation in response to climatic shifts or disturbance. Experimental studies can rely on
natural extremes in seasonal events to reveal couplings or can use purposeful
manipulations. Experimental studies should be performed at carefully selected sites that
provide a range of climate regimes (e.g., humid/warm temperate, humid/cool temperate,
arid, etc.) and land use types (e.g., forest, grassland, urban, agricultural). The
experiments must be designed for effective interfaces with observational studies and
models. Well-designed experiments can be powerful tools for making results from
research accessible to a broad range of stakeholders. Additionally, paleo-records can
reveal past changes in vegetation distribution and provide parallel records of climate for
cross comparison.
4.2.1 Natural experiments
Long-term experimental nested basins for characterizing the water balance, flow
pathways in terrestrial ecosystems, and aquatic ecosystem and biogeochemical
processes that control reactive transport and storage of C and N provide the essential
data for developing models and methods to scale hydrological variables and C and N
species, characterize basin-scale variability, and understand the limits of predictability.
The goal of this program element is to operate three to five nested basins in the United
States in a decadal program. These basins will represent several bioclimatic zones,
geological settings, and land-use characteristics, as well as scales, with the largest
being of truly continental scale. The basins will represent water-limited, energy-limited,
and nutrient-limited systems. Closed basins may prove to be valuable in this research
because they can be used to study long-term budgets (accumulation) of nutrients,
carbon, salinity, and so forth, in addition to water itself. Such closed basin networks are
common in the central US and Canada, in particular the glaciated prairie wetlands
spanning from Saskatchewan to Iowa with forested closed wetlands continuing into
northern Canada. Sites must be selected taking full advantage of existing programs with
historical records and a record of continuing research.
Natural experiments play a key role in the continuum between long-term observations
and purposeful manipulations. One significant limitation of the existing network of
experimental sites is the relatively small scale of most basins, which limits the
opportunity for observing a natural experiment as well as restricting the scale. Thus,
larger-scale study areas need to be designated, which include relatively undisturbed,
agricultural, and developed lands.
4.2.2 Manipulative experiments
10
Long-term studies of nested small to large scale basins provide a framework for
designing and conducting small scale manipulative experiments. These experiments will
be developed to address the process understanding needs from the model development
component of the science plan. For example, experiments involving whole-catchment
additions of reactive tracers are needed to study the controls on hydrologic fluxes from
land to water, particularly how hydrologic variability and interactions between nutrient
and water cycles affect the transfer of nutrients from terrestrial to aquatic systems.
4.3. Program Element 3: Improve representation of Physical and Biological
Processes in Land-Surface Models (Goals 2 and 3)
The Carbon Cycle Science Plan (CCSP, 1999) describes the need for improving process
models with regard to the terrestrial component of the carbon cycle, and associated
changes in the affected ecosystems. This need extends to coupling with the water and
nitrogen cycles as well, as noted in the CCSP. Thus, the need for improved process
models, linked to observational and experimental data, is an important element of the
water cycle plan as well.
Some existing models have been relatively successful in reproducing the range
boundaries of major trees and shrubs as a function of current climate conditions. These
models have been used to estimate shifts in these ranges under potential climate
change scenarios. However, the existing models were not designed to account for the
rates at which ecotones may shift in response to changes in temperature and
precipitation. Shifts in ecotones, such as the conversion of wetlands to drier upland
vegetation, can alter not only fluxes of carbon and nitrogen, but also rates of infiltration
and evaporation. These modifications to hydrologic processes, in turn, create feedbacks
to the water cycle. Such feedbacks to the water cycle will act in conjunction with
feedbacks from changes in the C and N cycles. The results of these feedbacks are
presently uncertain; together they could either stabilize ecotones or induce further
changes in vegetation communities. Past-recorded changes in vegetation may be
interlinked with climate changes. For example, Claussen et al. (1999) hypothesize that
the desertification of North Africa that occurred some 5000 years ago resulted from a
complex feedback between solar insolation, vegetation, and sea ice (see Box 4.2).
Understanding of terrestrial processes requires that ongoing observations be linked to
the continued development and testing of models. Often the limitations of models serve
as signposts in formulating and testing new hypotheses. Examples of current model
frontiers are the effects of CO2 on a full suite of plant processes (including allocation),
the simulation of dynamic interactions between carbon and nitrogen budgets, hydrologic
changes (such as drying or thawing of boreal peat), and the simulation of vegetation
dynamics such as successional changes over long time scales. Models must also be
improved to incorporate information about human and natural disturbance of the land
surface. Current models emphasize physiological and biogeochemical processes and
largely neglect the carbon storage dynamics induced by cultivation, forest harvest, fire,
fire suppression.
Knowledge of the rate of sequestration of carbon in lakes, reservoirs and peatlands, as
well as in oceans, and controls on those rates, is essential to understanding the global
carbon cycle. An improved modeling ability is needed to anticipate changes in delivery of
carbon to freshwater aquatic ecosystems and export from them, as affected by changes
in the hydrologic cycle, specifically the supply of water to these systems and changes in
11
residence times within them. Both the storage of carbon in peat and carbon release by
methane emissions are greatest when water tables are high. This is another mechanism
by which variability in the water cycle is linked directly with variability in the carbon cycle.
There are also important feedbacks among the nitrogen cycle, ecosystem functioning,
and global and regional water cycles. The next level of improvement in estimating
regional evaporation from plants is likely to result from a combination of new
observations (e.g., remote sensing) and models of vegetation that include
photosynthesis and the assimilation of nutrients. Coupling between the cycling of
nitrogen − a limiting factor for growth in many diverse aquatic and terrestrial ecosystems
− and the cycling of water and carbon in models of land surface-atmosphere interactions
is needed to improve our ability to estimate regional fluxes of water in terrestrial
ecosystems.
4.4 Program Element 4: Improve Basin-scale Models of Nutrient Sources
and Transport (Goals 2 and 3)
Nitrogen export from major river basins to coastal ecosystems is the cause of significant
eutrophication worldwide, and results in extensive zones of low oxygen concentration in
coastal waters. We recognize that elements such as phosphorus and iron often limit
production in aquatic systems, but we emphasize nitrogen in this science plan. The
problem has important direct impacts on human well being, including impacts on
fisheries caused by toxic algal blooms and by diminishment of recreational resources. To
inform decisions about possible management actions to mitigate the effects of or control
nitrogen export, models linking hydrological transport of nitrogen from forested,
agricultural, and urban-suburban lands with in-river process and routing models must be
developed, refined and tested.
Although nutrient export from basins can be a significant problem, nutrients are crucial to
the sustainability of riparian ecosystems, which often harbor a large percentage of a
regions biological diversity. Riparian ecosystems exist due to fortuitous intertwining of
the biogeochemical cycles and the hydrologic cycle. The availability of water and
nutrients sustain the ecosystem and determine the aquatic and terrestrial ecosystems.
Interrupting either cycle can cause major ecosystem perturbations and can shift the
species distribution. Although the interconnectivity of the cycles is not well understood,
hydrologic inputs have been tied to major nutrient inflows and cycling rates. Many
semiarid systems are potentially nitrogen limited and have the ability to take up nitrogen
throughout the year. If, however, there are interruptions or perturbations to the
hydrologic inputs (e.g. intercepted groundwater or major flooding events) and
concomitant decrease in vegetation, the ability of the riparian system to retain nitrogen
will be decreased, and will result in a net export of nitrogen from the system.
The current state of modelling river-basin export of nitrogen is not well advanced. A
simple expedient in the face of a lack of a comprehensive theoretical framework is to rely
on empirical approaches (e.g., Johnes et al. ,1996; Johnes and Heathwaite 1997;
Meeuwig, 1999; and Valiela et al., 1997). Such approaches lack a significant
hydrological component and are quite limited in ability to predict impacts from future
changes in land use. Process-based models have been developed but are limited by
gaps in scientific knowledge (e.g., Whitehead et al., 1998); Choi and Blood, 1998;
Thomann, 1998). Also, ecologists have advanced a conceptual model of aquatic
functioning called the "river continuum model” but this conceptual model has not been
12
formulated in mathematical terms, in large part because a comprehensive set of field
observations on a major river system is not available. A program should be established
to foster the development and testing of integrated river-basin models for nitrogen
export. Because of the close linkages, these will necessarily include carbon (and
therefore sediment) transport, and changes brought about during the transport through
rivers, reservoirs, and their associated ecosystems.
4.5. Program Element 5: Initiate a knowledge-transfer program to study
human influences (Goal 3)
Three key societal needs are related directly to the interaction between the water and
carbon and other constituent cycles. The first is understanding of the implications of
land use decisions on carbon sequestration, and in particular the storage, mobilization,
and movement of carbon in and among wetlands, lakes, and reservoirs. The second is
the riverine transport of sediment and associated pollutants to estuarine and coastal
systems. The third is prediction of organic carbon concentrations in drinking water
supplies, which increasingly is the focus of disinfection requirements. Notwithstanding
important applications needs related to prediction of movement of water at the land
surface (see applications sections in Chapters 2 and 3), predictive capability for carbon
and other constituents is in its infancy. There nonetheless is a critical need for
development of parallel science and applications pathways in this area, as important
policy decisions are rarely deferred pending scientific advances. In addition to the need
for better predictive tools that link the water and carbon cycles (detailed elsewhere in this
chapter), there is a need for studies of human activities that are linked to the water and
constituent cycles. The relevant human activities include, for instance, extraction of
water from surface and underground sources, release of water into surface waters and
the ground, alterations in the technology of water use, as well as change in water
management institutions.
It is important also to understand the forces driving these human activities in order to
develop accurate forecasts of changes in hydrological systems and to estimate future
vulnerabilities of societies and ecosystems to change in the water cycle. Understanding
the hydrological effects of human activities will help inform land-use planning decisions.
To benefit fully from this research, it will be necessary to couple water-use models to
models of water supply (e.g., aquifer recharge, stream flow, precipitation) and to models
that describe influences of land-use changes on the water and nutrient cycles and to
sediment transport.
Applications research in the coupling of the water and constituent cycles will reflect the
increasing emphasis of the USGCRP on integrated assessment of the regional impacts
of global change. There is a need to involve not only researchers in natural and social
sciences, but also decision makers, resource users, educators, and others who need
more and better information about climate and its impacts. Applications research must
be initiated to develop better means to connect water cycle research and its applications
in water resources management, i.e. two-way communication and knowledge transfer
between researchers and the management and applications community.
5.0 Initiatives
5.1 Integrated data and information system
An integrated data and information system should be developed. Because the relevant
data are currently archived by different agencies, an interagency planning group would
13
be required at the outset. [The National Water Quality Monitoring Council, as suggested
by Powell (1995), might be an appropriate body.] This group should identify the location
and formats of the relevant data, evaluate the current databases which may already be
structured as relational databases and prioritize a sequence for entraining old data and
new data in to the integrated database. The NASA Distributed Active Archive Center
(DAAC) at the Oak Ridge National Laboratory (see http://www-eosdis.ornl.gov/) focuses
on biogeochemical dynamics and may be able to provide critical capabilities for this
effort. The effort should also include testing of the system in an ongoing manner by
analysis of the integrated data. The approach should rely on the commercially available
software in order to ensure the ability to upgrade as more advanced versions of the
commercial relational databases are implemented. The effort should include data
recovery and "data mining". It is critical that sufficient resources be devoted to this effort
to ensure the availability of important data to the entire community.
5.2 Measurement technology development.
Advanced technology should be imported into this field to enable order-of-magnitude
improvements to in situ monitoring. The initiative could be set up to use a consortium of
private, university and government labs. The program should be designed with priorities
for different chemical species, and to try parallel development of several types of
detector systems. These detectors would also be useful in a wide range of routine water
quality studies and should include sediment monitoring. This investment would have
beneficial carry over into other applied areas.
5.3 Observations
5.3.1 Establish a network of in-situ monitoring stations near mouths of major
rivers in the U.S. along with a program to encourage international partners to
establish comparable networks globally.
As the in-situ measurement technology is developed, the sensors could be deployed at
gauging stations near the mouths of major rivers, with an emphasis on rivers draining
into estuaries which have had hazardous algal blooms in the past decade. These data
from these stations would be immediately useful for both indicating modeling directions
for coupled water cycle C and N models, and for indicating the complexity of the
conditions involving nutrient loadings, current patterns an weather that induce hazardous
algal blooms. These data would provide an early indication of level of observational and
modeling complexity required to provide useful predictive understanding. The
establishment of similar networks using in situ sensors with international partners should
also be encouraged with their initial development. This could lead to comparable data
sets for inflows to shared oceanic regions, such as the Gulf of Mexico, and to consistent
information on nutrient loading for other regions. Also, any developments in using such
data to anticipate hazardous algal could then be rapidly shared. Finally, rapid transfer of
these advances could allow less developed countries to “leap-frog” past the current
limited field sampling and laboratory intensive technology, in a manner comparable to
the widespread use of cellular telephones in areas without a land-based telephone line
network.
5.3.2 Development of a suite of aircraft and satellite based sensors for monitoring
parameters (turbidity, color, pigments) related to carbon and nitrogen
concentrations in freshwater ecosystems.
14
Remote sensing technology has advanced to’ a degree that will now permit frequent,
distributed observation of water quality in major rivers, following the lead of the ocean
sciences research community. High spatial resolution and moderate spectral resolution
instruments will be needed. Use of commercial data and commercial partnerships
should be considered, as alternative to development of separate research missions.
5.3.3 Strategic expansion and augmentation of 200-300 existing stream flow and
water quality monitoring stations to provide long term, high frequency records of
C, N and for characterizing fluxes and for comparison with and calibration of
remotely sensed data.
New measurement stations within river basins need to be developed, in parallel with
those at the mouths of major rivers. The expansion of the network should be carefully
planned to optimize for advancing as rapidly as possible the science objectives of this
plan and for encouraging the application of advanced technology to ongoing water
quality monitoring studies. The first wave of deployment beyond the mouths of major
rivers should support the nested watershed studies and allow the extension of the
results from these studies to larger spatial scales. The first wave deployment should also
include key stations upriver on the Mississippi River system because of the significant
research base that exists and the critical questions related to hypoxia at the outlet
5.3.4 Build cooperative linkages of world-wide programs, including volunteer
efforts, that can monitor water quality parameters of interest. Compile results of
these programs to improve the existing data base.
It is recognized that while U.S. government-supported measurement systems will form
the backbone of the research efforts, global coverage can only occur through
cooperative efforts with other professional and volunteer programs. A body to set
standards, maintain quality assurance and quality control and guide scientific
cooperation should be established and supported
5.4 Process Studies
5.4.1 Establish nested basin studies in three to five river systems with varying
land cover and levels of human disturbance and regulation (some examples of
possible basins include the Mississippi, the Potomac, and at least one high
latitude river).
These studies should employ in-situ measurements and remote sensing technologies to
characterize and improve understanding of linked water, C and N transport and
transformation processes. Studies on both terrestrial and aquatic ecosystems should be
part of the program. These studies should be coordinated with on-going studies and with
new ones that constitute portions of other program elements, so that costs of streamflow
and other hydrologic monitoring will be borne by these other studies. Basins should be
selected over a range of bio-hydro-climatic conditions (water-limited, energy-limited, and
nutrient-limited systems) so that models will be adequately stressed and tested. Where
practical, the research program should interact with local watershed management
organizations. Such organizations could assist the research effort by developing and
maintaining detailed local data on land use and management practices including fertilizer
applications, water use, animal husbandry and other relevant variables.
5.5 Modeling
15
5.5.1 Development of process models of coupled water, carbon, and nitrogen
transport and transformation in aquatic ecosystems and other terrestrial
components of the hydrologic cycle (e.g. through soil, groundwater etc.) that can
be tested against data from integrated data bases and results of field studies.
Model development and testing will also require focussed, small-scale experimental
studies to elucidate processes. It is anticipated that this would be supported through
competitive grants programs and through agency research staff.
5.5.2 Augment, as appropriate, modeling efforts being conducted as part of the
Carbon Cycle Science Plan to improve linkages among the carbon, water and
nitrogen cycles.
Process studies at intensively instrumented sites should be conducted to define
appropriate model structures for predicting the coupled cycling of C-N-water across
vegetated land surfaces. The existing network of tower sites (Ameriflux/fluxnet) should
be expanded with dual objectives of covering representative biomes and being arrayed
in a multi-scale grid that would support analysis of prediction efforts over regional and
continental areas. Rigorous data analysis tools must be developed and adopted to
identify the flow and transformation of information content through these biosphysical
systems, such as how rainfall anomalies affect soil moisture. Nitrogen cycling, root
function, foliar chemistry, stomatal function, and ultimately latent heat exchange to the
atmosphere with impacts to space-time fields of downwind precipitation need to be
studied. The next generation of studies must not only provide predictive skill across the
coupled systems, they must resolve the variable sensitivities across the spectrum of time
scales of variation in the forcing series.
5.5.3 Develop dynamic vegetation models to serve as realistic boundary
conditions in long-term integrations of atmospheric models.
The past successes of including vegetation functional controls on surface water and
energy balances over meteorological time scales, must be followed with efforts to handle
the vegetation's slower, structural responses to changes in climate and land use. Since
these changes in vegetation (e.g. structure, density, and species distribution) affect the
cycling of water, they must be dynamically included in water cycle models to accurately
understand and predict the behavior of the water cycle over the longer time scales on
which vegetation distributions shift and change. Through these efforts we can identify
the attributes of changes in energy and water inputs that induce positive (and negative)
feedbacks on the energy and water balances. This initiative would draw equally on data
sets from the network of tower sites (including vegetation) and joint historical records of
climate and vegetation changes; the need to deconvolve human induced changes from
natural changes in past vegetation distribution is an important component of this effort.
Analysis of the pathways by which a changing climate interacts with a dynamic
biosphere (with respect to carbon, water, and energy exchange) would support model
development integrating atmospheric forcing, land surface mass and energy fluxes, and
vegetation dynamics. The combined influences of carbon, nitrogen, and water must be
harnessed to predict the trajectory of vegetation shifts and their feedbacks on the spatial
and temporal distribution of infiltration, retention, and transpiration.
5.6 Establish a knowledge-transfer program
In Section 4.5, three focus areas for applications research related to linkages
between the water and constituent transport cycles were identified. This
applications initiative is intended to foster advances in all three areas, by “fast
tracking” connections between the research communities, operational agencies,
16
and other decision makers. Of the three areas, the first (implications of land use
decisions on transport and fate of carbon and other constituents) must have as its
target a relationship with land use planners. This could be addressed initially
through a series of workshops targeted at land use planners that would promote
information transfer about the implications of land use planning decisions on
aquatic transport and storage of carbon and other constituents. Depending on
“weak links” identified at these workshops, an applications research agenda could
be developed. The second thrust area, riverine transport of constituents to
estuarine and coastal systems, should first attempt to coordinate research in landbased runoff and transport processes, and coastal and estuarine research. Some
modest effort in this area is currently being supported through NASA’s Office of
Global Programs, though much more is needed. Initially, a workshop to address
gaps in ongoing research should be held, with subsequent outreach activities
aimed at the coastal zone management community. In the third area, the target
operational community is municipal water supply agencies, who need better
information about the dynamics of organic carbon in water supply sources in order
to plan treatment requirements. This need would have to be addressed through a
combination of targeted research, and cooperative projects with water supply
agencies. In this respect, the fellowship system suggested in the applications
initiative in Chapter 2, along with solicitations for research targeted at applications,
is one suggested mechanism.
17
Box 4.1 - Oxygen depletion results from the combination of several physical and
biological processes. In the Gulf of Mexico, hypoxia results from the stratification of
marine waters due to Mississippi River system freshwater inflow and the decomposition
of organic matter stimulated by Mississippi River nutrients. As a general rule, the
nutrients delivered to estuarine and coastal systems support biological productivity.
Excessive levels of nutrients, however, can cause intense biological productivity that
depletes oxygen. The remains of algal blooms and zooplankton fecal pellets sink to the
lower water column and seabed. The rate of depletion of oxygen during processes that
decompose the fluxed organic matter exceeds the rate of production and resupply from
the surface waters, especially when waters are stratified. Stratification in the northern
Gulf of Mexico is most influenced by salinity differences year-round, but is accentuated
in the summer due to solar warming of surface waters and calming winds. Following a
fairly predictable annual cycle beginning in the spring, oxygen depletion becomes most
widespread, persistent and severe during the summer months.
Midsummer coastal hypoxia in the northern Gulf of Mexico was first recorded in the early
1970s. In recent years (1993-1999), the extent of bottom-water hypoxia (16,000 to
20,000 km2) has been greater than twice the surface area of the Chesapeake Bay,
rivaling extensive hypoxic/anoxic regions of the Baltic and Black Seas. Prior to 1993, the
hypoxic zone averaged 8,000 to 9,000 km2 (1985-1992) (Rabalais et al., 1998, 1999).
The hypoxic area covered 12,400 km2 in 1998, about the size of Connecticut.
Source: Nancy N. Rabalais, Louisiana Universities Marine Consortium, 8124 Highway 56, Chauvin,
Louisiana 70344 (http://www.csc.noaa.gov/products/gulfmex/html/rabalais.htm)
Background photograph (taken by Philipp Hoelzmann) shows rock paintings of mid-Holocene
fauna near Zolat el Hammad, North Sudan. Today this region, like the largest part of the Sahara,
is a hyperarid desert. The desert formed some 5000-6000 years ago. Ensemble simulations using
a coupled atmosphere-ocean-vegetation model reveal a rather abrupt change in Saharan
vegetation (superimposed blue lines indicate fractional vegetation cover), which was triggered by
subtle changes in Northern hemisphere summer insolation (see paper by Claussen et al.,1999).
Copyright American Geophysical Union, reprinted with permission.
Box 4.2. Climate variability during the present interglacial, the Holocene, has been
rather smooth in comparison with the last glacial. Nevertheless, there were some
rather abrupt climate changes. One of these changes, the desertification of the
Saharan and Arabian region some 4 - 6 thousand years ago, was presumably quite
important for human society. It could have been the stimulus leading to the
foundation of civilizations along the Nile, Euphrat and Tigris rivers. Here we argue
that Saharan and Arabian desertification was triggered by subtle variations in the
Earth's orbit which were strongly amplified by atmosphere- vegetation feedbacks in
the subtropics. The timing of this transition, however, was mainly governed by a
global interplay between atmosphere, ocean, sea ice, and vegetation. [From Claussen
et al (1999). Copyright American Geophysical Union, reprinted with permission.]