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Sustainable Society:  A society that balances the environment, other life forms, and human interactions over an indefinite time period.

A Plan for a New Science Initiative on the
Global Water Cycle
 

Chapter 3: Predictability of Variations in
Global and Regional Water Cycles
 

Report to the USGCRP from the Water Cycle Study Group, 2001*
Updated October 12, 2003

Synopsis
Background
    Figure 3-1 Schematic of selected atmospheric, surface, and subsurface hydrologic processes
    Figure 3-2 Relative contributions of observations and models
    Figure 3-3 Example of error growth in a chaotic dynamical system
        Box 3-1 The 1997 Red River of the North Flood
        Box 3-2 The Importance of Predicting the Effects of El Niño on North America
    Figure 3-4 Schematic of the Temporal Scales of Water Cycle Variations
    Figure 3-5 Decision Making in Managing Water Resources and Ecosystems, Schematic
    Figure 3-6 Systems Modeling Framework for Prediction and Transfer of Information to end users
    Figure 3-7 Nesting Climate and Hydrologic Models
Goals
    Goal 1: Demonstrate the degree of predictability of variations in the water cycle on three time scales
    Goal 2: Improve predictions of water resources by quantifying fluxes between key hydrologic reservoirs
    Goal 3: Establish a systems modeling framework for making predictions and estimates of uncertainty
Program Elements
    Program Element 1: (Goal 1): Identify inherent predictability and limits of prediction
    Program Element 2: Goal 2): Quantify fluxes between water reservoirs.
        Box 3.3 A Resource Management Decision Constrained by Inadequate Understanding of Surface-Groundwater Fluxes
    Program Element 3: (Goal 3): Transfer information from physical to socioeconomic models
    Program Element 4: Data Assimilation
    Program Element 5: Water and Energy Budget Studies
    Program Element 6: Knowledge Transfer
Initiatives
    Initiative 1: Develop a comprehensive data, modeling, and knowledge transfer framework for each of the three fundamental time scales, linking observability, predictability, and controllability, and taking into account physical processes with large-scale dynamics.
        Figure 3-9 Example of Available Data that can be used to help Develop Distributed Hydrologic Models
    Initiative 2: Begin process research for the three time scales to characterize heterogeneity, guide the development of models, reduce knowledge uncertainty, and enhance predictions of state variables.
    Initiative 3: Develop and implement instruments, methods, networks, and assimilation techniques to estimate the two presently unobserved fluxes linking (1) surface with subsurface reservoirs over land (discharge/recharge) and (2) ocean-land-biosphere with the atmosphere (evaporation)
    Initiative 4: Establish an interdisciplinary forum that uses systems modeling frameworks to integrate users' requirements into the design and implementation of observing systems, model-based prediction, and forecast validation
    Initiative 5: Transfer knowledge

Synopsis

Societal Need

• Quantitative predictions of water cycle variability, along with estimates of the confidence in these predictions -- information that is critical for water resources and ecosystem management, and for hazard mitigation)

Scientific Gaps 

• A complete description of the spatial (catchment, regional, and continental) and temporal (daily, seasonal to interannual, and decadal to century) regimes within which hydrologic variables can be accurately predicted to forecast floods and droughts

• The understanding and quantifying of fluxes between key hydrologic reservoirs, including evapotranspiration, recharge, and surface water/groundwater interactions, to enhance prediction accuracy and reliability

• Methods to apply knowledge effectively from physical climate and hydrologic models to strategies for water resources management

Proposed Actions

• Identifying predictable water cycle components at daily, seasonal to interannual, and decadal to century time scales and all spatial scales; assessing the limits of predictability for less predictable components; and quantifying prediction uncertainty through a program of monitoring, process studies, and model development

• Developing and implementing instruments, methods, networks, and assimilation techniques to estimate the two presently unobserved fluxes linking surface and subsurface reservoirs over land (recharge/discharge), and ocean-land-biosphere with the atmosphere (evaporation)

• Undertaking an interdisciplinary initiative using a systems modeling framework to link climate, hydrologic, and socioeconomic systems -- while integrating users' requirements into the design and implementation of observing systems, model-based prediction, and forecast verification

Background

From both scientific and practical points of view, predicting the components of the Earth's water cycle is central to improving our understanding of climate variability and change. As discussed in Chapter 2, water cycle variability and process interactions (in the world's oceans, global atmosphere, land surface biosphere, surface and groundwater zones, and through fluxes among these reservoirs) vary over a wide range of spatial and temporal scales (Figure 3.1).

Some of these processes manifest themselves at long time scales, as in the deep ocean and in groundwater, while others, as in 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, isolating the slower modes of variability from the faster, for enhanced predictive ability at a wide range of scales.

Figure 3-1 Schematic of selected atmospheric, surface, and subsurface hydrologic processes

Schematic of selected atmospheric, surface, and subsurface hydrologic processes and their temporal and spatial scales of occurrence (adapted from Bloschl and Sivalapan, 1995).

Predicting water cycle components is among the most difficult problems facing both science and society. Those elements of the physical climate system most difficult to model -- water vapor, clouds, rain, snow, and groundwater -- are coupled to the terrestrial biosphere. These elements are also the quantities of critical importance for ecosystem conditions and water resource management. Essential decision making for ecosystem and water resource management, near-term policy, and long-term regulation and legislation are all growing more dependent on quantitative predictions of water cycle variability and change.

Because the water cycle is intimately related to living organisms, 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 can be considered as occurring over one of three time scales. 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 how more advanced weather forecasts on this time scale could improve mitigation of infrastructure damage from large-scale floods. Water cycle variability at a given location depends on both the propagation of disturbances from elsewhere (e.g., through 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 some disturbance.

To what degree 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, not to be confused with our current ability to make forecasts. Accurate forecasts depend on how fully the initial state can be accurately characterized and the fidelity of the forecasting model (Figure 3.2). There is enough evidence to suggest that atmospheric circulation is a chaotic system and is therefore depends sensitively on the initial conditions; that is, small errors in specifying initial conditions will amplify as the prediction evolves, as illustrated in Figure 3.3.

Figure 3-2 Relative contributions of observations and models

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. The three lower curves depict hypothetical contributions of observations and models and the sum of the two 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. 

In this case, models may provide a first-guess field from a previous forecast or assimilate limited observations to provide a consistent analysis of the variables of interest.  In this early part of the forecast period, the value of observations exceeds that of models in providing information; however, the contribution of observations at time t to prediction decays rapidly.  With lead time, the information provided by the models may actually increase, because dynamic adjustments in the model bring components of the system into balance. 

For example, numerical model predictions of precipitation in the 12 to 24 hour range are often superior to the forecasts in the 0 to 12 hour range because it takes time for realistic vertical motion and moisture fields to develop in the models.  (Diagram adapted from Anthes, 1984).

Figure 3-3 Example of error growth in a chaotic dynamical system

Example of error growth in a chaotic dynamical system, illustrated by a series of weather maps showing the distribution of geopotential height (analogous to pressure) at 500 hPa (a mid-tropospheric level in the atmosphere).  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 (top right) before the forecast model was run.

The two initial states are nearly indistinguishable in this presentation.  After three days, the forecasts have diverged somewhat, but the general weather pattern they predict over the continental United States is quite similar.  By day six, the forecasts have diverged so much 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.

Box 3-1 The 1997 Red River of the North Flood

On seasonal to interannual time scales, there is evidence that 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; Masutani and Leetmaa, 1999). To predict such short-term phenomena, it is necessary to have a comprehensive description of the recent evolution of the Earth's near-surface climate -- including upper ocean heat distribution, soil wetness, snow, and similar phenomena. In other words, seasonal to interannual predictions depend on conditions at the boundary between the atmosphere and the oceans and continents.

Climate predictions several seasons in advance of the present can help narrow the uncertainty in hydrologic predictions for better water resources management, natural hazard mitigation, and related decision making and policy guidance. There is evidence that the low-frequency predictable components of water cycle variability (e.g., seasonal variations) that are superposed on higher frequency chaotic fluctuations (e.g., daily variations) can be isolated and predicted (Shukla, 1998).

The value of seasonal predictions was seen in the successful forecast of the El Niño event of 1997-1998. The combination of improvements in the global observing system (primarily the deployment of moored buoys in the tropical Pacific Ocean), in the understanding of coupled ocean-atmosphere dynamics responsible for ENSO, and in global climate models together led several research groups around the world to predict tropical Pacific sea surface temperature. The predictions were used in a number of regions in several sectors for the first time to provide socioeconomic benefits (Box 3.2).

Box 3-2 The Importance of Predicting the Effects of El Niño
on North America:  the 1997-98 ENSO Event

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 variations may occur over decades, as in the case of the North Pacific Oscillation (Graham 1994 and Mantua et al., 1997); or they may be temporal trends like those recently observed by Karl et al. (1999). Such variations are nonstationary behavior with respect to the period for which we have direct observations of the Earth's climate. The challenge for predicting these fluctuations therefore depends on our ability to understand and model the fundamental processes that affect secular climate change.

Successful modeling of hydrologic processes and the global water cycle on all three time scales require detailed representations of the physical processes controlling water and energy fluxes. Such modeling also requires higher spatial resolution by one to two orders of magnitude than that currently used in dynamical weather forecasting models and climate change simulation models. The predictability of global water cycle components is typically quantified in the context of models that embody both theoretical understanding of the relevant processes and representations of all available observations. Thus, formulating an effective modeling strategy that encompasses the diverse temporal and spatial scales is a high priority.

Global and regional climate models capture the fluid dynamic and physical processes that cause large-scale (100 to 10,000 km) 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. Such models were first devised to predict weather, and only later applied to simulate the general circulation of atmosphere and oceans that is driven by radiative forcing at the top of the atmosphere and by the Earth's rotation. Water is a critically important component of this general circulation. Its vapor phase is the most active absorber/emitter of radiation, thus warming the Earth's surface by the so-called greenhouse effect.

In 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 through weathering, the slow changes in the nature and structure of the land surface. General circulation models and regional climate models approximate each of the water cycle components.

In general, the smaller the scale of a phenomenon's natural variability, the cruder the representation of the phenomenon in the model. Climate models solve equations governing the fluid dynamics and thermodynamics of the atmosphere and oceans by stepping the numerical approximations to those equations forward 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).

Hydrologic models are generally formulated on the catchment scale. This spatial scale is appropriate for water resources management and natural hazard mitigation (Figure 3.4). However, uncertainties remain in describing key processes at this scale. Much of the hydrologic research in the past three decades has focused on water movement within hydrologic reservoirs, with minimal attention to interactions between these reservoirs, such as freshwater flux between the atmosphere and oceans, groundwater recharge and evapotranspiration between land and atmosphere, and stream-groundwater interactions near the land surface.

As a resource to evaluate and manage, water must be considered along a continuum encompassing all its reservoirs. A major gap in our knowledge of the water cycle is quantification and modeling of fluxes between reservoirs. Some of these fluxes (e.g., evaporation and transpiration) provide critical information and feedback to the climate models. This knowledge gap results in large uncertainties in predictions.

Figure 3-4 Schematic of the Temporal Scales of Water Cycle Variations
and Associated Water Resource Problems


The three major temporal scales are (1) weather scale (a few hours to a few days), (2) the seasonal to interannual scale (several days to several years), and (3) climate scale (decadal to longer time scale).  Different processes dominate in each of these scales, but the same systems modeling framework can be used to quantify all models' predictive power and the practical value of predictions.  The systems modeling framework is presented in Figure 3.6.

For water resources' regional sustainability, the basic functions of measurement, understanding (through synthesis and modeling), education/outreach, decision making, and intervention are not now sufficiently linked (Figure 3.5 shows the general nature of current links).

Figure 3-5 Schematic of Decision Making in Managing Water Resources
and Ecosystems

 

Decision making in managing water resources and ecosystems is a complex, multifaceted process involving many stakeholders. Such decision making involves the economics of supply and demand, regulatory actions of government authorities at various levels, and litigation of disputes among stakeholders. This process is iterative and dynamic.  Multiple contractual, legislative, and judicial actions change the priorities and rights of interested individuals and groups as well as the water resources and ecosystems themselves.

The historical record and predictions of water cycle variations can be used in this process to provide quantitative information about the physical system. This information typically is provided in a probabilistic format, so the various stakeholders need to be able to use such information effectively.  Finally, the decisions that are made result -- both intentionally and unintentionally -- in changes to the water cycle, both directly and indirectly.

Societal interests in the water cycle concern water allocation, environmental hazard mitigation (including of floods and droughts), long-term variations in water availability, and policies related to all these areas. In resource planning, facility design, and management, time horizons extend from 30 to 100 years depending on the enterprise. Interstate and international water conflicts generally concern long-term water allocation to the different entities, as well as river flow maintenance for ecological and conservation needs. These contentious issues are increasingly coming to the fore.

The limitations of historical records for policy analyses are highlighted by the scientific and political debates about the adequacy of methods and data in analyzing extreme floods -- including the 1927 and 1993 Mississippi River floods, the 1986 and 1997 American River floods, the 1982-87 Great Salt Lake flood, and the 1992-present Devils Lake flood. Each such event appears to be dramatic in the context of the local historical record. However, the connections of such basin-scale events to the larger climate picture are now emerging. Both the Devils Lake and Great Salt Lake variations have been tied to persistent, hemispheric scale, atmospheric and oceanic circulation features.

The longer (1847-present) Great Salt Lake record allows for a considerably better characterization of the probabilities of different climate regimes and their ability to forecast the 1980s extreme, than the 1910-present record of Devils Lake does to forecast this lake's levels. 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 have also been made in constructing paleo-flood bounds for a number of rivers. The time is now ripe to move from such demonstrations to a more comprehensive synthesis of joint space-time-flood-climate state properties, across continental North America.

Droughts are also natural hazards with 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 multiyear events that make the short historical data base even more limited in its value 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, to improve estimates of the return time of such extreme events.

Such work, however, has focused on climatic drought indices, and hence is not directly relevant to river system management, which would require hydrologic drought indices. Yet, with the proper choice of paleoclimatic records, it is clearly possible to develop useful estimates of important hydrologic. Streamflow reconstruction for the Upper Colorado River flow from tree rings has had an impact on renegotiation of the Colorado River Compact. As in the case of floods, there is an opportunity to develop and test low flow and climate reconstructions for drought using paleoclimatic and historical data sources, reconstructions that could significantly aid in water resources management. At a minimum, future water availability could be assessed on the basis of projected future demands calculated from population growth and the historical record of past drought conditions.

Traditionally, surface water and groundwater have been managed separately. However, interactions between these two reservoirs are forcing the integrated management of them. Increased groundwater production (pumping) generally results in reduced baseflow to 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.

Most of the above-described physical models and processes in place to manage water resources and ecosystems have been developed to answer particular questions about the environment or about the human systems that use and control elements of the environment. The models differ among each other significantly in design and function, and typically, the output from the physical models is used as information input to decision-making models. This view that users of scientific information exist outside the process of developing that information provokes difficulties in the effective use and exchange of information. By considering physical and socioeconomic models together 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 tropical sea surface temperatures and their 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 ways that predictions are made, the information can be made significantly more useful, even for regimes for which predictability is quite modest (e.g., Barnston et al., 1999).

Figure 3-6 Systems modeling framework for prediction and transfer of information to end users

Systems modeling framework for prediction and transfer of information to end users.  Users of scientific information and environmental predictions should not be seen as outside the process.  A systems modeling framework can explicitly provide a valuable feedback loop by stating societal needs in terms already identified by users through national and regional assessments, and by including social science perspectives on the value of information. In this way, the value of information that can be extracted from predictions of water cycle variations can be better 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, users require probabilistic predictions for risk-based decision making.  To achieve these predictions, the model must run in an ensemble mode where initial conditions or model physics are perturbed to produce a suite of predictions and characterize their likelihood (and conversely, their level of uncertainty).

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 water management decisions) must necessarily take into account larger scale anomalies. To achieve this requires coupling global, climate, hydrologic, and land surface models. A conceptual scheme for such coupling 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 storage), are passed from one scale to another to estimate the basin response, for example, the flood hydrograph needed for water management decisions. 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 (owing to uncertainty in model parameterizations and input data) to provide valuable information to the end users (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, provide better information for decision making and management of risk. Information produced by models (as shown in Figure 3.7) and transferred to users (as shown in Figure 3.5) should not only project "mean states," but also identify measures of uncertainty for the projected states. Uncertainty estimates are crucial inasmuch as vulnerability to climate change varies regionally and by 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.

The quantities that numerical weather and climate models correctly predict are not necessarily the quantities needed by decision makers for managing water resources or mitigating natural hazards. Such information gaps indicate a need to develop a framework in which model predictions can be related to decision-making requirements for the complex context of multiple parties, uses and values.

Figure 3-7 Nesting Climate and Hydrologic Models

Nesting climate and hydrologic models. A coupled ocean-atmosphere model is used to compute global sea surface temperatures (SSTs), which are transferred as input, along with the land-surface state, to a global atmospheric model.  The global model output drives a large-scale atmospheric circulation model, 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 basin resolution, provide the input for a basin-scale coupled hydrology-ecosystem model.  The hydrologic model predictions along with risk statistics can provide input to a complex decision making process.

Why? It is necessary to concentrate efforts on predictable components of the water cycle, because progress in this area will be the most cost-effective and fast. At the same time, less predictable or inherently unpredictable processes must be estimated and their limits of predictability assessed. Because predictability at daily, seasonal to interannual, and decadal to century time scales is limited by different processes, the predictability of water cycle components must be assessed for each time scale.

How? This goal will be accomplished by identifying predictable components of the water cycle at daily, seasonal to interannual, and decadal to centennial time scales, and all spatial scales; by assessing the limits of predictability for less predictable components; and by quantifying prediction uncertainties through a program of monitoring, process studies, and model development.

Why? Quantifying fluxes between hydrologic reservoirs, such as recharge and evapotranspiration in terrestrial systems, is critical for predicting water resources. Climate trends may also be affected through 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 among these components to effectively manage water resources.

How? Fluxes between reservoirs will be quantified through a comprehensive program, including 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 will allow the development of distributed numerical models simulating 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.

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 times. In addition, efforts to balance competing water uses and to achieve 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 the predictability of water cycle components at all three time scales.

How? These goals will be accomplished through process studies that quantify fluxes between hydrologic reservoirs, model improvements, and effective transfer of knowledge among scientists using climate, hydrologic, socioeconomic, and ecosystem management models.

Fundamental research must be devoted to isolating potentially predictable components of water cycle variability at each of the three time scales. For daily and seasonal to interannual fluctuations, this amounts to separating the low-frequency or persistent components of water cycle variability from the more unpredictable high-frequency components. Low-frequency variations often provide the forcing for rapidly varying processes; thus, they 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 specifying the initial conditions (here, in low-frequency variations) helps assess the limits of predictability for fast-varying processes.

In theory, predictability limits of a deterministic dynamical system can be assessed analytically. However, water cycle dynamics at all scales are so complex that they 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 in this case is limited to relatively sparse observations. Thus, a plethora of scientific issues arises:

These and other related questions are also discussed further below.

Nested Models. The use of nested models to overcome the problem of translating information from larger to smaller scales (in a computationally efficient manner) raises many concerns. Fundamental problems may result because of inconsistent representations 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 forcing (outputs of the outer domain and initial conditions for the inner domain) can result in large differences as the system evolves over time.

Efforts are needed to investigate what scales of nesting are more appropriate to reduce uncertainty propagation across spatial scales and optimize the exchange of information at the interfaces. Issues that must be carefully addressed include one-way vs two-way nesting, effects of the model resolution in each nested domain, and the effect of including subgrid-scale parameterizations. Also, the question should be explored of whether a nested model framework or a single model of high resolution for the whole domain (a model that might soon be computationally feasible) will provide more accurate predictions at all scales.

Probabilistic Modeling Framework. Because of uncertainty in model inputs (measurement errors, natural variability of atmospheric variables, inadequacy of space-time resolution of these variables) and model formulations (simplifications, parameterizations, and incomplete understanding of all the interactions and nonlinear feedbacks in atmospheric dynamics), the output of any numerical weather prediction or climate simulation model must be viewed as a probabilistic rather than a deterministic framework. This fact gives rise to the need for ensembles of forecasts to provide reliable estimates of the probability distribution of the atmospheric state. Ensemble forecasting also provides information about forecast uncertainty from the dispersion of ensemble members.

How this ensemble would best (i.e., most economically) be generated, given the atmosphere's chaotic character and errors in observables, needs continuous research at global, regional and local scales. In particular, effective initial perturbation fields must be designed to characterize forecast uncertainty with a minimum number of ensemble members, and as a function of global, regional, and storm scales, for both short-term and long-term predictions. In addition, alternative ensemble strategies must be explored, such as perturbation of the model's physical parameterizations, and multimodel (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 of up to several days, and climate, for lead times of months or longer, offers 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 United States, made use of this information, if often in ad hoc ways. Evolving ensemble weather and climate forecast methods now routinely used in the global forecast community have yet to be adopted by the water management community, either at 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 surface variables (especially precipitation) predicted by both weather and climate forecast models are biased to the extent that forecast products are unusable without some kind of post-processing. 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 inflows is attractive, because it avoids the inevitable limitations of record length suffered by traditional methods that "train" forecast models to historical data. Additionally, 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.

Ensemble forecasts can also be readily updated as model improvements become available -- notwithstanding the fact that model assessment is complicated by continuing revisions of model physics. There is thus a strong argument for encouraging the use of ensemble forecast methods in water management applications. At the same time, the climate (and weather) modeling communities, on one hand, and water resource management communities, on the other, must better understand the needs and constraints of each other in terms of forecast products. While there is a demand for technology transfer, this is not a one-way street. The forecast products required for water management applications must likely meet a higher standard than has previously been achieved by the climate and weather forecast communities. Use of their forecast products in water management may well provide diagnostic information that will also help to improve the forecast models.

Scaling Models and Observing Systems. Observations of atmospheric, hydrologic and land-surface variables are often available at more than one scale, for example, point measurements of precipitation from rain gauges, and areal averages of precipitation from radar and satellites. Moreover, the scales of these observations are not typically the same as the scale (resolution) of the numerical prediction models. To validate a model, and 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's 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 data-collection strategies (in terms of sampling frequency or instrument spacing ) to be addressed to reduce prediction errors. Mathematical advancements in multiscale filtering and conditional simulation, which have mostly been developed for efficient image processing and transmission, have a lot to offer in this area (e.g., see Kumar, 1999). 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 to improve predictions at all scales, (3) improve model parameterizations and methods for state variables (e.g., temperature and precipitation) to be incorporated into innovative data assimilation and initialization methods.

Improved predictability of water cycle fluctuations depends 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.

Basin-scale Recharge. Accurate estimates of fluxes between surface and subsurface systems are required to evaluate 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, several approaches that have evolved independently must be merged. Techniques for estimating recharge include catchment-scale water budgets, inverse groundwater models, and deploying chemical and isotopic tracers in unsaturated and saturated zones. Numerical models of surface and subsurface systems have benefited greatly from faster computers, improved analytic 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 for surface and subsurface systems. Advances in inverse modeling also allow the 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 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 to obtain valuable information on recharge and discharge processes. Although the cost of tracer analysis might be considered high, sampling of tracers is generally only required a limited number of times (once or twice) because of the long times many tracers represent. Costs are thus offset by the limited number of analyses required. Tracer studies can complement long term monitoring programs and provide valuable information to validate numerical models.

Stream-Aquifer Interactions. Increasing emphasis is being given to stream-aquifer interactions 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 be equal to or less than recharge levels. This approach ignores the fact that many streams rely on groundwater discharge to maintain baseflow. With increasing importance placed on ecosystem maintenance, a shift has been seen toward managing surface water and groundwater as a single resource, and to recognize the importance of maintaining minimum streamflows 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. Disparity in the principal time scales governing stream-flow (minutes to hours) and groundwater flow (months to years) has hindered development of numerical models that include both systems. In addition, direct measurement of fluxes between surface water and groundwater have not been systematic (Box 3.3).

Box 3.3 A Resource Management Decision Constrained by Inadequate
Understanding of Surface-Groundwater Fluxes

Evaporation. Evaporation from land surfaces 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 basin-scale (using remote sensing), models of vegetation that cover growth and plant succession, and (using theories of hydraulic limitations within plants, xylem, cavitation etc.) photosynthesis and biochemical assimilation of nutrients, and soil moisture limitations. 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 of recharge depends on accurate partitioning of residual water (after runoff is calculated) between evapotranspiration and recharge. As water resources become more limited, particularly in the semiarid southwest, there may be increased emphasis on reducing evapotranspiration and enhancing recharge by replacing deep-rooted nonnative vegetation, such as creosote and mesquite, with shallow-rooted grasses.

Spatial Inhomogeneity. One important difficulty for the science is the pronounced spatial inhomogeneity of most variables involved -- precipitation, soil moisture, topography, land surface and other characteristics -- and the need to derive upscaling relationships for modeling purposes. Since the interactions of these processes are nonlinear, subgrid-scale variability in one variable can 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.

Program Element 3 (Goal 3): Transfer information from physical to socioeconomic models.

The hypothetical transfer of information model is shown in Figures 3.6 and 3.7, in which forcing functions and state variable information cascade from climate models to socioeconomic models involving multiple decision makers using intermediate basin-scale hydrologic models. For this transfer to be successful, disaggregation strategies must be developed for downscaling large-scale climate model predictions to provide input to smaller scale hydrologic models. 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 necessarily propagate to the hydrologic models. When hydrologic models are used to forecast droughts, floods, and other hazards, such predictions are then uncertain because of the uncertainties in forcings generated by the climate models, with additional uncertainties inherent in the hydrologic models. Output from hydrologic models will provide probabilistic input to 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 in improving the coordination of decision-making entities. For example, public agencies and other parties engaged in local watershed management initiatives could use this information to develop alternative management strategies to address climate variability.

Quantitative information on water resources and associated uncertainties is critical for planning and developing a policy framework to respond to and mitigate extreme hydrologic events. One of the challenges to successfully implementing a climate-hydrologic-economic model framework is clear coordination of the scientific requirements of the water management decision process and the predictive ability of the hydrologic models.

Figure 3-8 Schematic of how a Probabilistic Model Forecast
can be used for Risk-based Decision-making

Depending on a tolerance level that is user-specified and application-dependent and the sensitivity of decisions to that tolerance level and prediction uncertainty, valuable feedback might result for model verification (i.e., no need to demand from the model more than the user can make use of), model improvements (when the model predictions are not reliable enough to support a decision), and observational requirements (collection or assimilation of more observations to improve the reliability of predictions).

Initiatives

Initiative 1: Develop a comprehensive data, modeling, and knowledge transfer framework for each of the three fundamental time scales, linking observability, predictability, and controllability, and taking into account physical processes with large-scale dynamics.

Efforts to predict water cycle components are particularly useful for forecasting extremes (e.g., droughts and floods) that may be sensitive to large-scale climate variability and local atmosphere-land-groundwater coupling. For this reason, an initiative must be directed toward improving the capabilities of physical (numerical) climate, hydrology, and water quality models. However, improvements in these physical models must be guided by end users' requirements, so the modeling framework must include a knowledge transfer element.

Determine optimal modeling strategies to handle multiple sources of uncertainty and multiple scales of natural variability. Uncertainty in predicting water cycle variations is introduced from the inherently chaotic character of the atmosphere and limitations in the process of making predictions, including undersampling, short record length, measurement error, analysis error, parameter value identification, and systematic errors in numerical models. These uncertainties arise at different spatial scales and different lead times for a given prediction.

A fundamental question in seasonal and longer lead prediction of water fluxes is the choice of a modeling strategy to minimize propagation of uncertainty among components of a predictive model. For example, predictability of regional hydrologic systems is limited by predictability of large-scale climate fluctuations when large-scale climate forces regional hydrology. If there is two-way land-atmosphere coupling or climate modulation by local hydrologic processes, there is potentially greater predictability that can be exploited through coupled modeling.

Given the relevant scales for predicting floods and droughts -- at global, continental, regional, and catchment scales -- two methodologies may be employed to model the relevant processes: nested models and high-resolution global models. The nested model approach most often used predicts each phenomenon with the model relevant to that scale and uses the 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 SST anomalies is most appropriately modeled with a global atmospheric general circulation model. A regional model in which seasonal predictability is high can be nested in the global model to help predict regional details of the distribution of precipitation and temperature anomalies consistent with the large-scale moisture and energy fluxes in the global model.

Nested regional climate models help bridge the spatial scales of atmospheric, land-surface, and subsurface processes. Much weather and climate simulation research has recently been done with nested regional models; and several operational weather forecasting products are generated using regional models. Further nesting is needed of surface and subsurface hydrology models to simulate the runoff and streamflow from the precipitation predicted by regional models for many catchments where basin response is sensitive to small-scale inhomogeneities.

While the suitability of the nested model approach has been demonstrated, such models have well-known limitations; for example, they are not formulated to obey integral constraints associated with mass and energy conservation laws, which can limit their accuracy for climate simulation. In principle, such limitations could be removed by using a high-resolution global model strategy. The initiative proposed here 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 is to link global coupled ocean-atmosphere models to integrated system models such that resulting predictions and uncertainty estimates that provide a useful basis for decision making in water resources management: such a result requires high spatial and temporal resolution, placing huge demands on computational resources and observational data to initialize and verify the models.

A systematic approach to model design and development is therefore needed to help determine, based on basic research outcomes, the scales at which predictive information should be exchanged within a nested modeling approach, or whether a global integrated system model is required. The research must help identify the appropriate way to nest models (whether one-way coupling, two-way coupling with "fuzzy" matching at the boundaries, perturbation expansion, adaptive mesh techniques, or etc.) and to develop ensembles of model integrations for robust measures of forecast uncertainty (such as ensembling over possible initial states, over model parameter estimates, etc.).

This research in modeling strategy will be heavily computational and will require enhancements to the nation's computing capabilities. In particular, a virtual distributed modeling software engineering infrastructure must be developed to facilitate model comparison. Also needed will be the interchange of modeling components and data sets among models, and adequate computing facilities distributed to participating modeling centers.

Additionally, new possibilities for developing distributed hydrologic models (Figure 3.9) are provided by the expanded analytic capabilities of geographical information systems, along with the availability on the web of information on input parameters, including elevation, vegetation type, soil type, land use land cover, river reaches, and hydrologic unit boundaries, at relatively fine spatial and temporal scales. Improvements in visualization enhance our ability to understand hydrologic systems and allow researchers to transfer model results more readily to the user community.

Figure 3-9 Example of Available Data that can be used
to help Develop Distributed Hydrologic Models

Many kinds of information are readily available from the web for distributed hydrologic modeling, such as vegetation type (U.S. Forest Service), soil type (USDA), and land use/land cover (USGS).

Determine the parameter, accuracy, and sampling requirements through observing system simulation experiments at all spatial scales. A powerful method for determining gaps in our measurements to best 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, to determine whether the sampling strategy adequately captures the relevant variability modes in the natural process. The OSS methodology provides important information for 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. Assimilation of observations in hydrologic (surface and groundwater) models has yet to be attempted in a systematic way. To adequately assess the impact of new observing systems on the understanding of predictability and on the ability to predict at seasonal and longer lead times, it will be necessary to develop an OSS methodology to effectively combine hydrology models and observations. This approach can then be used to estimate the scales appropriate to couple or nest component models. New observing systems may also affect the ability of predictive models to make more accurate forecasts.

In addition to OSS experiments to help shape future observing systems, companion observing-system experiments (OS) are needed to assess prediction impacts, sampling and deployment strategy, and improved use 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 on 4DDA, to determine optimal atmospheric observing-system strategies, including impacts on prediction model accuracy.

For these observing systems to be possible, methods will be required for optimally merging data from heterogeneous observing systems, such as weather radar, satellite and surface and upper air stations.

Make fundamental advances in mathematical theories of predictability, based on principles in probability, stochastic processes, nonlinear dynamics, and numerical methods. Mathematical theories are continually developed in other physical sciences, but they have not been systematically applied often to characterize water cycle components. A new program in the science and mathematics of water cycle predictability is needed to guide applications of atmospheric and hydrologic theories over a range of space and time scales. This program would forge collaboration and links among mathematicians, statisticians, hydrologists, and climate modelers to advance predictability in hydrology. Such a program can capitalize on the proposed long-term monitoring network (described below), as well as planned remote-sensing measurements and climate model outputs for wealth of measurement and model results on hydrologic processes.

Until recently, climate and hydrologic models were used for deterministic predictions. In other words, initial and boundary conditions have been applied to obtain a single realization of the weather or climate system, and to predict a single value. As is well known, however, deterministic predictions of, for example, the atmosphere's instantaneous state cannot be made with lead times longer than a few days because of chaotic fluctuations (Figure 3.3). Climate predictions on seasonal and longer time scales must be made within a probabilistic framework. Such predictions are currently made by using an ensemble of model predictions, each with slightly different initial or boundary conditions that are nearly indistinguishable from the actual environmental state given the available measurements. Research is required to place this ad hoc methodology on a firmer theoretical basis.

Promote knowledge transfer among scientists and end users. Earth system models may be able to predict global water cycle variations with some skill. For these predictions to be useful to water resources managers or other decision makes, however, the predictions must take the right form. Coarse-resolution predictions, for example, may need to be converted to the local watershed scale using empirical disaggregation techniques or the additional application of a higher resolution nested model. Uncertainties in the model predictions must also be communicated to the end users in an understandable and quantitatively useful way. The key to achieving this knowledge transfer, of course, is communication and coordination among the scientists and society's end users early in developing prediction systems. Modelers should design their end-to-end systems with the users in mind. Users, in turn, must be sufficiently versed in the limitations of the prediction systems and in the proper interpretations of model-generated data.

Toward this end, a prediction system framework must be developed to identify and quantify 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. Users of water cycle prediction information must help define what aspects of water resources and ecosystems should be managed, and help establish the requirements for predictive information. The modelers must use this information in designing and realizing predictability experiments and operational prediction systems. The information exchange among them all must be iterated with new developments in observing systems, new predictive models, and new water resources and ecosystems management experience. The effective transfer of knowledge gained through this process must be enabled and strengthened.

Large-scale and basin-scale experiments are needed to improve models' representations of the relevant processes, estimate model parameters, validate model simulations and predictions, and evaluate fluxes among hydrologic reservoirs (as through evapotranspiration, recharge, and surface water -- groundwater interactions in watersheds with different types of land cover or major anthropogenic effects. Large-scale models must have greatly improved representations of cloud dynamics; microphysics of precipitation; interaction of cloud liquid water, water vapor, and radiation; transport of water vapor in the planetary boundary layer; and aggregation/disaggregation of surface hydrologic processes over complex landscapes. In land surface models, a number of processes are poorly represented. Model parameter estimation and model validation require the integrating of observations, process-resolving models, and large-scale (parameterized) models through data assimilation and adjoint techniques.

Biospheric responses are inadequately represented for both short- and long-term processes. For example, how vegetation in different biomes around the world will respond to increasing concentrations of atmospheric CO₂ is poorly known; only a limited number of plant species have been studied for such applications. Further, the geographic distribution of different biomes is strongly controlled by climate, especially precipitation and temperature regimes. The manner in which 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 clear in comparing 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 as observations are integrated with process-resolving models and large-scale parameterized models.

The experiments for evaluating fluxes should help 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. New models must be developed based on quantitative understanding of interactions between these reservoirs, as determined from detailed field experiments.

It is critical to develop a monitoring network to determine initial states for model predictions, verify model forecasts, and validate 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. A high priority is exploiting these advances to verify and validate predictive models, and to determine the scales at which measurements are required to simulate hydrologic variability realistically and accurately. Beyond developing 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.

They should be augmented with an intensive array of soil moisture sensors, precipitation and interception gauges, a network of groundwater monitoring wells, and frequent remote-sensing measurements, particularly using NASA's new canopy lidar. The addition of soil moisture instruments to existing eddy-covariance instruments will help quantify soil moisture content controls for transpiration modeling and for feedback to climate models. Long-term experimental sites for characterizing water balance and flow pathways provide the data essential for developing models and methods to scale hydrologic variables, characterize basin-scale variability, and understand the limits of predictability. The monitoring program covers several components.

Precipitation. Assessing precipitation over the entire globe at sufficiently high resolution will capture its diurnal variability and spatial inhomogeneities to improve understanding of water cycle exchanges and predictions at all scales. The Global Precipitation Mission, according to currently planned specifications, promises 3-hourly, global, 4-kilometer precipitation coverage, and could be the cornerstone of the needed effort. Accurate snow cover mapping by integrating satellite and ground-based networks is also a high priority.

Ocean Fluxes. A global ocean surface flux monitoring program is needed to obtain, for the first time, an estimate of the freshwater flux from atmosphere to ocean (precipitation) and from the oceans to the atmosphere (evaporation). These aspects of the global water cycle remain a major source of uncertainty in attempting 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 millennial-scale changes in ocean circulation. Accurate measurement programs are needed to estimate interannual variability and long-term changes in these ices sheet masses.

Fluxes Among Reservoirs. Another requirement is the monitoring of hydrologic components to quantify fluxes among atmospheric, surface, and subsurface reservoirs. Monitoring efforts should cover a spectrum of land cover variability, with times 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 water cycle variations. 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 for locations 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 water for recharge estimation and to evaluate flow mechanisms (piston vs preferential flow). A national network of groundwater monitoring wells to assess both water level and water chemistry is essential for groundwater recharge characterization and for identifying long-term trends resulting from pumping, drought, and land use change.

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 among scientists in these various disciplines and stakeholders. An intellectual forum and creative funding arrangements are needed to assure opportunities for cross-disciplinary collaboration on important water resources and environmental management issues that are sensitive to climate variability. One essential task of this intellectual forum would be identifying basins that are particularly vulnerable to climate variability and developing climate and hydrologic databases including environmental and socioeconomic variables that would be useful in assessing vulnerability and in making decisions.

To understand how uncertainties propagate through these systems we need to consider both uncertainties in climate variability and uncertainties in human impacts and evaluate the feedback between these elements. Output from such analyses can be used to evaluate policy options and assess their implications for mitigating the potential adverse impacts of 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 vulnerable to climate variability and, in any event, especially receptive to the use of improved forecast information.

Practical use of 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" that can guide decision making. Methods for translating an uncertain forecast (with forecast here meaning a suite of multidimensional variables) to products useful for decision making are in their infancy and much research is needed to advance them. This area must be developed in parallel with the efforts to improve forecast quality -- even forecasts with large uncertainties 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 made or influenced by parties with strongly divergent interests can result in suboptimal resource allocation and ineffective overall risk management. Efforts to overcome such problems 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 benefit from improved access to information on the implications of water cycle variability for local resource management concerns.

The rationale for introducing ensemble forecast methods to the water resources management community is a strong one. Yet despite their clear advantages, ensemble forecasts of weather and climate water cycle variations have not penetrated the water resources management community. The applications initiative should assist water managers in using ensemble forecast products in their operation of water resource systems. The primary focus should be on reservoir systems (or, in some cases, free-flowing rivers). At the same time, implications for groundwater in systems that conjunctively use surface and groundwater should be recognized. The necessary technology transfer might be accomplished through a cooperative applications program such as the program proposed in Chapter 2 for development of improved design protocols incorporating climate information.

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 they would also require evidence of the participation of a credible science-based organization. This arrangement might be accomplished through 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 agency or university. Applications should go well beyond the water resources management community, to embrace other regional economic development and resource management decisions where climate and hydrologic information can play an important role.

Continue to Next Chapter, study References or return to The Global Water Cycle Study.
_____
* Courtesy of United States Climate Change Global Research Program.
1717 Pennsylvania Ave, NW, Suite 250, Washington, DC 20006.
Telephone: 1-202-223-6262.
See original at < http://www.usgcrp.gov/usgcrp/Library/watercycle/wcsgreport2001/wcsg2001chapter3.htm >.

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