Climate Change 2001:
Working Group II: Impacts, Adaptation and Vulnerability
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3.4.6. Water Resource Scenarios Reference Conditions

Water is a resource of fundamental importance for basic human survival, for ecosystems, and for many key economic activities, including agriculture, power generation, and various industries. The quantity and quality of water must be considered in assessing present-day and future resources. In many parts of the world, water already is a scarce resource, and this situation seems certain to worsen as demand increases and water quality deteriorates, even in the absence of climate change. Abundance of the resource at a given location can be quantified by water availability, which is a function of local supply, inflow, consumption, and population. The quality of water resources can be described by a range of indicators, including organic/fecal pollution, nutrients, heavy metals, pesticides, suspended sediments, total dissolved salts, dissolved oxygen, and pH.

Several recent global analyses of water resources have been published (Raskin et al., 1997; Gleick, 1998; Shiklomanov, 1998; Alcamo et al., 2000). Some estimates are shown in Table 3-3. For regional and local impact studies, reference conditions can be more difficult to specify because of large temporal variability in the levels of lakes, rivers, and groundwater and human interventions (e.g., flow regulation and impoundment, land-use changes, water abstraction, effluent return, and river diversions; Arnell et al., 1996).

Industrial wastes, urban sewage discharge, application of chemicals in agriculture, atmospheric deposition of pollutants, and salinization negatively affect the quality of surface and groundwaters. Problems are especially acute in newly industrialized countries (UNEP/GEMS, 1995). Fecal pollution of freshwater basins as a result of untreated sewage seriously threatens human health in some regions. Overall, 26% of the population (more than 1 billion people) in developing countries still do not have access to safe drinking water, and 66% do not have adequate environmental sanitation facilities—contributing to almost 15,000 deaths each day from water-related diseases, nearly two-thirds of which are diarrheal (WHO, 1995; Gleick, 1998; see Chapter 9).

Table 3-4: The role of various types of climate scenarios and an evaluation of their advantages and disadvantages according
to the five criteria described in the text. Note that in some applications, a combination of methods may be used—for example,
regional modeling and a weather generator (WGI TAR Chapter 13, Table 13.1).
Type or Tool
  • Testing system
  • Identifying key
    climate thresholds
  • Easy to design and apply (5)
  • Allows impact response surfaces to be
    created (3)
  • Potential for creating unrealistic
    scenarios (1,2)
  • Not directly related to GHG forcing (1)
  • Characterizing
    warmer periods in
  • Physically plausible changed climate
    that really did occur in the past of a
    magnitude similar to that predicted for
    ~2100 (2)
  • Variables may be poorly resolved in
    space and time (3,5)
  • Not related to GHG forcing (1)
  • Exploring
    vulnerabilities and
    some adaptive
  • Physically realistic changes (2)
  • Can contain a rich mixture of well-resolved,
    internally consistent,
    variables (3)
  • Data readily available (5)
  • Not necessarily related to GHG forcing
  • Magnitude of climate change usually
    quite small (1)
  • No appropriate analogs may be available (5)
  • Extrapolating
  • Pedagogic
  • May contain a rich mixture of well-resolved
    variables (3)
  • Not related to GHG forcing (1,4)
  • Often physically implausible (2)
  • No appropriate analogs may be available
  • Starting point for
    most climate
  • Large-scale
    response to
  • Information derived from the most
    comprehensive, physically based
    models (1,2)
  • Long integrations (1)
  • Data readily available (5)
  • Many variables (potentially) available
  • Spatial information poorly resolved (3)
  • Daily characteristics may be unrealistic
    except for very large regions (3)
  • Computationally expensive to derive
    multiple scenarios (4,5)
  • L a rge control run biases may be a
    concern for use in certain regions (2)
  stretched grid
  • Providing
    information at
  • Provides highly resolved information
  • Information derived from physically
    based models (2)
  • Many variables available (3)
  • Globally consistent and allows for
    feedbacks (1,2)
  • Computationally expensive to derive
    multiple scenarios (4,5)
  • Problems in maintaining viable
    parameterizations across scales (1,2)
  • High resolution dependent on SSTs and
    sea ice margins from driving model
    (AOGCM) (2)
  • Dependent on (usually biased) inputs
    from driving AOGCM (2)
  • Providing high
  • Provides very highly resolved
    information (spatial and temporal) (3)
  • Information derived from physically
    based models (2)
  • Many variables available (3)
  • Better representation of some weather
    extremes than in GCMs (2,4)
  • Computationally expensive, thus few
    multiple scenarios (4,5)
  • Lack of two-way nesting may raise
    concern regarding completeness (2)
  • Dependent on (usually biased) inputs
    from driving AOGCM (2)
Climate Model-Based
  • Providing point/
    high spatial
  • Can generate information on high-resolution
    grids or nonuniform regions
  • Potential, for some techniques, to
    address a diverse range of variables (3)
  • Variables are (probably) internally
    consistent (2)
  • Computationally (relatively)
    inexpensive (5)
  • Suitable for locations with limited
    computational resources (5)
  • Rapid application to multiple GCMs (4)
  • Assumes constancy of empirical
    relationships in the future (1,2)
  • Demands access to daily observational
    surface and/or upper air data that span
    range of variability (5)
  • Not many variables produced for some
    techniques (3,5)
  • Dependent on (usually biased) inputs
    from driving AOGCM (2)
  • Integrated
  • Exploring
  • Pedagogic
  • May allow for sequential quantification
    of uncertainty (4)
  • Provides "integrated" scenarios (1)
  • Multiple scenarios easy to derive (4)
  • Usually rely on linear pattern-scaling
    methods (1)
  • Poor representation of temporal
    variability (3)
  • Low spatial resolution (3)
  • Generating
    baseline climate
    time series
  • Altering higher
    order moments of
  • Statistical
  • Generates long sequences of daily or
    subdaily climate (2,3)
  • Variables usually are internally
    consistent (2)
  • Can incorporate altered
    frequency/intensity of ENSO events (3)
  • Poor representation of low-frequency
    climate variability (2,4)
  • Limited representation of extremes
  • Requires access to long observational
    weather series (5)
  • In absence of conditioning, assumes
    constant statistical characteristics (1,2)
  • Exploring
    probability and risk
  • Integrating current
    thinking on
    changes in climate
  • May allow for "consensus" (4)
  • Has potential to integrate very broad
    range of relevant information (1,3,4)
  • Uncertainties can be readily represented
  • Subjectivity may introduce bias (2)
  • Representative survey of experts may
    be difficult to implement (5)
a Numbers in parentheses within the Advantages and Disadvantages columns indicate that they are relevant to the criteria described. The five criteria follow: 1) Consistency at regional level with global projections; 2) physical plausibility and realism, such that changes in different climatic variables are mutually consistent and credible and spatial and temporal patterns of change are realistic; 3) appropriateness of information for impact assessments (i.e., resolution, time horizon, variables); 4) representativeness of potential range of future regional climate change; and 5) accessibility for use in impact assessments.

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