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NWWG meets the third Wednesday of every month from 4:30-6PM at the Navarro River Resource Center. All are welcome! Find Out More

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Latest Tree Tip

Getting the most from your Firewood
By Clare Nunamaker

If you're relying on firewood for heating this winter, you're probably already aware of the importance of burning se... Read More

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Watershed Managers' Lessons Learned at the 2002 World Watershed Summit

The technical part is easier and faster than the human part.

  • Political challenges are greater than scientific/technical challenges.

Education is the single most important and effective "management practice."

  • The command and control approach is important but secondary. Complementary collaborative strategies are needed.

  • Perception is everything — If the resource is not aesthetically pleasing, people won't believe it's worth saving.
  • More aggressive demand for conservation measures should be incorporated into society.
  • Youth respond better to youth vs. adult —led workshops; they are easily motivated and often more willing to protect waters than parents.
  • Keep lessons entertaining. Humor helps, as do hands-on activities.

Keep it small, Keep it confidential, Keep it moving, Keep it flexible and Keep it local

  • Watershed protection is a long-term endeavor.

  • You can’t steer a parked car - Delivering tiny successes while planning the big one helps to keep the public committed.
  • Adaptive management is helpful to guide the frequently necessary corrections. Water quality forecasting/modeling often is not reliable enough to provide a scientific basis.
  • Pilot projects are useful.

Watershed plans must be the property of the local community.

  • The "Three Rs" are key: Respect, Recognition, and Reward

  • The watershed protection process is more a "bottom up" than a "top down" approach.
  • Governments have to provide leadership but not ownership.
  • It is tough sledding without a good local coordinator.
  • Organization and long-term support are key to maintaining energy and interest. Assessment establishes synergy.
  • One-on-one technical assistance is important.

Water is a shared resource -- all sectors need to be engaged.

  • Effective efforts are collaborative and comprehend the entire array of affected agencies, interests, concerns, issues, and opportunities (multi-functionally).

  • Scientists and resource managers need to frequently interact.
  • Integration of staffing and programs helps to better manage resources.
  • Success is based, in large measure, on the information revolution.

Watershed management is primarily about land use management

  • Land use decision makers are a chronically underserved audience.

  • There are simple methodologies for determining prior

    ities.

  • Land conservation nonprofits and public land managers can be effective partners.
  • Land conservation helps mitigate water quality challenges.
  • The tools exist to minimize imperviousness.

Resources are limited, thus we are wise to clearly identify goals and endpoints, correctly diagnose impaired waters, and compare benefits and risks of alternatives.

  • States may need to thoughtfully and rigorously reconsider their water quality standards.

  • Legally based objectives and requirements facilitate planning and practice. Methodology should be standardized.
  • Instream habitat is relatively ignored in risk assessment.
  • An experimental approach is required to determine cause-effect relationships: continuous simulation modeling can generate realistic alternative scenarios.
  • Well-designed monitoring programs and statistical hypothesis testing are important.
  • Adequate monitoring and assessment and refined WQS can simultaneously support multiple water quality management program needs and objectives
  • State monitoring, assessment, and water quality standards must be sufficiently adequate and refined to support watershed-based management. They must be at the same spatial scale.
  • Sophisticated statistics are needed to understand the relative importance of some factors.
  • Models must be well calibrated — error and bias must be evaluated.

New tools offering flexibility and incentives are needed to achieve CWA goals.

  • Tools are lacking to comprehensively address interrelated watershed issues

  • Successful models are needed to fit local circumstances.
  • Water quality trading is a flexible market-based tool with growing acceptance and use; other market-based approaches are also needed for ecosystem management.

Significant improvement will require management of many nonpoint and diffuse urban contributions as well as physical alterations to the landscape.

  • Surface water, groundwater, and the interaction between them are important. Geology, soils, climate, hydrology and other factors are important determinants of water conditions.
  • Urban watersheds tend to show a unique signature:
    • As watersheds are developed, biological communities degrade; Physical factors are as (or even more) important than chemical pollution.
    • Downstream of urban discharges show greater impacts than their rural counterparts, despite better wastewater treatment.
  • The natural flow regime must be protected or restored to extent possible: both high and low flows are important to system integrity.
    • Stormwater runoff volume is a very big pollutant and the one that is growing the most rapidly. If we cannot manage that, waters will not be fishable or swimmable.
  • Monitoring for dry weather combined sewer overflows is a priority and requires a high commitment of resources. Floatables control is not easy or inexpensive.
  • Imperviousness is a good indicator of watershed potential

Beware the data loop, but:

  • Integration of habitat, chemistry and biology into geographic information systems (GIS) is an essential assessment tool.

  • Better data sets are needed to construct predictive models of watersheds.
  • Long-term monitoring data is needed to assess effectiveness of management actions.
  • High-resolution (spatial and temporal) data is necessary to adequately manage and restore impaired streams.
  • Models should drive data collection so that data can be interpreted in the right context.
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