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Optimizing precipitation monitoring networks is crucial. Reducing gauges at Hubbard Brook Forest by over 50% minimally impacted precipitation estimates, allowing resource reallocation.

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Area of Science:

  • Environmental Science
  • Hydrology
  • Ecology

Background:

  • Precipitation monitoring network design requires balancing data accuracy with resource constraints.
  • Network designs must adapt to evolving monitoring objectives and resource availability.

Purpose of the Study:

  • To evaluate the impact of reducing precipitation gauge density on estimate quality.
  • To develop a decision-making framework for optimizing monitoring network design.

Main Methods:

  • Simulated reduced sampling schemes by analyzing all gauge removal combinations for two slopes.
  • Assessed the impact of gauge reduction on long-term average annual precipitation estimates.

Main Results:

  • 95% of simulated scenarios with fewer gauges yielded precipitation estimates within 2% of the best estimate.
  • Reducing gauges from 23 to 10 at Hubbard Brook resulted in <2% change in long-term precipitation estimates.

Conclusions:

  • Precipitation estimates are often insensitive to significant reductions in gauge density.
  • Network redesign decisions can incorporate factors beyond data accuracy, such as safety and logistical efficiency.