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Area of Science:

  • Hydrology
  • Environmental Modeling
  • Data Science

Background:

  • Hydrological models are essential for forecasting but are subject to inherent uncertainties from data and parameter variations.
  • Additional data collection aims to reduce forecast uncertainty, yet financial limitations necessitate prioritizing data with maximal information content.
  • Current qualitative expert-based decisions for data collection may not yield optimal designs for complex hydrogeological problems.

Purpose of the Study:

  • To extend previous data worth analyses by incorporating simultaneous selection of multiple new measurements.
  • To consider multiple forecasts of interest within the data collection optimization framework.
  • To provide a quantitative approach for optimizing data collection strategies in hydrology.

Main Methods:

  • Developed an extended data worth analysis framework.
  • Integrated simultaneous selection of multiple measurements and consideration of multiple forecasts.
  • Utilized probability mapping to identify informative areas for specific forecasts.

Main Results:

  • Demonstrated an approach to optimize data collection for hydrological forecasting.
  • Showcased the ability to suggest specific measurement sets or generate probability maps for informative areas.
  • Provided evidence that sequential measurement selection can lead to suboptimal designs.

Conclusions:

  • The proposed method optimizes data collection by maximizing information content for hydrological forecasts.
  • Including estimates of data covariance is critical for selecting effective future measurement sets.
  • This approach enhances the efficiency and effectiveness of hydrological data acquisition strategies.