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<p>Comprehensive nutrient monitoring is essential for managing water quality and mitigating the risk of harmful algal blooms in complex riverine systems. This study introduces and applies a unified Data Gaps Score (DGS) to assess the spatial and temporal adequacy of nutrient monitoring in the Peace River Basin (PRB), a 2350-square-mile watershed in Southwest Florida. The DGS integrates four components—sampling density, sampling distribution, concentration variability, and land use risk—into a single composite metric for identifying deficiencies in monitoring networks. Analysis of more than 103,000 water quality records for total nitrogen (TN), total phosphorus (TP), and orthophosphate (ORP) revealed disproportionate sampling in certain regions and extensive clustering throughout the network, with many central tributary regions exhibiting low sampling coverage and heightened risk factors. The DGS highlighted Payne Creek and Bowlegs Creek as substantial monitoring gaps, both bearing high land use pressures and elevated nutrient variability, with Payne further compounded by low sampling intensity. Temporal analysis further detected stagnation or decline in sampling effort across several central regions, with similar declines observed even amongst the more densely monitored areas at the basin margins. These results highlight the local need for more proportional distribution of monitoring resources toward central, high-risk sub-basins to enhance spatial representativeness, increase the detection of nutrient loading, and support more robust risk modeling. Unlike station‑placement optimization approaches, the DGS framework is designed as a diagnostic tool for existing monitoring networks, providing a single, interpretable measure of spatial and risk-weighted monitoring deficiency across predefined management units. It offers actionable, transferable guidance for optimizing monitoring network design and prioritizing data collection to address persistent and emerging environmental risks in riverine nutrient management.</p>

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A data gaps score to optimize nutrient monitoring networks in the Peace River Basin, Florida

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月8日

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  • John Peller,
  • Rachel Rotz,
  • Leandro Nunes de Castro

摘要

Comprehensive nutrient monitoring is essential for managing water quality and mitigating the risk of harmful algal blooms in complex riverine systems. This study introduces and applies a unified Data Gaps Score (DGS) to assess the spatial and temporal adequacy of nutrient monitoring in the Peace River Basin (PRB), a 2350-square-mile watershed in Southwest Florida. The DGS integrates four components—sampling density, sampling distribution, concentration variability, and land use risk—into a single composite metric for identifying deficiencies in monitoring networks. Analysis of more than 103,000 water quality records for total nitrogen (TN), total phosphorus (TP), and orthophosphate (ORP) revealed disproportionate sampling in certain regions and extensive clustering throughout the network, with many central tributary regions exhibiting low sampling coverage and heightened risk factors. The DGS highlighted Payne Creek and Bowlegs Creek as substantial monitoring gaps, both bearing high land use pressures and elevated nutrient variability, with Payne further compounded by low sampling intensity. Temporal analysis further detected stagnation or decline in sampling effort across several central regions, with similar declines observed even amongst the more densely monitored areas at the basin margins. These results highlight the local need for more proportional distribution of monitoring resources toward central, high-risk sub-basins to enhance spatial representativeness, increase the detection of nutrient loading, and support more robust risk modeling. Unlike station‑placement optimization approaches, the DGS framework is designed as a diagnostic tool for existing monitoring networks, providing a single, interpretable measure of spatial and risk-weighted monitoring deficiency across predefined management units. It offers actionable, transferable guidance for optimizing monitoring network design and prioritizing data collection to address persistent and emerging environmental risks in riverine nutrient management.