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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Inferencia bayesiana para predecir concentraciones pasadas y futuras de nitrato

Matt Dumont1, Connor Cleary1, Richard McDowell2

  • 1Komanawa Solutions Ltd, 4 Ash Street, Christchurch, 8011, Canterbury, New Zealand.

Journal of contaminant hydrology
|February 24, 2026
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La gestión del nitrógeno de nitrato (NO₃N) en aguas subterráneas requiere tener en cuenta los desfases temporales. Un nuevo modelo bayesiano predice con precisión los niveles de NO₃N, mejorando las decisiones de gestión y detectando reducciones más rápido que los métodos tradicionales.

Palabras clave:
Aguas subterráneasRetrasoGestión del sueloLixiviaciónModelo de parámetros agrupadosNitrato

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Área de la Ciencia:

  • Ciencias Ambientales
  • Hidrología
  • Ciencia de Datos

Sus antecedentes:

  • La gestión eficaz de la calidad de las aguas subterráneas requiere tener en cuenta el retraso temporal entre la implementación de las estrategias de gestión y la observación de los cambios en los niveles de nitrógeno de nitrato (NO₃N).
  • Los métodos tradicionales a menudo luchan por incorporar con precisión estos retrasos temporales, lo que puede llevar a decisiones de gestión subóptimas.

Objetivo del estudio:

  • Desarrollar y validar un modelo de inferencia bayesiana rápido y basado en datos para estimar las concentraciones históricas y futuras de NO₃N en aguas subterráneas.
  • Evaluar la capacidad del modelo para detectar reducciones de NO₃N de manera más efectiva y con un tamaño de efecto mayor en comparación con los enfoques frecuentistas, particularmente en sistemas con una desnitrificación mínima.

Principales métodos:

  • El estudio empleó un modelo de inferencia bayesiana que integra modelos de edad de parámetros agrupados con concentraciones de NO₃N medidas.
  • Se realizaron experimentos numéricos para evaluar la precisión y el rendimiento del modelo frente a métodos frecuentistas.

Principales resultados:

  • El modelo desarrollado demostró una precisión razonable en experimentos numéricos.
  • Aceleró significativamente la detección de reducciones de NO₃N y aumentó el tamaño del efecto detectado, mostrando tasas de detección del 20 % -60 % frente al 5 % -25 % para métodos frecuentistas (tiempo de residencia medio > 10 años).
  • La aplicación en sitios de aguas subterráneas de Nueva Zelanda predice un aumento significativo de NO₃N, con un 20 % de los pozos que potencialmente superan los estándares de agua potable en estado estacionario.

Conclusiones:

  • El modelo proporciona una herramienta valiosa para incorporar retrasos temporales en la gestión de NO₃N, lo que permite investigaciones más rápidas y rentables con requisitos de datos reducidos.
  • Son necesarias reducciones significativas de NO₃N (≥20 %) para mantener los estándares actuales de calidad del agua en Nueva Zelanda.
  • El modelo apoya la prueba de hipótesis sobre la gestión histórica de la tierra y ofrece evidencia complementaria para una toma de decisiones informada.