Speaker
Description
Missing data is one of the most persistent challenges in environmental monitoring, undermining the reliability of analyses and limiting effective resource management. This issue is particularly critical under European regulations such as the Nitrates Directive (91/676/EEC), which requires accurate monitoring of nitrate concentrations in groundwater to protect ecosystems and public health. Yet, frequent gaps in monitoring data make compliance and risk assessment challenging.
Although numerous imputation techniques exist, there is no clear consensus on how to evaluate their quality. To address this, we adopted a comprehensive framework that goes beyond simple error metrics. We examined the effect of imputation on the data structure by comparing the distributions of the percentage of stations that exceed nitrate thresholds before and after gap-filling. Marginal distributions were assessed using estimated density functions, with agreement quantified through divergence measures such as Hellinger and Kullback–Leibler distances.
Instead of relying on global linear correlation–which often fails to capture complex dependencies—we applied monotonic dependence measures. The generalised Lorenz curve provided a robust tool for revealing dependence patterns, thereby offering deeper insight into how imputation reshapes relationships within the data.
Using two decades of national groundwater monitoring data (2000–2022), we tested six gap-filling strategies, spanning geostatistical methods and modern predictive algorithms. Our findings reveal that indicator kriging consistently outperforms other approaches, preserving spatial patterns and enabling reliable risk forecasting. By leveraging spatial relationships and introducing advanced evaluation tools, this approach strengthens predictive models and supports more informed decision-making.
Why does this matter? Reliable data is the foundation of effective water management. Addressing missing information and improving evaluation practices can enhance compliance with EU directives, optimise monitoring systems, and protect communities from nitrate pollution.
(Analyses were performed in R 4.3.1 and ArcGIS Pro 3.4.0.)
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