资源环境科技发展态势分析平台

The management of water resources needs robust methods to efficiently reduce nitrate loads. Knowledge on where natural denitrification takes place in the subsurface is thereby essential. Nitrate is naturally reduced in anoxic environments and high-resolution information of the redox interface, that is, the depth of the uppermost reduced zone is crucial to understand the variability of the denitrification potential. In this study we explore the opportunity to use random forest (RF) regression to model redox depth across Denmark at 100-m resolution based on 13,000 boreholes as training data. We highlight the importance of expert knowledge to guide the RF model in areas where our conceptual understanding is not represented correctly in the training data set by addition of artificial observations. We apply random forest regression kriging in which sequential Gaussian simulation models the RF residuals. The RF model reaches a R-2 score of 0.48 for an independent validation test. Including sequential Gaussian simulation honors observations through local conditioning, and the spread of 800 realizations can be utilized to map uncertainty. Emphasis is put on adequate handling of nonstationarities in variance and spatial correlation of the RF residuals. The RF residuals show no spatial correlation for large parts of the modeling domain, and a local variance scaling method is applied to account for the nonstationary variance. Moreover, we present and exemplify a framework where newly acquired field data can easily be integrated into random forest regression kriging to quickly update local models.

Plain Language Summary Nutrients, such as nitrogen in form of nitrate are essential for plant growth. Despite their benefits, nutrient surplus can cause adverse health and ecological effects. In fact, nitrate leaching from agricultural fields is one of the major water resources management challenges in today's agricultural landscapes. During the transport through the subsurface, nitrate can potentially be degraded in anoxic layers below the redox interface. This is referred to as denitrification and its potential varies spatially depending on the local landscape. The location of the redox interface is thus essential knowledge toward a spatially differentiated water resources management. We applied a machine learning method to model the redox depth at 100m spatial resolution across Denmark, based on redox data obtained from 3,000 boreholes and environmental variables that explain redox variability. We highlight the importance of expert knowledge in guiding the machine learning model by adding artificial observations in underrepresented landscapes. As an add-on, a geostatistical model is used to honor local data at grid scale and quantify the spatial uncertainty elsewhere. Lastly, we outline and exemplify a framework that allows an easy integration of newly acquired field data for updating local models of the depth to the redox interface.