Regional Annual Flow Estimation by Machine Learning Tool in QGIS for Data-Scarce Catchments

Within the APRIORA project, an open-source, geospatial QGIS plugin was developed to support the implementation of the EU Urban Wastewater Treatment Directive in 2025 by assessing environmental risks from human pharmaceuticals. This multidisciplinary deterministic model estimates annual loads from wastewater treatment plants, distributes them spatially through river networks and calculates the Predicted Environmental Concentration (PEC) for each reach.

The practical application of the tool encountered a key limitation in data-scarce regions, where missing catchment-scale flow data and API consumption data prevented the calculation of PECs. Existing hydrological models often present barriers due to high computational demands, intensive calibration needs and strict data requirements. To bridge this gap, a new, integrated hydrological module for the QGIS plugin was developed, offering a flexible, efficient solution that operates with minimal and easily accessible geospatial inputs. In that way, the tool became applicable in data scarce catchments of the project with limited monitoring networks as Poland and Latvia.

The module consists of four tools designed to operate sequentially. The first, “Fix river network”, establishes topological contributing relationships between river sections. The second, “Contributing area of gauging station”, delineates subcatchments contributing to any available stream gauges, defining the areas for model calibration and validation. This step can be omitted in fully ungauged catchments. The third, “Calculate geofactors”, computes physiographic and climatic predictors (e.g., mean elevation, slope, share of forest and settlement area, mean annual precipitation) for each subcatchment. It is important to note that the model makes use of freely available continental-scale datasets (e.g., Copernicus DEM (30m resolution), Corine Land Use Land Cover (100m resolution) and ERA5 monthly total precipitation) thereby ensuring its applicability in regions where data is scarce. The fourth tool, “Flow estimation”, employs a machine learning approach (specifically a Random Forest Regressor) where the previously calculated geofactors act as independent variables to predict the flow measured in gauged subcatchments.

In order to guarantee its applicability in regions without local gauges, the tool allows the use of pre-calibrated, averaged model parameters derived from the project’s partner countries. This provides a transferable solution despite underlying regional hydrological uncertainties. The model estimates annual mean flow and annual mean low flow for regional river sections. This temporal resolution aligns with annual API consumption statistics and also represents the worst-case condition for pollution dilution and environmental risks.

In this presentation, we will present the tool itself as well as results from three different Baltic Sea catchments.

 

Acknowledgement - The authors thank the Interreg Baltic Sea region funding programme – co-founded by the European Union (ERDF) – and all the APRIORA project partners contributing to this work.