Environmental DNA (eDNA) metabarcoding is raising expectations for biomonitoring of organisms that have hitherto been neglected. To bypass current limitations in taxonomic assignments due to incomplete or erroneous reference databases, taxonomy-free approaches are proposed for biomonitoring at the level of operational taxonomic units (OTUs). This is challenging, because OTUs cannot be annotated and directly compared against classically derived taxonomic data. The application of good stringency treatments to infer the validity of OTUs and clear understanding of the consequences of such treatments is especially relevant for biodiversity assessments. We investigated how common practices of stringency filtering affect eDNA diversity estimates in the statistical framework of Hill numbers. We collected water eDNA samples at 61 sites across a 740-km2 river catchment, reflecting a spatially realistic scenario in biomonitoring. After bioinformatic processing of the data, we studied how different stringency treatments affect conclusions with respect to biodiversity at the catchment and site levels. The applied stringency treatments were based on the consistent appearance of OTUs across filter replicates, a relative abundance cut-off and rarefaction. We detected large differences in diversity estimates when accounting for presence/absence only, such that detected diversity at the catchment scale differed by an order of magnitude between the treatments. These differences disappeared when using stringency treatments with increasing weighting of the OTU abundances. Our study demonstrated the usefulness of Hill numbers for biodiversity analyses and comparisons of eDNA data sets that strongly differ in diversity. We recommend best practice for data stringency filtering for biomonitoring using eDNA.