The implementation process of the EU Water Framework Directive (WFD) requires a set of management measures to achieve good ecological status for all surface waters until the year 2027. It can be assumed that particularly multiple-stressor effects and excess fine sediment are the key obstacles to achieving good ecological status. Therefore, both (i) quantified stressor effects and interactions and (ii) the knowledge of fine sediment impacts and an applicable assessment scheme can help river basin managers to derive adequate management actions. However, the knowledge about multiple-stressor effects is still incomplete and an agreed method for assessing/monitoring fine sediment impact does not exist. In order to address these gaps, this thesis focused on the following objectives: (i) Investigation of the hierarchy and interactions of multiple anthropogenic stressors using standard WFD monitoring data, (ii) Development of a stream type-specific index, which reflects the fine sediment impact in small mountain streams based on the taxon-specific response of macroinvertebrates and (iii) Establishment of a bioassessment scheme, which makes the developed fine sediment index applicable for river managers. According to these objectives, this thesis is divided into three chapters. The first chapter identified stressor hierarchy and interactions using standard WFD monitoring data. Here, the impact of 12 environmental predictors on fish, benthic invertebrates and aquatic macrophytes was addressed. The response variables were traits, ecological metrics and biological indices. The results obtained in this chapter showed that riverine habitat degradation was the dominant stressor group for the river fauna, notably the bed physical habitat structure. Overall, the explained variation in benthic invertebrate metrics was higher than it was in fish and macrophyte metrics. In particular, general integrative (aggregate) metrics such as percentage Ephemeroptera, Plecoptera and Trichoptera (EPT) taxa performed better than ecological traits (e.g., percentage feeding types). Overall, additive stressor effects dominated, while significant and meaningful stressor interactions were generally rare and weak, thus implying independently-acting stressors. In the second chapter a stream type-specific biological index (DFSI) was developed, which evaluates the fine sediment impact in small mountainous gravel-bed streams. The DFSI is based on the taxon-specific response of macroinvertebrates to deposited fine sediment. Fine sediment was sampled at 73 sampling sites applying a sediment remobilization approach. Macroinvertebrate taxalists from these sites originated from monitoring schemes. Threshold Indicator Taxa ANalysis (TITAN) was applied on the macroinvertebrate taxalists and fine sediment data to identify indicator taxa, which were then used for index development. Finally, the performance of the index was tested on an independent data set and compared with other fine sediment indices and standard WFD metrics. In total, TITAN identified 95 reliable indicator taxa, of which some taxa tolerated large amounts of fine sediment (e.g., Gammarus roeselii and Tubificidae Gen. sp.), while others were found to be highly sensitive to increased fine sediment mass (e.g., Elodes sp. and Limnius perrisi). Applied on the independent data set, the index performed well in detecting the magnitude of deposited fine sediment. Furthermore, the index was better related to the deposited fine sediment mass as compared to other fine sediment indices and standard metrics used for monitoring purposes under the WFD. In the third chapter, a bioassessment scheme was developed, which makes the DFSI (chapter II) applicable for river managers by transforming DFSI results into quality classes. Data from 489 macroinvertebrate sampling sites were used to derive a specific value for the fine sediment basic state condition (FSBS). Subsequently, five fine sediment quality classes (“high”, “good”, “moderate”, “poor” and “bad”) of increasing deviation from the FSBS were elaborated. Moreover, the results were validated with a second independent data set. An additional objective was to analyse the influence of catchment land cover on the fine sediment loads and to identify change points of the DFSI along the land use gradient. For this purpose, a Random Forest analysis was applied. A reliable value for the FSBS was derived by applying a mathematical procedure and testing on an independent data set. Following on from the value of FSBS, the boundaries of five fine sediment quality classes were calculated by using a method similar to the classification of Ecological Quality Ratios (EQR). Furthermore, catchment arable land was identified as the most powerful predictor for fine sediment loads in the Random Forest model followed by catchment urban area. However, the latter one accounted for a clearly smaller part of the variation in the DFSI. DFSI change points were detected at ~12% arable land and at ~3% urban area indicating that abrupt macroinvertebrate response may occur at relatively low levels of catchment land use.