Development and testing of a phytoplankton index of biotic integrity (P-IBI) to assess the ecological conditions of German lowland rivers

This dissertation developed and tested a phytoplankton index of biotic integrity (P-IBI) to assess the ecological conditions of German lowland rivers, and simulated the daily phytoplankton biomass (as indicated by chlorophyll a) dynamics for the purpose of water quality forecasting and watershed management. Prior to that, this study determined whether phytoplankton assemblages generated by the two sampling protocols were related to different environmental variables and therefore might yield different bioassessment, and investigated the relationships between phytoplankton and environmental variables. The study catchment of Kielstau catchment (50 km²) is located in the North German lowlands. It is characterized by low hydraulic gradients, shallow groundwater, and flat topography. Sandy, loamy and peat soils are characteristic for this catchment. The water quality is not only influenced by the predominating agricultural land use in the catchment as cropland and pasture, but also by municipal wastewater treatment plants and drainage systems. At first, phytoplankton assemblages generated by two sampling protocols in the Kielstau catchment were compared to select a better sampling protocol for phytoplankton based bioassessment. Plankton net (PLNET) protocol was a better method compared with sedimentation (SEDIM) protocol because algal data collected by PLNET protocol had higher relationship with environmental variables. Then the spatial and temporal dynamics of phytoplankton community and its relationship with environmental variables were investigated. Phytoplankton community in the Kielstau catchment was a typical riverine diatom-dominated community and prevailed by species of Achnanthes, Cocconeis, Cyclotella, Fragilaria, Navicula and Tabellaria. Remarkable spatial and temporal variations of phytoplankton community were observed and canonical correspondence analysis (CCA) indicated that physical factors (e.g. hydrological variables) and major nutrients (e.g. total phosphorus, dissolved inorganic nitrogen) were of equal importance controlling the variation in structure of riverine phytoplankton assemblages. To assess the ecological conditions of German lowland rivers, a phytoplankton index of biotic integrity (P-IBI) was developed and tested in the Kielstau catchment. It consisted of six metrics (out of 36 original metrics) based on Cumulative_R2 and correlation index (CoI) between biotic metrics and environmental variables. The developed P-IBI and its six metrics were indicative for ecological integrity and water quality and it could be a useful tool to measure the long-term status of streams and the effectiveness of various watershed managements. By implementing the developed P-IBI in the study area, the ecological status varied seasonally and the general ecological status was 'Moderate' regardless of seasonal variations, which was lower than the requirement ('Good' status) of the Water Framework Directive (WFD) by 2015. The relative lower ecological status was probably caused by human disturbances such as point sources, diffuse sources emissions and artificial drainage systems. Thereafter, based on a 1.5-y measured data set of chlorophyll a and environmental variables, two modeling approaches (artificial neural networks, ANN and multiple linear regression, MLR) were developed to simulate the daily chlorophyll a dynamics. In general, the developed ANN and MLR models achieved satisfactory accuracy in predicting daily dynamics of chlorophyll a concentrations. The predicted and the observed data matched closely by MLR model with coefficient of determination (R2) and Nash-Sutcliffe efficiency (NS) of 0.53 and 0.53 for calibration period and 0.63 and 0.62 for validation period. Likewise, the results of ANN model also illustrated a good agreement between observed and predicted data during calibration and validation periods, which was demonstrated by R2 and NS values (0.68 and 0.68 for calibration period, 0.55 and 0.66 for validation period). For watershed management, combined with an automatic water quality monitoring device and a telemetry system, the developed models can be used as a real-time forecasting system. The results of prediction can help to assess the future changes of ecological status and develop efficient water management policy.


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