Description: The latest version of the Soil and Water Assessment Tool (SWAT+) features several improvements compared with previous versions of the model, for example, the definition of landscape units that allow for a better representation of spatio‐temporal dynamics. To evaluate the new model capabilities in lowland catchments characterized by near‐surface groundwater tables and extensive tile drainage, we assess the performance of two SWAT+ model setups in comparison to a setup based on a previous SWAT model version (SWAT3S with a modified three groundwater storage model) in the Kielstau catchment in Northern Germany. The Kielstau catchment has an area of about 50 km2, is dominated by agricultural land use, and has been thoroughly monitored since 2005. In both SWAT+ setups, the catchment is divided into upland areas and floodplains, but in the first SWAT+ model setup, runoff from the hydrologic response units is summed up at landscape unit level and added directly to the stream. In the second SWAT+ model setup, runoff is routed across the landscape before it reaches the streams. Model results are compared with regard to (i) model performance for stream flow at the outlet of the catchment and (ii) aggregated as well as temporally and spatially distributed water balance components. All three model setups show a very good performance at the catchment outlet. In comparison to a previous version of the SWAT model that produced more groundwater flow, the SWAT+ model produced more tile drainage flow and surface runoff. Results from the new SWAT+ model confirm that the representation of routing processes from uplands to floodplains in the model further improved the representation of hydrological processes. Particularly, the stronger spatial heterogeneity that can be related to characteristics of the landscape, is very promising for a better understanding and model representation of hydrological fluxes in lowland areas. The outcomes of this study are expected to further prove the applicability of SWAT+ and provide useful information for future model development.

Description: The model performance of all three model setups was very good, but the SWAT+ model setup with runoff routing between landscape units performed best. Moreover, the SWAT+ model applications predicted a greater spatial heterogeneity of the water balance components. The representation of hydrological fluxes particularly with regard to groundwater flow, surface runoff, and tile drainage flow differed considerably between the SWAT and SWAT+ model setups.

Description: Rivers and related freshwater ecosystems are facing increasing natural disturbance and anthropogenic stressors. Understanding the key ecological processes that govern the riverine biota in aquatic ecosystems under multiple pressures has crucial importance. However, there is still insufficient knowledge in quantifying of stressors interactions. Moreover, the understanding of the responses of riverine phytoplankton to multiple stressors is still scarce from catchment aspect. As an interdisciplinary study, the catchment hydrological processes were linked to ecological responses in this study, and we chose phytoplankton functional groups (PFGs) instead of taxonomic classifications of algae to examine their responses to land-use pattern (L), hydrological regime (H), and physicochemical condition (P) across two contrasting hydrological periods (dry, wet). The traits-based phytoplankton functional groups are highly suggested as robust bio-indicators for better understanding the current ecological status. The hydrological regime was described by a matrix indices of hydrological alteration based on the outputs of a well-established ecohydrological model (SWAT). The results from variation partitioning analysis showed that P and H dominate during the dry period and P in high flows. Structural equation models (SEM) showed that the skewness of 7 days discharge emerged as a key driver of H, and had always an indirect effect on functional group TB (benthic diatoms) during both hydrological periods. The functional group M (mainly composed by Microcystis) has directly related to phosphorous in both periods, while indirectly to L of urban area in high flow period, and water bodies in low flow period. This study emphasized that climate change and anthropogenic activities such as altering flow regime and land-use pattern affect directly or indirectly riverine phytoplankton via physicochemical conditions. In addition, our findings highlighted that biomonitoring activities require detailed investigation in different hydrological periods. SEM is recommended for improved understanding of phytoplankton responses to the changing environment, and for future studies to fulfill the increasing demand for sustainable watershed management regarding aquatic biota.

Description: To summarize, we suggest a guideline for water quality modelling: 1. Spatial and temporal patterns of land use and land management are critical to adequately represent water quality in models. Remote sensing and land use models are very useful resources to be exploited. 2. The transfer of a model diagnostic analysis to water quality leads to a better understanding of how water quality variables are controlled by model structures and corresponding model parameters. 3. Assessing multiple model outputs regarding their temporal, spa- tial and process performance using observed time series, remotely sensed spatial patterns, knowledge about transport pathways and even soft data can significantly enhance model consistency. 4. Multi-metric calibration using performance metrics and signa- ture measures both for discharge and water quality, such as FDC and NDC, leads to more balanced model simulations that represent all magnitudes of discharge and water quality accurately. 5. Scenarios and storylines should be co-developed with stake- holders in the river basin to make them more realistic and increase the acceptance of model results. They should be realistic in space and time, and provide a mix of available management options. 6. The interpretation of BMPs can be supported by diagnostic tools to show the effectiveness of measures and their combinations while considering their costs and impacts on ecosystem services.