Microplastic river emissions are known to be one of the major sources for marine microplastic pollution. Especially urbanized estuaries localized at the land-sea interface and subjected to microplastic emissions from various sources exhibit a high microplastic discharge potential to adjacent coasts. To adapt effective measures against microplastic emissions a more detailed knowledge on the importance of various microplastic sources is necessary. As field data is scarce we combined different approaches to assess microplastic emissions into the Warnow estuary, southwestern Baltic Sea. Resulting microplastic emission estimates are based on in-situ measurements for the catchment emissions, whereas for the remaining microplastic sources within the estuary literature data on microplastic abundances, and various parameters were used (e.g. demographical, hydrological, geographical). The evaluation of the different emission scenarios revealed that the majority of microplastic is likely discharged by the Warnow river catchment (49.4%) and the separated city stormwater system (43.1%) into the estuary, followed by combined sewer discharges (6.1%). Wastewater treatment plant emissions exhibit the lowest percentage (1.4%). Our approach to estimate anti-fouling paint particles emissions from leisure and commercial shipping activities was associated with highest uncertainties. However, our results indicate the importance of this source highlighting the necessity for future research on the topic. Based on our assumptions for microplastic retention within the estuary, we estimate a potential annual emission of 152–291 billion microplastics (majority within the size class 10–100 µm) to the Baltic Sea. Considering all uncertainties of the different applied approaches, we could assess the importance of various microplastic sources which can be used by authorities to prioritize and establish emission reduction measures. Additionally, the study provides parameters for microplastic emission estimates that can be transferred from our model system to other urbanized Baltic estuaries.

Land use changes influence the water balance and often increase surface runoff. The resulting impacts on river flow, water level, and flood should be identified beforehand in the phase of spatial planning. In two consecutive papers, we develop a model-based decision support system for quantifying the hydrological and stream hydraulic impacts of land use changes. Part 1 presents the semi-automatic set-up of physically based hydrological and hydraulic models on the basis of geodata analysis for the current state. Appropriate hydrological model parameters for ungauged catchments are derived by a transfer from a calibrated model. In the regarded lowland river basins, parameters of surface and groundwater inflow turned out to be particularly important. While the calibration delivers very good to good model results for flow (Evol =2.4%, R = 0.84, NSE = 0.84), the model performance is good to satisfactory (Evol = −9.6%, R = 0.88, NSE = 0.59) in a different river system parametrized with the transfer procedure. After transferring the concept to a larger area with various small rivers, the current state is analyzed by running simulations based on statistical rainfall scenarios. Results include watercourse section-specific capacities and excess volumes in case of flooding. The developed approach can relatively quickly generate physically reliable and spatially high-resolution results. Part 2 builds on the data generated in part 1 and presents the subsequent approach to assess hydrologic/hydrodynamic impacts of potential land use changes.

Various process-based models are extensively being used to analyze and forecast catchment hydrology and water quality. However, it is always important to select the appropriate hydrological and water quality modeling tools to predict and analyze the watershed and also consider their strengths and weaknesses. Different factors such as data availability, hydrological, hydraulic, and water quality processes and their desired level of complexity are crucial for selecting a plausible modeling tool. This review is focused on suitable model selection with a focus on desired hydrological, hydraulic and water quality processes (nitrogen fate and transport in surface, subsurface and groundwater bodies) by keeping in view the typical lowland catchments with intensive agricultural land use, higher groundwater tables, and decreased retention times due to the provision of artificial drainage. In this study, four different physically based, partially and fully distributed integrated water modeling tools, SWAT (soil and water assessment tool), SWIM (soil and water integrated model), HSPF (hydrological simulation program-FORTRAN) and a combination of tools from DHI (MIKE SHE coupled with MIKE 11 and ECO Lab), have been reviewed particularly for the Tollense River catchment located in North-eastern Germany. DHI combined tools and SWAT were more suitable for simulating the desired hydrological processes, but in the case of river hydraulics and water quality, the DHI family of tools has an edge due to their integrated coupling between MIKE SHE, MIKE 11 and ECO Lab. In case of SWAT, it needs to be coupled with another tool to model the hydraulics in the Tollense River as SWAT does not include backwater effects and provision of control structures. However, both SWAT and DHI tools are more data demanding in comparison to SWIM and HSPF. For studying nitrogen fate and transport in unsaturated, saturated, and river zone, HSPF was a better model to simulate the desired nitrogen transformation and transport processes. However, for nitrogen dynamics and transformations in shallow streams, ECO Lab had an edge due its flexibility for inclusion of user-desired water quality parameters and processes. In the case of SWIM, most of the input data and governing equations are similar to SWAT but it does not include water bodies (ponds and lakes), wetlands and drainage systems. In this review, only the processes that were needed to simulate the Tollense River catchment were considered, however the resulted model selection criteria can be generalized to other lowland catchments in Australia, North-western Europe and North America with similar complexity.

Lowland river basins are characterised by complex hydrologic and hydraulic interactions between the different subsystems (aerated zone, groundwater, surface water), which may require physically-based dynamically-coupled surface water and groundwater hydrological models to reliably describe these processes. Exemplarily, for a typical north-eastern Germany lowland catchment (Tollense river with about 400 km 2 ), an integrated hydrological model, MIKE SHE, coupled with a hydrodynamic model, MIKE 11, was developed and assessed. Hydrological and hydraulic processes were simulated from 2010 to 2018, covering strongly varying meteorological conditions. To achieve a highly reliable model, the calibration was performed in parallel for groundwater levels and river flows at the available monitoring sites in the defined catchment. Based on sensitivity analysis, saturated hydraulic conductivity, leakage coefficients, Manning's roughness, and boundary conditions (BCs) were used as main calibration parameters. Despite the extreme soil heterogeneity of the glacial terrain, the model performance was quite reasonable in the different sub-catchments with an error of less than 2% for water balance estimation. The resulted water balance showed a strong dependency on land use intensity and meteorological conditions. During relatively dry hydrological years, actual evapotranspiration (ETa) becomes the main water loss component, with an average of 60%-65% of total precipitation and decreases to 55%-60% during comparatively wet hydrological years during the simulation period. Base flow via subsurface and drainage flow accounts for an approximate average of 30%-35% during wet years and rises up to 35%-45% of the total water budget during the dry hydrological years. This means, groundwater is in lowland river systems the decisive compensator of varying meteorological conditions. The coupled hydrologic and hydraulic model is valuable for detailed water balance estimation and seasonal dynamics of groundwater levels and surface water discharges, and, due to its physical foundation, can be extrapolated to analyse meteorological and land use scenarios. Future work will focus on coupling with nutrient transport and river water quality models.Appl. Sci. 2020, 10, 1281 2 of 23 water resources. Hydrological research and forecast has become increasingly important due to the growing problems with the available water resources, and due to this reason, the hydrological forecasts have been modernised, extending from simple flood predictions to detailed water management decisions and practices that require extensive hydrological processes information [1][2][3][4]. Integrated hydrological models play an essential role by providing the necessary information regarding biological, physical, and chemical processes and their interactions within a catchment [5,6]. In general, the temporal and spatial variations of GW and SW resources are imprecisely known which effects the proper management of water resources [7][8][9]. Physically-based dynamically-coupled SW a...

Evapotranspiration (ET) has a decisive effect on groundwater recharge and thus also affects the base flow of the receiving water. This applies above all to low-lying areas with a low depth to groundwater (GW), as is often the case in the north German lowlands. In order to analyze this relation, a coupled rainfall-runoff and hydraulic stream model was set up using the software SWMM-UrbanEVA, a version of the software SWMM that was upgraded by a detailed ET module. A corresponding model was set up for the same site but with the conventional software SWMM to compare the water balance and hydrographs. The total amount of ET calculated with the SWMM software is 7% higher than that computed with the upgraded version in the period considered. Therefore, less water is available for soil infiltration and lateral groundwater flow to the stream. This generally leads to a slight underestimation of base flows, with the exception of a notably wet summer month when the base flows were highly overestimated. Nevertheless, the base flow hydrograph shows a good adaptation to observed values (MAE = 0.014 m3s−1, R = 0.88, NSE = 0.81) but gives worse results compared to SWMM-UrbanEVA. The latter is very well able to reflect the GW-fed base flow in the sample stream in average (MAE = 0.011 m3s−1) and in its dynamics (R = 0.93, NSE = 0.85). By applying the UrbanEVA upgrade, SWMM is applicable to model the seasonal dynamics of near-natural river basins.