MONERIS is a semi-empirical, conceptual model, which has gained international acceptance as a robust meso-to macro scale model for nutrient emissions. MONERIS is used to calculate nitrogen (N) and phosphorus (P) emissions into surface waters, in-stream retention, and resulting loads, on a river catchment scale. This paper provides the first (i) comprehensive overview of the model structure (both the original elements and the new additions), (ii) depiction of the algorithms used for all pathways, and for retention in surface waters, and (iii) illustration of the monthly disaggregation of emissions and the implementation of measures. The model can be used for different climatic conditions, long term historical studies, and for future development scenarios. The minimum validated spatial resolution is 50 km 2 , with a temporal resolution of yearly or monthly time steps. The model considers seven emission pathways (atmospheric deposition on surface waters, overland flow, erosion, tile drainage, groundwater, emissions from sealed urban areas, and point sources), and six emission sources (natural background, fertilizer application, nitrogen atmospheric deposition on arable land and other areas, urban sources, and point sources); and these are calculated separately for different land-uses. The pathway and source-related approach is a prerequisite for the implementation of measures to reduce non-point and point-source emissions. Therefore, we have modified MONERIS by the addition of a "management alternative" tool which can identify the potential effectiveness of nutrient reduction measures. MON-ERIS is an appropriate tool for addressing the scientific and political aspects of river basin management in support of a good surface water quality.

Aim: Systematic conservation planning is vital for allocating protected areas given the spatial distribution of conservation features, such as species. Due to incomplete species inventories, species distribution models (SDMs) are often used for predicting species' habitat suitability and species' probability of occurrence. Currently, SDMs mostly ignore spatial dependencies in species and predictor data. Here, we provide a comparative evaluation of how accounting for spatial dependencies, that is, autocorrelation, affects the delineation of optimized protected areas.Location: Southeast Australia, Southeast U.S. Continental Shelf, Danube River Basin. Methods:We employ Bayesian spatially explicit and non-spatial SDMs for terrestrial, marine and freshwater species, using realm-specific planning unit shapes (grid, hexagon and subcatchment, respectively). We then apply the software gurobi to optimize conservation plans based on species targets derived from spatial and non-spatial SDMs (10%-50% each to analyse sensitivity), and compare the delineation of the plans.Results: Across realms and irrespective of the planning unit shape, spatially explicit SDMs (a) produce on average more accurate predictions in terms of AUC, TSS, sensitivity and specificity, along with a higher species detection probability. All spatial optimizations meet the species conservation targets. Spatial conservation plans that use predictions from spatially explicit SDMs (b) are spatially substantially different compared to those that use non-spatial SDM predictions, but (c) encompass a similar amount of planning units. The overlap in the selection of planning units is smallest for conservation plans based on the lowest targets and vice versa.Main conclusions: Species distribution models are core tools in conservation planning. Not surprisingly, accounting for the spatial characteristics in SDMs has drastic impacts on the delineation of optimized conservation plans. We therefore encourage practitioners to consider spatial dependencies in conservation features to improve the spatial representation of future protected areas. K E Y W O R D SBayesian hierarchical modelling, connectivity, gurobi, integer linear programming, spatial autocorrelation, spatial unit | 759 DOMISCH et al.

Freshwater species are adapted to and depend on various discharge conditions, such as 32 indicators of hydrologic alteration (IHA). Knowing how these indicators will be altered under climate change is essential for predicting species response and to develop mitigation concepts. The simulation of IHA under climate change is subject to considerable uncertainties which should be considered to obtain credible and robust predictions. Therefore, we investigated the major uncertainties inherent in climate change data and processing: General circulation model (GCM) and regional climate model (RCM) choice, representative concentration pathway (RCP) scenario, bias correction (BC) method, all within three mesoscale catchments in the European ecoregions: Central Plains, Central Highlands, and Alpine. Highest uncertainties were caused by the GCM and RCM choice, followed by the type of BC and the RCP. For the prediction, we reduced these uncertainties tailored to the ideal depiction of the IHA in each ecoregion. Together with a significance test, this enabled a robust depiction of the change in IHA for two future time periods. We found diverging changes within the ecoregions, caused by the complex interaction between precipitation, temperature and the governing catchment hydrological processes. The results provide an important basis for further impact research, especially for ecological freshwater studies.

Artificial drainage systems affect all components of the water and matter balance. For the proper simulation of water and solute fluxes, information is needed about artificial drainage discharge rates and their response times. However, there is relatively little information available about the response of artificial drainage systems to precipitation. To address this need, we analysed 11 datasets from artificial drainage study sites (daily or hourly resolution), one daily dataset from an open ditch system, and three datasets from rainfall simulations on tile-drained fields.When we considered all 11 artificial drainage study sites, we found that artificial drainage discharge responded to 70% of all rainfall events during the year, and that the response rate differed significantly between 56% summer and 84% in winter. A median of 23% of the yearly precipitation rate is discharged by artificial drainage systems, varying from 9% of the precipitation in summer to 54% of the precipitation in winter. The artificial drainage systems usually started to respond within the first hour under rain fed conditions, and the response time increased at lower rainfall intensities ( < 1 mm h -1 ). The peak outflow normally occurred within the first two days. The influence of soil texture and land use on artificial drainage discharge rates could not be reproduced properly, due to the spatial high variability caused by other site-specific properties.

1. Freshwater ecosystems have a higher percentage of threatened and extinct species than terrestrial or marine realms, but remain under-represented in conservation research and actions arguably as a consequence of less popularity and promotion.