Climate and land-use change drive a suite of stressors that shape ecosystems and interact to yield complex ecological responses, i.e. additive, antagonistic and synergistic effects.Currently we know little about the spatial scale relevant for the outcome of such interactions and about effect sizes. This knowledge gap needs to be filled to underpin future land management decisions or climate mitigation interventions, for protecting and restoring freshwater ecosystems. The study combines data across scales from 33 mesocosm experiments with those from 14 river basins and 22 cross-basin studies in Europe producing 174 combinations of paired-stressor effects on a biological response variable. Generalised linear models showed that only one of the two stressors had a significant effect in 39% of the analysed cases, 28% of the paired-stressor combinations resulted in additive and 33% in interactive (antagonistic, synergistic, opposing or reversal) effects. For lakes the frequency of additive and interactive effects was similar for all spatial scales addressed, while for rivers this frequency increased with scale. Nutrient enrichment was the overriding stressor for lakes, generally exceeding those of secondary stressors. For rivers, the effects of nutrient enrichment were dependent on the specific stressor combination and biological response variable. These results vindicate the traditional focus of lake restoration and management on nutrient stress, while highlighting that river management requires more bespoke management solutions.

A new version of the Integrated Nitrogen in Catchments model (INCA) was developed and tested using flow and streamwater nitrate concentration data collected from the River Kennet during 1998. INCA is a process-based model of the nitrogen cycle in the plant/soil and instream systems. The model simulates the nitrogen export from different land-use types within a river system, and the in-stream nitrate and ammonium concentrations at a daily time-step. The structure of the new version differs from the original, in that soil-water retention volumes have been added and the interface adapted to permit multiple crop and vegetation growth periods and fertiliser applications. The process equations are now written in terms of loads rather than concentrations allowing a more robust tracking of mass conservation when using numerical integration. The new version is able to reproduce the seasonal dynamics observed in the streamwater nitrogen concentration data, and the loads associated with plant/soil system nitrogen processes reported in the literature. As such, the model results suggest that the new structure is appropriate for the simulation of nitrogen in the River Kennet and an improvement on the original model. The utility of the INCA model is discussed in terms of improving scientific understanding and catchment management.

Microbial processes in soil are moisture, nutrient and temperature dependent and, consequently, accurate calculation of soil temperature is important for modelling nitrogen processes. Microbial activity in soil occurs even at sub-zero temperatures so that, in northern latitudes, a method to calculate soil temperature under snow cover and in frozen soils is required. This paper describes a new and simple model to calculate daily values for soil temperature at various depths in both frozen and unfrozen soils. The model requires four parameters: average soil thermal conductivity, specific heat capacity of soil, specific heat capacity due to freezing and thawing and an empirical snow parameter. Precipitation, air temperature and snow depth (measured or calculated) are needed as input variables. 2 -values of the testing period were between 0.87 and 0.94 at a depth of 20cm, and between 0.80 and 0.98 at 50cm. Thus, despite the simplifications made, the model was able to simulate soil temperature at these study sites. This simple model simulates soil temperature well in the uppermost soil layers where most of the nitrogen processes occur. The small number of parameters required means that the model is suitable for addition to catchment scale models.

No abstract

No abstract