Impacts of changing society and climate on nutrient loading to the Baltic Sea.

This paper studies the relative importance of societal drivers and changing climate on anthropogenic nutrient inputs to the Baltic Sea. Shared Socioeconomic Pathways and Representative Concentration Pathways are extended at temporal and spatial scales relevant for the most contributing sectors. Extended socioeconomic and climate scenarios are then used as inputs for spatially and temporally detailed models for population and land use change, and their subsequent impact on nutrient loading is computed.

Future socioeconomic conditions may have a larger impact than climate change on nutrient loads to the Baltic Sea.

The Baltic Sea is suffering from eutrophication caused by nutrient discharges from land to sea, and these loads might change in a changing climate. We show that the impact from climate change by mid-century is probably less than the direct impact of changing socioeconomic factors such as land use, agricultural practices, atmospheric deposition, and wastewater emissions. We compare results from dynamic modelling of nutrient loads to the Baltic Sea under projections of climate change and scenarios for shared socioeconomic pathways.

Nitrate leaching losses from two Baltic Sea catchments under scenarios of changes in land use, land management and climate.

Pollution with excess nutrients deteriorate the water quality of the Baltic Sea. The effect of combined land use and climate scenarios on nitrate leaching and nitrogen (N) loads to surface waters from two Baltic Sea catchments (Norsminde in Denmark and Kocinka in Poland) was explored using different models; the NLES and Daisy models for nitrate leaching, and MIKE SHE or MODFLOW/MT3DMS for N transport. Three Shared Socioeconomic Pathways (SSP1, SSP2 and SSP5) defined change in land use and agricultural activities.

Diverging importance of drought stress for maize and winter wheat in Europe.

Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984-2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat.

Reducing uncertainty of estimated nitrogen load reductions to aquatic systems through spatially targeting agricultural mitigation measures using groundwater nitrogen reduction.

The need to further abate agricultural nitrate (N)-loadings to coastal waters in Denmark represents the main driver for development of a new spatially targeted regulation that focus on locating N-mitigation measures in agricultural areas with high N-load. This targeting makes use of the spatial variation across the landscape in natural N-reduction (denitrification) of leached nitrate in the groundwater and surface water systems. A critical basis for including spatial targeting in regulation of N-load in Denmark is the uncertainty associated with the effect of spatially targeting measures, since the effect will be critically affected by uncertainty in the quantification of the spatial variation in N-reduction.

Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions.

This corrects the article DOI: 10.1038/nplants.2017.

Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions.

This corrects the article DOI: 10.1038/nplants.2017.

The uncertainty of crop yield projections is reduced by improved temperature response functions.

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C.