• Energy Research

Numerous climate futures are now available fromglobal climate models. Translation of climate data such as precipitation and temperatures into ecologicallymeaningful outputs formanagers and planners is the next frontier. We describe a model-based open platform to assess vulnerabilities of agricultural systems to climate change on pixel-wise data. The platformincludes a simulationmodeling engine and is suited towork with NetCDF format of input and output files. In a case study covering a region (Auvergne) in theMassif Central of France, the platformis configured to characterize climate (occurrence of arid conditions in historical and projected climate records), soils and human management, and is then used to assess the vulnerability to climate change of grassland productivity (downscaled to a fine scale). We demonstrate how using climate time series, and process-based simulations vulnerabilities can be defined at fine spatial scales relevant to farmers and land managers, and can be incorporated into management frameworks.

Over the last century, the management of pastoral systems has undergone major changes to meet the livelihood needs of alpine communities. Faced with the changes induced by recent global warming, the ecological status of many pastoral systems has seriously deteriorated in the western alpine region. We assessed changes in pasture dynamics by integrating information from remote-sensing products and two process-based models, i.e. the grassland-specific, biogeochemical growth model PaSim and the generic crop-growth model DayCent. Meteorological observations and satellite-derived Normalised Difference Vegetation Index (NDVI) trajectories of three pasture macro-types (high, medium and low productivity classes) in two study areas - Parc National des Écrins (PNE) in France and Parco Nazionale Gran Paradiso (PNGP) in Italy - were used as a basis for the model calibration work. The performance of the models was satisfactory in reproducing pasture production dynamics (R2 = 0.52 to 0.83). Projected changes in alpine pastures due to climate-change impacts and adaptation strategies indicate that: i) the length of the growing season is expected to increase between 15 and 40 days, resulting in changes in the timing and amount of biomass production, ii) summer water stress could limit pasture productivity; iii) earlier onset of grazing could enhance pasture productivity; iv) higher livestock densities could increase the rate of biomass regrowth, but major uncertainties in modelling processes need to be considered; and v) the carbon sequestration potential of pastures could decrease under limited water availability and warming.

The occurrence of hydrological extremes in the Amazon region and the associated sediment loss during rainfall events are key features in the global climate system. Climate extremes alter the sediment and carbon balance but the ecological consequences of such changes are poorly understood in this region. With the aim of examining the interactions between precipitation and landscape-scale controls of sediment export from the Amazon basin, we developed a parsimonious hydro-climatological model on a multi-year series (1997–2014) of sediment discharge data taken at the outlet of Óbidos (Brazil) watershed (the narrowest and swiftest part of the Amazon River). The calibrated model (correlation coefficient equal to 0.84) captured the sediment load variability of an independent dataset from a different watershed (the Magdalena River basin), and performed better than three alternative approaches. Our model captured the interdecadal variability and the long-term patterns of sediment export. In our reconstruction of yearly sediment discharge over 1859–2014, we observed that landscape erosion changes are mostly induced by single storm events, and result from coupled effects of droughts and storms over long time scales. By quantifying temporal variations in the sediment produced by weathering, this analysis enables a new understanding of the linkage between climate forcing and river response, which drives sediment dynamics in the Amazon basin.

European livestock agriculture is extraordinarily diverse, and so are the challenges it faces. This diversity has contributed to the development of a fragmented set of research communities. As a result, livestock research is often under-represented at policy level, despite its high relevance for the environment and food security. Understanding livestock systems and how they can sustainably adapt to global change requires inputs across research areas, including grasslands, nutrition, health, welfare and ecology. It also requires experimental researchers, modellers and stakeholders to work closely together. Networks and capacity building structures are vital to enable livestock research to meet the challenges of climate change. They need to maintain shared resources and provide non-competitive arenas to share and synthesize results for policy support. Long term strategic investment is needed to support such structures. Their leadership requires very different skills to those effective in scientific project coordination.

AbstractSnow cover duration is a crucial climate change indicator. However, measurements of days with snow cover on the ground (DSG) are limited, especially in complex terrains, and existing measurements are fragmentary and cover only relatively short time periods. Here, we provide observational and modelling evidence that it is possible to produce reliable time-series of DSG for Italy based on instrumental measurements, and historical documentary data derived from various sources, from a limited set of stations and areas in the central-southern Apennines (CSA) of Italy. The adopted modelling approach reveals that DSG estimates in most settings in Italy can be driven by climate factors occurring in the CSA. Taking into account spatial scale-dependence, a parsimonious model was developed by incorporating elevation, winter and spring temperatures, a large-scale circulation index (the Atlantic Multidecadal Variability, AMV) and a snow-severity index, with in situ DSG data, based on a core snow cover dataset covering 97 years (88% coverage in the 1907–2018 period and the rest, discontinuously from 1683 to 1895, from historical data of the Benevento station). The model was validated on the basis of the identification of contemporary snow cover patterns and historical evidence of summer snow cover in high massifs. Beyond the CSA, validation obtained across terrains of varying complexity in both the northern and southern sectors of the peninsula indicate that the model holds potential for applications in a broad range of geographical settings and climatic situations of Italy. This article advances the study of past, present and future DSG changes in the central Mediterranean region.

Vulnerability assessment to climate change is an issue of concern.We develop Java-based software for vulnerability assessment to climate change.We illustrate the software in vulnerability assessments of European grasslands. Vuln-Indices Java-based software was developed on concepts of vulnerability to climate change of agro-ecological systems. It implements the calculation of vulnerability indices on series of state variables for assessments at both site and region levels. The tool is useful because synthetic indices help capturing complex processes and prove effective to identify the factors responsible for vulnerability and their relative importance. It is suggested that the tool may be plausible for use with stakeholders to disseminate information of climate change impacts.