Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to +9°C) and precipitation (-50 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development.

Improving runoff estimates from regional climate models: a performance analysis in Spain

An important step to assess water availability is to have monthly time series representative of the current situation. In this context, a simple methodology is presented for application in large-scale studies in regions where a properly calibrated hydrologic model is not available, using the output variables simulated by regional climate models (RCMs) of the European project PRUDENCE under current climate conditions (period 1961–1990). The methodology compares different interpolation methods and alternatives to generate annual times series that minimise the bias with respect to observed values. The objective is to identify the best alternative to obtain bias-corrected, monthly runoff time series from the output of RCM simulations. This study uses information from 338 basins in Spain that cover the entire mainland territory and whose observed values of natural runoff have been estimated by the distributed hydrological model SIMPA. Four interpolation methods for downscaling runoff to the basin scale from 10 RCMs are compared with emphasis on the ability of each method to reproduce the observed behaviour of this variable. The alternatives consider the use of the direct runoff of the RCMs and the mean annual runoff calculated using five functional forms of the aridity index, defined as the ratio between potential evapotranspiration and precipitation. In addition, the comparison with respect to the global runoff reference of the UNH/GRDC dataset is evaluated, as a contrast of the “best estimator” of current runoff on a large scale. Results show that the bias is minimised using the direct original interpolation method and the best alternative for bias correction of the monthly direct runoff time series of RCMs is the UNH/GRDC dataset, although the formula proposed by Schreiber (1904) also gives good results

Spatial and temporal precipitation patterns of the Ebro River Basin, Spain

The Ebro River Basin, with around 85 000 km2 and located in NE Spain, is characterized by the high spatial heterogeneity of its geology, topography, climatology and land use. Rainfall is one of the most important climatic variables studied owing to its non-homogenous behaviour in event and intensity, which creates drought, water runoff and soil erosion with negative environmental and social consequences. In this work we characterized the rainfall variability pattern in the Ebro River Basin using universal multifractal (UM) analysis, which estimates the concentration of the data around the precipitation average (C1, codimension average), the degree of multiscaling behaviour in time (? index) and the maximum probable singularity in the rainfall distribution ( s). A spatial and temporal analysis of the UM parameters is applied to study the possible changes. With this porpoise, 60 daily rainfall series were selected from 132 synthetic series generated by Luna and Balairón (AEMet). These daily rainfall series present a length of 60 years, from 1950 to 2009. Each one of them was subdivided (1950?1970 and 1980?2009) to analyse the difference between the two periods. The range of variation of precipitation amounts and the frequency of dry events between both periods are discussed, as well as the evolution of the UM parameters through the years.

Study of PM10 and PM2.5 levels in three European cities: Analysis of intra and inter urban variations

In the present paper, 1-year PM10 and PM 2.5 data from roadside and urban background monitoring stations in Athens (Greece), Madrid (Spain) and London (UK) are analysed in relation to other air pollutants (NO,NO2,NOx,CO,O3 and SO2)and several meteorological parameters (wind velocity, temperature, relative humidity, precipitation, solar radiation and atmospheric pressure), in order to investigate the sources and factors affecting particulate pollution in large European cities. Principal component and regression analyses are therefore used to quantify the contribution of both combustion and non-combustion sources to the PM10 and PM 2.5 levels observed. The analysis reveals that the EU legislated PM 10 and PM2.5 limit values are frequently breached, forming a potential public health hazard in the areas studied. The seasonal variability patterns of particulates varies among cities and sites, with Athens and Madrid presenting higher PM10 concentrations during the warm period and suggesting the larger relative contribution of secondary and natural particles during hot and dry days. It is estimated that the contribution of non-combustion sources varies substantially among cities, sites and seasons and ranges between 38-67% and 40-62% in London, 26-50% and 20-62% in Athens, and 31-58% and 33-68% in Madrid, for both PM10 and PM 2.5. Higher contributions from non-combustion sources are found at urban background sites in all three cities, whereas in the traffic sites the seasonal differences are smaller. In addition, the non-combustion fraction of both particle metrics is higher during the warm season at all sites. On the whole, the analysis provides evidence of the substantial impact of non-combustion sources on local air quality in all three cities. While vehicular exhaust emissions carry a large part of the risk posed on human health by particle exposure, it is most likely that mitigation measures designed for their reduction will have a major effect only at traffic sites and additional measures will be necessary for the control of background levels. However, efforts in mitigation strategies should always focus on optimal health effects.

Building intuition of iron evolution during solar cell processing through analysis of different process models

An important aspect of Process Simulators for photovoltaics is prediction of defect evolution during device fabrication. Over the last twenty years, these tools have accelerated process optimization, and several Process Simulators for iron, a ubiquitous and deleterious impurity in silicon, have been developed. The diversity of these tools can make it difficult to build intuition about the physics governing iron behavior during processing. Thus, in one unified software environment and using self-consistent terminology, we combine and describe three of these Simulators. We vary structural defect distribution and iron precipitation equations to create eight distinct Models, which we then use to simulate different stages of processing. We find that the structural defect distribution influences the final interstitial iron concentration ([Fe-i]) more strongly than the iron precipitation equations. We identify two regimes of iron behavior: (1) diffusivity-limited, in which iron evolution is kinetically limited and bulk [Fe-i] predictions can vary by an order of magnitude or more, and (2) solubility-limited, in which iron evolution is near thermodynamic equilibrium and the Models yield similar results. This rigorous analysis provides new intuition that can inform Process Simulation, material, and process development, and it enables scientists and engineers to choose an appropriate level of Model complexity based on wafer type and quality, processing conditions, and available computation time.