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Der Weinbau ist einer Zunahme von Extremwetterereignissen und dem Klimawandel ausgesetzt und reagiert in besonderen Masse hierauf. Aufgrund der hohen soziookonomischen Bedeutung des Weinbausektors in Europa ist die Entwicklung von geeigneten Anpassungsstrategien zur Abschwachung der Auswirkungen des Klimawandels von groster Bedeutung fur seine kunftige Nachhaltigkeit und Wettbewerbsfahigkeit. In diesem Artikel sollen als Output des Forschungsprojektes Clim4Vitis (https://clim4vitis.eu/) einige mogliche kurzfristigen Anpassungsstrategien (Abbildung 1) zusammengefasst werden. Langzeitanpassungsstrategien werden in einem gesonderten Technical Review beschrieben.

Meteorological conditions are important factors in the development of fungal diseases in winter wheat and are the main inputs of the decision support systems used to forecast disease and thus determine timing for efficacious fungicide application. This study uses the Fourier transform method (FTM) to characterize temporal patterns of meteorological conditions between two neighbouring experimental sites used in a regional fungal disease monitoring and forecasting experiment in Luxembourg. Three meteorological variables (air temperature, relative humidity, and precipitation) were included, all conducive to infection of wheat by Zymoseptoria tritici cause of Septoria leaf blotch (STB) in winter wheat, from 2006 to 2009. The intraday, diurnal, dekadal and intra-seasonal variations of the meteorological variables were assessed using FTM, and the impact of existing contrasts between sites on the development of STB was analyzed. Although STB severities varied between sites and years (P ≤ 0.0003), the results indicated that the two sites presented the same patterns of meteorological conditions when compared at larger temporal scales (diurnal to intra-seasonal scales, with time periods >11 h). However, the intraday variations of all the variables were well discriminated between the sites and were highly correlated to STB severities. Our findings highlight and confirm the importance of intraday meteorological variation in the development of STB in winter wheat fields. Furthermore, the FTM approach has potential for identifying microclimatic conditions prevailing at given sites and could help in improving the prediction of disease forecast models used in regional warning systems.

Septoria tritici blotch (STB) is among the most important crop diseases causing continuous threats to wheat production worldwide. STB epidemics are the outcome of interactions between susceptible host cultivars, favorable environmental conditions, and sufficient quantities of pathogen inoculum. Thus, to determine whether fungicide sprays should be applied to prevent the risk of epidemics that might otherwise lead to yield loss, weather-based systems as stand-alone or combined with other disease or agronomic variables have been implemented in decision-support systems (DSS). Given the economic importance of wheat in Morocco and increasing concerns caused by fungal plant pathogens in wheat-growing regions, DSS integrating a disease risk model would help to limit potentially harmful side effects of fungicide applications while ensuring economic benefits. Here we describe the use of an artificial intelligence algorithm, i.e. the artificial neural network, within a weather-based modelling approach to predict the progress of STB in wheat in Luxembourg. The reproducibility of area-specific modelling approaches is often a hurdle for their application in operational disease warning system at a regional scale. Hence, we explore the potential of coupling artificial intelligence algorithms with weather-based model for predicting in-season progress of a major economically important fungal disease – wheat stripe rust – in selected wheat-producing regions in Morocco.

Changes in the frequency and intensity of heat waves have shown substantial negative impacts on public health. At the same time, climate change towards increasing air temperatures throughout Europe will foster such extreme events, leading to the population being more exposed to them and societies becoming more vulnerable. Based on two climate change scenarios (Representative Concentration Pathway 4.5 and 8.5) we analysed the frequency and intensity of heat waves for three capital cities in Europe representing a North&ndash, South transect (London, Luxembourg, Rome). We used indices proposed by the Expert Team on Sector-Specific Climate Indices of the World Meteorological Organization to analyze the number of heat waves, the number of days that contribute to heat waves, the length of the longest heat waves, as well as the mean temperature during heat waves. The threshold for the definition of heat waves is calculated based on a reference period of 30 years for each of the three cities, allowing for a direct comparison of the projected changes between the cities. Changes in the projected air temperature between a reference period (1971&ndash, 2000) and three future periods (2001&ndash, 2030 near future, 2031&ndash, 2060 middle future, and 2061&ndash, 2090 far future) are statistically significant for all three cities and both emission scenarios. Considerable similarities could be identified for the different heat wave indices. This directly affects the risk of the exposed population and might also negatively influence food security and water supply.

Aim: Grape (Vitis vinifera L.) phenology is mainly temperature-driven. Consequently, the shift in thermal conditions due to climate change is supposed to have a distinct influence on grape phenology, grape maturity and wine typicity. This study aims to investigate (i) the future phenological development, as well as (ii) the consequences on the temperature conditions in specific phenophases under the conditions of the Luxembourg grapegrowing region.Methods and Results: A budburst model and a phenological model were combined with an ensemble of ten regional climate change projections for Luxembourg. Analyses comparing four 30-year time spans (reference period: 1971-2000; present: 2001-2030; near future: 2031-2060; far future: 2061-2090) demonstrated that each of the 27 phenological stages according to BBCH code is projected to be reached significantly earlier than in the reference period. According to these projections, the length of phenophases at the early stages is not affected, whereas the ripening period length is significantly shortened. The air temperature increase in the ripening period (far future compared to reference period: + 4.6 °C to + 5.3 °C) is projected to be markedly higher than in the annual averages (+ 2.6 °C).Conclusions: Since (i) air temperatures are generally projected to increase in the future and (ii) the ripening period will take place earlier (usually in the warmer parts of the season), climate change is implicating a two-fold impact on air temperature increase in the ripening period.Significance and impact of the study: This two-fold impact potentially threatens the wine typicity of the traditional grapegrowing regions and therefore calls for specific adaptation strategies.

The aim of the present investigations was to simulate the annual risk of bunch rot (Botrytis cinerea) on Vitis vinifera L. cv. Riesling grapes based on three long-term (n = 3 × 7 = 21 cases) assessment data sets originating from three Central European grape-growing regions. Periods when meteorological parameters were significantly (p

Air quality and thermal stress lead to increased morbidity and mortality. Studies on morbidity and the combined impact of air pollution and thermal stress are still rare. To analyse the correlations between air quality, thermal stress and morbidity, we used a two-stage meta-analysis approach, consisting of a Poisson regression model combined with distributed lag non-linear models (DLNMs) and a meta-analysis investigating whether latitude or the number of inhabitants significantly influence the correlations. We used air pollution, meteorological and hospital admission data from 28 administrative districts along a north-south gradient in western Germany from 2001 to 2011. We compared the performance of the single measure particulate matter (PM10) and air temperature to air quality indices (MPI and CAQI) and the biometeorological index UTCI. Based on the Akaike information criterion (AIC), it can be shown that using air quality indices instead of single measures increases the model strength. However, using the UTCI in the model does not give additional information compared to mean air temperature. Interaction between the 3-day average of air quality (max PM10, max CAQI and max MPI) and meteorology (mean air temperature and mean UTCI) did not improve the models. Using the mean air temperature, we found immediate effects of heat stress (RR 1.0013, 95% CI: 0.9983-1.0043) and by 3 days delayed effects of cold stress (RR: 1.0184, 95% CI: 1.0117-1.0252). The results for air quality differ between both air quality indices and PM10. CAQI and MPI show a delayed impact on morbidity with a maximum RR after 2 days (MPI 1.0058, 95% CI: 1.0013-1.0102; CAQI 1.0068, 95% CI: 1.0030-1.0107). Latitude was identified as a significant meta-variable, whereas the number of inhabitants was not significant in the model.

Wheat leaf rust caused by Puccinia triticina Eriks. was identified for the first time in 2000 in the Grand Duchy of Luxembourg on the basis of orange-to-brown, round-to-ovoid, erumpent uredinia (1 to 1.5 mm in diameter) scattered on the upper and lower leaf surfaces and producing orange-brown urediniospores that are subgloboid, approximately 20 μm in diameter, and with up to eight germ pore scattered in thick, echinulate walls. In a second phase, wheat was monitored weekly (starting from Zadoks growth stage 30, pseudo stem erection) during the 2003–2008 cropping seasons for wheat leaf rust. Disease severity (percentage of leaf area with symptoms) was recorded in four, replicated field experiments located in three villages (Diekirch District: Reuler; and Grevenmacher District: Burmerange and Christnach), which are representative of the different agroclimatological zones of Luxembourg. A significant difference in severity was observed between the sites (P < 0.01) and the years (P < 0.05). Over the 6-year period, Burmerange and Reuler consistently showed the highest and lowest disease severity, respectively. In 2003 and 2007, Burmerange (a southern site with the highest average spring temperatures of 13.6 and 14.0°C, respectively) showed the highest disease severity with 66 and 57%, respectively, whereas the lowest severity (

Wheat stripe rust (caused by Puccinia striiformis f. sp. tritici) is a major threat in most wheat growing regions worldwide, which potentially causes substantial yield losses when environmental conditions are favorable. Data from 1999 to 2015 for three representative wheat-growing sites in Luxembourg were used to develop a threshold-based weather model for predicting wheat stripe rust. First, the range of favorable weather conditions using a Monte Carlo simulation method based on the Dennis model were characterized. Then, the optimum combined favorable weather variables (air temperature, relative humidity, and rainfall) during the most critical infection period (May-June) was identified and was used to develop the model. Uninterrupted hours with such favorable weather conditions over each dekad (i.e., 10-day period) during May-June were also considered when building the model. Results showed that a combination of relative humidity >92% and 4°C < temperature < 16°C for a minimum of 4 continuous hours, associated with rainfall ≤0.1 mm (with the dekad having these conditions for 5 to 20% of the time), were optimum to the development of a wheat stripe rust epidemic. The model accurately predicted infection events: probabilities of detection were ≥0.90 and false alarm ratios were ≤0.38 on average, and critical success indexes ranged from 0.63 to 1. The method is potentially applicable to studies of other economically important fungal diseases of other crops or in different geographical locations. If weather forecasts are available, the threshold-based weather model can be integrated into an operational warning system to guide fungicide applications.

The first step in the formulation of disease management strategy for any cropping system is to identify the most important risk factors. This is facilitated by basic epidemiological studies of pathogen life cycles, and an understanding of the way in which weather and cropping factors affect the quantity of initial inoculum and the rate at which the epidemic develops. Weather conditions are important factors in the development of fungal diseases in winter wheat, and constitute the main inputs of the decision support systems used to forecast disease and thus determine the timing for efficacious fungicide application. Crop protection often relies on preventive fungicide applications. Considering the slim cost−revenue ratio for winter wheat and the negative environmental impacts of fungicide overuse, necessity for applying only sprays that are critical for disease control becomes paramount for a sustainable and environmentally friendly crop production. Thus, fungicides should only be applied at critical stages for disease development, and only after the pathogen has been correctly identified. This chapter provides an overview of different weather-based disease models developed for assessing the real-time risk of epidemic development of the major fungal diseases (Septoria leaf blotch, leaf rusts and Fusarium head blight) of winter wheat in Luxembourg.

Foliar fungicides in wheat are typically used to safeguard against economic losses from diseases. In this study, we assessed the effects of three fungicide spray regimes [single, double, and triple treatments] on four different grain quality parameters [thousand grain weight (TGW), test weight (TW), grain protein content (GPC), and Zeleny sedimentation volume (ZSV)] during the 2006–2009 period at two sites in Luxembourg. The fungicides used were generally a mix of chlorothalonil and triazoles. At Burmerange,(cultivar Cubus), the values of TGW, TW, GPC and ZSV ranged from 38 to 62 g, 67 to 83 kg hl−1, 12.0%to 14.7% dry matter (DM), and 27 to 54 ml, respectively. Whereas, at Everlange (cultivar Achat), the ranges of TGW, TW, GPC and ZSV were 42 to 65 g, 65 to 81 kg hl−1, 11.0% to 15.0% DM, and 21 to 66 ml,respectively. In more than 75% cases, the results indicate that fungicides did not significantly affect TW or ZSV at either sites (P > 0.05). However, there was a significant and positive fungicide effect on GPC in 2006 and 2009 at Burmerange, and only in 2006 at Everlange (P < 0.05). On the contrary, TGW was significantly affected at Burmerange in all years, except 2008 when a positive increase was observed compared to control plots; and in 2006 and 2007 at Everlange. Interestingly, when there was an effect of fungicides on a quality parameter, there was no difference among different fungicide treatments. Thus under conditions prevailing in Luxembourg, a single fungicide treatment applied with judicious timing generally resulted in statistically similar grain quality parameters when compared with a double or triple fungicide treatment.

We evaluated the cost effectiveness of a decision–support system (DSS) developed for assessing in real time the risk of progression of the main fungal diseases (i.e., Septoria leaf blotch, powdery mildew, leaf rusts and Fusarium head blight) of winter wheat in the Grand-Duchy of Luxembourg (GDL). The study was conducted in replicated field experiments located in four agricultural locations (representative of the main agro-ecological regions of the country) over a 10-year period (2003–2012). Three fungicide spray strategies were compared: a single DSS-based system and two commonly used spray practices in the GDL, a double- (2T) and a triple-spray (3T) treatment; there was also a non-treated control. In years with a high disease pressure, the DSS-based recommendation resulted in protection of the three upper leaves comparable to that achieved with the 2T and 3T treatments, with significant grain yield increases (P > 0.05) compared to the control (a 4 to 42% increase, depending on the site and year). Overall, the financial gain in treated plots compared with the control ranged from 3 to 16% at the study sites. Furthermore, in seasons when dry weather conditions precluded epidemic development, the DSS recommended no fungicide spray, reducing use of fungicide, and thus saving the cost of the product. The gain in yield for the 2T and 3T plots (compared with control) did not necessarily result in a financial gain during the duration of the experiment. This study demonstrates the potential advantages and profitability of using a DSS-based approach for disease management.

Tourism destinations often require information about climate to assess their climate potential. This can be performed in terms of mean conditions of relevant climatological parameters. For a user-friendly analysis and visualization of climate data relevant for tourism application in Luxembourg, information is prepared based on the facets of climate in tourism. Information on thermal comfort/stress conditions as well as aesthetical and physical parameters is considered. In the present study, relevant and sensible factors were identified and presented. Therefore, physiologically equivalent temperature, precipitation patterns and the Climate-Tourism/Transfer-Information-Scheme are applied. In addition, extreme events relevant for heat stress are analysed based on existing data sets (i.e. heat waves of 2010). Expected climatic conditions for the future are investigated using the projections of two different regional climate models. The results concerning climate change conditions reveal increasing heat stress and sultriness but decreasing cold stress. This information is the basis for an adequate assessment to provide relevant information for different environmental planning issues as well as for the growing tourism sector of Luxembourg.

Septoria tritici is the causal agent of leaf blotch in wheat and among the most damaging fungal cereal pathogens in the humid regions of central Europe. The percentage of the leaf area colonized by S. tritici was recorded weekly between April and July every season between 2004 and 2010. A total of 11 cultivars with moderate susceptibility [ratings of 4–6 on a 1 (resistant) to 9 (susceptible) scale] were included. The disease level was assessed on the upper three leaf layers at 2 locations between 2004 and 2006 and at 3 locations between 2007 and 2010. The period between sowing and the point of time, when 50% of the leaf area was necrotized due to colonization by S. tritici ( T 50 ) was estimated for each year, site, cultivar and leaf layer by non-linear regression. T 50 values followed a bimodal distribution with one maximum at 245 days after sowing (DAS; early epidemics) and one maximum at 270 DAS (late epidemics). Early epidemics were preceded by almost constant daily average temperatures of 13.2 ± 0.8 °C between 181 and 210 DAS. Late epidemics were preceded by an approximately linear increase in temperature from 8.7 ± 0.9 to 12.1 ± 0.9 °C during the same period of time. Based on these differences, it seems possible to predict whether an early or a late epidemic can be expected at least 35 days before the epidemic outbreak. Temperature sums calculated with a base temperature of 6.6 °C starting at sowing and ending when T 50 was reached were not significantly different between early and late epidemics ( P = 0.73) and averaged 1721 ± 49°days. Fungicide applications, which resulted into a delay of the epidemic development similar to the difference between early and late epidemics, resulted in a yield increase between 11.7 and 12.6%.

Fungi of the genus Fusarium are prominent plant pathogens producing various toxic secondary metabolites. Digital microscopic images were taken from the asexual spores of Fusarium avenaceum, F. cerealis, F. culmorum, F. graminearum, F. poae, F. oxysporum, F. sambucinum, and F. verticillioides. The asexual spore type formed first on Potato Dextrose Broth (PDB) medium was used. Fusarium avenaceum, F. cerealis, F. culmorum, F. graminearum and F. sambucinumformed macroconidia first, whereas F. poae, F. oxysporum and F. verticillioides formed microconidia first. Different shape parameters of the spore images were calculated using a freely available software package (FRAGSTATS 3.3), which was originally designed for spatial pattern analysis of maps. Fusarium cerealis could neither be distinguished unambiguously from F. sambucinum nor from F. graminearum based on the spore shape parameters analysed here. All other species could be distinguished from each other based on significant differences of at least one of the calculated spore shape parameters.

An impact related daily air quality index (DAQx), calculated for 15 air quality monitoring stations (traffic, background, and industry) in Belgium, France, Germany and Luxembourg, was compared to mesoscale atmospheric patterns between 2001 and 2007. Meteorological conditions were described by the Hess and Brezowsky synoptic weather classification system and gridded data of the EU FP6 ENSEMBLES project of total precipitation and mean surface temperature. DAQx values indicate sufficient to poor air quality in the urban area of Brussels and at urban traffic stations, as well as satisfactory air quality at the background stations. The air quality index refers to more than 90% to the presence of high PM10, O3 and NO2 concentrations. SO2 and CO play only a minor role. The investigation of weather regimes indicates that zonal and mixed cyclonic circulation regimes are associated with better air quality than meridional and anticyclonic weather regimes. In general, weather regimes with high daily precipitation lead to better air quality than dryer air masses because of lower contribution of PM10 to the air quality index. A trend analysis of weather regimes from 1978 to 2007 shows significant (α = 0.05) positive trends for weather classes associated with lower PM10 concentrations. The results of a case study at a German station examining the relationship between PM10 concentrations and local meteorological quantities (wind speed and precipitation) confirm the results of the regional analysis.

The model PROCULTURE has been developed by the Université Catholique de Louvain – UCL (Belgium) to simulate the progress of the septoria leaf blotch disease on winter wheat during the cropping season. The model has been validated in Luxembourg for four years at four distinct representative sites. It is able to identify infection periods due to the causal agent Mycosphaerella graminicola on the last five leaf layers by combining meteorological data with phenological data from PROCULTURE's crop growth model component. The meteorological forcing consists of hourly time-series of air temperature, relative humidity and cumulative rainfall since the time of sowing, retrieved from automatic weather stations for hindcast and numerical weather prediction model outputs for the forecast periods. In order to improve the model, leaf wetness – which is one of the most important drivers for the spread of the disease – shall be added as an additional predictor. Therefore leaf wetness sensors were set up at four test sites during the 2007 growing season. To get a continuous spatial coverage of the country, it is planned to couple the PROCULTURE model offline to 12-hourly operational weather forecasts from an implementation of the Weather Research and Forecasting (WRF) model for Luxembourg at 1 km resolution. Because the WRF model does not provide leaf wetness directly, an artificial neural network (ANN) is used to model this parameter.

Long-term records of solar UV radiation reaching the Earth's surface are scarce. Radiative transfer calculations and statistical models are two options used to reconstruct decadal changes in solar UV radiation from long-term records of measured atmospheric parameters that contain information on the effect of clouds, atmospheric aerosols and ground albedo on UV radiation. Based on earlier studies, where the long-term variation of daily solar UV irradiation was derived from measured global and diffuse irradiation as well as atmospheric ozone by a non-linear regression method [Feister et al. (2002) Photochem Photobiol 76:281-293], we present another approach for the reconstruction of time series of solar UV radiation. An artificial neural network (ANN) was trained with measurements of solar UV irradiation taken at the Meteorological Observatory in Potsdam, Germany, as well as measured parameters with long-term records such as global and diffuse radiation, sunshine duration, horizontal visibility and column ozone. This study is focussed on the reconstruction of daily broad-band UV-B (280-315 nm), UV-A (315-400 nm) and erythemal UV irradiation (ER). Due to the rapid changes in cloudiness at mid-latitude sites, solar UV irradiance exhibits appreciable short-term variability. One of the main advantages of the statistical method is that it uses doses of highly variable input parameters calculated from individual spot measurements taken at short time intervals, which thus do represent the short-term variability of solar irradiance.

Solar erythemal UV radiation (UVER) is highly relevant for numerous biological processes that affect plants, animals, and human health. Nevertheless, long-term UVER records are scarce. As significant declines in the column ozone concentration were observed in the past and a recovery of the stratospheric ozone layer is anticipated by the middle of the 21st century, there is a strong interest in the temporal variation of UVER time series. Therefore, we combined groundbased measurements of different meteorological variables with modeled ozone data sets to reconstruct time series of daily totals of UVER at the Meteorological Observatory Potsdam, Germany. Artificial neural networks were trained with measured UVER, sunshine duration, the day of year, measured and modeled total column ozone, as well as the minimum solar zenith angle. This allows for the reconstruction of daily totals of UVER for the period from 1901 to 1999. Additionally, analyses of the long-term variations from 1901 until 1999 of the reconstru...

[1] Solar erythemal UV radiation (UVER) is highly relevant for numerous biological processes that affect plants, animals, and human health. Nevertheless, long-term UVER records are scarce. As significant declines in the column ozone concentration were observed in the past and a recovery of the stratospheric ozone layer is anticipated by the middle of the 21st century, there is a strong interest in the temporal variation of UVERtime series. Therefore, we combined ground-based measurements of different meteorological variables with modeled ozone data sets to reconstruct time series of daily totals of UVER at the Meteorological Observatory, Potsdam, Germany. Artificial neural networks were trained with measured UVER, sunshine duration, the day of year, measured and modeled total column ozone, as well as the minimum solar zenith angle. This allows for the reconstruction of daily totals of UVERfor the period from 1901 to 1999. Additionally, analyses of the long-term variations from 1901 until 1999 of the reconstructed, new UVER data set are presented. The time series of monthly and annual totals of UVERprovide a long-term meteorological basis for epidemiological investigations in human health and occupational medicine for the region of Potsdam and Berlin. A strong benefit of our ANN-approach is the fact that it can be easily adapted to different geographical locations, as successfully tested in the framework of the COSTAction 726.

[1] This paper is based on a comparative study on ultraviolet radiation (UV) measurements and UV reconstruction models for eight sites in Europe. Reconstruction models include neural network techniques and radiative transfer modeling combined with empirical relationships. The models have been validated against quality-controlled ground-based measurements, 8 to 20 years, on time scales ranging from daily to yearly UV sums. The standard deviations in the ratios of modeled to measured daily sums vary between 10 and 15%. The yearly sums agree within a 5% range. Depending on the availability of ancillary measurements, reconstructions have been carried out to the early 1960s. A method has been set up to educe one best estimate of the historical UV levels that takes into account the long-term stability and underlying agreement of the models, and the agreement with actual UV measurements. Using this best estimate, the yearly sums of erythemally weighted UV irradiance showed a range of 300 kJ/m2 at 67°N to 750 kJ/m2 at 40°N. The year-to-year variability was lowest at 40°N with a relative variation of 4.3%; for central and northern European latitudes this year-to-year variation was 5.2 to 6.5%. With regard to the period 1980 to 2006, first-order trend lines range from 0.3 ± 0.1 to 0.6 ± 0.2% per year, approximately two thirds of which can be attributed to the diminishing of cloudiness and one third to ozone decline.

Artificial Neural Networks (ANN) are efficient tools to derive solar UV radiation from measured meteorological parameters such as global radiation, aerosol optical depths and atmospheric column ozone. The ANN model has been tested with different combinations of data from the two sites Potsdam and Lindenberg, and used to reconstruct solar UV radiation at eight European sites by more than 100 years into the past. Special emphasis will be given to the discussion of small-scale characteristics of input data to the ANN model. Annual totals of UV radiation derived from reconstructed daily UV values reflect interannual variations and long-term patterns that are compatible with variabilities and changes of measured input data, in particular global dimming by about 1980/1990, subsequent global brightening, volcanic eruption effects such as that of Mt. Pinatubo, and the long-term ozone decline since the 1970s. Patterns of annual erythemal UV radiation are very similar at sites located at latitudes close to each other, but different patterns occur between UV radiation at sites in different latitude regions.

To conceptualize strategies for regional environmental management in the Trier region, extensive urban meteorological measurements were undertaken. Weather stations from the German Weather Service and the state Pollution Monitoring Network were used as well as a number of our automatic meteorological stations and a mobile platform (instrumented van). The bioclimatic conditions in the city of Trier are affected by the valley of the Moselle River. Both the wind field and the thermal stratification in the urban boundary layer showed local characteristics especially marked in the diurnal variation and monthly mean concentrations of the air pollutants nitrogen and sulfurdioxide (NO x , SO2), ozone (O3) and particle matter (PM10). Catabatic flows from the side valleys partially reduce the urban heat island and increase the ozone concentration in the city in the evening during calm weather conditions. The impact-based air-quality index is mostly determined by a high PM10 concentration. Strategies to reduce air pollutions in the Trier region are discussed.

Projected climate changes will affect wheat crop production both in the main processes of plant growth and development but also in the occurrences and severities of plant diseases. We assessed the potential infection periods of wheat leaf rust (WLR) at two climatologically different sites in Luxembourg. A threshold-based model, taking hourly values of air temperatures, relative humidity and precipitation during night-time into account, was used for calculating favourable WLR infection days during three periods throughout the cropping season. Field experiments were conducted during the 2003–2013 period at the selected sites. Projected climate data, from a multi model ensemble of regional climate models (spatial resolution 25 km) as well as an additional projection with a higher spatial resolution of 1.3 km, were used for investigating the potential WLR infection periods for two future time spans. Results showed that the infections of WLR were satisfactorily simulated during the development of wheat at both sites for the 2003–2013 period. The probabilities of WLR detection were close to 1 and the critical success index ranged from 0.80 to 0.94 (perfect score = 1 for both). Moreover, the highest proportions of favourable days of WLR infection were simulated during spring and summer at both sites. Regional climate projections showed an increase in temperatures by 1.6 K for 2041–2050 and by 3.7 K for 2091–2100 compared to the reference period 1991–2000. Positive trends in favourable WLR infection conditions occur at both sites more conducive than in the reference period due to projected climatic conditions.

A multi-model ensemble of 15 climate change projections from regional climate models was used to assess the impact of changes in air temperature and precipitation on the phenology of pest species in agriculture. This allowed the bandwidths of expected changes in both meteorological variables to be calculated, forming the basis for assessing and clearly communicating the uncertainties related to the model results. More specifically, we investigated the potential impact of regional climate change effects on the crop inva- sion of the rape stem weevil, Ceutorhynchus napi Gyllenhal (Coleoptera: Curculionidae), in Central Europe (Luxembourg). Multisite and perennial data from field observations were used to choose a biological model from the literature, based on daily maximum air temperature and daily totals of precipitation to describe the migration of C. napi. Based on this statistical relation, we were able to reproduce the observed crop invasion with a mean root mean squared error (RMSE) of 10 days. Daily values of projected maximum air temperatures and daily totals of precipitation of the multi-model ensemble were used as input data for the threshold-based biologi - cal model that projects the immigration of this pest species into oilseed rape crops (Brassica napus L.). We examined three thirty-year timespans, the near (2021 to 2050) and the far future (2069 to 2098) and compared them to a reference timespan (1961 to 1990). The projections showed a significant shift of crop invasion to an earlier onset for the near (14 days) and far future (21 days) compared to the reference period. In addition, the timespan in which the potential crop invasion will take place increased from 53 days in the reference timespan to 73 days in the near and 65 days in the far future based on the ensemble median values. It could be expected that a shifting of the immigration period will increase the risk of missing the appropriate time frame for an insecticide application. A depletion of stored nutrient resources, leading to starvation after diapause, can be eliminated for C. napi under climate change effects, as this species hibernates motionless as an adult in earth cocoons until emergence in early springtime driven by temperature.