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Highlights • Adverse weather conditions were analysed using a range of adverse weather indices. • Future UK climate is expected to remain favourable for wheat production. • Wetter winter and spring could, however, increase the risk of waterlogging. • Use of global climate model ensembles to quantify prediction uncertainty is advised., Winter wheat is an important crop in the UK, suited to the typical weather conditions in the current climate. In a changing climate the increased frequency and severity of adverse weather events, which are often localised, are considered a major threat to wheat production. In the present study we assessed a range of adverse weather conditions, which can significantly affect yield, under current and future climates based on adverse weather indices. We analysed changes in the frequency, magnitude and spatial patterns of 10 adverse weather indices, at 25 sites across the UK, using climate scenarios from the CMIP5 ensemble of global climate models (GCMs) and two greenhouse gas emissions (RCP4.5 and RCP8.5). The future UK climate is expected to remain favourable for wheat production, with most adverse weather indicators reducing in magnitude by the mid-21st century. Hotter and drier summers would improve sowing and harvesting conditions and reduce the risk of lodging. The probability of late frosts and heat stress during reproductive and grain filling periods would likely remain small in 2050. Wetter winter and spring could cause issues with waterlogging. The severity of drought stress during reproduction would generally be lower in 2050, however localised differences suggest it is important to examine drought at a small spatial scale. Prolonged water stress does not increase considerably in the UK, as may be expected in other parts of Europe. Climate projections based on the CMIP5 ensemble reveal considerable uncertainty in the magnitude of adverse weather conditions including waterlogging, drought and water stress. The variation in adverse weather conditions due to GCMs was generally greater than between emissions scenarios. Accordingly, CMIP5 ensembles should be used in the assessment of adverse weather conditions for crop production to indicate the full range of possible impacts, which a limited number of GCMs may not provide.

Climate resilience refers to the capacity of a system to buffer core functions against climate-related uncertainty and variability (1). The occurrence of diversity in responses to weather variability within a functional group or species (2, 3), such as European wheat that supplies bread and pasta, can ensure a reasonable yield regardless of weather conditions and provides genetic material for selection under changing climate (4). Genetic diversity is not directly related to response diversity, as shown for forage crops (5). Since most of the forage crop species were distributed among several weather response clusters and most of the clusters contained several species, the genetic closeness did not fully explain responses to critical weather conditions. This phenomenon may represent a keystone for breeding and thus deserves to be explored further. Indeed, genetic response diversity deserves more attention with respect to yield and quality (6). The suitability of response diversity to describe agronomic fitness in wheat monocultures is questioned by Snowdon et al. (7), but no arguments are presented. We (8) consider the approach suitable to assess and enhance the resilience of monocultures and thereby increase the stability of total yield under weather variability regardless of whether the complementary cultivars are cultivated on one farm or within a region, national borders, or Europe. Cultivar (or crop) mixtures common in forage cultivation and sometimes used with cereals … [↵][1]1To whom correspondence should be addressed. Email: helena.kahiluoto{at}lut.fi. [1]: #xref-corresp-1-1

The occurrence of major wildfires in countries such as Portugal, Italy and Spain (2017) and Sweden (2018) indicated that wildfires pose a risk across Europe. While Central Europe has not been at the center of such events, observed climate data and climate projections suggest a tendency toward more years with wet and warm winters and dry and hot summers as well as fuel accumulation, leading to more hazardous conditions. Although some existing studies analyzed the differences in wildfire occurrence in this territory based on terrain, soil or vegetation characteristics, the effects of climate change have not been properly appraised. To fill this knowledge gap, we used and tested an ensemble of nine fuel aridity metrics, including three dedicated fire danger rating indices, and evaluated their level of agreement with actual fire occurrence, their ability to explain the interannual variability in wildfire frequency, and their temporal trends. The analysis covered the entire territory of the Czech Republic at 500 m spatial resolution. Two periods were included based on observed (1956–2015) and projected (2020–2100) meteorological data using ensembles of five regional climate models (RCMs) and five global circulation models (GCMs) based on Euro-CORDEX and CMIP5 datasets. For the future, we considered Representative Concentration Pathway (RCP) 4.5. Our results showed that since 1956, most of the Czech territory has exhibited an increasing frequency of fire weather days (i.e., days with highly conducive wildfire conditions) and an increasing area affected by weather conducive to wildfire occurrence, with the trends accelerating after 2000. The annual variation in the fuel aridity levels (derived solely from meteorological data) explained more than 2/3 of the reported wildfire variability during 1991–2015 over the Czech Republic. The future projections based on the RCM or GCM ensembles indicated a significant increase in fuel aridity and an increase in the area under fire-conducive conditions. Recommendations derived from such robust results are provided for stakeholders seeking to implement adaptation measures.

Rapid development of Micro Electro Mechanical Systems (MEMS) and the minimization of sensor cost, size and energy consumption in the last two decades leads to an effort to replace traditional sensors with their MEMS alternatives. The power consumption is one of the key problems, due to necessity to provide long term device power supply. Therefore a newly developed device was designed with the accent to low power consumption, to be able to operate with one small internal battery at range several months to years. The main goal was to develop a wireless monitoring system capable of continuous stability monitoring of various building structures. The sensor is designed to measure slow inclination variations or changes and in combination with variant designed for high frequency monitoring should represent complete solution of real time structure health monitoring. The STATOTEST compact measurement system is mainly composed of triaxial MEMS accelerometer as a sensing unit, motherboard containing IOT modules and battery, all placed in single waterproof box. The raw signal measured by MEMS accelerometer is preprocessed inside the unit and the data are sent to the cloud via LoRaWan, NBIoT or satellite. The results can be displayed, managed and exported through the web application. This paper presents current state of sensor development, refer to number of problems, which were solved during the process and deals with estimation of its accuracy characteristics in the laboratory conditions. During the laboratory experiment, small defined changes of inclination were performed and compared with values registered by the inclination sensor. The testing was performed before and after calibration procedures. After eliminating of accelerometers production errors, the accuracy of the unit measurement RMSE is less than 0.002° for the step change of 0.09°, tested in six different orientations of the sens or. One measurement is mean of 1000 measurements and its residual random error for one measurement is 2°x10e-5. Series of laboratory tests proved high short-term device accuracy in stable conditions. It is well known, that MEMS accelerometers strongly depend on the sensor temperature. To perform temperature compensation, we built own climate chamber, which is able to change automatically temperature of the several devices at once in specified ranges. Temperature compensation was then performed by using of polynomial approximation to obtain the field measurement accuracy close to laboratory conditions. This task is challenging because it is necessary to improve the proper material composition between the MEMS and the monitored structure and the device fixing methods.

Indicators based on evapotranspiration (ET) provide useful information about surface water status, response of vegetation to drought stress, and potential growth limitations. The capability of ET-based indicators, including actual ET and the evaporative stress index (ESI), to predict crop yields of spring barley and winter wheat was analyzed for 33 districts of the Czech Republic. In this study, the ET-based indicators were computed using two different approaches: (i) a prognostic model, SoilClim, which computes the water balance based on ground weather observations and information about soil and land cover; (ii) the diagnostic Atmosphere–Land Exchange Inverse (ALEXI) model based primarily on remotely sensed land surface temperature data. The capability of both sets of indicators to predict yields of spring barley and winter wheat was tested using artificial neural networks (ANNs) applied to the adjusting number and timeframe of inputs during the growing season. Yield predictions based on ANNs were computed for both crops for all districts together, as well as for individual districts. The root mean square error (RMSE) and coefficient of determination (R2) between observed and predicted yields varied with date within the growing season and with the number of ANN inputs used for yield prediction. The period with the highest predictive capability started from early-June to mid-June. This optimal period for yield prediction was identifiable already at the lower number of ANN inputs, nevertheless, the accuracy of the prediction improved as more inputs were included within ANNs.The RMSE values for individual districts varied between 0.4 and 0.7 t ha–1 while R2 reached values of 0.5–0.8 during the optimal period. Results of the study demonstrated that ET-based indicators can be used for yield prediction in real time during the growing season and therefore have great potential for decision making at regional and district levels.

This letter offers a critical evaluation of the use of an inexpensive camera mounted on a recreational unmanned aerial vehicle as a tool for landslide research. The constructed 3D surface models are compared to those generated using a DSLR camera, Total Station and Terrestrial Laser Scanning. It is shown that the investigated approach has considerable potential despite the fact that its performance was slightly inferior to the other methods. This was demonstrated also in a real case study of a small shallow landslide. Nonetheless, in order to assess the real accuracy and precision of the constructed models, it is emphasised that the operator should still have a basic knowledge of the principles of photogrammetry.

The risk of severe water scarcity events simultaneously affecting key wheat-producing areas doubles despite CO2 mitigation efforts., Global warming is expected to increase the frequency and intensity of severe water scarcity (SWS) events, which negatively affect rain-fed crops such as wheat, a key source of calories and protein for humans. Here, we develop a method to simultaneously quantify SWS over the world’s entire wheat-growing area and calculate the probabilities of multiple/sequential SWS events for baseline and future climates. Our projections show that, without climate change mitigation (representative concentration pathway 8.5), up to 60% of the current wheat-growing area will face simultaneous SWS events by the end of this century, compared to 15% today. Climate change stabilization in line with the Paris Agreement would substantially reduce the negative effects, but they would still double between 2041 and 2070 compared to current conditions. Future assessments of production shocks in food security should explicitly include the risk of severe, prolonged, and near-simultaneous droughts across key world wheat-producing areas.

Winter oilseed rape (Brassica napus L., WOSR) is the main oilseed crop in Europe, and it is mainly grown for biofuel and edible oil. The crop is grown widespread in Europe with most of the production area located in Germany, Poland, Czech Republic and France. How current and future WOSR production is influenced by climatic risk factors was explored by integrating a phenology model with climatic indices relating to crop yield. The phenology model based on the BRASNAP-PH model performed well when tested against data from variety trials across Europe. The phenology model was integrated into the Agriclim model. To quantify non-optimal agroclimatic growing conditions with respect to physical and biological stressors, a total of nine agroclimatic indices were calculated, of which one reflects a major disease (Phoma stem canker). Agriclim simulations across Europe show that current cropping area agree well with the area with least calculated stress factors. Projections under climate change were based on climate scenarios generated by the LARS-WG weather generator for GISS and HadGEM models RCP4.5 and RCP8.5 climate projections (2081–2100), which predicted that the crop will most likely suffer from low temperatures (early and late frost) in areas where the crop is currently grown. In the southern regions of Europe, the crops will suffer from droughts. Based on the model runs with climate scenarios, the North European regions (Boreal environmental zone) will become more favourable for growing WOSR. The type of adverse conditions for European WOSR growing areas is likely to change under climate change, underpinning the need for developing adaptive management strategies and future breeding strategies for crop cultivars to guarantee a well-established and healthy crop under climate change.

An increase in drought frequency, duration and severity is expected for the Central European region as a direct consequence of climate change. This will have profound effects on a number of key sectors (e.g. agriculture, forestry, energy production and tourism) and also affect water resources, biodiversity and the landscape as a whole. However, global circulation models significantly differ in their projections for Central Europe with respect to the magnitude and timing of these changes. Therefore, analysis of changes in drought characteristics during the last 54 yr in relation to prevailing climate trends might significantly enhance our understanding of present and future drought risks. This study is based on a set of drought indices, including the Standardized Precipitation Index (SPI), the Palmer Drought Severity Index (PDSI), the Palmer Zindex (Z-index) and the Standardized Precipitation-Evapotranspiration Index (SPEI), in their most advanced formulations. The time series of the drought indices were calculated for 411 climatological stations across Austria (excluding the Alps), the Czech Republic and Slovakia. Up to 45% of the evaluated stations (depending on the index) became significantly drier during the 1961-2014 period except for areas in the west and north of the studied region. In addition to identifying the regions with the most pronounced drying trends, a drying trend consistency across the station network of 3 independent national weather services was shown. The main driver behind this development was an increase in the evaporative demand of the atmosphere, driven by higher temperatures and global radiation with limited changes in precipitation totals. The observed drying trends were most pronounced during the April-September period and in lower elevations. Conversely, the majority of stations above 1000 m exhibited a significant wetting trend for both the summer and winter (October-March) half-years.

In recent years, two drought monitoring systems have been developed in the Czech Republic based on the SoilClim and AVISO soil moisture models. The former is run by Mendel University and Global Change Research Institute (CAS), while the latter, by the Czech Hydrometeorological Institute. SoilClim is based more on real soil properties and aimed primarily at agriculture, while AVISO complements the system with more theoretical presumptions about soil, showing, rather, climatological potential. Both soil moisture models were complemented by forecasts on a daily basis, taking meteorological inputs from NWP (Numerical Weather Prediction) models and thus giving short- to mid-range outlooks up to 9 days ahead. Validation of the soil moisture and drought intensity prediction was performed and is presented in this article showing its prediction reliability and potential. In the analysis, we focus mainly on the past year, 2017. The tool has strong predictive power for soil moisture and drought intensity so it is suitable for farmers who need to make decisions about irrigation and production activities. The presented system is fully functional and can be applied in the coming years.

The World Centre of Excellence (WCoE) on Landslide Risk Reduction entitled “Landslide risk assessment and development guidelines for effective risk reduction” (2014–2017) was designed to contribute to the risk reduction effort formulated in the Sendai Partnership initiative. Several research activities were developed and their results were presented to a broad public through a series of articles, informative web pages and documentary movies. The research focused on improving landslide hazard assessment in a variety of natural environments, including deep-seated as well as shallow landslides. Landslide hazard assessment was applied practically through development projects in Ethiopia and Peru. Within the scope of the WCoE we proposed and conducted two projects of the International Program on Landslides (IPL). One of them is dedicated to compilation and analysis of glacial lake outburst floods (Database of glacial lake outburst floods (GLOFs)–project No. 179) at the global level. This potentially highly damaging natural phenomenon combines characteristics of floods and debris flows and often also involves landslides in the initiation process. The other IPL project focuses on the main challenges of landslide risk reduction in the Czech Republic (Challenges for landslide hazard and risk management in “low risk” regions, Czech Republic, IPL project No. 197), which is a country with abundant landslide-related knowledge and rather low annual occurrence frequencies. Despite that, landslides cause considerable damage and financial losses, which often could be prevented if the available hazard information were to be used.

Open image in new window A small mesh of sensors which monitor movements across detachment planes of the giant San Andres Landslide on the northeastern lobe of El Hierro in the Canary Islands was established in 2013. In this paper we present the results obtained over a two year period spanning from October 2013 to October 2015. Our results demonstrate that the detachment planes are affected by sinistral strike slip displacements and subsidence of the depleted mass of the landslide. While these general trends are consistent the movements recorded at particular monitoring points differ in detail as one site is characterised by progressive strike slip and dip slip trends while another is characterised by movement pulses and reversals in the sense of movement. These findings contrast markedly with suggestions that the giant landslide is inactive and demonstrate that its reactivation is a possibility which cannot be dismissed categorically. Big data analytics have been used to identify interdependence between the recorded movements and a range of climatic and geophysical variables such as seismic data, tidal data, and geomagnetic data. We have found that the recorded movements correlate only weakly or moderately with climatic and seismic parameters but strongly to the horizontal and vertical intensity of the magnetic field. These findings are rather unexpected and we emphasise that special care must be taken in pushing the conclusions of a purely numerical analysis. The advantages of adopting a big data mindset led us to make significant improvements to the instrumental infrastructure in early 2016. These incremental improvements to the small mesh of sensors are driven partly by our desire to understand the kinematic behaviour of landslide itself and partly by our desire to explore the potential of big data analytics in geoscientific research.

The Permian pyroclastic deposits on the Principalek Hill SW of Vrchlabi (Czech Republic) were investigated by means of geological mapping and geophysics. The pyroclastic rocks are exposed in several small coherent outcrops, yet many interpretations are based on debris. The preserved textures enable reconstruction of eruptive styles. The volcanism started with phreatomagmatic eruptions documented by the fine-grained tuff with accretionary lapilli at the base. Subsequently, the activity changed to phreato-Strombolian/Surtseyan producing lapilli-tuffs and lapillistones. These events were followed by a Strombolian phase as evidenced by ill-sorted scoriaceous tuff-breccias with volcanic bombs. The uppermost unit consists of welded lava agglutinates and basaltic lavas suggesting a Hawaiian style of eruption. One of the basaltic feeder necks is exposed in a small abandoned quarry. Geophysical surveys were carried out over the extent of pyroclastic deposits. These included gravimetry, magnetometry, and DC resistivity tomography. The magnetic data indicate the occurrence of basaltic dykes in the central part of the pyroclastic deposits. The DC tomography confirms the presence of dykes, enables estimation of the pyroclastic deposits thickness, and provides evidence for the size and position of the maar-diatreme. Gravity data yield an insignificant low associated with the diatreme, which is likely a product of low rock-density contrast between the diatreme and country rocks. We argue that this is due to short time gap between sedimentation and volcanic activity and also related to conjoint burial and diagenetic history of the diatreme and the surrounding country rocks.

Europe is, after Asia, the second largest producer of wheat in the world, and provides the largest share of barley. Wheat (and to a similar extent, barley) production in Europe increased by more than 6-fold during the 20th century. During the first half of the 20th century, this was driven by expanding the harvested area. This was followed, from the mid-20th century, by a mas- sive increase in productivity that in many regions has stalled since 2000. However, it remains unclear what role climatic factors have played in these changes. Understanding the net impact of climatic trends over the past century would also aid in our understanding of the potential impact of future climate changes and in assessments of the potential for adaptation across Europe. In this study, we compiled information from several sources on winter wheat and spring barley yields and climatological data from 12 countries/regions covering the period from 1901−2012. The studied area includes the majority of climatic regions in which wheat and barley are grown (from central Italy to Finland). We hypothesized that changes in climatic conditions have led to measurable shifts in climate−yield relationships over the past 112 yr, and that presently grown wheat and bar- ley show a more pronounced response to adverse weather conditions compared to crops from the early 20 th century. The results confirm that climate−yield relationships have changed significantly over the period studied, and that in some regions, different predictors have had a greater effect on yields in recent times (between 1991 and 2012) than in previous decades. It is likely that changes in the climate−yield relationship at the local level might be more pronounced than those across the relatively large regions used in this study, as the latter represents aggregations of yields from various agroclimatic and pedoclimatic conditions that may show opposing trends (1) (PDF) Changing regional weather-crop yield relationships across Europe between 1901 and 2012. Available from: https://www.researchgate.net/publication/307899712_Changing_regional_weather-crop_yield_relationships_across_Europe_between_1901_and_2012 [accessed Nov 10 2018].

This paper presents a snow avalanche hazard map of the most avalanche-prone mountain range in the Czech Republic, the Krkonose Mountains. The map was prepared using historical records of 1132 avalanches which occurred over the last 54 years and state-of-the-art modelling of avalanche propagation and the spatial distribution of potential avalanche source areas. The map provides not only reliable and easy to understand information for the Mountain Rescue Service of the Czech Republic and mountain tourists, but also for land use managers to identify areas where new avalanche paths may develop under favourable conditions, including the total removal of forest cover.

Chapter summarizes the major impacts of changing climatic conditions in the Czech agriculture. Specific case studies are performed for the whole country (arable land) and are processed through GIS in the spatial grid 500 x 500 m respectively 1 x 1 km if middle Europe is considered. Contribution presents the impacts of climate change on the production of two major field crops (winter wheat and spring barley) in the Czech Republic for different future time horizons (2030, 2050 and 2100). The yield study includes not only the effect of climatic conditions but also the fertilization effect of carbon dioxide. Study is completed by effects of rising temperatures on the spread of temperature-depending biotic factors (selected pests) and changes in agroclimatic conditions for field crops. The basic data which are needed and used are long-term database of the national meteorological service and agricultural organizations which was used for evaluation of growth models (e.g. CERES). Other used tools are models which allow describe the evolution of pests in new climate conditions (e.g. CLIMEX or ECAMON) and various meteorological indieces. Description of expected weather conditions are based on two emission scenarios, according to the IPCC (mostly SRES-A2 and -B1) and three GCM models (NCAR-PCM, ECHAM5 and HadCM3). Their open access monthly outputs are published for the individual time horizons (e.g. 2030, 2050 and 2100) and are prepared in the daily time step by stochastic weather generator. The impacts of climate change are determined by comparing the current and expected state observed phenomena.