Martin Dubrovský

The apparent and potential effects of climate change on the inferred concentration of dissolved organic matter in a temperate stream (the Malse River, South Bohemia).

Long-term and seasonal changes in concentration of dissolved organic matter (DOM) and their possible drivers were evaluated for an upland stream in central Europe during 1969-2000. Two periods have been detected within this data set-years with decreased DOM until the middle of 1980s and then years with increased DOM until 2000. Temperature, hydrological regime of runoff from the catchment (namely the amount of interflow), and changes in atmospheric deposition of acidity coincided with the variations in DOM concentrations. The analysis of single runoff events confirmed the relation between the export of increased DOM concentrations from the catchment and interflow. A multiple linear regression model based on monthly averages of temperature and interflow explained 67% of DOM variability. This model suggested a 7% increase in DOM concentration under the scenarios of possible future climate change related to doubled CO(2) concentration in the atmosphere. The scenarios were based on results of several global circulation models.

HIGH-FREQUENCY AND LOW-FREQUENCY VARIABILITY IN STOCHASTIC DAILY WEATHER GENERATOR AND ITS EFFECT ON AGRICULTURAL AND HYDROLOGIC MODELLING

The high-frequency and low-frequency variabilities, which are often misreproduced by the daily weather generators, have a significant effect on modelling weather-dependent processes. Three modifications are suggested to improve the reproduction of the both variabilities in a four-variate daily weather generator Met&Roll: (i) inclusion of the annual cycle of lag-0 and lag-1 correlations among solar radiation, maximum temperature and minimum temperature, (ii) use of the 3rd order Markov chain to model precipitation occurrence, (iii) applying the monthly generator (based on a first-order autoregressive model) to fit the low-frequency variability. The tests are made to examine the effects of the three new features on (i) a stochastic structure of the synthetic series, and on (ii) outputs from CERES-Wheat crop model (crop yields) and SAC-SMA rainfall-runoff model (monthly streamflow characteristics, distribution of 5-day streamflow) fed by the synthetic weather series. The results are compared with those obtained with the observed weather series.Results: (i) The inclusion of the annual cycle of the correlations has rather ambiguous effect on the temporal structure of the weather characteristics simulated by the generator and only insignificant effect on the output from either simulation model. (ii) Increased order of the Markov chain improves modelling of precipitation occurrence series (especially long dry spells), and correspondingly improves reliability of the output from either simulation model. (iii) Conditioning the daily generator on monthly generator has the most positive effect, especially on the output from the hydrological model: Variability of the monthly streamflow characteristics and the frequency of extreme streamflows are better simulated. (iv) Of the two simulation models, the improvements related to the three modifications are more pronounced in the hydrological simulations. This may be also due to the fact that the crop growth simulations were less affected by the imperfections of the unmodified version of Met&Roll.

CLIMATE CHANGE IMPACTS AND ADAPTATION STRATEGIES IN SPRING BARLEY PRODUCTION IN THE CZECH REPUBLIC

The crop model CERES-Barley was used to assess the impacts of increased concentration of atmospheric CO2 on growth and development of the most important spring cereal in Central and Western Europe, i.e., spring barley, and to examine possible adaptation strategies. Three experimental regions were selected to compare the climate change impacts in various climatic and pedological conditions. The analysis was based on multi-year crop model simulations run with daily weather series obtained by stochastic weather generator and included two yield levels: stressed yields and potential yields. Four climate change scenarios based on global climate models and representing 2 × CO2 climate were applied. Results: (i) The crop model is suitable for use in the given environment, e.g., the coefficient of determination between the simulated and experimental yields equals 0.88. (ii) The indirect effect related to changed weather conditions is mostly negative. Its magnitude ranges from −19% to +5% for the four scenarios applied at the three regions. (iii) The magnitude of the direct effect of doubled CO2 on the stressed yields for the three test sites is 35–55% in the present climate and 25–65% in the 2 × CO2 climates. (iv) The stressed yields would increase in 2 × CO2 conditions by 13–52% when both direct and indirect effects were considered. (v) The impacts of doubled CO2 on potential yields are more uniform throughout the localities in comparison with the stressed yields. The magnitude of the indirect and direct effects ranges from −1 to −9% and from +31 to +33%, respectively. Superposition of both effects results in 19–30% increase of the potential yields. (vi) Application of the earlier planting date (up to 60 days) would result in 15–22% increase of the yields in 2 × CO2 conditions. (vii) Use of a cultivar with longer vegetation duration would bring 1.5% yield increase per one extra day of the vegetation season. (viii) The initial water content in the soil water profile proved to be one of the key elements determining the spring barley yield. It causes the yields to increase by 54–101 kg.ha−1 per 1% increase of the available soil water content on the sowing day.

Creating Daily Weather Series with Use of the Weather Generator

Estimation of quantitative impacts of potential climate change on environment and various aspects of human existence requires high-resolution surface weather data. Since the direct output from general circulation models (GCMs) is unreliable at the local scale, alternative approaches - most frequently based on statistical techniques - should be used to downscale coarsely resolved GCM output patterns to finer spatial and/or temporal resolution. The downscaling techniques are briefly reviewed in the paper. Two of the approaches were followed in developing two versions of the stochastic weather generator (WG) called MetR (ii) downscaling the GCM-simulated daily circulation pattern, using statistical linkage between the circulation patterns and the surface weather characteristics. Met&Roll-1 is a four-variate surface weather generator which employs a Markov chain approach to model precipitation occurrence and an autoregressive model to simulate the solar radiation and the diurnal extreme temperatures. The validation of the generator is performed by comparison of the stochastic structure of observed and synthetic series. Uncertainties in projecting the climate change scenario into the parameters of the WG are discussed. Met&Roll-2 is a generator which links the four surface weather variables with upper-air circulation patterns (CPs). CPs are characterized by principal components derived from 500 hPa geo-potential field. The series of CPs is either generated by autoregressive model or taken from the GCM output. The first test of this generator is focused on the correlation between CPs and surface weather characteristics.

Sensitivity of Ceres-Maize Yields to Statistical Structure of Daily Weather Series

To study impacts of climate variations on cropproduction, the growth models are used to simulateyields in present vs. changed climate conditions.Met&Roll is a four-variate (precipitation amount,solar radiation, minimum and maximum temperatures) stochasticweather generator used to supply synthetic dailyweather series for the crop growth model CERES-Maize.Three groups of experiments were conducted in thisstudy: (1) Validation of Met&Roll reveals some discrepanciesin the statistical structure of synthetic weatherseries, e.g., (i) the frequency of occurrence of longdry spells, extreme values of daily precipitationamount and variability of monthly means areunderestimated by the generator; (ii) correlations andlag-1 correlations among weather characteristicsexhibit a significant annual cycle not assumed by themodel. On the whole, the best fit of the observed andsynthetic weather series is experienced in summermonths. (2) The Wilcoxon test was employed to comparedistributions of maize yields simulated with use ofobserved vs. synthetic weather series. As nostatistically significant differences were detected,it is assumed that the generator imperfections inreproducing the statistical structure of weatherseries negligibly affect the model yields. (3) Thesensitivity of model yields to selectedcharacteristics of the daily weather series wasexamined. Emphasis was placed on the characteristicsnot addressed by typical GCM-based climate changescenarios: daily amplitude of temperature, persistenceof the weather series, shape of the distribution ofdaily precipitation amount, and frequency ofoccurrence of wet days. The results indicate that someof these characteristics may significantly affect cropyields and should therefore be considered in thedevelopment of climate change scenarios.

Multi-GCM projections of future drought and climate variability indicators for the Mediterranean region

Abstract Future climate conditions for the Mediterranean region based on an ensemble of 16 Global Climate Models are expressed and mapped using three approaches, giving special attention to the intermodel uncertainty. (1) The scenarios of mean seasonal temperature and precipitation agree with the projections published previously by other authors. The results show an increase in temperature in all seasons and for all parts of the Mediterranean with good intermodel agreement. Precipitation is projected to decrease in all parts and all seasons (most significantly in summer) except for the northernmost parts in winter. The intermodel agreement for the precipitation changes is lower than for temperature. (2) Changes in drought conditions are represented using the Palmer Drought Severity Index and its intermediate Z-index product. The results indicate a significant decrease in soil moisture in all seasons, with the most significant decrease occurring in summer. The displayed changes exhibit high intermodel agreement. (3) The climate change scenarios are defined in terms of the changes in parameters of the stochastic daily weather generator calibrated with the modeled daily data; the emphasis is put on the parameters, which affect the diurnal and interdiurnal variability in weather series. These scenarios indicate a trend toward more extreme weather in the Mediterranean. Temperature maxima will increase not only because of an overall rise in temperature means, but partly (in some areas) because of increases in temperature variability and daily temperature range. Increased mean daily precipitation sums on wet days occurring in some seasons, and some parts of the Mediterranean may imply higher daily precipitation extremes, and decreased probability of wet day occurrence will imply longer drought spells all across the Mediterranean.

Consequences of climate change for the soil climate in Central Europe and the central plains of the United States

This study aims to evaluate soil climate quantitatively under present and projected climatic conditions across Central Europe (12.1°–18.9° E and 46.8°–51.1° N) and the U.S. Central Plains (90°–104° W and 37°–49° N), with a special focus on soil temperature, hydric regime, drought risk and potential productivity (assessed as a period suitable for crop growth). The analysis was completed for the baselines (1961–1990 for Europe and 1985–2005 for the U.S.) and time horizons of 2025, 2050 and 2100 based on the outputs of three global circulation models using two levels of climate sensitivity. The results indicate that the soil climate (soil temperature and hydric soil regimes) will change dramatically in both regions, with significant consequences for soil genesis. However, the predicted changes of the pathways are very uncertain because of the range of future climate systems predicted by climate models. Nevertheless, our findings suggest that the risk of unfavourable dry years will increase, resulting in greater risk of soil erosion and lower productivity. The projected increase in the variability of dry and wet events combined with the uncertainty (particularly in the U.S.) poses a challenge for selecting the most appropriate adaptation strategies and for setting adequate policies. The results also suggest that the soil resources are likely be under increased pressure from changes in climate.

Effect of Estimated Daily Global Solar Radiation Data on the Results of Crop Growth Models

The results of previous studies have suggested that estimated daily global radiation (RG) values contain an error that could compromise the precision of subsequent crop model applications. The following study presents a detailed site and spatial analysis of the RG error propagation in CERES and WOFOST crop growth models in Central European climate conditions. The research was conducted i) at the eight individual sites in Austria and the Czech Republic where measured daily RG values were available as a reference, with seven methods for RG estimation being tested, and ii) for the agricultural areas of the Czech Republic using daily data from 52 weather stations, with five RG estimation methods. In the latter case the RG values estimated from the hours of sunshine using the Ångström-Prescott formula were used as the standard method because of the lack of measured RG data. At the site level we found that even the use of methods based on hours of sunshine, which showed the lowest bias in RG estimates, led to a significant distortion of the key crop model outputs. When the Ångström-Prescott method was used to estimate RG, for example, deviations greater than ±10 per cent in winter wheat and spring barley yields were noted in 5 to 6 per cent of cases. The precision of the yield estimates and other crop model outputs was lower when RG estimates based on the diurnal temperature range and cloud cover were used (mean bias error 2.0 to 4.1 per cent). The methods for estimating RG from the diurnal temperature range produced a wheat yield bias of more than 25 per cent in 12 to 16 per cent of the seasons. Such uncertainty in the crop model outputs makes the reliability of any seasonal yield forecasts or climate change impact assessments questionable if they are based on this type of data. The spatial assessment of the RG data uncertainty propagation over the winter wheat yields also revealed significant differences within the study area. We found that RG estimates based on diurnal temperature range or its combination with daily total precipitation produced a bias of to 30 per cent in the mean winter wheat grain yields in some regions compared with simulations in which RG values had been estimated using the Ångström-Prescott formula. In contrast to the results at the individual sites, the methods based on the diurnal temperature range in combination with daily precipitation totals showed significantly poorer performance than the methods based on the diurnal temperature range only. This was due to the marked increase in the bias in RG estimates with altitude, longitude or latitude of given region. These findings in our view should act as an incentive for further research to develop more precise and generally applicable methods for estimating daily RG based more on the underlying physical principles and/or the remote sensing approach.

Determination of areas with the most significant shift in persistence of pests in Europe under climate change.

BACKGROUND This study aimed to estimate the impact of climate change on the ranges of crop pest species in Europe. The organisms included in the study were species from the family Tortricidae (Cydia pomonella, Lobesia botrana) and the family Pyralidae (Ostrinia nubilalis), Chrysomelidae beetles (Leptinotarsa decemlineata, Oulema melanopus) and species from the family Aphididae (Ropalosiphum padi, Sitobion avenae). Climate conditions in the year 2055 were simulated using a subset of five representative global circulation models. Model simulations using these climate change scenarios showed significant shifts in the climatic niches of the species in this study. RESULTS For Central Europe, the models predicted a shift in the ranges of pest species to higher altitudes and increases in the number of generations (NG) of the pests. In contrast, in the southern regions of Europe, the NG is likely to decrease owing to insufficient humidity. The ranges of species are likely to shift to the north. CONCLUSION Based on the ensemble-scenario mean for 2055, a climate-driven northward shift of between 3° N (O. nubilalis) and 11° N (L. botrana) is expected. The areas that are most sensitive to experiencing a significant increase in climate suitability for future pest persistence were identified. These areas include Central Europe, the higher altitudes of the Alps and Carpathians and areas above 55° N.

Simulation of Surface Air Temperature by GCMs, Statistical Downscaling and Weather Generator: Higher-Order Statistical Moments

The third and fourth statistical moments, that is, skewness and kurtosis, are compared for daily maximum temperature in summer and daily minimum temperature in winter between observations, outputs of two global climate models, four versions of statistical downscaling, and weather generator. The comparison is performed at six stations in central Europe. None of the simulation models can be considered as superior to the others. Causes of a good correspondence with and differences from observations are identified e.g. in the treatment of physics in the models, imperfections in physical parameterizations, or a linear transfer of properties from predictors onto predictands in statistical downscaling.