Country

Czech Republic

Explore historical and projected climate data, climate data by sector, impacts, key vulnerabilities and what adaptation measures are being taken. Explore the overview for a general context of how climate change is affecting Czech Republic.

Climate by Sector Agriculture

The exposure of the irrigation, crops and land management, livestock, rural transport, and storage and processing to climate variability and change increases at the local and regional scale. Climate change is enhancing the risks, acting as a threat multiplier, particularly with regard to the availability of water and the changes in thermal environment. In many places, climate change is expressing itself through higher variations in moisture, increase in dryness when dry, and increase in wetness when wet.

This section provides the visualization of four climate indices that are most relevant for agriculture sector.

Temperature: Seasonal Variability

Implications

The warm conditions of the day are important for crop growth cycles. However, there are upper heat thresholds beyond which crop productivity is reduced or stalled. This threshold is different with each crop type. As temperatures rise globally, assessing the local trends in daily maximum temperatures its important as it provides a way to assess if upper thresholds might get reached more frequently and the potential impacts this might have on overall yields.

Data

The graph shows projected change in Monthly Mean of Daily Maximum Temperature by 2050 compared to the reference period (1986-2005) under all RCPs of CIMP5 ensemble modeling. Positive values indicate that warmest daily maximum temperatures will likely to increase compared to the baseline, and vice versa. The shaded area represents the range or spread between 10th and 90th percentile of all analyzed models.

Caveats

Across the collection of climate models, the overall temperature trends, independent of maximum, mean or minimum temperatures, show relatively systematic warming across the seasonal cycle. The exact values of change can be considerable different between future climate models as they exhibit different sensitivities to the climate forcings. Low emissions connected to the RCP2.6 will likely lead to smaller warming than high emissions of the RCP8.5, with the intermediate scenarios somewhere in-between. Note, the shaded range is a difference between models, not the year to year variability. Particularly in higher latitudes, the inter-annual variability can be substantial and by far exceed the climatological change.

Precipitation: Spatial Variability

Implications

Annual precipitation is one of the most fundamental climatic conditions for rain-fed agriculture and livestock productivity. A gain or a decrease over the coming decades could determine if certain crops or farm practices remain viable, and if reduced water availability might require a shift to more drought resistant crops or if farmers are required to shift investments into irrigation. The annual rainfall amount provides a critical background on top of which other factors can become important, such as the temporal gaps between individual rainfall episodes, the availability of water during critical times of the seasonal cycle, or the intensity of individual rainfall events. Together with these other indicators, mean annual rainfall is a useful measure toward estimating water balance to ensure sustainable food production.

Data

The map shows change in projected Mean Annual Precipitation by 2050 compared to the reference period (1986-2005) under RCP 8.5 of CIMP5 ensemble modeling. Blue/Green areas are likely to receive more annual rainfall compared to the reference period and to Brown/Yellow areas.

Caveats

Global climate models have substantially improved their spatial representation of rainfall, but it is important to recognize that a number of important issues remain, particularly with regard to the interaction with local topography. Additionally, there are still systematic data biases in some parts of the world. Nevertheless, caution should be applied not to interpret the field too narrowly at the level of individual grid points, particular in warm regions where local patterns of rainfall dominates. Instead, a more regional context of precipitation changes should be considered.

Precipitation: Time Series

Implications

A "dry day" is a day without any agriculturally meaningful rainfall, which is generally defined by a threshold of 0.1 mm/day. The maximum number of consecutive dry days is an important metric for rain-fed agriculture as it directly impacts soil moisture, and thus crop growth. As climate warms, one of the signals is the increase in contrast: when it rains, it might rain harder, but when its dry it might get drier. The trend toward more consecutive dry days and higher temperatures will increase evaporation and add stress to limited water resources, affecting irrigation and other water uses. Long periods of consecutive days with little or no precipitation also can lead to drought. In general, the average annual maximum number of consecutive dry days are projected to increase for the higher emissions scenarios. Some crops, however, might benefit from this change, particularly when the dry conditions exist in specific parts of the crop cycle.

Data

The graph shows the recorded maximum number of Consecutive Dry Days (CDD) per year for 1986-2005, and projected maximum number of CDD for 2020-2100 under all RCPs of CIMP5 ensemble modeling. Note, the shaded ranges (or model spread) illustrate the inter-model differences, here using the +/- one standard deviation. The reason for using a narrower metric compared to the 10th and 90th percentile is that the inter-model difference is large for precipitation, and in particular for the count of days with rainfall.

Caveats

Temporal variations in the projections of the maximum length of consecutive dry days reflect the multi-decadal variability in weather and climate. At regional scales, that inter-annual to decadal variability is often larger than the difference between emission scenarios, particularly in the first half of the 21st century. Depending on the size of the domain being averaged, the separation between emission scenarios is not always clearly recognizable. One difficulty is that not all modeling groups contributed data from all the different RCPs, and thus the multi-model median values shown in the dashboard figure are based on a different set of underlying models. Particularly RCP6.0 generally is represented by the fewest members, which leads to a somewhat noisier projection. The general tendencies over time joined by the increasing separation of the RCPs is the most robust representation of the expected changes.

Precipitation: Extreme Events

Implications

An anticipated impact of climate change is the increase in climate variability. In particular, warmer air has a higher capacity to carry moisture in form of water vapor, which is then available for rainfall. It generally depends on location if the long-term average rainfall will exhibit a positive or negative trend, but it is thought that extremes could change more systematically towards higher intensity events. With every Degree-Celsius, the moisture carrying capacity of air increases ~7%. Therefore, the systematic warming of multiple degrees can lead to non-trivial increases in moisture that could potentially be transported and thus rained out (though not every rainfall event will increase). Extreme rainfall directly affects agriculture where it can damage crop, flood fields and streams, and the water can strip soils of their nutrients or the soil mass itself.

Data

The boxplot shows recorded Maximum Monthly Rainfall for 1986-2005 and projected Maximum Monthly Rainfall 10-yr Return Level by 2050 under all RCPs of CIMP5 ensemble modeling. This indicator focuses on the maximum monthly rainfall amount that can be expected within a 10-yr period.

Caveats

The focus of the analysis is on the largest accumulated monthly rainfall to analyze excessively wet periods. All monthly rainfall values were used in an extreme value analysis to estimate the upper tail of the distribution. This is necessary as in many regions of the world, this extreme tail is found to be heavy, meaning that these very large events are occurring more frequently than one might expect when looking at smaller events. Note, using just 20-year time interval is somewhat short to best measure rare events, and thus trends might not be clear. When averaged over a country, the results provide a first glance at expected changes in these monthly extremes. But for specific applications, a more targeted local study with the longest possible records should be performed.