Decreasing soil moisture and increasing frequency and intensity of soil drought episodes are among the frequently discussed consequences of ongoing global climate change. To address this topic, a water balance model SoilClim forced by climate reanalysis ERA5-Land was applied on a global scale to analyze the spatiotemporal variability of changes in soil moisture anomalies. The results revealed that the soil relative available water (AWR) significantly decreased on 31.1% of global non-glaciated land and significantly increased on 5.3% of such global non-glaciated land in 1981–2021. Decreasing AWR trends were detected over all continents and were particularly pronounced in South America, which experienced significant drying on more than half of the continent. The main drought ‘hotspots’ were identified in equatorial Africa, a large part of South America, the Midwest United States, and in a belt extending from eastern Europe to eastern Asia. A seasonal analysis of region-specific patterns further suggested drying in Europe in summer but an absence of a drying trend in winter. These results were supported by an analysis of the area affected by percentile-based drought on individual continents, revealing statistically significant increasing trends of 5th- and 10th-percentile droughts on all continents except Australia at an annual scale. Nevertheless, summer and autumn drought frequency increases were also detected in Australia. The seasonal trends were the most rapid in South America and Europe (except in winter). The distributions of AWR values, evaluated by Z scores, shifted remarkably toward drier conditions during the 2001–2021 period, particularly in South America and Asia. These results underscore the alarming increase in soil drought on a global scale, highlighting the need for effective drought management strategies.

Fire weather indices are one of the basic components of any fire danger early warning system. The Canadian Fire Weather Index (FWI) was developed to indicate the danger due to wildfires in boreal and temperate forests in Canada in the second half of the 20th century. Over time, the FWI has been adapted all over the world and is considered the most widely used fire weather index. This study provides a comprehensive review and meta-analysis of 750 research papers, generalizing the adoption of the FWI across 18 global environmental zones. The objective was to determine FWI values for various fire danger classes worldwide, from very low to extreme. The values of FWI and hydrometeorological variables were compared with wildfire occurrence. Key findings indicate that in drier and warmer climates, higher FWI values (around 50) correspond to high fire danger, whereas in cooler and moister climates, lower FWI values (around 25) signify extreme danger. The analysis of hydrometeorological variables reveals that relative humidity, aridity index, and vapor pressure deficit are significant factors influencing extreme minimum FWI, while average solar radiation has minimal impact. These insights have critical implications for developing effective wildfire prevention and management strategies tailored to specific environmental conditions. By establishing new fire danger classes reflective of regional meteorological and hydroclimatic characteristics, this study enhances the global applicability of the FWI. The ability to quickly adapt the FWI for fire danger forecasting in new areas is particularly beneficial for regions with previously low study coverage. The results underscore the importance of integrating regional climate variables into fire danger assessment frameworks to improve early warning systems and mitigate wildfire risks. The conclusions highlights the effectiveness of the FWI in diverse geographic contexts and its potential to enhance fire danger forecasting globally, thereby aiding in the prevention and management of wildfires.

In the Czech Republic, soil moisture content during the growing season has been decreasing over the past six decades, and drought events have become significantly more frequent. In 2003, 2015, 2018 and 2019, drought affected almost the entire country, with droughts in 2000, 2004, 2007, 2012, 2014 and 2017 having smaller extents but still severe intensities in some regions. The current methods of visiting cadastral areas (approximately 13,000) to allocate compensation funds for the crop yield losses caused by drought or aggregating the losses to district areas (approximately 1000 km${}^2$) based on proxy data are both inappropriate. The former due to the required time and resources, the later due to low resolution, which leads to many falsely negative and falsely positive results. Therefore, the study presents a new method to combine ground survey, remotely sensed and model data for determining crop yield losses. The study shows that it is possible to estimate them at the cadastral area level in the Czech Republic and attribute those losses to drought. This can be done with remotely sensed vegetation, water stress and soil moisture conditions with modeled soil moisture anomalies coupled with near-real-time feedback from reporters and with crop status surveys. The newly developed approach allowed the achievement of a proportion of falsely positive errors of less than 10% (e.g., oat 2%, 8%; spring barley 4%, 3%; sugar beets 2%, 21%; and winter wheat 2%, 6% in years 2017, resp. 2018) and allowed for cutting the loss assessment time from eight months in 2017 to eight weeks in 2018.

Photosynthetically active radiation (PAR) is the 400–700 nm portion of the solar radiation spectrum that photoautotrophic organisms including plants, algae, and cyanobacteria use for photosynthesis. PAR is a key variable in global ecosystem and Earth system modeling, playing a prominent role in carbon and water cycling. Alongside air temperature, water availability, and atmospheric CO₂ concentration, PAR controls photosynthesis and consequently biomass productivity in general. The management of agricultural and horticultural crops, forests, grasslands, and even grasses at sports venues is a non-exhaustive list of applications for which an accurate knowledge of the PAR resource is desirable. Modern agrivoltaic systems also require a good knowledge of PAR in conjunction with the variables needed to monitor the co-located photovoltaic system. In situ quality-controlled PAR sensors provide high-quality information for specific locations. However, due to associated installation and maintenance costs, such high-quality data are relatively scarce and generally extend over a restricted and sometimes non-continuous period. Numerous studies have already demonstrated the potential offered by surface radiation estimates based on satellite information as reliable alternatives to in situ measurements. The accuracy of these estimations is site-dependent and is related, for example, to the local climate, landscape, and viewing angle of the satellite. To assess the accuracy of PAR satellite models, we inter-compared 11 methods for estimating 30 min surface PAR based on satellite-derived estimations at 33 ground-based station locations over several climate regions in Europe, Africa, and South America. Averaged across stations, the results showed average relative biases (relative to the measurement mean) across methods of 1 to 20%, an average relative standard deviation of 25 to 30%, an average relative root mean square error of 25% to 35% and a correlation coefficient always above 0.95 for all methods. Improved performance was seen for all methods at relatively cloud-free sites, and quality degraded towards the edge of the Meteosat Second Generation viewing area. A good compromise between computational time, memory allocation, and performance was achieved for most locations using the Jacovides coefficient applied to the global horizontal irradiance from HelioClim-3 or the CAMS Radiation Service. In conclusion, satellite estimations can provide a reliable alternative estimation of ground-based PAR for most applications.

Freshwater fish production is significantly correlated with water temperature, which is expected to increase under climate change. This study evaluated changes in water temperature and their impact on productive ponds at fisheries in the Czech Republic. A model was developed to calculate surface water temperature based on the five-day mean of the air temperature and was then tested in several ponds in three major Czech fish production areas. The output of the surface water temperature model was compared with independently measured data (r = 0.79–0.96), and the verified model was then applied to predict climate change conditions. The results were evaluated with regard to the thresholds characterizing the water temperature requirements of fish species and indicated that the limitation of Czech fish farming results from (i) an increased number of continuous periods during which given fish species are threatened by high water temperatures and (ii) the extension of continuous periods with stressful water temperatures. For Czech fisheries, the model suggests a sharp increase in unprecedented temperature regimes, which will pose critical challenges to traditional forms of common carp farming within several decades. Although reducing the level of eutrophication and loading them with organic substances might alleviate expected threads, farming current fish species in deeper and colder ponds at higher elevations might be inevitable.