Microclimatic conditions contrast strongly with the macroclimatic conditions measured by standard weather stations and commonly represented by gridded climate data. This is evident for instance in forests, where variations in structures (e.g. canopy openness) form temperature and humidity regimes that are significantly buffered from the conditions outside forests. Although there is ample evidence that microclimates drive many ecosystem functions and ecological processes, fine-scale variation in climate is still rarely considered in environmental research, management and applications. Thus, a better understanding of the current and future microclimates can support the provision of ecosystem services and enhance the efficacy and benefits of nature conservation.
This session aims to advance our understanding of interactions between weather and climate variability and change across scales and forest, agricultural, and natural vegetation systems. We invite presentations related but not limited to:
- Micrometeorology and microclimate, measuring (e.g. ground-based, remote-sensing, citizen science, Big Data etc.) and modeling (both statistical and mechanistic) at scales operating below the conventional climate grids, from meters to hundreds of meters
- Impact of weather and climate extremes on agriculture and forests
- Biometeorology and bioclimatology, agrometeorological modeling
- Climate-smart management in mitigating the impacts of weather and climate induced disturbances (e.g. droughts, fires, pests, diseases)
- Development of approaches to produce future climate projections operating at fine-spatial scales
- Decision support systems & the representation of uncertainty and added values of increased resolution for end-users
- Interactions/feedback of forestry and agriculture end users
The cooling efficiency of the land surface, i.e. its ability to dissipate absorbed radiation and moderate temperature rise, is expressed through its apparent heat capacity, a dynamic surface property that varies throughout the day in response to turbulent fluxes and boundary layer development. Under continuous clear-sky conditions, daytime changes in apparent heat capacity can be estimated from geostationary satellite observations, allowing the derivation of the Cooling Efficiency Factor Index (CEFI), a physically based metric characterizing the local temperature response to incoming radiation at spatial scales of few kilometers. The spatial distribution of CEFI is primarily controlled by land cover and further modulated by evapotranspiration and near-surface wind speed, enabling the detection of microclimatic contrasts that are not resolved by conventional meteorological and climate products. Temporal variations in CEFI provide useful proxies for surface processes that are otherwise difficult to observe directly. In vegetated systems, CEFI acts as an indicator of drought stress associated with stomatal closure under soil moisture limitation and high atmospheric evaporative demand, and is therefore strongly related to vegetation productivity. In arid and sparsely vegetated regions, CEFI can serve as a direct proxy for surface wind stress. Here we present the theoretical background of CEFI and demonstrate the feasibility of several practical applications, including early detection of flash drought, estimation of wildfire risk in natural ecosystems, assessment of crop production losses, and identification of dust formation hotspots in desert environments. CEFI is available in near real time, providing suitable data to support operational environmental monitoring and early warning systems.
How to cite: Zampieri, M., Ceccherini, G., Saharwardi, S. M., Girardello, M., Migliavacca, M., Hoteit, I., and Cescatti, A.: The Cooling Efficiency Factor Index (CEFI): a geostationary satellite metric for near-real-time monitoring of land-surface processes and environmental stress, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-199, https://doi.org/10.5194/ems2026-199, 2026.
Evapotranspiration links the earth's water and energy budgets. It also connects macro- and microclimate, as atmospheric evaporation requirements often reflect conditions at the large scale, but the rate at which the surface releases water vapor into the atmosphere is ultimately determined by processes at the local scale. In an agricultural context, evapotranspiration determines crop water requirements and is a key factor in relation to the initiation and development of agricultural drought. It has been shown in the past that in temperate climates drought occurrence and the severity of extreme drought events are sensitive with respect to the maximum evapotranspiration rates permitted by the prevailing environmental conditions. Understanding this sensitivity, and how it may evolve in the future in response to changes in the precipitation (rate of storm arrivals and mean storm depth), thermal and radiative regimes, is of fundamental importance for assessing the impacts of climate change on agricultural production.
The aim of this contribution is to provide a brief overview of the relevant concepts and the current state of knowledge, to assess whether historical variations in evapotranspiration (in relation to variations in temperature, solar radiation and atmospheric CO2 concentrations) have influenced the occurrence of drought in the Alpine region, and to present a preliminary assessment of future changes in evapotranspiration and their implications for extreme drought events with high impact on agricultural systems, based on a new set of regional climate change scenarios for Switzerland. Results of simulations with ecosystem and ecohydrological models will be use to illustrate the main points of discussion. The issue of the need to take microclimatic constraints into account will be addressed.
How to cite: Calanca, P.: Agricultural drought in the Alpine region – What is the role of evapotranspiration in the context of climate change?, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-501, https://doi.org/10.5194/ems2026-501, 2026.
Agricultural productivity is constrained by climate variability and the increasing frequency of extreme events, such as drought, salinity, and heat stress. These events are largely mediated by water availability and soil moisture dynamics. The European sustainability targets framework is driving the need for solutions that boost crop resilience while maximising water and nutrient efficiency in field conditions. In the face of climate variability, there is growing interest in microbial biostimulants, especially microbial consortia, as a means to enhance plant resilience to abiotic stress and optimise water usage. Through a systematic review of the literature, this study provides a synthesis of field-based evidence on the effects of microbial consortia in tomato (Solanum lycopersicum L.) under abiotic stress. Following PRISMA 2020 guidelines, a comprehensive screening of the Scopus database (up to September 2025) identified only 34 inherent records, of which 7 papers (30 treatment combinations) met the pre-defined inclusion criteria. The results show that microbial consortia usually improve how crops in the field perform under abiotic stress conditions. When yield was measured per unit area, it increased in 76.9% of cases, with a mean improvement of +94.1% compared to non-inoculated controls. In the experiments s where yield was quantified also at the plant level, fruit fresh weight increased in 85.7% of treatments (+481.0% on average). Fruit quality traits (Brix or total soluble sugars) improved in 46.2% of cases (+65.6%), suggesting partial mitigation of stress-induced physiological limitations. Resource optimization was demonstrated in specific cases, where consortia allowed for irrigation cuts of up to 50% and phosphorus savings while substantially increasing yields under stress compared to controls; however, these benefits were not ubiquitous, as other studies showed mixed effects under abiotic stress or lacked the precise nutrient data required for a definitive assessment. These findings emphasize the potential of microbial consortia to enhance tomato resilience under fluctuating climates. However, a critical research gap remains: the limited number of field trials and the lack of standardized quantitative data across agronomic, physiological, and input parameters hinder predictable deployment. Despite promising tomato yield gains, future research must prioritise multi-site and multi-year field validations using a standardized core set of metrics to bridge the gap between experimental success and the reliable adoption of consortia in climate-resilient agriculture.
How to cite: Lugari, F., Martelli, A., Donati, I. I. M., Rapinesi, D., Bongiorno, G., and Altobelli, F.: Field evidence of the effects of microbial consortia on tomato performance and water-use efficiency under climate-driven abiotic stress, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-723, https://doi.org/10.5194/ems2026-723, 2026.
Evapotranspiration monitoring provides essential information for weather prediction, for instance, through sensible heat flux, and for water management, particularly in regions where land use is a critical factor. Given the increasing impacts of drought and the challenges associated with monitoring, detailed ET measurements are vital for explaining meteorological and hydrological extremes from a management perspective. This study focuses on micrometeorological measurements of ET across different land use types to address key challenges in water balance estimation.
We analysed data from multiple LI-COR 710 eddy-covariance systems in agricultural and forested areas in the Netherlands to help close the gap between incoming and outgoing water fluxes in terrestrial environments. Our results reveal that the influence of ET on the water balance varies significantly within the same catchment area, depending on land use. From a hydrological perspective, these differences have important implications for policy development and management strategies, particularly under conditions of climate extremes. From an atmospheric perspective, as evapotranspiration is the primary link between the hydrological cycle and the surface energy balance, these differences suggest shifts in how net radiation is partitioned into latent and sensible heat fluxes.
This is particularly relevant due to increasing droughts in the Netherlands during the last decades. Our findings highlight the critical role of evapotranspiration in the soil–water–atmosphere continuum and highlight how land use adjust the balance between surface cooling through evaporation and the direct heating of the atmosphere. These complex surface-atmosphere interactions demonstrate the suitability of high-frequency micrometeorological measurements for achieving water balance closure and predicting local climate impacts.
How to cite: Vega Briones, J., Terink, W., and van den Eertwegh, G.: Evapotranspiration Across Land Use Types: A Micrometeorological Approach to Water Balance Closure, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-781, https://doi.org/10.5194/ems2026-781, 2026.
In the context of climate change, plant-based food production is heavily dependent on changing weather conditions, which can lead to crop instability, reduced self-sufficiency, and consequently, a decline in the competitiveness of the region's agricultural economy. Wheat (Triticum aestivum) is the most important cereal crop in Slovenia, cultivated on approximately 57% of arable land. Rising temperatures have already resulted in earlier phenological phases, and further warming is expected to shorten the grain filling period for wheat and other cereals. Even under unchanged photosynthetic capacity, yield reductions are projected to be directly proportional to the shortening of this critical growth stage. The relationship between temperature and water deficit is also important.
This study presents a comprehensive assessment of 34 agroclimatic indicators relevant to winter wheat production in Slovenia, including indicators related to frost and dormancy conditions, temperature regimes during the growing season, heat stress, and extreme precipitation events. To reduce dimensionality and identify dominant climatic patterns, Principal Component Analysis (PCA) was applied to transform the correlated indicators into orthogonal principal components (PCs). In addition, Spearman correlation analysis was used to determine the most influential agroclimatic indicators driving winter wheat yield variability, accounting for non-linear crop responses to climatic factors. The analysis was conducted using historical climate data (1981–2010), climate model projections from six regionally downscaled EURO-CORDEX simulations (RCP4.5 and RCP8.5), and long-term winter wheat yield data from two field experiments spanning 1993–2023.
Climate projections of the dominant PCs indicate substantial increases in Temperature conditions during the growing season, Night-time heat stress and high humidity, Moderate heat stress, and High precipitation compared with the 1981–2010 reference period. Statistically significant positive correlations were identified between winter wheat yield at the Rakičan site and the PCs representing High precipitation conditions, Spring frost conditions, and Dormancy period conditions. These findings provide an improved understanding of how future climatic conditions are likely to affect winter wheat production in Slovenia and contribute to broader insights relevant for cereal production across Europe under climate change.
This work was supported by the Slovenian Research Agency, Research Program P4−0085, Research Project V4-2423 and partially financed within the project SN-ZRD/22-27/0510 (ARISE).
How to cite: Žnidaršič, Z., Kolmanič, A., and Pogačar, T.: Winter Wheat (Triticum aestivum) in a Warming Climate: Key Agroclimatic Indicators Shaping Yield Variability in Slovenia, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-18, https://doi.org/10.5194/ems2026-18, 2026.
Viticulture is driven by climate, which governs grapevine phenology, water balance, yield, and grape quality. Ongoing climate change is expected to disrupt the historical climatic suitability of established wine regions while enabling expansion into new areas. This study presents a bioclimatic zoning approach to identify preferential future areas for grape growing across the Iberian Peninsula.
High-resolution climate data from CHELSA (30 arc-seconds, ~1 km) were used for the historical period 1981–2010 and the future period 2041–2070, under SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Traditional viticulture-related bioclimatic indices were calculated, including mean annual temperature, growing season temperature, the Winkler Index, annual precipitation, and the dryness index.
Historical vineyard conditions were characterised using Protected Designation of Origin (PDO) regions, which were used to define the reference climatic envelope for grape growing. For each index, threshold ranges were established based on values observed in PDO vineyard areas. Future regions were classified as preferential only when all selected indices simultaneously fell within the defined thresholds, ensuring a conservative suitability assessment.
For the historical period, around 80% of the Iberian Peninsula exhibited preferential conditions for viticulture. Future projections indicate a precipitation decrease ranging from −20 to −50 mm/year and temperature increases between 1.6 and 2.6 °C. These changes, as reflected in the bioclimatic indices, lead to a reduction in preferential areas, particularly in southern and interior regions, driven by increasing temperatures and greater aridity. Coastal and higher-elevation areas show greater persistence of suitable conditions, while some expansion potential is observed in northern regions. The preferential area for viticulture in the Iberian Peninsula is projected to decline to between 34% (SSP5-8.5) and 50% (SSP1-2.6).
The framework provides a basis for climate-informed site selection, regional planning, and targeted adaptation strategies for viticulture in the Iberian Peninsula under future climate change.
How to cite: Fernandes, A., Pereira, N., Pinho, J., Serra, E., Fonseca, A., Fraga, H., and Santos, J.: Bioclimatic Zoning of Future Viticulture Suitability in the Iberian Peninsula under Climate Change, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-165, https://doi.org/10.5194/ems2026-165, 2026.
While weather data have always been important for agriculture, they have become even more crucial with the emergence of precision agriculture and data-driven crop production. Applications such as pest management, irrigation scheduling and yield prediction rely on weather data.
As farmers’ weather stations (FWS) are now more accessible and affordable, they are increasingly used to monitor local environmental conditions in real time and support agricultural decision-making (Rosillon et al., 2024). However, FWS are prone to measurement errors, and the impact of these errors on agrometeorological model outputs remains unclear. Few studies exist, and their scope is limited by the time required for field trial data collection (Trilles et al., 2020; Mokhtarzadeh et al., 2025). Extended performance evaluations under diverse climatic conditions are nevertheless essential to test these stations (Kalaany et al., 2025).
This study aims to develop a FWS data simulator based on a three-year field experiment to simulate plausible FWS measurements beyond the observation period. These simulations are then used to model potato late blight (PLB) risk using a weather-based PLB model, and to assess the suitability of FWS across a wide range of climatic conditions.
Six FWS are installed alongside a reference weather station over three years to quantify sensor measurement errors. Modelling FWS data consists of modelling these errors and adding them to the reference weather data (Equations 1 and 2).
The age of the station (to account for sensor drift) and weather conditions are used as predictors. Three algorithms are tested: (1) a naïve model, based on random sampling of measurement errors from the marginal empirical distribution; (2) a multilinear regression combined with a first-order autoregressive model accounting for the temporal autocorrelation of model residuals; (3) a random forest combined with a random sampling of model residuals.
Algorithms are evaluated using cross-validation to assess their ability to appropriately simulate FWS data and FWS PLB risk for unsampled years. FWS data and corresponding PLB risk are then simulated over a historical period (e.g. 2006–2025), enabling extended performance evaluation of FWS in PLB modelling.
This analysis will first quantify the relative contribution of four key components (sensor drift, weather conditions, temporal autocorrelation, and random variability) to measurement errors. Then, it will evaluate algorithms performance in simulating FWS datasets. Finally, it will assess the ability of the FWS data simulator to accurately reproduce PLB risk.
Preliminary results show that random forest outperforms multilinear regression for simulating FWS data, with significant impact on PLB risk modelling. Detailed methods and results will be presented at the congress.
How to cite: Rosillon, D., Bogaert, P., Brun, F., César, V., Journée, M., Planchon, V., and Dandrifosse, S.: Simulating farmers’ weather station data for an extended evaluation of agricultural decision support, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-161, https://doi.org/10.5194/ems2026-161, 2026.
Damaging effects of sudden meteorological phenomena, such as spring frosts, pose a significant threat to fruit tree and shrub crops. Such events impact the flowers and fruit buds of the plants, causing substantial losses in yield in a given year. In some cases, the yield may be completely lost. A significant example of this phenomenon was the cold wave that occurred in April 2024 across substantial regions of Poland, Germany, and Czechia. It is also important to note that fruit orchards have a considerable economic importance; they underpin the livelihoods of numerous farmers and fruit constitutes a significant export commodity, for example in Poland. In order to ensure the optimal implementation of agrometeorological services, it is imperative to consider not only forecasts of the probability of frost based on meteorological parameters (i.e. the hazard) or, in climatological terms, relevant multiannual statistics. The risk approach should be consistently applied, incorporating the elements of hazard, exposure, and vulnerability. The latter is a particularly challenging task. In the context of damage to sensitive plant parts, the phenological approach is employed. It is imperative to consider the developmental stage of individual crop varieties at a given time and location in order to fully ascertain the risk related to frost. The methods currently available for tracking generative plant stages, such as flowering and fruiting, remain limited in comparison to those available for monitoring the rate of vegetative development. The latter can be more easily measured using satellite remote sensing. Consequently, the observation of phenological stages must be conducted directly. In this study, these observations serve as in-situ data for the validation of a model based on the concept of effective temperature sums: growing degree days. Consequently, the model results, corroborated by phenological observations in orchards across Poland, are employed to formulate concepts for novel operational products that demonstrate frost-related risk in a more comprehensive manner. The same base has the potential to be applied to evaluate climatologically future frost risk to fruit crops, after taking into account adequate climate change scenarios in the context of adaptation of the agricultural sector.
How to cite: Lewicki, M.: Enhancing agrometeorological services for frost risk assessment in fruit crops: from meteorological parameters to risk evaluation and adaptation, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-743, https://doi.org/10.5194/ems2026-743, 2026.
Gentiana pneumonanthe L. (marsh gentian) is a wetland specialist and the exclusive host plant of the Alcon blue (Phengaris alcon). This species is considered a ‘flagship species' of wetland conservation, helping to raise awareness and support protection efforts. However, its long-term viability is increasingly threatened by climate change. In the Iberian Peninsula (IP), the species currently occurs within protected areas designated under Natura 2000 and RAMSAR. This study aims to identify the bio-ecological indicators that influence the viability and growth of marsh gentian, determine the suitable regions for its persistence, and assess the future suitability zones, particularly within protected areas. From an initial set of 14 bioclimatic variables (ISIMIP3b, processed using the CHELSA method at 1 km² resolution) and two topographic variables, four bio-ecological indicators were selected based on Pearson correlation and Variance Inflation Factor analyses (VIF): Thermicity Index (It), Ombrothermic Index (Io), Accumulated summer precipitation from June to August (RR_summer), and Maximum of the daily maximum temperature of the hottest month (August) (TXX_aug). Species distribution modelling was performed using the Biomod2 platform, applying six modelling algorithms (GLM, GAM, RF, GBM, CTA and MARS), which were evaluated using a combination of non-spatial metrics (TSS, AUCroc, BIAS, CSI) and a spatial metric (Boyce index). Projections were produced for a historical baseline (1995–2014) and future periods (2041–2060 and 2081–2100), under two anthropogenic radiative forcing scenarios (SSP3-7.0 and SSP5-8.5). The ensemble model demonstrated strong predictive performance (Boyce index = 0.98). Historically, 13.4% of the IP was climatically suitable for the species, primarily in mountainous regions. Under SSP3-7.0, suitable areas are projected to decline by 74.2% (2041–2060) and 99.3% (2081–2100), while under SSP5-8.5, the reductions are estimated at 75.5% and 99.9%, respectively. Although minor gains may occur in the Pyrenees (up to 3.5% under SSP3-7.0 in 2041–2060 and 0.05% under SSP5-8.5 in 2081–2100). Furthermore, most protected areas are expected to lose climatic suitability for species persistence. Such declines may disrupt ecological processes and directly threaten the survival of P. alcon. These findings underscore the urgent need for climate-informed land-use planning and effective habitat conservation strategies.
How to cite: Freitas, T. R., Martins, S., Jesus, J., Campos, J., Fernandes, A., Menz, C., Maravalhas, E., Fraga, H., and Santos, J. A.: Evaluating the climatic suitability of Gentiana pneumonanthe L. in the Iberian Peninsula through bio-ecological indicators, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-242, https://doi.org/10.5194/ems2026-242, 2026.
We developed a new approach to climate change impact and adaptation analysis at regional level in Finland in close collaboration with stakeholders (Carter & Fronzek 2022, Fronzek et al. 2022). It advances methods of analysis by applying impact models across sectors within a standard analytical framework for representing three aspects of relevance for policy: (i) sensitivity: examining the sensitivity of the sectors to changing climate by constructing impact response surface of relevant indicators; (ii) urgency: estimating risks of approaching or exceeding key thresholds of impact under alternative scenarios as a basis for determining urgency of response; and (iii) response: determining the effectiveness of potential adaptation and mitigation responses.
The approach is demonstrated by evaluating multi-hazards to ecosystems with risk indicators of drought, fire and pest outbreaks estimated with the process-based forest growth model PREBAS. Forest productivity for locations in Finland was simulated for systematic perturbations in temperature and precipitation, and results were plotted as impact response surfaces (IRS) that depict the model sensitivity to changes in climate in a scenario-neutral manner. IRSs were overlaid with probabilistic projections of climate change to estimate likelihoods of exceeding critical impact thresholds.
Our results indicate that more severe climate change and rising CO2 levels increase growth and carbon uptake, but also the risk of disturbances. The net biome exchange is strongly related to harvest levels, but combined effects of climate change and forest management are complex and vary regionally. Our impact response surface approach helps to stress-test and improve sector-based models and provides a framework for model intercomparison. It can also be used, for example, to study multi-hazard impacts.
References:
Carter T.R. and S. Fronzek (2022) A model-based response surface approach for evaluating climate change risks and adaptation urgency. In: Kondrup C. et al. (eds) Climate Adaptation Modelling, Springer Climate. Springer, Cham., p. 67-75, doi:10.1007/978-3-030-86211-4_9
Fronzek, S., Y. Honda, A. Ito, J.P. Nunes, N. Pirttioja, J. Räisänen, K. Takahashi, E. Terämä, M. Yoshikawa and T.R. Carter (2022) Estimating impact likelihoods from probabilistic projections of climate and socio-economic change using impact response surfaces. Climate Risk Management 38, 100466, doi:10.1016/j.crm.2022.100466
How to cite: Fronzek, S., Akujärvi, A., Carter, T. R., Junttila, V., Mäkelä, A., Minunno, F., and Pirttioja, N.: A multi-hazard evaluation of climate change impacts on forests in Finland using impact response surfaces, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-444, https://doi.org/10.5194/ems2026-444, 2026.
Numerous studies have documented a significant relationship between seasonal average temperatures and flowering phenology across a wide range of species and regions However, seasonal averages flatten the temporal structure of thermal forcing, potentially obscuring the thermal patterns that drives biological processes. Building on evidence that spring mean Tmax is a primary predictor of Robinia pseudoacacia L. (black locust) flowering phenology, this study presents a novel framework to assess the independent contribution of ETCCDI-based thermal extreme indices, using Piedmont (NW Italy) as a case study.
Three indices were computed for all phenological observation cells across Piedmont using AgERA5 daily temperature data and ETCCDI-compliant 90th percentile thresholds (5-day centered window, 1991–2020 baseline): TX90p_spring (count of spring days with Tmax exceeding the 90th percentile, March 1 – April 15); Warm Spell Duration Index (WSDI, count of spring days belonging to runs of at least 6 consecutive days above the threshold); TX90p_winter (count of winter days with Tmax above the 90th percentile, December–February). Each index was then correlated with the corresponding seasonal mean Tmax and with observed first flowering Day of Year (DOY) to evaluate its predictive power. A residual analysis was subsequently conducted to isolate phenological variability not accounted for by seasonal means alone.
Results show that TX90p_spring retains a significant relationship with flowering DOY even after removing the effect of spring mean Tmax, confirming that the frequency of anomalously warm spring days carries phenological information independent of the seasonal mean. WSDI and TX90p_winter both showed significant direct associations with flowering timing, warm spell years and anomalously warm winter days were each associated with earlier flowering, but neither retained significance after controlling for the respective seasonal mean Tmax, suggesting their phenological signal is not independent of the seasonal mean.
These findings support the hypothesis that the temporal structure of spring warming, specifically the frequency of anomalously warm days, modulates flowering timing beyond what seasonal means alone can predict. This distinction carries direct implications for projecting phenological change under climate scenarios in which means and extremes may shift non-proportionally. Notably, while ETCCDI indices are typically interpreted as indicators of thermal stress, TX90p_spring behaves here as a biological forcing agent accelerating phenological development. This advance, however, in a season characterized by inherently unstable weather conditions, may increase exposure to late frost or precipitation events during a phenological sensitive phase, with potential consequences for nectar production and apicultural yields.
How to cite: Carbonari, F. and Epifani, C.: Beyond seasonal means: ETCCDI thermal extreme indices and Robinia pseudoacacia flowering phenology in Piedmont (NW Italy), EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-552, https://doi.org/10.5194/ems2026-552, 2026.
Climate change is expected to bring significant transformations to viticultural systems in long-established wine regions, including the Douro Wine Region (DWR) in northern Portugal. In this work, the influence of projected climate conditions on major viticultural indicators, namely grape yield, timing of phenological stages, and potential alcohol levels, is assessed through an ensemble of high-resolution downscaled climate projections. Future conditions are analysed in comparison with a recent baseline period (1986 to 2015) under two Shared Socioeconomic Pathways: SSP1–2.6 (low emissions) and SSP5–8.5 (high emissions) for the period 2041 to 2070. The results reveal a consistent and statistically significant climate signal for all variables considered, with stronger effects and greater spatial variability under the high-emissions scenario. Model projections indicate a widespread reduction in yields across the DWR, typically ranging from about −1 to −3 t ha⁻¹, with the largest decreases occurring in areas that currently have the highest productivity, particularly in the Douro Superior subregion. This pronounced spatial differentiation points to increased regional susceptibility and underscores the need for location-specific adaptation strategies. The phenological response shows a clear shift toward earlier flowering, advancing by as much as roughly 30 days compared with the reference period, especially under SSP5–8.5. Such changes are expected to modify grape development patterns, increase the risk of exposure to late frost during early growth stages, and challenge existing vineyard management schedules. At the same time, potential alcohol content is projected to rise throughout the region, in some places surpassing an increase of +2% vol under the high-emissions scenario. This trend may affect wine style and typicity, complicate compliance with appellation regulations, and create new demands for harvest timing and winemaking practices to control sugar accumulation. These projections indicate that climate change will drive marked and spatially uneven shifts in Douro viticulture. Although the low-emissions pathway limits the severity of these impacts, the outcomes associated with SSP5–8.5 highlight the urgency of implementing forward-looking adaptation measures to safeguard wine quality, economic sustainability, and the cultural identity of the region.
How to cite: Fraga, H., Serra, E., Guimarães, N., Crespo, N., Fernandes, A., Menz, C., and Santos, J.: Updated climate change impacts for viticulture in the Douro Winemaking Region, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-44, https://doi.org/10.5194/ems2026-44, 2026.
Potential evapotranspiration (PET) is a primary driver of agricultural water demand, yet its estimation remains uncertain in topographically complex and water-limited regions such as the Iberian Peninsula. This study develops a high-resolution, climate-driven PET framework to evaluate present and future atmospheric water demand and its implications for Mediterranean perennial agriculture. PET was calculated using the Hargreaves method from multiple climate datasets representing observations, reanalysis, and climate projections (CHELSA, E-OBS, ERA5-Land, and C3S CMIP6 Atlas) for the historical period 1981–2010. Dataset intercomparison shows that the high-resolution CHELSA product best reproduces regional climatic gradients while maintaining consistency with reanalysis products, making it suitable for impact assessment in complex terrain.
Future PET projections were analysed using CHELSA under three CMIP6 scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5) for mid- and late-21st century periods. Results indicate a robust intensification and spatial expansion of evaporative demand across Iberia, scaling with greenhouse gas forcing. Under SSP5-8.5, large areas of central and southern Iberia are projected to exceed 1400–1600 mm yr⁻¹ by the late century, while even Atlantic and mountainous regions experience unprecedented increases.
A ranking analysis of temperature, precipitation, PET, and precipitation-evapotranspiration balance at the NUTS-2 level reveals increasing climatic stress in interior regions, particularly Castilla-La-Mancha, Extremadura, Madrid, Castilla-y-León, and Centro-Portugal. To assess agricultural implications, PET was combined with FAO-56 crop coefficients to estimate crop evapotranspiration (ETc) for vineyards, olive groves, and fruit trees. All crop systems show substantial increases in water demand, with olive and fruit-tree systems in southern and eastern Iberia exhibiting the largest intensification.
These findings highlight the added value of high-resolution climate data for regional impact assessments and indicate a transition toward a more water-demanding climate across Mediterranean agroecosystems. Without adaptation measures—including improved irrigation efficiency, crop management adjustments, and strategic land-use planning—future evaporative pressure may threaten the sustainability of perennial agriculture and regional water resources in the Iberian Peninsula.
How to cite: Fonseca, A., Cruz, J., Andrade, C., Fernandes, A., Menz, C., and Santos, J.: Rising Atmospheric Water Demand in Iberia: High-Resolution Projections of Potential Evapotranspiration and Agricultural Impacts, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-168, https://doi.org/10.5194/ems2026-168, 2026.
Accurate and timely forecasting of crop irrigation demand is a critical agrometeorological challenge, particularly in regions experiencing intensifying climate variability, water scarcity, and growing food insecurity. Weather conditions, including temperature extremes, precipitation deficits, and elevated evaporative demand, directly drive crop water requirements and can significantly reduce yields when irrigation scheduling is poorly timed. Existing studies on crop water demand have employed machine learning (ML) for actual evapotranspiration (ETa) prediction, incorporating feature selection to reduce data dimensionality and mitigate multicollinearity, thereby improving model performance. However, existing ETa models have limited spatial continuity and restricted applicability under changing climate conditions.
This study addresses these limitations by developing a machine learning-based Irrigation Demand Forecasting Model (AI-IDFM) that integrates multi-sensor satellite data, spectral indices, and various climatic variables to determine crop water requirements under changing climate conditions. The actual crop evapotranspiration (ETact), a key agrometeorological variable linking atmospheric demand and crop water use is estimated through a surface energy balance framework using Landsat satellite data with 30 m spatial resolution, complemented by high-resolution (3 m) land cover information, capturing fine-scale microclimatic variability relevant for field-level irrigation decision-making. The key variables, including NDVI, SAVI, LST, and net radiation (Rn), along with climatic variables such as precipitation, temperature, and humidity, are incorporated into the model. Deep learning and machine learning approaches, including Convolutional Neural Network–Multilayer Perceptron (CNN-MLP), Random Forest (RF), and Gradient Boosting (GB), were used for training and validation. The CNN-MLP model performed best, achieving an R² of 0.90, followed by RF and GB with R² values of 0.87 and 0.80, respectively. From this study, the short-range irrigation advisories are generated by coupling AI-IDFM with daily meteorological outputs from the ICON ensemble model, providing 7-day forecasts of key atmospheric drivers, including temperature, total precipitation, and relative humidity. Forecasted inputs for April 2025 indicated temperatures ranging from 36.2-42.8°C, average humidity between 18.5-43.7%, reflecting intense thermal stress during the pre-Kharif transition period that directly amplify crop water demand.
Model performance showed that CNN predictions closely matched observed ETact for rice (6.798 vs. 6.99 mm/day) and wheat (2.041 vs. 1.86 mm/day), validated against eddy covariance flux tower observations across Kharif and Rabi cropping seasons in Okara district, Punjab. The model consistently outperformed ensemble methods across diverse crop types. During the Rabi wheat growth phase, CNN forecasted ET at 29.87 mm/day compared to a flux tower measurement of 33 mm/day, with the lowest deviation recorded in December (6.99 vs. 6.89 mm/day). The results demonstrate that AI-IDFM provides a robust, weather-informed irrigation advisory framework capable of supporting farm-level decision-making across diverse cropping systems. The approach links weather data to actionable irrigation decisions, enabling scalable management from field to regional levels, improving water use efficiency and supporting climate-resilient agriculture in data-scarce systems.
How to cite: Aeman, H., Hafeez, M., and Munir, S.: AI-Based Irrigation Demand Forecasting for Climate-Adaptive Crop Water Management in Irrigated Agriculture, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-244, https://doi.org/10.5194/ems2026-244, 2026.
Spain is one of the world’s leading wine producers and is therefore particularly vulnerable to the impacts of climate change on this sector. This work assesses the impact of climate change on viticulture using bioclimatic indices specifically designed for grapevines. To this end, precipitation- and temperature-based indices were analyzed across the protected designations of Origin (PDOs) of Spain, computed from daily outputs at very high-resolution (5 km) regional climate simulations performed with the Weather Research and Forecasting (WRF) model (v4.3.3) for the Iberian Peninsula. Specifically, two simulations were conducted, including a 30-year simulation representing a recent past period, with WRF driven by ERA5. A second 30-year simulation representing severe climate change conditions was also performed, with WRF forced with ERA5 combined with a mean climate change signal derived from a CMIP6 multi-model ensemble under the SSP5-8.5 scenario, using the pseudoglobal warming (PGW) approach. The simulated indices were previously evaluated against those computed from the gridded observational ROCIO_IBEB product, developed by the Spanish Meteorological Agency (AEMET).
Overall, the evaluation showed that WRF has good skill in representing the main spatiotemporal characteristics of wine-related bioclimatic indices. Concerning potential changes under severe climate change conditions, PGW results suggest a northward and northwestward shift of optimal climatic conditions for vineyards, while PDOs in the southern plateau and along the Mediterranean coast show a decline in climatic suitability for wine production. These changes are driven by a general increase in temperatures, including warmer nights during the ripening period, and an extension of the growing season in northern PDO regions. Moreover, southern and coastal PDOs are expected to experience a marked increase in extremely hot days, which could negatively affect grape yield and quality. Additionally, a substantial reduction in precipitation and a trend toward drier conditions are projected, leading to a decrease in areas with optimal moisture availability and an expansion of regions under water stress.
These findings may provide a robust basis for designing adaptation strategies to climate change in the viticultural sector.
Acknowledgements: This research has been carried out within the framework of project PID2021-126401OB-I00, funded by MICIU/AEI/10.13039/501100011033 and by ERDF, EU.
How to cite: García-Valdecasas Ojeda, M., Sola-Alonso, C., Tacoronte, N., Donaire-Montaño, D., Castro-Díez, Y., Esteban-Parra, M. J., and Gámiz-Fortis, S. R.: Impact of Severe Climate Conditions on Wine Production in Peninsular Spain , EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-305, https://doi.org/10.5194/ems2026-305, 2026.
The black locust (Robinia pseudoacacia L.) tree is a highly valuable species for both forestry and apiculture, despite its invasive spread and ecological impacts on grasslands and steppe areas. Under favourable weather conditions, it can produce high honey yields and represents a primary source of income for beekeepers in Hungary. In recent years, yields have become more unpredictable due to early warm springs followed by sudden frost events. To investigate the effects of current environmental conditions and the weekly dynamics, 24-hour nectar measurements were conducted over two years, paired with honey bee foraging data. We have compared the nectar weight, sugar concentration and sugar value in open flowers along with the hourly beehive weight and meteorological data. Our results showed that nectar secretion started at the bud stage and the nectar weight in open flowers averaged 1.9 ± 1.7 mg per flower, with a mean sugar concentration of 41 ± 18%. Optimal conditions for nectar secretion were observed at air temperatures between 15–25 °C and relative humidity levels of 60–80%. Nectar weight increased on cloudy, wetter days, while peak values occurred on subsequent days, accompanied by a decrease in sugar concentration. Honey bees were more active at temperatures above 20 °C and relative humidity below 40%.
We found that meteorological variables, mainly air temperature, have a high impact on nectar secretion and consequently on foraging activity as well. Our results may contribute to a better understanding of weather-driven effects on nectar secretion and their impact on pollinators, especially in the context of climate change.
How to cite: Ilyés-Vincze, C., Leelőssy, Á., Magyar, D., Mészáros, R., Déri, H., and Zajácz, E.: Nectar secretion of black locust and its impact on honey bee foraging under variable weather conditions , EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-410, https://doi.org/10.5194/ems2026-410, 2026.
Over the past decade, the Sudoe region (Spain, Portugal, and France) has faced increasing pressure on water resources due to climate change, recurrent droughts, and growing agricultural demand. These challenges highlight the need for more efficient irrigation practices and improved access to high-resolution agrometeorological information. In many areas, the limited density of meteorological observation networks constrains the provision of accurate, parcel-scale data required for optimized water management.
To address this issue, the e-Rigation project proposes an innovative solution based on virtual meteorological stations. The initiative aims to generate operational, high-resolution meteo-climatic indicators at plot level, enabling more precise irrigation scheduling and supporting sustainable water resource management. The project is structured around three main pillars: development of the virtual station system, its implementation and validation through pilot experiences, and knowledge transfer and capacity building among end users. The project brings together a multidisciplinary consortium from across the Sudoe area, including research institutes, meteorological agencies and agricultural organizations and authorities. Basque partners play a key role, with Neiker as project coordinator and Euskalmet contributing expertise in meteorological data analysis, modeling, and operational services.
This contribution presents an overview of the project objectives and activities, with emphasis on the role of Euskalmet, which leads Work Package 1 on the design and development of the virtual meteorological station system. This includes system requirements definition, implementation and calibration of analysis and prediction tools, and improvement of applications for irrigation water management. Euskalmet also contributes to implementation by integrating virtual station data into irrigation management tools and participating in pilot testing and evaluation. These actions aim to ensure the operational delivery of daily agrometeorological indicators at parcel scale and to assess the technical and economic viability of the proposed solution. Key activities include the development of advanced interpolation and downscaling techniques to generate high-resolution meteorological fields; integration of observational data, numerical weather prediction outputs, and remote sensing products; generation of key irrigation variables such as reference evapotranspiration and soil water balance indicators; and adaptation of decision-support tools to incorporate virtual station data.
The project also emphasizes user engagement through training, dissemination, and knowledge transfer. Euskalmet contributes to training materials, supports pilot users, and participates in system performance evaluation and impact assessment on water efficiency and agricultural practices. Overall, the e-Rigation project aims to advance smarter and more sustainable irrigation management in the Sudoe region through innovative meteorological services and collaboration between scientific, technical, and agricultural communities
How to cite: Gaztelumendi, S., Hernandez, R., and Landeras, G.: Interreg Sudoe e-Rigation Project: The Contribution of the Basque Meteorological Agency, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-553, https://doi.org/10.5194/ems2026-553, 2026.
One of the hot challenges in agrometeorology is seasonal crop yield forecasting, which is a critical aspect of food production planning. The seasonal crop yield forecasting may be optionally based on crop growth models. In this approach, the meteorological data fed into the crop models typically consist of observational weather data up to the forecast date, followed by weather data from weather forecasts (WFs), climate models, or weather generators (WGs).
In our contribution, we propose an improvement of the WG-based methodology. In contrast to approaches described in the literature, where WGs synthesize data independently of any WF, we are developing a methodology in which our single-site parametric M&Rfi WG (run with daily step) synthesizes multiple realisations of weather series conditionally on available WFs. Two approaches are proposed: (A) For use in operational crop yield forecasting, WG produces synthetic weather series starting with D0 day (which comes after the last day with weather observations and for which WF is available), so that the synthetic series smoothly follows available observations. In our experiments, (a) WF is defined for the upcoming days/weeks/months either in terms of the absolute values of individual weather variables or deviations from their climatological normals, (b) WF may optionally include information on its accuracy (e.g. in terms of standard errors or min-max intervals), (c) Precipitation forecast is assumed to be given in terms of amount and probability of precipitation occurrence, (d) WF may be defined separately for a set of time intervals (e.g. for next three days, next week, next months, etc.). The procedure for linking the generation process with WF is based on a continuous adjusting of the stochastically generated series in a way resulting in a series that fits the weather forecast while the internal structure (e.g. relations between variables) of the series remains realistic. (B) the “Research” approach: Unlike A approach, the B approach aims to answer the question: How the use of WF of given accuracy may contribute to the accuracy of seasonal forecast of the crop yields? The process of adjusting the stochastically generated series is similar to A method, but now, we care only about the dispersion of individual realisations, so that the magnitude of the dispersion corresponds to the known accuracy of the forecast.
To demonstrate the two methodologies, we run crop models Daisy and Hermes for 1 experimental site in Czechia and five main crops – winter wheat, spring barley, winter rape, maize and sugar beet.
Acknowledgements: The experiments were made within the frame of projects PERUN (supported by TACR, no SS0203004000), OP JAK (supported by MSMT, no. CZ.02.01.01/00/22_008/0004605) and AdAgriF (supported by MSMT, no. CZ.02.01.01/00/22_008/0004635).
How to cite: Dubrovský, M., Trnka, M., Bartošová, L., Štěpánek, P., Pohanková, E., and Bálek, J.: Linking the Weather Generator with Weather Forecasts for Use in Crop Yield Forecasting , EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-701, https://doi.org/10.5194/ems2026-701, 2026.
Spring phenology in temperate trees is strongly controlled by air temperature, yet the role of short-term thermal anomalies preceding phenological events remains insufficiently understood. In our previous analyses of silver birch (Betula pendula) phenology in Poland, we showed that the timing of both ground-observed leaf unfolding (LU) and satellite-derived start of season (SOS) was closely related to temperature anomalies in March and April, and that not only the magnitude but also the within-season timing of warming played an important role in shaping spring onset. Preliminary anomaly-based analyses further suggested that the period of approximately 20 days preceding leaf-out may be particularly important. However, contrasting early-spring seasons indicated that similar phenological outcomes may arise from different thermal trajectories, including both sustained warming and short but intense late-season warm pulses.
In this study, we revisit these relationships using an extended dataset comprising LU observations for 2007–2026 and SOS estimates for 2018–2026. LU data were obtained from the Institute of Meteorology and Water Management – National Research Institute, while SOS was derived from Sentinel-2 imagery using the Enhanced Vegetation Index. Temperature conditions were assessed from in-situ measurements and a high-resolution station-based gridded dataset. We focus specifically on pre-event temperature anomalies, examining their magnitude, persistence, and timing in relation to the observed dates of spring onset. Rather than relying solely on calendar-month means, we analyse temperature anomalies in moving windows preceding LU and SOS to identify the period of greatest phenological sensitivity and to test whether exceptionally early spring onset is more strongly associated with sustained positive anomalies or with brief but intense warm episodes occurring shortly before the event.
By combining long-term ground observations with satellite-based estimates, this study provides a more process-oriented perspective on spring phenology and its thermal controls. The results are expected to improve our understanding of how silver birch responds to anomalous thermal conditions and to help refine interpretations of phenological sensitivity under ongoing climate warming.
How to cite: Sulikowska, A., Grabska, E., Dížková, P., Bartošová, L., and Wypych, A.: Tracking pre-event temperature anomalies and spring onset in silver birch in Poland, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-724, https://doi.org/10.5194/ems2026-724, 2026.
Supporters & sponsors
