Climate change also influences crop suitability in certain regions where livestock can be negatively affected by migrating diseases and available food. To complicate matters the agricultural sector is also trying to become more sustainable and environmentally friendly in an attempt to meet greenhouse gas emission targets.
This session intends to examine our increasing knowledge of agricultural meteorology, while also attempting to identify opportunities in our changing environment.
We invite presentations related but not limited to:
• Agrometeorological modelling (e.g. modelling agrometeorological related diseases, frost protection warning methods, drought indices etc.)
• Impact of weather and climate extremes on agriculture
• Methods of measurements and observations (e.g. ground based equipment, remote sensing products, citizen science, Big Data etc.)
• Decision support systems & the representation of uncertainty
• Interactions/feedback of farmers and other end users
• Use of future climate projections on agrometeorological models
The memory of dynamic processes refers to the characteristic of dynamic systems where historical states influence their current and future behaviour. In modelling dynamic systems, it is crucial to ensure that the model design allows simulations that consider the system’s history and its impact on future dynamics beyond the integration interval. An important step toward this goal is identifying the characteristic time scale of the memory, i.e. memory longevity.
In this research, we use the atmosphere as a dynamic system and air temperature at 2 m height as a variable that can carry memory information. To assess the impact of environmental conditions on memory characteristics, we selected meteorological data measured at climate stations and data from rural areas near Novi Sad and Vrsac (Serbia) measured at automatic weather stations (AWS) as part of the Forecasting and Warning Service for Plant Protection of Serbia (PIS) meteorological monitoring system.
Following Caballero et al. (2002), we analyzed the power spectral density (PSD) and autocorrelation functions (AC) of three air temperature time series. According to results of previous studies, processes represented with PSD following power law function, S(w) at low frequencies (w), [S(w) = w-2d] are collectively referred to as “long memory” processes if 0<d<0.5. Parameter d is commonly described as the “intensity of long memory”.
Initial results obtained for climate station data series show clear differences between high- and low-frequency trends in PSD estimates for air temperature at the selected locations. Differences in AC are particularly noticeable for longer lags.
Caballero, R., Jewson S., Brix, A., 2002: Long memory in surface air temperature: Detection, modelling, and application to weather derivative valuation. Clim. Res., 21, 127-140. 10.3354/cr021127.
How to cite: Lalic, B. and Firanj Sremac, A.: Impact of Environmental Conditions on Long-Memory of Surface Air Temperature, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-988, https://doi.org/10.5194/ems2024-988, 2024.
A useful resource for understanding and describing worldwide climatic patterns is the Köppen-Geiger (KG) climate classification system. The classification of climates suited for viticulture has been crucial to the wine sector, given the importance of climate in cultivating grapes for wine production. Wine production has come to be associated with Mediterranean climates due to the combination of the KG climate classification system and the geographic location of Europe's old-world wine areas. These climate types can also be found in the fynbos of South Africa, the Mallee of southern Australia, the matorral of Chile and Argentina, the chaparral and coastal valleys of western North America, and in the Mediterranean basin of Europe. But today, wine is produced in many different sorts of climates. Overall, a region's climate has a significant impact on wine production since it decides whether particular grape types can be grown there, greatly influencing the type of wine that can be produced and affecting the wine's quality.
This study examines the KG classification system's application to the most recent CMIP6 experiments. Using an ensemble of 14 global climate models and the WorldClim dataset, a baseline for the historical period 1970–2000 was established. Climate variability in winemaking regions is assessed using future estimates from 2041 to 2060, based on several scenarios of human radiative forcing (SSP2-4.5 and SSP5-8.5). The findings represent the most thorough record of past climate classifications for most wine regions globally, as well as prospective future changes to these categories.
Globally, temperate and arid zones (climate types C and B, respectively) are expected to undergo a substantial transition from a warm summer temperature to a hot summer climate. High temperatures can have a major impact on the grape development process. They may cause early ripening, alter the aromatic components in the grape berry, and perhaps change the acidity balance. Wine producers must modify their vineyard management practices in response to climate shifts and employ appropriate countermeasures to mitigate the negative effects of abiotic pressures on grape quality and vineyard health. These adaptation tactics could involve relocating to other microclimatic zones, adopting irrigation techniques, modifying canopy and soil management, or utilizing different variety-clone-rootstock combinations. Wine producers need to consider regional climate change projections to ensure the long-term sustainability of the environment and the socioeconomic landscape. This will help them make more informed decisions about vineyard management practices, and ultimately strengthen the wine industry's resilience and adaptability to the ongoing effects of climate change.
Acknowledgments: Research funded by National Funds by FCT under the project UIDB/04033/2020 and LA/P/0126/2020. Vine & Wine Portugal – Driving Sustainable Growth Through Smart Innovation, PRR e pelos Fundos Europeus Next Generation EU, no âmbito das Agendas Mobilizadoras para a Reindustrialização, Projeto n.º C644866286-011.
How to cite: Andrade, C., Fonseca, A., A. Santos, J., Bois, B., and V. Jones, G.: Worldwide Historical Shifts and Projections with CIMP6 Experiments for Köppen-Geiger Climate Classifications in Wine Regions, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-91, https://doi.org/10.5194/ems2024-91, 2024.
Understanding global plant phenology dynamics is essential for predicting ecosystem responses to climate variability. This study introduces a novel approach to analyse plant phenology dynamics across Köppen climate zones using the Normalised Daily Temperature Range (DTRT) and minimum daily relative humidity (R1) indices. By coupling these selected indices with phenology-related satellite data, we investigate the seasonality of plant development worldwide. We use global conventional temperature and humidity reports to explore how environmental factors influence phenological patterns across diverse ecosystems, from tropical rainforests to polar regions. The Köppen climate classification system serves as a framework for categorising climatic regions based on temperature, precipitation, and vegetation characteristics, facilitating a comprehensive analysis of regional climate variations and their impact on plant phenology. Integrating observed meteorological data and satellite imagery, we explore complex interactions between climate indices and vegetation dynamics. Satellite-derived metrics, such as the normalised difference vegetation index (NDVI), provide valuable insights into vegetation greenness and growth stages, complementing the meteorological data used to calculate DTRT and R1 indices. Our study, conducted on a global scale, focuses on identifying the start and end of the vegetation season and examining changes in seasonality across different Köppen climate zones. We hypothesise that DTRT and R1 indices can serve as standalone tools for determining these key phenological events and fill the gaps in satellite-derived data. By comparing DTRT and R1 indices with satellite NDVI data, we aim to assess their accuracy in capturing vegetation phenology dynamics globally. The comparative analysis of indices and satellite data allows a comprehensive understanding of how climate variability influences the timing and duration of the vegetation season. This research enhances our ability to predict ecosystem responses to climate change and offers valuable insights for ecosystem management and conservation efforts on a global scale.
Funding: This work is supported by contract for the Implementation and Financing of Scientific Research Project NIO in 2024 No. 451-03-66/2024-03/200117 dated February 5, 2024, COST Action CA20108 - FAIR NEtwork of micrometeorological measurements (FAIRNESS) and Centre of Excellence One Health, Ministry of Science, Technological Development and Innovations 451-03-1524/2023-04/16
Acknowledgements: Serbian micrometeorological measurements and phenological observations by Forecasting and Reporting Service for Plant Protection of the Republic of Serbia (PIS) are financed by the Ministry of Agriculture, Forestry and Water Management of the Republic of Serbia and Provincial Secretariat for Agriculture, Water management and Forestry of the Vojvodina province, Republic of Serbia. FAIRNESS FMP 2.0 portal is used as one of the sources of micrometeorological data.
How to cite: Firanj Sremac, A., Lalic, B., and Marcic, M.: Normalised DTR and Minimum Relative Humidity Across Köppen Climate Zones: Analysing Responses in Plant Phenology, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-426, https://doi.org/10.5194/ems2024-426, 2024.
As the Mediterranean region is affected by climate change, it is crucial to understand how the variability in meteorological parameters and human activities impacts the natural resources in agriculture. Higher temperatures, reduced precipitation and extreme events like droughts and floods are fundamental variables that contribute to a negative water balance in nature. Additionally, while intensive farming has increased agricultural productivity, concerns about its sustainability and the potential consequences for ecosystems have emerged. Developing natural-based solutions is necessary to address major challenges, including environmental degradation, water scarcity, loss of soil fertility and biodiversity.
This study focuses on integrated olive orchard agroecosystems management in Messenia regional unit, northwest Peloponnese, known for its rich agricultural heritage and olive oil production. Messenia has a Mediterranean climate, characterized by hot, dry summers and mild, wet winters, with a unique topography combining coastal plains and mountain area. In collaboration with local stakeholders we have developed two field experiments to monitor agrometeorological indicators and assess the current agricultural practices regarding soil and water management.: (i)The first experiment is being conducted in a hilly terrain orchard where we have applied a methodology to collect surface runoff and quantify soil erosion after rainfall events in three different treatments (use of herbicides, mowing of natural vegetation and cover crops seeding). (ii) The second experiment is being developed at a different orchard plant over flat terrain, where we are testing three different irrigation practices, namely: rainfed, irrigation based on common local practices and irrigation based on the phenological stages of olive trees. The agrometeorological conditions (e.g. ambient and soil temperature and humidity, potential evapotranspiration, vegetation properties) are monitored at both experiments with ground based instrumentation and airborne remote sensing. The three major aspects under study are soil characteristics, plant growth, and olive oil quality. First results indicate a difference of more than 30% in soil erosion rates for the different cover treatments. An improvement in oil quality without significant changes in olive oil content between rainfed and irrigated treatments is also found during the first year of the experimental study. Both experiments will remain active in the following years, and based on the results of our research we aim to consult local farmers on enhancing the resilience of olive tree cultivation and reducing the environmental footprint.
How to cite: Pantazis, C., Solomos, S., Maneas, G., Centrito, M., Marino, G., Roggero, P. P., Mihail, J., Fountoulakis, I., Kapsomenakis, J., Gkisakis, V., Nastos, P., Kourgialas, N., and Zerefos, C. S.: Agrometeorological assessment of olive cultivation practices in Messenia, Peloponnese, under climate change, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-107, https://doi.org/10.5194/ems2024-107, 2024.
Montenegro is located on the Balkan Peninsula. This country portrays a complex topography, and so various climate types are found, from alpine conditions in inland mountains to a Mediterranean climate in valleys and near the coast. The Mediterranean conditions allowed Montenegro to develop a viticulture heritage, which is becoming threatened by climate change. Most of the vineyards are located in the vicinity of the capital, Podgorica, where the temperature will certainly increase. In this sequence, the present study aimed to expose the climate change impacts on Montenegrin Viticulture, resorting to a high-resolution dataset CHELSA (≈ 1km). Temperature data from the weather station was compared to the CHELSA dataset for the historical period, revealing that the dataset is accurate. Thermal bioclimatic indices were analyzed for the historical period 1981-2010, and for the future period 2041-2070. Projections were ensembled for 5 climate models, and encompassed 3 Shared Socioeconomic Pathways, SSP1—2.6, SSP3—7.0, SSP5—8.5. The results revealed that, regardless of the SSP scenario, the temperature increase threatens Montenegrin viticulture, as the baseline conditions will be surpassed. Temperature increase will be major during the warmest quarter, which will be around 4 °C. Regarding the growing season, the average temperature from April to October, will reach 24 °C, which is above traditional thresholds for successful grape growing. The mean annual temperature will increase by more than 2 °C, and as a consequence, the growing season will be expanded, by more than 1 month, anticipating phenological events. During the historical period, the Winkler Index is 2530 °C, suitable for wine production, but in future, there will be excessive heat in grapevines, as the Winkler index will become higher than 3000 °C, increasing the risk of thermal and hydric stress. These findings are alarming, serving as a careful alert that adaptation measures should be applied in the short term, to guarantee the long-term sustainability and productivity of Montenegrin Viticulture.
Acknowledgments:
Research funded by National Funds by FCT – Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020 and LA/P/0126/2020 (https://doi.org/10.54499/UIDB/04033/2020)
This research was funded by the MONTEVITIS project “Integrating a comprehensive European approach for climate change mitigation and adaptation in Montenegro viticulture”, funded by the European Union’s Horizon Europe—the Framework Programme for Research and Innovation (2021–2027)—under grant agreement nº 101059461
How to cite: Fernandes, A., Kovač, N., Fonseca, A., Fraga, H., Radonjić, S., Simeunović, M., Menz, C., Costafreda-Aumedes, S., and Santos, J.: Climate Change Impacts on Montenegrin Viticulture, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-137, https://doi.org/10.5194/ems2024-137, 2024.
Phenological observations are expensive and demanding in terms of manpower to monitor the vegetation stages. Therefore, satellite products have opened new possibilities for easier and more widespread data collection. Nowadays biomass estimations extensively rely on these tools, given their extensive spatial and temporal coverage, which are defined by indicators such as vegetation indices, which describe the biomass growth, canopy structure, vegetation health and even water management etc. However, the detection of flowering stages through remote sensing is less explored, with fewer established methods available.
This study investigates temporal phenological changes during the blooming period of the most widely cultivated oilseed crops in Hungary in 2021, specifically the sunflower (Helianthus annuus L.) and the winter-cultivated oilseed rape (Brassica napus L.). The objective is to characterize the blooming phase and dynamics of these two crop species utilizing various vegetation indexes and satellite-derived products. The investigation is conducted across seven distinct regions, using honey bees as bioindicators of the fields.
Methodologies outlined in prior scientific literature, focusing on the analysis of anthesis timing and duration in major nectar-producing crops utilizing Sentinel-1 SAR and Sentinel-2 optical products, serve as the basis for this research. Within each radius study areas, the parcel-averaged and smoothed daily time series were acquired. The estimation of the blooming phases was achieved through parcel smoothing methods using daily non-parametric local regression (loess) approach which showed better performance compared to the Savitzky-Golay (SG) algorithm. In our flowering detection analysis, we also examined the differences in the ascending and descending orbits and their combined results. In the case of oil seed rape, the NDVI index reached its maximum after flowering, while for sunflower it varied. Additionally, we investigated the outcomes of all polarization and method combinations within each crop type.
Our study enables the comprehension of temporal flowering patterns in bee pasture crops through the integration of SAR and optical measurements. Additionally, it supports the utilization of beehive scales to provide field-based reference data for estimating anthesis.
The research was funded by the National Multidisciplinary Laboratory for Climate Change, RRF-2.3.1-21-2022-00014 project. Project No. 993788 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the KDP-2020 funding scheme.
How to cite: Vincze, C., Birinyi, E., Leelőssy, Á., Kristóf, D., Mészáros, R., and Kern, A.: Field-level analysis of phenological cycles and dynamics of sunflower (Helianthus annuus L.) and oil seed rape (Brassica napus L.) flowering within various regions of Hungary, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-415, https://doi.org/10.5194/ems2024-415, 2024.
Wireworms, the soil living larvae of several click-beetle species (Fam. Elateridae), are serious pests in various crops, e.g. vegetables, potatoes and maize, due to their feeding on underground plant parts. Controlling wireworm infestations in Europe has become increasingly challenging due to the lack of efficient insecticides and presumably the effects of reduced tillage and climate change. It is to be expected that rising temperatures will further enhance the population densities and spread of thermophilic species.
In the frame of the ACRP-project “RIMPEST1”, the relationships between weather and wireworm damages in potatoes were investigated for an important potato production region in North-eastern Austria. An analysis was conducted using anonymised data on annual wireworm damages from 2002 to 2022, along with corresponding temperature and precipitation data. The resulting regression model calculates the damage level in potatoes in North-eastern Austria during late summer/autumn at the time of harvest, based on soil temperature sums from the previous months. The underlying significant positive correlation between soil temperatures and wireworm damages in the investigated region might be due to promoting effects of higher temperatures on the thermophilic species Agriotes ustulatus (Schäller) and A. brevis (Candeze).
By utilizing the model, farmers and extension workers can calculate a first forecast of the damage risk prior to potato harvest, making it a decision support tool for determining the optimal harvesting date. Additionally, the model approach can estimate future trends of wireworm infestation in potatoes in the respective region based on representative climate scenarios. First results indicate a clear increase in the risk of severe wireworm damages in potatoes in the period from 2031 to 2060. To ensure optimal performance in the future, the model approach should be continuously validated and calibrated with new data from further years.
[1] ACRP-13th Call Project RIMPEST (KR20AC0K17957) ("The effect of changing climate on potential risks from important insect pests on plant production in Austria and related adaptation options").
https://www.klimafonds.gv.at/report/acrp-13th-call-2020/
https://rimpest.boku.ac.at
How to cite: Hann, P., Trska, C., Kamptner, A., Eitzinger, J., and Wechselberger, K.: Forecasting regional wireworm damage levels in potatoes based on soil temperatures, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-405, https://doi.org/10.5194/ems2024-405, 2024.
Global warming is one of the most significant characteristics of climate change. Serbia is no exception; it was noted that the daily mean air temperature and the number of days with extreme weather conditions have increased. The tropical days that used to be common for the summer months recently have been registered in May and even April.
Extreme weather events can disrupt our usual perception of the time of occurrence of the development stages of pests and growth stages of plants. Since air temperature is the main trigger of plant and harmful organism development, a warm period occurrence earlier than usual in the spring could cause the earlier emergence of the pest and the earlier start of plant growing season.
Over the past fourteen years, meteorological conditions have been measured in the plant canopies as part of the Forecasting and Warning Service for Plant Protection of Serbia (PIS) monitoring system. Extremely high winter temperatures and early winter-spring transition compared to the region's climatology are recorded, affecting plant and pest phenology dynamics. This research is focused on effects registered in the moth family Tortricidae (Lepidoptera) and their host plants.
The occurrence of different stages for the codling moth (Cydia pomonella) and apples, the oriental fruit moth (Grapholita molesta) and peach, the plum fruit moth (Grapholita funebrana) and plum, and the grape berry moth (Lobesia botrana) and grapevine, were analyzed and their association with air temperature and season transition indices. The data used in the study are gathered through the PIS monitoring network. Biological data series are collected on production plantations where protection measures were regularly applied. Biofix data, as well as timing and population of adults, are gathered using pheromone traps. Eggs and larvae presence, as well as the growth stages of host plants, result from visual inspections in the orchards and vineyards. Depending on the species, 20-40 locations were analyzed annually for each pest for 2012-2024.
Our research aims to explore the impact of extreme winter temperatures and winter-spring transitions on the development of pests and host plants. Monitoring and understanding these effects is crucial for accurately forecasting and effectively managing adverse impacts on agriculture.
How to cite: Marcic, M., Lalic, B., Firanj Sremac, A., and Koci, I.: Impact of Early Winter-Spring Transition on the Development Dynamics of theMoth Family Tortricidae and Host Plants, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-826, https://doi.org/10.5194/ems2024-826, 2024.
In the scope of the ACRP-project RIMPEST1 we tested prognosis models for thermophilic, economically relevant insect pests in agriculture in Austria. We focused on Diabrotica virgifera virgifera and Ostrinia nubilalis, as pests in corn as well as Helicoverpa armigera, Aphis fabae, Acyrtosiphon pisum and Myzus persicae as pests in vegetables.
The respective models collected via extensive systematic literature research, were further processed, tested, and validated using historical monitoring and weather data to choose models with the best fit for Austria for each investigated species.
For Diabrotica v.v. the zero-inflated Poisson mixture model (Falkner (2018) was chosen as best fit, since it was developed with Austrian monitoring data (2002 – 2015) and utilizes spatial data on corn crop abundance.
The best fitting prognosis model for O. nubilalis in Austria is the model by Maiorano and Donatelli (2014). Prediction of the flight peak of the first (=winter) generation of the bivoltine race adults has an R² of 0.324 (RMSE 8.424 days, n =35). For the flight peak of the univoltine race, a priori set to 560 degree-days in the model for testing, the R² is 0.288 (RMSE 8.694 days, n = 105).
For H. armigera, a model for prognosis of the development from egg to adult (Dalal and Arora, 2019). performed well calculating the full development of an individual during the season, but did not account for winter diapause and needs an additional model to set the first biofix per year for simulations. We will utilize our monitoring data to design a negative prognosis model for this purpose, which will predict the earliest day an immigrating H. armigera adult can be detected in Austria.
For the aphids two models per species were tested, either for aphid development (A. fabae, A. pisum and M. persicae) or a phenological model (A. pisum and M. persicae). Further testing of these models is still in progress.
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1 ACRP-13th Call Project RIMPEST (KR20AC0K17957) ("The effect of changing climate on potential risks from important insect pests on plant production in Austria and related adaptation options").
https://www.klimafonds.gv.at/report/acrp-13th-call-2020/
https://rimpest.boku.ac.at
https://www.ages.at/en/research/project-highlights/rimpest
References:
Dalal, P.K./ Arora, R.: Model‑based phenology prediction of Helicoverpa armigera (Hübner) (Noctuidae: Lepidoptera) on tomato crop. Journal of Plant Diseases and Protection 2019, 126, S. 281-291.
Falkner, K.: Analysing the influences of climate and land use on the spread and abundance of the Western Corn Rootworm in Austria. In, Department of Economics and Social Sciences, Institute for Sustainable Economic Development. Vienna: University of Natural Resources and Life Sciences 2018, S. 133.
Maiorano, A./ Donatelli, M.: Validation of an insect pest phenological model for the European corn borer (Ostrinia nubilalis Hbn) in the Po Valley in Italy. Italian Journal of Agrometeorology 2014, 18, S. 43-50.
How to cite: Manhalter, S., Moyses, A., and Wechselberger, K.: Changes in vegetable and arable crop insect pest occurrence caused by climate change – models investigated in the scope of the ACRP-project RIMPEST1, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-298, https://doi.org/10.5194/ems2024-298, 2024.
Sustainable crop production will in future face further challenges from insect pests due to climate change. Rising temperatures will enable higher overwintering rates and accelerate the development of thermophilic insects, leading to increased damage risks for regional crop production systems. These changes pose problems for optimum timing of monitoring and control measures, which could be countered with improved and new prediction models.
Within the ACRP-Project RIMPEST[1] new prediction models were developed for the European grapevine moth, Lobesia botrana (Denis & Schiffermüller) and the European grape berry moth, Eupoecilia ambiguella (Hübner) (Lepidoptera: Tortricidae) in Austria including monitoring data from 60 selected monitoring sites and measured weather data from adjacent reference weather stations in the period 1980 to 2023. Stepwise multiple linear regression (MLR) analysis was applied to generate prediction models for the first seasonal occurrence of the different developmental stages (egg, larvae, adult) of the first and second generation of L. botrana and E. ambiguella. As input data for the MLR analysis nine processed weather parameters, different calculation periods and the DOYs (day of year on which the first seasonal occurrence was observed) were used.
The performance evaluation of the six generated MLR models for predicting the different generations and developmental stages of L. botrana resulted in an R2 of 0.51 to 0.92, a RMSE of 2.18 to 3.97 and an average prediction range of 1.90 days too early to 1.40 days too late. For E. ambiguella the validation resulted in an R2 of 0.33 to 0.69, a RMSE of 3.48 to 4.15 and an average prediction range of 2.85 days too early to 1.00 day too early. The MLR models for E. ambiguella first generation egg and larvae were not sufficiently validated as too few datasets were available.
The implementation of the new MLR models for impact assessments under regional climate scenarios can help to determine the potential future risks of L. botrana and E. ambiguella occurrence in Austrian wine-growing regions. The inclusion of future observation data into the analysis, especially from years with extreme weather events, can further improve the prediction accuracy of the MLR models.
[1] ACRP-13th Call Project RIMPEST (KR20AC0K17957) ("The effect of changing climate on potential risks from important insect pests on plant production in Austria and related adaptation options").
https://www.klimafonds.gv.at/report/acrp-13th-call-2020/
https://rimpest.boku.ac.at/
https://www.ages.at/en/research/project-highlights/rimpest
How to cite: Kolkmann, K., Blümel, S., Thaler, S., and Eitzinger, J.: Multiple linear regression models to predict seasonal occurrences of two important grapevine pest Lepidoptera in Austria, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-311, https://doi.org/10.5194/ems2024-311, 2024.
Global warming will modify the dynamics of thermophilic pest insect populations and their spread, which could raise the risk of crop damage and plant production. Long-term insect pest monitoring can provide important data for developing pest models, which can help to predict future pest risk trends in the face of climate change. Within the ACRP-project RIMPEST1 ("The effect of changing climate on potential risks from important insect pests on plant production in Austria and related adaptation options") pest trends under crop land-use and climate scenarios by applying pest models are therefore investigated. Corresponding databases from monitoring programs for various insect pests of major crops are used for pest model development and testing for Austrian case study regions. To estimate the range of potential pest risks in the various Austrian crop growth regions in the future (2021-2050; 2071-2100), an ensemble of downscaled Austrian climate scenarios (ÖKS15) of two emission scenarios, RCP 4.5 and RCP 8.5, is used. Using validated pest algorithms on a site-specific and grid-based application reveals variable-sized shifts in pest phenology, depending on the climate region and the specified future time periods.
First results for the American grapevine leafhopper (Scaphoideus titanus), European grapevine/berry moths (Lobesia botrana and Eupoecilia ambiguella), and plum moth (Grapholita funebrana) indicate a significant response to climate change toward earlier first occurrence dates of relevant development stages, which partially coincides with shifted growing areas of the host plants. While hardly any changes are likely in the near future (2021-2050) compared to current conditions, a significantly earlier occurrence of the pests can be expected at the end of the century, which varies regionally and depending on the projection. On average, for example, European grapevine moths can be expected to appear around 4 days earlier (RCP 4.5) / 7 days earlier (RCP 8.5) than today.
The findings of the project should help practitioners and policymakers to develop future strategies for optimised cultivation and pest control options.
1ACRP-13th Call Project RIMPEST (KR20AC0K17957) ("The effect of changing climate on potential risks from important insect pests on plant production in Austria and related adaptation options"). https://www.klimafonds.gv.at/report/acrp-13th-call-2020/; https://rimpest.boku.ac.at
How to cite: Thaler, S., Kolkmann, K., Blümel, S., and Eitzinger, J.: Use of climate change scenarios for pest algorithms in Austria, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-302, https://doi.org/10.5194/ems2024-302, 2024.
In the context of climate change and globalisation, the risks for invasive species are increasing. One example of this is Spodoptera frugiperda (JE Smith), commonly known as the fall armyworm (FAW). The FAW originates from the tropical and subtropical regions of America and has spread to most continents worldwide in recent years. Since the FAW has recently also spread to the Mediterranean regions of Europe, the aim of this study was to analyse the potential susceptibility of Slovenian agriculture to this pest, with specific emphasis on maize (Zea mais). Additionally, the climate suitability was assessed for four possible natural enemies of FAW. The climate suitability of the past and future climate in Slovenia was evaluated in relation to the latest available data on the global occurrence of FAW and its natural enemies. The analysis was carried out on the basis of historical data and climate model projections for temperature and precipitation. The climate projections included 6 sets of regionally downscaled model results from the EURO-CORDEX project for the RCP4.5 and RCP8.5 scenarios. The results show that an increasing probability of FAW occurrence in Slovenia throughout the 21st century, especially in the maize-growing areas of Slovenia with the largest areas under maize cultivation. The natural enemies whose climatological niches were calculated to be the most comparable to those of the FAW are parasiotids Telenomus remus and Trichogramma Pretiosum. The results of this study can serve as a tool for the possibilities of pre-emptive introduction of natural enemies of FAW to Slovenia, which is a method of biological pest control, in which the use of synthetic pesticides is replaced by the introduction and release of natural enemies of the pests.
How to cite: Žnidaršič, Z., Curk, M., Trdan, S., and Pogačar, T.: Exploring the climate suitability of Slovenia for the invasive species Spodoptera frugiperda (JE Smith), EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-116, https://doi.org/10.5194/ems2024-116, 2024.
Among wild pollinators, diurnal butterflies are important in natural ecosystems and contribute significantly to agricultural productivity. Worryingly, a growing body of literature suggests that climate change (CC) may result in the extinction and decline of many butterfly species1. Understanding which species and areas are most vulnerable to CC is essential for planning conservation and mitigation efforts. The project LIFE project BEEadapt aims to improve pollinator climate resilience in four areas in Central Italy, including protected areas, natural and agro-ecosystems.
The LIFE BEEadapt project focuses on four study areas: the Pontine plain (PP) (Latium), the Roma Natura protected areas (RMPA) (Latium), lowland/hilly areas, the Torricchio Natural Reserve (TNR) (Marche) and the Appennino Tosco-Emiliano National Park (ATENP) with buffer zone (Emilia Romagna), two mountainous areas.
After a preliminary climatological study of the studied areas, authors developed species distribution models (SDMs) using biomod2 R package2, the algorithm Maxent, and two CC scenarios by 2050: sustainable (SSP1-2.6) and fossil-fueled development (SSP5-8.5), to assess the potential impacts of CC on 114 butterflies. Species data was obtained from the Global Biodiversity Information Facility (https://www.gbif.org/) and bioclimatic data from Worldclim (https://www.worldclim.org/) at a resolution of 1 km2. Model performance was assessed using the ROC, and Boyce Index.
Results show first that CC signals are evident in the studied areas. Furthermore, they show that butterflies have a consistent vulnerability pattern at both the species and multispecies level. In the study areas, CC appears to favor lowland and generalist species, which increase their climatic suitability under both scenarios, particularly in mountains. Mountain and specialist species are expected to have reduced climatic suitability, especially under the SSP5-8.5.
Findings are comparable with recent studies on the effects of CC on pollinators, which revealed similar sensitivity patterns based on species ecology, and provide new insights into species potential local responses to CC, allowing to set conservation priorities and direct LIFE BEEadapt mitigation actions. Conservation measures such as habitat restoration and connectivity enhancement will be critical to the long-term survival of these butterfly populations.
How to cite: Baldi, M. and Biancolini, D.: Effects of climate change on wild pollinators: the case of butterflies in Central Italy, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-484, https://doi.org/10.5194/ems2024-484, 2024.
The complex and nonlinear characteristics of atmospheric dynamics, along with their high dimensionality and interconnections across several spatial and temporal factors, pose significant challenges in comprehending, modeling, and predicting hydroclimatic extreme events. Understanding complex systems such as the atmosphere requires establishing causal relationships; therefore, this study focuses on hydroclimatic extremes, including heat waves, droughts, and extreme precipitation, which continue to be emphasized in climate change projections. Hydroclimatic extreme events pose a significant threat to agricultural sustainability, impacting crop yields, public health, migration patterns and ecological balance. The present research delves into the spatial and temporal dynamics of extreme precipitation, droughts, and heatwaves, specifically analyzing their influence on corn and soybean yields. Moreover, we aim to discover the causal relationships between these extreme events and crop yields by employing cross convergent mapping (CCM). CCM is a technique that relies on nonlinear state space reconstruction and is capable of distinguishing between causality and correlation. In accordance with Takens’ embedding theory, the attractor is reconstructed by CCM using time series data. This reconstruction allows for the identification and measurement of causation through cross-mapping prediction. The study uses the Evaporative Demand Drought Index (EDDI) as a metric of agricultural drought, the Palmer Drought Severity Index (PDSI) as an indicator of hydrological drought, together with maximum air temperature, extreme precipitation, and data on corn and soybean yields. Beyond that, this investigation is carried out on rainfed agricultural lands in Iowa with the specific aim of elucidating the impacts of hydroclimatic extreme events. Agricultural sustainability research and the quantification of economic consequences and impacts of extreme events on agriculture will benefit significantly from the findings of this investigation.
How to cite: Baydaroğlu, Ö., Yeşilköy, S., and Demir, I.: A Causality Perspective on the Impact of Hydroclimatic Extremes on Crop Yields, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-291, https://doi.org/10.5194/ems2024-291, 2024.
Accurate feature identification of drought disaster events is required for proper risk management in agriculture. This study improved the crop water deficit index (CWDI) by including the daily meteorological, crop development stage, soil moisture content, and yield data for 1981–2020 in northeastern China. Two drought characteristic variables (drought duration and intensity) were extracted using the theory of runs to produce the improved crop water deficit index (CWDIwp). Thresholds for the bivariate indicators were also determined for agricultural drought events of varying severity. A joint distribution model for drought variables was constructed based on five types of Archimedean copulas. The joint probability and the joint recurrence period for agricultural drought events were analyzed for drought events with varying intensities in northeast China. The results suggest that the CWDIwp can reliably identify the onset, duration, and intensity of drought events over the study area and can be used to monitor agricultural drought events. The conditional probability of drought intensity (duration) decreased as the drought duration (intensity) threshold increased, whereas the drought recurrence period increased as the threshold for drought duration and intensity rose. In the period (1981–2020), drought intensity in the three Northeastern provinces showed an increasing trend in the order Jilin Province > Liaoning Province > Heilongjiang Province. The spatial distribution of the joint probability and the joint recurrence period was obvious, and the joint probability showed a decreasing distribution trend from west to east. The distribution trend for the joint probability was opposite to that of the joint recurrence period. Furthermore, the areas with high drought probability values corresponded to the areas with low values for the recurrence period, indicating that the drought occurrence probability was higher, and the recurrence period value was lower in the drought-prone areas. The high-risk drought areas (60–87%) were in western Liaoning and western Jilin, with a recurrence period of 1–3 years, whereas the low-risk areas (<40%) were located in the mountainous areas of eastern Liaoning and eastern Jilin. The joint probability and joint recurrence period for agricultural drought events of varying severity were quite different, with the probability following the order light drought > moderate drought > severe drought > extreme drought. The order for the recurrence period was light drought < moderate drought < severe drought < extreme drought. The results provide technical support for disaster prevention and mitigation in drought risk management.
How to cite: zhang, S.: Identification and Risk Characteristics of Agricultural Drought Disaster Events Based on the Copula Function in Northeast China, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-46, https://doi.org/10.5194/ems2024-46, 2024.
The Douro region is renowned for its exceptional wines, notably the renowned Port Wine. Vintage years, occurring approximately 2-3 times per decade, signify outstanding quality, driven by optimal climatic conditions that enhance grape attributes. However, climate change presents challenges as rising temperatures and extreme weather events impact viticulture. This study evaluates the climatic influence on vintage years and forecasts climate change effects for forthcoming decades, utilizing machine learning algorithms. Historical vintage data spanning from 1850 to 2014 was gathered alongside monthly climatic variables including temperature, precipitation, humidity, solar radiation, and wind components from the 20th Century Reanalysis dataset. Diverse machine learning algorithms were deployed for classification, augmented by statistical analysis to pinpoint pertinent climate variables. Model training and evaluation employed cross-validation, followed by hyperparameter tuning for the most effective models. Future climate projections from 2030 to 2099, under various socio-economic scenarios (IPCC SSP2, SSP3, and SSP5), were integrated, with quantile mapping bias adjustment applied to refine future climate data. Historical data revealed vintage years occurring 23.6% of the time, averaging two vintage years per decade, with a slight upward trend. Crucial climate variables influencing vintage year occurrence were identified, including precipitation in March, air temperatures in April and May, humidity in March and April, solar radiation in March, and meridional wind in June. The study found promising results from Logistic Regression, SVC, and XGBClassifier models. This research offers valuable insights into the nexus between climate variables and wine vintage years, empowering winemakers to make informed decisions regarding vineyard management and grape cultivation. The projections underscore the importance of adaptation strategies in confronting the challenges posed by climate change to the wine industry. This research was funded by National Funds by FCT – Portuguese Foundation for Science and Technology, under the projects UIDB/04033/2020 (https://doi.org/10.54499/UIDB/04033/2020) and LA/P/0126/2020 (https://doi.org/10.54499/LA/P/0126/2020).
How to cite: Fraga, H., Guimarães, N., and A. Santos, J.: Vintage Port quality under climate change: A machine learning approach using Twentieth Century Reanalysis and CMIP6 data., EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-70, https://doi.org/10.5194/ems2024-70, 2024.
The ALADIN-Climate/CZ regional climate model, operating at a high resolution of 2.3 km, was utilised to project future occurrences of soil drought in Czechia. GCM ARPEGE-Climate drives the regional climate model. To assess the effects of weather conditions on soil drought, we developed a simple model simulating the soil moisture based on API30 and near-surface air temperature, calibrated against observations. Using simulations of future climate, we predict soil drought. Drought was defined as a soil water content below 30% of the maximum water-holding capacity.
According to the medium emissions scenario, there is an expected average rise in the number of days with soil drought in the arable layer by 46% and 106% during 2021-2050 and 2051-2080, respectively, compared to the baseline period of 1991-2020. Additionally, there is a notable increase in the percentage of land affected by drought, with areas experiencing drought for over 50 days increasing by 15.5% and 53.2% by 2050 and 2080, respectively. Higher intensity of soil drought is evident across the country, particularly pronounced at higher elevations due to rising temperatures and reduced precipitation amounts. These observed changes emphasise the urgent need to implement adaptation measures in agriculture and alter agricultural land management practices.
This work was supported by the Technology Agency of the Czech Republic (Prediction, Evaluation and Research for Understanding National Sensitivity and Impacts of Drought and Climate Change for Czechia, SS02030040 and Centre for Landscape and Biodiversity, SS02030018).
Keywords: soil drought, soil water, precipitation, temperature, climate change, regional climate model, climate change scenario, adaptation measures in agriculture
How to cite: Mozny, M., Vlach, V., Hajkova, L., and Musilova, A.: Projected soil drought development in Czechia (2021–2050; 2051–2080) using high-resolution ALADIN-Climate/CZ regional model., EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-461, https://doi.org/10.5194/ems2024-461, 2024.
In order to achieve sustainable and optimised grape production within vineyard plots, it is crucial to have a deep understanding of the spatial variability of microclimates. This study utilises a microclimate model (NicheMapR) in conjunction with multiple climate data sources to analyse microclimatic conditions in two vineyard plots: Quinta do Bomfim in northern Portugal and Herdade do Esporão in southern Portugal. The innovative approach achieves a spatial resolution of 10 meters for climate variables. Local station hourly data is combined with ERA5-land data using quantile mapping bias correction. The microclimate model output is further used to correct biases in a EURO-CORDEX model ensemble. Specific climate extreme and bioclimatic indices designed to viticulture are computed for each vineyard plot. By conducting analysis at the 10-meter scale, it becomes possible to identify any potential shifts in temperature extremes, precipitation patterns, and other crucial climatic variables that are relevant to grape cultivation within each individual plot. In regions with complex topography, the importance of microclimate analysis is highlighted, as there are significant variations in climatic variables. However, in areas with gentle slopes, the differences in climatic variables are minimal and therefore the significance of microclimate analysis is less pronounced. According to the projections, it is estimated that there will be a median temperature increase of around 3.5°C and 3.6°C in Quinta do Bomfim and Herdade do Esporão, respectively, when comparing future scenarios for the periods 2071–2100 and 1981–2010. Additionally, there is expected to be a decrease in precipitation of approximately 98 mm and 105 mm in these areas. Thus, this study provides a comprehensive and forward-looking approach to analysing microclimates in vineyard plots. By integrating geospatial data, ERA5-land data, and the microclimate NicheMapR model, the research aims to provide viticulturists with a better understanding of current microclimates and future climate scenarios.
Acknowledgments: Research funded by National Funds by FCT – Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020 and LA/P/0126/2020 (https://doi.org/10.54499/UIDB/04033/2020). Vine & Wine Portugal—Driving Sustainable Growth Through Smart Innovation, PRR & NextGeneration EU, Agendas Mobilizadoras para a Reindustrialização, Contract Nb. C644866286-011.
How to cite: Fonseca, A., Cruz, J., Fraga, H., Andrade, C., Valente, J., Alves, F., Neto, A., Flores, R., and Santos, J.: Towards Resilient Viticulture: Vineyard Microclimatic Zoning as a Tool for Sustainable Adaptation, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-138, https://doi.org/10.5194/ems2024-138, 2024.
Determination of climate variability and change for agriculture define necessity to map tropical relevant indices and carry out meteorological measurements capable for agrometeorological services. In this study ERA5 reanalysis data were used to characterise precipitation and climate based indices spatial patterns from 1991 to 2020 (30 years) climate normal. In our study, five precipitation-based and two temperature-based climate indices were analysed and visualised based on their agricultural importance and tropical position of Kenya. They include annual total wet day (PRCP), consecutive dry days (CDD), consecutive wet days (CWD), very wet days (R95P), extremely wet days (R99P), summer days (SU35) and diurnal temperature range (DTR). ERA5 data was pre-processed in climate data operator (CDO).The resultant network common data Form (NetCDF) generated from CDO were visualized in own python programme to obtain spatial pattern distribution maps. In addition, high-quality temperature, humidity and soil moisture data measurements were carried out in from 2023. Trends, variation and correlations for the measurements were analysed and compared using different regression methods, ordinary least squares (OLS) and Theil-Sen regression. Results indicate average S35 of between 260.7 to 365 days in arid Northwestern Kenya in the 2011-2020 decade. Most parts of the country show average DTR between 10 °C to 12 °C while mean annual average CDD ranged between 0 (minima) to 300 days per decade with Kenyan highlands and coastal strip recording 0 to 60 days. Higher number of CWD were observed with an average between 219 to 292 per decade in highlands. However, spatial distribution patterns and the changes in the extreme events are inconsistent from one decade to the other. Increasing CDD and decreasing CWD is a constraint to crop yield and maturity and determinants to crop developmental stages. The risk of reduced yields can be monitored. Meteorological and soil moisture preliminary results indicate variability between seasons. Comparison of temperature (2m) and temperature from the soil moisture sensor from two different elevations and depths reveal a partly linear and heteroscedastic pattern. Regression models produced similarities in behavioural patterns of measurements from the two elevations. Soil moisture deficiency informs irrigation requirements (IRR), a strategy for crop's evapotranspiration and optimal yields. Further, the measurements act as a reference for comparison with nearby weather station measurements as well as gridded datasets for better understanding of quality control. Overall, the results demonstrates importance of quality measurements and indices analysis for agrometeorological services for farmers during cropping and irrigation seasons and monitoring of agricultural yields in Kenya.
Keywords: Agriculture, Climate indices, Measurements, Services, Soil moisture, Kenya
How to cite: Musyimi, P. K., Székely, B., and Weidinger, T.: Climate Indices and Measurements Application for Agrometeorological Services in Kenya, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-196, https://doi.org/10.5194/ems2024-196, 2024.
The Meteorological Service of Catalonia (SMC) has been carrying out phenological monitoring of crops since 2013 to study the impact of climate change on the ecosystem. For this purpose, it was created the Phenological Network of Catalonia (Fenocat), which is made up of eighty observers who monitor the main phenophases of fruit trees such as almond, cherry, olive, apple, orange, or vine, as well as annual winter crops as wheat or barley, annual summer crops as maize or sunflower, and forage herbs such as alfalfa. In addition, 14 birds and 6 butterflies are also monitored.
The Fenocat is a citizen science network whose observers report data two or three times per week, carrying out a periodic assessment of the status of the phenophases for a plant, rather than simply recording the date of an event. This observation system allows the recording of second occurrences in the same season or year (second flowering, second leaf sprouting, second fruit formation), events that happen more frequently due to climate change. The observers insert the information directly into the database using a web application in situ; the web application lets them compare their observations with those produced by other observers.
Fenocat uses BBCH codification and is a data provider of the Pan-European Phenology Database (PEP725).
As genetic factors determine the phenological response, it was carried out a cultivar identification using genetic markers (DNA extraction, Genotyping using molecular markers -SSRs or SNPs- and a calculation of the similarity between the analysed samples and the database information of the Centre for Research in Agricultural Genomics).
After ten years of observations and more than 1,150,000 data collected, we observe a general advance in phenophases such as bud burst, full flowering and maturation, and a delay in the leave fall. For example, in the specific case of the almond tree, the sprouting of the first leaves has advanced 13 days in 10 years, full flowering has advanced by 9 days in 10 years, and the maturation of the almond happens 23 days earlier than 10 years ago (average data for all of Catalonia).
How to cite: Busto, M., Cunillera, J., and de Yzaguirre, X.: Ten years monitoring crops with citizen science, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-282, https://doi.org/10.5194/ems2024-282, 2024.
Evapotranspiration is an essential element in the water cycle, being estimated through various methods such as the Penman-Monteith equation, the Priestley-Taylor equation, the FAO-56 Penman-Monteith equation, the Surface Energy Balance Algorithm etc. With the advent of satellite imagery, constant efforts have been made to improve the spatial resolution of ET estimates through the integration of high-resolution remote sensing data. Evapotranspiration estimated based on MODIS data utilizes the Penman-Monteith equation, resulting in a product at a resolution of 500 meters over a period of over 20 years (2001-2023). Recently, new estimation tools for ET based on Sentinel 2 and Sentinel 3 data have been developed, such as Sen-ET, which uses the Two-Source Energy Balance (TSEB-PT) model and a Data Mining model to bring the product to a resolution of 20 meters. Furthermore, Python libraries developed for the Google Earth Engine platform provide the capability to estimate ET at 30 meters based on Landsat data using the Surface Energy Balance Algorithm. In this study, a comparison of ET will be performed, based on products derived from the integration of satellite imagery obtained from Sentinel 2 and Sentinel 3, Sentinel 2 and Landsat 8, Landsat 8 alone, as well as the ET product from MODIS. The analysis extends over a period of 8 years between 2016 and 2023, which is the common timeframe for all datasets. The study area is located in southeastern Romania, where extensive agricultural fields frequently suffer from drought. The results obtained from satellite imagery will be compared with those obtained from meteorological data, as well as with other satellite products capable of highlighting moisture deficits or drought.
Acknowledgments: This study has received funding from the European Union Agency for the Space Programme under the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101082189 (MAGDA project).
How to cite: Ontel, I., Angearu, C.-V., Irimescu, A., Chitu, Z., and Irasoc, A.: A comparison of remote sensing evapotranspiration products over agriculture crops, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-748, https://doi.org/10.5194/ems2024-748, 2024.
The concept of frost safety margins has been used in previous research as an indicator associated with the period between the occurrence of the meteorological phenomenon and the most sensitive phenological stage to freeze damage in deciduous perennial plants in mid and high latitudes. It is a relevant indicator in agriculture due to its use in agricultural planning and territorial management of productive areas, as it has been linked to damages from late frosts and as a fundamental criterion for false springs events in fruit trees. However, its implementation requires robust phenological records or experimentation under controlled conditions. The aim of this study is to propose a concept of frost safety margins based on daily minimum temperature data (TMIN). To this end, we used TMIN in raster format at a spatial resolution of 1 km² for the Aragón region (northeast Spain). Calculations were performed in the R programming language and the cartography was built using ArcMap 10.7.1® software. The indicator represents a state composed of two elements: a numerical one, without hierarchical order, which incorporates the moment of occurrence; and a character one associated with the difference in days between the penultimate and last frost per year. Once the states were classified, an annual sequence was formed for the historical series and analyzed as a Markov chain. The results show that the margins are persistent and their autocorrelation with lag-1 is statistically significant, especially with increasing altitude. It was also found that, with a greater number of types of margins, the forecast using Markov chains is lower, indicating a more random behavior. In much of the Aragonese territory, the stationary probability of safety margins corresponds to type 4a, i.e., a margin during the month of April with a difference between 1 to 7 days, while in the lowlands it corresponds to type 2a, a margin during the month of March with a difference between 1 to 7 days. The proposed safety margins indicator can be used in conjunction with empirical or modeled phenological data to obtain agroclimatic risk, making it a line of research that allows for expansion with other future research lines.
How to cite: Pérez-Sosa, E., Serrano-Notivoli, R., Saz-Sánchez, M. Á., and Hernández-Navarro, M. L.: Frost safety margins: a proposal based on daily minimum temperature , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-795, https://doi.org/10.5194/ems2024-795, 2024.
Evapotranspiration (ET) is a key element of water cycle especially over vegetated surface. Vegetation in Hungary is often prone to drought events, while exact information on water need of different species is barely available. Vast majority of the agricultural lands are rainfed, without the option of irrigation, hence water balance components are in the focus of research interest in case of different agricultural settings. Moreover, crop growth models of different approaches rely heavily on ET estimations. Water balance components are essential to assess crop health, or estimate future crop growth.
In this work we present calculations of actual evapotranspiration using different methods. Flexibility in parameterization, data requirements and their availability and performance are tested in case of all methods to select the best candidates for Hungarian agricultural applications. We use empirical and process based approaches to evaluate the feasibility of the methods in case of Hungarian conditions. A multiannual analysis ensures that the differences among years are better considered. To ensure that data on other components of water balance are considered, we apply the ET estimation methods to selected stations of the drought monitoring network of General Directorate of Water Management, where continuous soil moisture content data are recorded. Measurement stations are located typically over agricultural fields. Representation of different crop and soil types are ensured in site selection. Weather data for the ET calculations are provided by the homogenized and gridded database of the HungaroMet (Hungarian Meteorological Service). In the future eddy covariance based and lysimeter data will serve as basis for the validation of potential models.
How to cite: Gelybó, G. and Shaifei, M.: Testing evapotranspiration retrieval approaches over Hungarian agricultural fields, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-967, https://doi.org/10.5194/ems2024-967, 2024.
Recent advancements in satellite phenology research demonstrate its effectiveness as a robust tool for monitoring vegetation and its responses to climate drivers. Satellite data provides insights distinct from ground-based observations, yet both sources complement each other well. Utilizing new-generation satellites, with high spatial and temporal resolution, facilitates a deeper understanding of climate-phenology relationships. These satellite-derived indices prove especially valuable in regions with scarce ground-based observations like Poland.
Air temperature plays a crucial role in driving various plant developmental processes, among which the initiation of spring activity is the most prominent. However, drawing ultimate conclusions regarding this relationship remains challenging as quantitative estimates strongly diverge. Recent observations indicate that climate warming has altered plant phenology across many European regions. Investigating these complex connections is crucial, given that changes in plant phenology affect fundamental ecosystem functions, including water, carbon, and energy fluxes, as well as interactions between plants and animals, ultimately shaping ecosystem productivity.
The main aim of this study is to evaluate the impact of air temperature conditions on the onset of spring activity of silver birch (Betula pendula) in Poland over the years 2007-2024. The phenology data used in the study include both ground observations of leaf unfolding and satellite-derived estimates of start-of-season (SOS) for silver birch, which is among one of the most abundant deciduous tree species in Poland. Ground-based data were sourced from the Institute of Meteorology and Water Management – National Research Institute and spans years 2007-2024, while the satellite-based SOS metric was derived from Sentinel-2 imagery and covers the 2018-2024 period. The SOS metric was based on EVI (Enhanced Vegetation Index) derivatives. Temperature conditions during individual seasons were assessed using in-situ measurements as well as the E-OBS (v28.0e) gridded dataset, and the relationship between air temperature and the start of the season was studied using temperature anomalies and the Growing Degree Days (GDD) index for 5°C base temperature.
The study showed large inter-annual variability in silver birch phenology – the estimated SOS varied between studied seasons by more than 20 days. The results also proved the usefulness of Sentinel-2 data in monitoring the phenology of deciduous tree species, while indicating that satellite-based estimators pertain to a slightly different phenological phase than ground-based observations. The study provided insights into the links between temperature conditions and the spring phenology of birch, showing that responses to air temperature are complex. The relationship remains evident, as, for instance, in 2018, characterized by extremely warm April and May, an advanced start of the season was observed. Conversely, in 2021, a delayed start of the season was noted as a consequence of cold temperatures during these months. However, quantitatively describing these relationships remains a challenge and further research is necessary.
How to cite: Sulikowska, A., Grabska-Szwagrzyk, E., and Wypych, A.: Satellite-based springtime phenology of silver birch (Betula pendula) in Poland and its relations to air temperature , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-951, https://doi.org/10.5194/ems2024-951, 2024.
At the annual scale, the long-term average of the ratio of actual to potential evapotranspiration, represents the so-called relative productivity index (kv) which depends on climate, soil, and vegetation conditions. We aim to assess drought based on the analysis of the spatio-temporal variability of kv. The study area is the governorate of Zaghouan, located northern Tunisia, between latitudes 36°00N and 36°40N and longitudes 9°35E and 10°25E, mainly occupied by cereal crops. It is composed by 48 administrative districts (Imadas) of size between 20 and 80 km². Field evidence of drought is obtained using the National reports on cereal crops drought assessment. Bank facilities are provided to farmers with crop lands reported as drought damaged areas. The study period is 2000-2001 to 2020-2021. During that period, 11 years out of 21 were declared drought in the national reports. The methodology adopts order statistics of the remote sensing kv. The latter is estimated by imada, using MODIS data, considering the accumulation of evapotranspiration during the cereal growth period, from November to May (herein year by extension). Thus, a kv matrix of N=21 columns (years) with n=48 raws (imadas) is analyzed. For a given year i, we consider the values of kv arranged in increasing order of magnitudes (X(i,1), X(i,2), …, X(i,n)) where n is the number of imadas. Four drought prognostics are evaluated. We see whether the droughts registered by the National reports are retrieved by the prognostic methods. The minimum of kv (X(i,1)) is evaluated in Methods 1 while the maximum X(i,n) is tested for Method 2. In Method 1 the 25% percentile of X(1) is considered to filter drought years. In Method 2 and Method 3, the 75% percentile of X(n) and 2.5% percentile of X(n/2) are respectively considered. In Method 4, a quantile-quantile plot approach is adopted based on the minimization of mean absolute error, considering X(10%), X(25%), X(50%) and X(75%). Four categories of years are assumed in Method 4: severe drought; moderate drought; as well as mild humid and humid (no drought). Method 1 resulted in one single undetected drought year. Method 2 resulted in 2 false droughts and one drought not detected. In Method 3 one single drought is false. Method 4 resulted in three drought years that were classed as humid. Though, these results encourage the use of order statistics of satellite relative productivity index as a procedure for drought identification over large areas.
How to cite: Bargaoui, Z. and Abid, N.: Analysis of spatio-temporal droughts using order statistics of remotely sensed relative productivity index , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-200, https://doi.org/10.5194/ems2024-200, 2024.
In addition to the historically sensitive Mediterranean regions, northern parts of Southeast Europe are increasingly vulnerable to forest fires exacerbated by climate change. Higher temperatures, more frequent heat waves, and erratic rainfall patterns in the region foster conditions conducive to swift wildfire ignition and propagation. Human-induced factors further exacerbate the risk, while the ecological consequences of these fires are far-reaching, affecting biodiversity and human lives.
The Forest Fire Danger Index is a tool for assessing wildfire risk under very dry soil conditions. It incorporates factors besides soil dryness such as daily wind speed, mean temperature, and humidity to assess the potential risk for wildfires. Elevated wind speed can hasten fire propagation, while low humidity and high temperatures increase the likelihood of ignition and accelerate spread in arid regions. To calculate soil dryness specifically developed for fire controls, we analyzed the widely-used Keetch-Byram Drought Index, which relies on daily precipitation, maximum temperature, and effectively captures the soil's evaporation-precipitation balance.
For the calculations, we assessed E-OBS version 29, a state-of-the-art, quality-controlled and interpolated observational dataset on 0.1° horizontal resolution for the reference period of 1971–2023. For the future, we used an ensemble of regional climate model simulations from the Euro-CORDEX initiative, which takes into account future anthropogenic activity through three distinct RCP scenarios until 2100 (RCP2.6, RCP4.5, and RCP8.5).
Results indicate that regions historically experiencing infrequent severe compound wildfire events, such as most of the northern lowland areas in Romania, Bulgaria, Serbia, Hungary, or Croatia, are exposed to significant changes with global warming. Particularly after 2060 in the case of the non-mitigation scenario of RCP8.5 compared to the immediate mitigation of RCP2.6. Consequently, these possibly high impacts require a substantial investment in fire monitoring, education, and a warning system in these countries as well.
Acknowledgments: Research leading to this study has been supported by the European Climate Foundation (G-2309-66801), the Hungarian National Research, Development and Innovation Fund (K-129162), and the National Multidisciplinary Laboratory for Climate Change (RRF-2.3.1-21-2022-00014).
How to cite: Szabó, P., Pongrácz, R., and Bartholy, J.: Analysis of a fire danger index over Southeast Europe, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-756, https://doi.org/10.5194/ems2024-756, 2024.
Drought is a natural phenomenon that occurred in Central Europe in the past, but it is becoming a more serious problem due to the changes in its occurrence and intensity related to climate change. We analysed changes in air temperature and precipitation amounts in Slovakia from 1931 to 2020 as well as changes in drought occurrence and intensity using the Standardised Precipitation and Evapotranspiration Index (SPEI) since 1961. Further, cluster analysis was used to determine regions with similar drought occurrences. For each of the identified five clusters, drought characteristics were determined and compared between two reference periods 1961–1990 and 1991–2020.
While a decrease in the number of months with SPEI-1 below − 1 was identified in autumn, spring and summer months showed a drying tendency. Overall, we can say that we observed a shift in drought occurrence from autumn and winter months to spring and summer months. The clearest tendency in drought events was observed in the western part of Slovakia covering areas with agriculturally intensive land use. Besides prolonging drought events, there was also a higher accumulated deficit for each event. Considering SPEI-12, the severely to extremely dry conditions repeatedly occurred in clusters 1 and 5 since 2011, affecting agriculturally intensively used areas and causing yield losses. These regions also experience the most severe change in water balance concerning the relative SPEI-12. Generally, approximately two-thirds of Slovakia experience a decreasing value of rSPEI-12.
Observed drying tendency in spring months is a great problem for the agriculture, because of the seeding and germination of some spring crops such as maize, sunflower or soya. This is also the main cause, why the farmers seed winter crops rather than spring crops. This is a reason, why the cultivation area of spring barley has had a decreasing tendency in south-western part of Slovakia since 2018 (Križanová 2022). The reports published in the last years by the Regional Agricultural and Food Chamber Galanta (south-western part of Slovakia) show that the farmers try to use autumn soil moisture for securing better crop yields next year. Therefore, they prefer rather winter cultivars of crops than their spring cultivars. They even sow spring barley in autumn months. It is a way, how to ensure higher yields and better use of agroclimatic conditions while maintaining malting parameters (Križanová 2022; Križanová 2023).
The same situation is in the forestry. According to internal information provided by the Forests of the Slovak Republic, state enterprise, the foresters start to plan the seeding of tree seedlings rather for autumn months, because the success of spring planting is lower due to the drying of seedlings as the result of lower soil moisture in spring.
How to cite: Labudová, L., Ivaňáková, G., Faško, P., and Kajaba, P.: Changing drought occurrence and severity affecting agriculture and forestry in Slovakia, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-75, https://doi.org/10.5194/ems2024-75, 2024.
