Chairperson: Branislava Lalic
The Agricultural Risk Information System (ARIS) is a GIS-based model calculating 24 agrometeorological indicators in a high spatial resolution based on 1km gridded weather input over Austrian agricultural areas. These include general or crop specific agroclimatic suitability and risk indicators as well as pest and disease risk indicators based on specific algorithms. Our study, within the framework of the project AGROFORECAST, focuses on the application potential and performance of three insect pest and one disease algorithm (European grapevine moth, grapevine cicada, plum moth and grapevine downy mildew) where the first occurrence of relevant development stages (i.e. adults, nymphs) are calculated for the pests and the start of infection and incubation period of grapevine downy mildew throughout the whole year. To enhance the resilience and sustainability of Austrian agricultural systems under changing climate, the application potential for short-term to seasonal forecasts as well as climate change scenarios were tested for these pest indicators. Tailored information methods of the forecasts and predictions for decision support (e.g. for pest management) and precision agriculture methods were tested.
For the evaluation, different statistical analyses were carried out at selected locations in Austria, using
(a) short-range and seasonal forecast: the current ECMWF long-range ensemble forecast system 5 (SEAS5, in operation since November 2017) with forecasts up to 7 months was used as meteorological input for the ARIS model in combination with ECMWF medium-range forecasts (10-d) and INCA analysis data. Spatial downscaling methods were applied to the forecasts during the harvest season and included downscaled 10-day ECMWF data as well as downscaled 7-month seasonal forecasts of precipitation, global radiation, minimum and maximum temperature on a daily basis with a resolution of 1x1 km. The study period ranged from 2018 to the present;
(b) the Austrian climate change projections ÖKS15 for the long-term assessments, comparing the two time periods present (1981-2010) and near future (2036-2065).
The sensitivity, uncertainties and performance of different prediction ranges for the studied indicators are demonstrated in our study. The results are analyzed in collaboration with stakeholders in regard to performance and potential adaptation needs for crop protection service and pest management applications. In order to determine the long-term changes and possible impacts, these indicators were applied to the Austrian agricultural regions using the different Austrian ÖKS15 climate projections and the two emission scenarios RCP 4.5 and RCP 8.5 for the near future.
How to cite: Thaler, S., Eitzinger, J., Kubu, G., Manschadi, A., Palka, M., and Schneider, S.: Application of seasonal weather forecasts and climate change scenarios on selected pest and disease algorithms in Austria, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-115, https://doi.org/10.5194/ems2022-115, 2022.
The astronomical and meteorological definitions of spring, summer, autumn, and winter are unique and quantitative. Nevertheless, in everyday life, season transition is more related to human perception of seasons than astronomical parameters (like the timing of equinox or solstice) or calendar. If any kind of vegetation is present, human perception of seasons strongly relays on changes in plant development: spring – the start of the growing season and intensive leaf appearance; summer – the intensive leaf development and flowering of gardens/yield formation in the field/crop harvest; autumn – a slowdown of development, and harvesting; winter – dormancy. If there is no vegetation whatsoever, human perception of seasons is probably based on one of the thermal comfort indices. For example, ten values of the universal thermal climate index (UTCI) that describe thermal stress categories corresponding to specific human physiological responses to the thermal environment can be associated with seasons and their transition.
Fitzjarrald et al. (2001) found the onset of spring in Eastern USA using springtime minimum in the afternoon relative humidity and first date when tendency Bowen ratio falls below one. Adopting an alternate approach, Lalic et al. (2022) linked changes in extreme values and inflection points of afternoon relative humidity and normalized daily temperature range (DTR/Td) with the growing stages of plants.
This study intends to offer indices based on daily air temperature and humidity records, which can be used as unique and quantitative descriptors of the timing of season transitions. As a case study, the timing of extreme values and inflection points of selected indices calculated using climate station data will be compared with phenology dynamics of dominant plants.
Literature
Fitzjarrald, D. R., Acevedo, O. C., Moore, K. E., 2001: Climatic Consequences of Leaf Presence in the Eastern United States. J. Climate, 14 (4), 598–614. https://doi.org/10.1175/1520-0442(2001)014<0598:CCOLPI>2.0.CO;2
Lalic, B., Fitzjarrald, D. R., Firanj Sremac, A., Marcic, M., Petric, M., 2022: Identifying Crop and Orchard Growing Stages Using Conventional Temperature and Humidity Reports, Atmosphere (Accepted for publication).
How to cite: Lalić, B. and Firanj Sremac, A.: Air temperature and humidity records-based indices as signals of seasonal changes, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-549, https://doi.org/10.5194/ems2022-549, 2022.
Over the last 1-2 decades, the UK has been one of many countries observing plateaus in key crop yields, despite farming practice innovations such as the adoption of precision agriculture and advanced plant breeding programmes. The food production sector also continues to suffer strong yield impacts arising from inter-annual weather variability. This is in the context of a rapidly growing global population and a changing climate, which combined threaten future food security worldwide. With ever increasing competition for land use, identification and further development of climate-resilient crop varieties is a key priority.
In this research, we present examples of the development of ‘best in class’ historical time-series records of key agro-meteorological variables and metrics by inter-comparing and combining different types of in-situ and remotely sensed observational and re-analysis products. We demonstrate the added value of applying these climate datasets to the modelling of UK crop yield and the interpretation of inter-annual crop production data using statistical models. Specifically, we combine this high-resolution climate data with Recommended List variety trials data in linear mixed models to isolate the genetic, environmental and genotype-by-environment drivers of winter wheat yield variability in the UK. Through this we demonstrate that the rate of genetic gain in winter wheat is slowing in the UK.
Through our analysis of historical time-series climate data we generate the first “State of the UK Agroclimate”. This climate service will provide breeders, farmers and the wider agricultural sector with long-term UK-wide and regional agro-climatological averages, trends and extreme weather metrics of relevance to crops widely grown in the UK, including winter wheat.
Our agro-climate analysis and results from our statistical models can be combined with projections of future climate, to identify current varieties that may perform well in the face of UK climate change and provide breeders with guidance of traits to breed into future varieties for enhanced resilience.
How to cite: Raymond, J., Dorling, S., Mackay, I., Lovett, A., Penfield, S., and Philpott, H.: Weatherproofing for a smarter, resilient and more sustainable agri-sector, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-12, https://doi.org/10.5194/ems2022-12, 2022.
The relationship between plant yield, food production, and climate conditions is becoming crucial, especially when climate extremes are considered.
Atmospheric numerical models and reanalyses generate valuable weather and climate information. They have been evaluated and used in the past to assess extreme climate events representation. However, they can also be used to force models from other research fields, such as plant production model used in agriculture. This approach allows to create scenarios and/or ensembles to assess the compound uncertainty deriving from the climatic and plant production aspect, especially under hydrometeorological extremes such as heatwaves and droughts.
In this work, we assess the response of the plant yield computed using the AquaCrop model to a prolonged heatwave and drought condition. We focus on the Po Valley, norther Italy, for the summer 2015 period due to its high dependency of irrigation.
The two atmospheric products used are a set of free simulations at 3km resolution using the Weather Research and Forecasting (WRF-ARW) model, which are used as scenarios for the irrigation water requirements, and the 6-km COSMO-REA6 reanalysis, which provides the best reference dataset within the atmospheric reanalyses. The AquaCrop model is forced only with the cropland gridpoints in the Po Valley, and we test the sensitivity of the crop model to parameters such as initial soil moisture, irrigation management, soil, and crop type. Further, we are going to investigate the relationship between plant state and CO2, by using in-situ observations.
Preliminary results show that for wheat, the yield response depends on the meteorological input data, the COSMO-REA6 yields are higher than the ones obtained with WRF-ARW, and the clay content in the soil. Further, classical and machine learning clustering techniques in parameter space are used to understand the dependency of the yield.
How to cite: Valmassoi, A., Esters, L. T., Friederichs, P., and Keller, J. D.: Investigating the drivers for crop yield changes during heatwaves and droughts, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-493, https://doi.org/10.5194/ems2022-493, 2022.
Temperature and precipitation have a significant affect on the growth of vines and consequently viticulture is highly affected by climate change. Measurements indicate changes in the occurrence of phenological phases in the present climate and it is expected that this trend will continue in future.
Istria is one of the most prosperous wine regions in Croatia with more than 3000 ha of vineyards. It is well known for cultivating traditional (Malvazija istarska, Teran, Hrvatica…) and introduced varieties (Chardonnay, Merlot…). Because of that, it is important to determine sign and the robustness of future changes in phenological stages as well as possibility of spring frost occurrence.
For this study climate change effect on 21 grapevine varieties cultivated in Institute of Agriculture and Tourism in Poreč, Croatia were analyzed. Linear correlation between meteorological parameters and phenological stages of each variety and Growing degree day (GDD) thresholds required to begin a particular phenological stage is calculated and tested in present climate and used as a constant in future period.
For future changes, daily output of temperature (Tmin, Tmax and Tmean) and total daily precipitation from three CORDEX Regional Climate Models (RCMs) simulations (CLMcom-CCLM4-8-17, SMHI-RCA4, CNRM-ALADIN5.3) for Croatian domain, were used to determine changes in beginning of three key phenological stages (budburst, flowering and veraison) in 30-year periods (2011-2040, 2041-2070) compared to historical run (1971-2000).
Results show that, as the temperature rises phenological stages occur earlier (especially budburst and harvest). This affects all varieties equally. Earlier budburst leads to possibility of spring frost damage particularly in the inland of Istrian peninsula. Because of that, occurrence of frost was studied in present and in future climate. Day with frost is determined with the use of minimum daily temperature and dew point temperature (Td) calculated from Tmin and relative humidity at 7CET. Conditions for frost occurrence are suitable if Tmin < 3°C and Td < 0°C. Results show that frost will be one of the determinal factors for grapevine in future, as it is in present.
How to cite: Omazić, B., Blašković, L., Telišman Prtenjak, M., Bubola, M., Karoglan, M., and Anić, M.: Future change in viticulture phenological stages and early frost risk in Istria region, Croatia, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-326, https://doi.org/10.5194/ems2022-326, 2022.
Several factors influence grapevine's quality and productivity; among them, there are weather and climate. Temperature, solar radiation, precipitation and soil moisture have relevant impacts on crop growth and yield. Recently crop growth models were used as tools to assess climate variability and change in crop yields and quality. Crop growth models are currently employed at a regional scale for agricultural needs, environmental applications, and/or for supporting the process of decision-making and planning in agriculture. Some crop growth models are adaptable to various crops and can simulate crop growth and plant development, as well as water and nitrogen balances. Specific crop growth models have also been developed to simulate grapevine growth and development. Generally, crop growth models include specific modules calculating the occurrence of phenological stages, that can also be used as stand-alone routines. Finally, grapevine phenology has recently been studied in connection with climate change by means of grape harvest dates used to reconstruct past climate and, more rarely, future climate. The numerical crop growth model IVINE (Italian Vineyard Integrated Numerical model for Estimating physiological values) was developed at our dept. to simulate grapevine phenological and physiological processes. The boundary conditions required by IVINE are hourly meteorological data related to some air and soil values (among which the more relevant are air temperature and soil moisture), while other inputs are initial conditions and parameters characterizing geography, soil texture, grapevine variety, and cultivation interventions. The main IVINE outputs are: the phenological stages (e.g. dormancy exit, bud-break, fruit set, veraison, and harvest), the leaf development, the yield, the berry sugar concentration, and the leaf water potential. The IVINE requires some experimental parameters depending on the cultivar, which have been evaluated through some calibration experiments for some popular Italian varieties. IVINE includes theoretical physically based equations for processes such as water balance and photosynthesis, and empirical equations for others. Long-term simulations carried out in Italian territory show, in the period 1980-2010, significant trends in almost all physio-phenological variables, as well as a reduction in interannual variability, correlated with the climate change still ongoing.
IVINE model performances depend on the quality of input data: the use of experimental data measured not far from the vineyard could improve the quality of the simulation, even if the model seems able to account for the interannual variability of the meteorological conditions, which reflects in the pheno-physiological trends interannual variability. During the presentation, the model will be introduced, and a sketch of the main results obtained will be discussed.
How to cite: Cassardo, C., Andreoli, V., Spanna, F., and Traversa, M.: IVINE: a crop model for simulating pheno-physiological processes on vineyards, also from a climatic point of view, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-475, https://doi.org/10.5194/ems2022-475, 2022.
Real-time soil moisture measurements are essential to manage for adaptive dynamic management of climate change adaptation and reduction of nutrient losses and greenhouse gas emissions from agriculture and forestry. Soil moisture status influences crop growth, run-off, groundwater recharge, land surface-atmospheric exchange dynamics and greenhouse gas emissions as well as the risk of forest fire danger. Here we present the new Irish Soil Moisture Observation Network (ISMON) as an umbrella to bring together several recently established long-term environmental observational networks. These are: 1) initiative of the AGMET group (agmet.ie), 2) COSMOS UK – Northern Ireland, 3) Teagasc NASCO (National Agricultural Soil Carbon Observatory) and 4) Terrain-AI, all of which include several different methodologies for measuring soil moisture at field scale. For instance, AGMET is installing novel cosmic ray neutron sensors which can provide field averaged soil moisture estimates (400m diameter) and Teagasc NASCO and Terrain AI are using Time Domain Reflectometry probes. Such networks are seen as necessary to resolving the problem of scale between point, field-based measurements and satellite-derived soil moisture products and are important in monitoring key biogeochemical processes that vary rapidly in time and space. In the initial phase of the implementation of the ISMON network, the current distribution of the stations in relation to the other networks are presented. The ISMON aims to represent the most relevant soil types, land cover, and regional climate regimes to corroborate direct measurements of soil moisture; it will adapt its design to improve the monitoring network as required. The ISMON will make a valuable contribution to, and expand the international soil moisture monitoring network.
How to cite: Finkele, K., Hochstrasser, T., Murphy, P. N. C., Daly, E., Jarmain, C., Richards, K., Fenton, O., Cummins, T., Saunders, M., Johnston, P. M., Bruen, M., Byrne, K. A., Delaney, D. T., Fealy, R., Green, S., Higgins, S., Hunter Williams, N., Lanigan, G., McCarthy, T., McCormack, T., Mellander, P.-E., Nicholson, O., Nugent, C., O'Loughlin, F., Tobin, B., Tuohy, P., and Whetton, R.: The new Irish Soil Moisture Observation Network – ISMON: an Umbrella for Integrating Several Recent Soil Moisture Measurements Initiatives, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-273, https://doi.org/10.5194/ems2022-273, 2022.
The repeated droughts that have affected Europe in 2018, 2019, and 2020, as well as increasing environmental and legal constraints that demand for a more sustainable management of soil water resources, stress the needs for terrestrial water budget forecasts at stakeholder-relevant high spatial resolutions, e.g., for the agricultural sector. In this context, we have established and operate a new quasi-operational forecasting experiment with the integrated hydrologic model ParFlow (www.parflow.org) which forecasts all relevant states and fluxes of the terrestrial water cycle over Germany and its neighbouring regions at 600m horizontal resolution. ParFlow simulates the complete 2D/3D surface and subsurface water budget, including the unsaturated and the saturated (groundwater) zones, as well as the energy and water fluxes at the atmosphere-surface-subsurface interface through its surface module CLM (Common Land Model). The simulations are driven by surface and near-surface atmospheric variables from different weather forecasting products from ECMWF (European Centre for Medium-Range Weather Forecasts). The 10-day hydrologic forecast, driven by the deterministic high-resolution weather forecast HRES, is complemented by a 50-member ensemble of ParFlow/CLM simulations forced by ECMWF’s ENS probabilistic forecast ensemble. This short-term ensemble approach allows for evaluating the impact of the uncertainties of the weather forecast, and especially of precipitation, on the different components of the terrestrial water budget. At the beginning of each season, we additionally run a 4-month seasonal prediction ensemble of the terrestrial water cycle using ECMWF’s SEAS long-term probabilistic 50-member ensemble forecast. Since soil moisture has a high inertia, especially for deeper soil layers, compared to, e.g., atmospheric variables, the spread of the ensemble remains acceptable over the prediction period. To provide the most useful information products on the evolution of water resources over the upcoming months to stakeholders, we derive diagnostics from the probabilistic seasonal forecasts that allow for a risk assessment. For example, for each 10-day period of the forecast, we calculate the probability (or risk) for the plant available water to reach values below a critical threshold for water stress, e.g., 30%. A prototypical application of these experiments is the ADAPTER project (www.adapter-projekt.de). In this framework, the monitoring and forecasting of relevant soil moisture-related diagnostics is meant to provide support for agricultural and water resources management, e.g., against droughts.
How to cite: Belleflamme, A., Wagner, N., Goergen, K., and Kollet, S.: Probabilistic short-term to seasonal soil moisture forecasts over Germany and surrounds using the hydrologic model ParFlow/CLM, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-660, https://doi.org/10.5194/ems2022-660, 2022.
The project AgriSens Demmin 4.0 aims to explore applications of remote sensing products for the agricultural sector. The developed methods and acquired knowledge will be made available for farmers and the public. Experiments and case studies are carried out in the project area, which is located about 180 kilometres north of Berlin and covers approximately 800 square kilometres with a large part of arable farming land. For this area, a rich collection of different data sources exists: Besides remote sensing data (from satellites, aircrafts and UAV systems), meteorological and soil data is collected by a dense network of environmental measuring stations. Additionally, comprehensive field measurement campaigns are conducted.
In the project, the agrometeorological department of the German National Meteorological Service prepares and processes the meteorological data and provides agrometeorological simulations to the project partners. The basis for these simulations is the SVAT model AMBAV (AgrarMeteorologische Berechnung der Aktuellen Verdunstung), which computes estimates for soil moisture and evapotranspiration based on the atmospheric conditions, soil types and current vegetation.
The above-mentioned richness of data for the project area enables the comparison of soil moisture estimates of different sources, each having specific scales, strengths, and weaknesses: Soil cores for gravimetric soil moisture samples have a high degree of accuracy, but only have limited spatial and temporal validity because of their sparsity. Installed soil sensors offer time-continuous measurements, but are prone to calibration errors and offsets. Satellite-derived products promise extensive monitoring of areas of interest, but provide only indirect and area-integrated measurements of surface soil water, which have limited informative value for deeper soil layers. Soil moisture modelling can be used to bridge the gap in scales since it is more flexible and may provide soil moisture estimation in arbitrary resolution if the relevant input data is available.
In our study, we compare in-situ soil moisture measurements and AMBAV model estimates for the AgriSens Demmin 4.0 area in order to evaluate the model but also asses the comparability of the different data sources. Preliminary results show that intercomparisons of soil core samples and AMBAV model results are satisfactory when there is good knowledge about the soil texture, whereas the agreement with automatic soil sensors remains problematic, most likely due to installation and calibration aspects.
High-quality in-situ data is generally important for the adjustment and verification of remote sensing products, and this includes the area of soil-vegetation-atmosphere transfer of water. Model validation and development is viewed as critical in order to provide an efficient linkage between the two domains.
How to cite: Haßelbusch, K., Böttcher, F., Lucas-Moffat, A., Falge, E., and Herbst, M.: Assessing the collection of soil moisture estimates in the scope of AgriSens Demmin 4.0: Comparison of in-situ measurements and AMBAV model results, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-540, https://doi.org/10.5194/ems2022-540, 2022.
Forest fires are one of the most severe natural disasters that periodically affect Mediterranean countries, as well as other countries with Mediterranean-like climates, such as the southwestern regions of the USA, or in more dry climates like in Australia. Forest fires though part of a natural forest renewal process, since they reduce the existing build-up load and thus the intensity of future fires, when frequent and assuming large-scale proportions have detrimental impacts on biodiversity, agroforestry systems, soil erosion, and economy. Portugal (PT) has a high inter-annual fire season variability and endures extreme forest fires, with a record of burned area in 2017 around 500 kha. These extreme wildfire events (EWE) concentrated in few days but with high burned areas, are linked to severe fire weather conditions.
In this study a comparison between several fire danger indices is performed for a reference period 2001‒2021 and 2017 (May‒October) for the Fire Weather Index (FWI), Continuous Haines Index (CHI), Keetch-Byram drought index (KBDI), Burning Index (BI) and Fire Danger index (FDI). A daily analysis for the so-called Pedrogão Grande wildfire (June 17th) and the October major fires (October 15th) included the Spread Component (SP) and Ignition Component (IC). Results revealed high above average values for all indices for 2017 in comparison with 2001‒2021 particularly, for October. High statistically significant monthly correlations between FWI, FDI and BI were found, along with lower between FWI and CHI as expected. These correlations are depicted in the spatial patterns between FWI and FDI for the two EWE. Furthermore, the spatial distribution of FDI has the best performance in capturing the locations of the occurrence of the two EWEs’. Though the remaining indices are informative, they lack some accuracy that can be achieved with a calibration procedure.
Overall, the implementation of a multi-index’s methodology might be a highly relevant tool for PT, whose complex orography and land cover, along with the projected increase in temperatures and intensification of drought conditions will lead to an increase in conditions prone to the occurrence of EWE. The outcomes allowed to conclude, that since fire danger depends on several factors a multi-index’s diagnosis is highly relevant, though calibration and scale adjustment are needed for some indices. The implementation of a Multi-index’s Prediction System should be able to further enhance the ability of tracking and forecast unique EWE, since the shortcomings of some indices are compensated by the information retrieved by others. Overall, a new forecast system can help ensuring the development of appropriate spatial preparedness plans, proactive responses by the civil protection regarding firefighter’s management, suppression efforts to minimize the detrimental impacts of wildfires in Portugal.
Funding: This research is supported by National Funds by FCT—Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020.
How to cite: Andrade, C. and Bugalho, L.: Multi-index’s assessment of the two 2017 major wildfires in Portugal, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-488, https://doi.org/10.5194/ems2022-488, 2022.
"Findability, Accessibility, Interoperability and Reusability – the FAIR principles – intend to define a minimal set of related but independent and separable guiding principles and practices that enable both machines and humans to find, access, interoperate and re-use data and metadata" (FAIR Data Maturity Model Working Group, 2020). In the further text term "FAIRness" will be used to describe how data meet FAIR principles, while FAIRNESS (capital letters) is the COST CA20108 action title.
The long-term need for a FAIR micrometeorological data is strongly boosted by two independent but equally important issues: a) full awareness of the time, effort, and money lost due to the lack of FAIR data in general (it is quantified in EC PwC EU Services report (PwC EU Services, 2018): "…the minimum true cost of not having FAIR data … is 78% of the Horizon 2020 budget per year) and b) urgent need to develop tailored adaptation and mitigation measures in rural and urban areas to reduce negative effects of adverse weather and climate change.
Enhanced standardization and integration between data bases&sets of micrometeorological measurements that are part of research projects or local/regional observational networks established for special purposes (agrometeorology, urban microclimate monitoring) by increasing FAIRness of data create a strong background for future research and modeling studies as well as a European Micrometeorological database.
Addressing identified challenges requires an effective transboundary network of researchers, stakeholders (extension services and environmental agencies, local authorities and ministries, SME), and civil society (specialized and general public) from Europe and beyond to identify and fill knowledge gaps, standardize, optimize and promote new environmental-tailored measurement and control procedures, enhance research effectiveness and improve dissemination. Therefore, FAIRNESS Cost action gathers 94 researchers, scholars, students, and stakeholders from 25 countries and 20 different specializations to work together to achieve goals set together.
Literature
FAIR Data Maturity Model Working Group. (2020). FAIR Data Maturity Model. Specification and Guidelines (1.0). https://doi.org/10.15497/rda00050
PwC EU Services. The cost of not having FAIR research data (2018) DOI 10.2777/02999.
How to cite: Lalić, B.: FAIRness of micrometeorological data: New community challenge?, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-448, https://doi.org/10.5194/ems2022-448, 2022.
