Environmental conditions (i.e., soil and climate constraints) define where and when crops can be grown and produced. However, climate change threatens agriculture productivity by modifying the distribution of temperatures, the hydrological cycle, and the frequency and intensity of extreme events. In New Zealand, agricultural production represents over 80% of the country’s exported goods. Hence, understanding the impacts of climate change on New Zealand agriculture is necessary to better adapt to future climate conditions.
We combined soil data and CMIP6 climate projections from five models and four Shared Socio-economic Pathways (SSPs) to conduct Land Suitability Analysis (LSA) for five crops: apple, cherry, maize, wheat and pasture. We applied a fuzzy-logic approach with crop-specific indicators to compute the agroclimatic suitability of the five crops across New Zealand. The LSA was performed for each climate model separately to estimate the climate-related uncertainty. The agroclimatic suitability corresponds to the means of computations from individual climate models. In addition, the crop water requirements were quantified considering precipitation and evapotranspiration.
The results show how the agroclimatic suitability patterns of crops will change in the future under different climate change scenarios. This allows for identifying where and when the agroclimatic suitability for a given crop is expected to decrease/increase. Moreover, the computed crop water requirement allows for estimating irrigation needs and water use.
While LSAs have been extensively used in New Zealand, there are gaps in previous applications that our work addresses. This research is the first LSA application in New Zealand that uses a consistent methodology across all agricultural sectors allowing for better inter-crop comparisons. Our study also provides estimates of climate-related suitability uncertainties, which are important to consider when exploring future climate conditions given the role of climate variability on agricultural production. Finally, our work estimates crop water requirements which are critical for future water management planning.
Future application of this methodology to other crops (e.g., winegrapes, kiwifruit, avocado, vegetables, nuts…) will extend our knowledge, give a more comprehensive view of climate change impacts on New Zealand’s agriculture landscape, and help develop better climate change adaptation options.
How to cite: Hamon, B., Quénol, H., Vannier, C., and Cochrane, T.: Bridging the Gaps in the Future Agroclimatic Suitability of Crops in New Zealand, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1157, https://doi.org/10.5194/egusphere-egu25-1157, 2025.
Climate change complicates pest management practices, particularly pesticide application, as pests’ responses to pesticides are temperature-dependent. Here, we investigated the effects of extreme heat events on the toxicity of the neonicotinoid imidacloprid to wheat aphid Sitobion avenae, considering factors including temperature, exposure duration, intervals between heat events and pesticide application, and exposure order. We found that toxicity is both temperature- and time-dependent, with longer exposure durations and shorter intervals between heat stress and pesticide treatment generally increasing toxicity. The sequence of exposure (whether heat or pesticide occurs first) also influenced efficacy, with variations observed between adult (F0) and offspring (F1) stages. Both indoor and field experiments demonstrated that factors like the temperature post-application, the interval between stresses, and their order are crucial for pest control outcomes. Based on these results, we proposed several guidelines for farmers: 1) apply pesticides on hotter days; 2) ensure that post-application temperatures are elevated; 3) minimize the interval between pesticide application and heat events. These strategies can optimize pesticide use, enhance efficacy, and reduce overall pesticide application, offering valuable insights for improving pest management in a warming climate. Further field studies are needed to confirm these findings.
How to cite: Wang, B.-X., Wang, X.-J., Wang, Y., and Ma, C.-S.: Optimizing pesticide efficacy: the impact of extreme heat events and timing in a warming climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2613, https://doi.org/10.5194/egusphere-egu25-2613, 2025.
Canada's viticultural growing climates are changing and redefining the potential of the Canadian wine industry. Growing seasons are evolving, as observed through the changing trends of important variables, such as near-surface temperature and seasonal precipitation. Using open access NEX-GDDP-CMIP6 data available in Google Earth Engine, this research investigated the trend evolution of key viticultural variables, near-surface temperature (minimum, average, maximum) and seasonal precipitation, across various temporal timespans within the primary Canadian wine-producing provinces of Ontario, British Columbia, Quebec and Nova Scotia between 1994-2100. In addition, two shared socioeconomic pathways (SSPs), SSP245 and SSP585, were used to help build an understanding of how the key viticultural variables of interest may change in the near-term (2015-2050) and the long-term (2051-2100). Statistically significant near-surface temperature increases were demonstrated across all wine-growing provinces alongside seasonal precipitation increases over the growing season. Temperature increases can have an impact on the quality of wine produced as well as the type of grape variety used, which could be beneficial to Canadian wine producers. The Canadian wine industry is typically dominated by grape varieties reflective of cooler growing climates. Increasing temperatures, especially over the growing season, may allow for the utilization of grape varieties found in other wine-growing areas with warmer climates, like southern Europe. However, the increasing frequency of extreme events, like rainstorms, droughts, and heat waves, will present barriers to the potential growth of the Canadian wine industry.
How to cite: Lippa, M. N., Straffelini, E., and Tarolli, P.: Climate Change Impacts on Viticulture in Canada, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8851, https://doi.org/10.5194/egusphere-egu25-8851, 2025.
Spring frost events pose a significant risk to viticulture (Poni S. et al., 2022). In Marlborough, New Zealand's leading wine region, the economy is heavily dependent on vineyard yields, making spring frost events a very important threat to consider.
This risk is exacerbated in the context of climate change, as the increase in warmer temperatures has led to the advancement of phenological stages in grapevines, notably bud break (Van Leeuwen C., et al., 2016). This advancement makes grapevines more vulnerable to frost, as new growth is then exposed to potential late frost events. Despite these critical implications, frost risk in Marlborough has not been thoroughly assessed since 2018, emphasizing the need for an updated analysis.
Initial research on frost occurrence patterns was conducted by Clark and Sturman in 2009 and revealed significant variations in observed climate across New Zealand's primary vineyard regions. This suggests that temperature changes are not uniform throughout the country, with Marlborough experiencing an increase in frost occurrence in contrast to other areas. These regional disparities have been attributed to changes in large-scale atmospheric circulation and its interaction with New Zealand's complex topography (Sturman, A. et al., 2013).
Given the high sensitivity of the wine industry to weather fluctuations and the considerable regional variability in trends of key parameters, such as temperature, a deeper understanding of the spatial and temporal variability of New Zealand's weather regimes is proving to be highly valuable for viticulture. This study investigates the variability of spring frost events in Marlborough by linking large-scale atmospheric circulation patterns to local climate impacts.
We use a combination of K-means clustering and Self-Organizing Maps (SOMs) on high-resolution WRF model data spanning three nested domains (27 km, 3 km, and 1 km resolution). While K-means is widely used to identify regional weather regimes (Polo, I. et al., 2011), its tendency to oversimplify the continuum of atmospheric variability is mitigated here by the Self-Organizing Maps, which preserve the topological relationships of the data and capture gradual transitions between climate states.
The proposed methodology aims to elucidate the large-scale and synoptic drivers of frost events by exploring their spatial and temporal distribution with fine precision. The results identify the role and recurrence of specific circulation patterns, responsible for triggering spring frost events. By improving the identification of weather patterns responsible for frost events, this research sets the groundwork for developing targeted frost risk forecasts and management strategies, ensuring resilience in the face of climate change for the New Zealand wine industry. This methodology is transferable to other regions around the world and can be applied to a wide range of crops and agricultural systems.
Stefano, P. et al. ; Am J Enol Vitic. 2022. doi:10.5344/ajev.2022.22011.
Van Leeuwen, C. et al. ; Journal of Wine Economics. 2016. doi:10.1017/jwe.2015.21.
Sturman, A. et al. ; Academic Journal. 2013. doi:10.1002/joc.3608.
Polo, I. et al. ; J. Climate. 2011. doi:10.1175/2011JCLI3622.1.
How to cite: Collet, E., Ullmann, A., Quénol, H., Sturman, A., and Pohl, B.: Connecting Large-Scale Atmospheric Circulation to Spring Frost Events in Marlborough Vineyards, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10223, https://doi.org/10.5194/egusphere-egu25-10223, 2025.
Land Surface Models (LSMs) are a fundamental component of climate modeling as they simulate the energy and water fluxes between terrestrial and atmospheric systems. Agriculture is a sector strongly affected by changing climate conditions and the increasing occurrence of extreme weather patterns. For these reasons, we use the state-of-the-art Noah LSM with multi-parameterizations (Noah-MP), enhanced with a crop module (Noah-MP-crop) that incorporates crop growth and its interactions with the atmosphere. The main goal is to use Noah-MP-crop and downscaled climate projections to generate seasonal and decadal predictions of the climate impacts of drought and heat stress on the growth and development of a typical Mediterranean annual rainfed crop. The specific objectives of the study are: (1) to perform a sensitivity analysis to identify the most influential Noah-MP-crop model parameters affecting the model outputs of leaf area index (LAI), total biomass, and the exchange of water and carbon with the atmosphere, and (2) to calibrate the model using field-scale observations of these variables. Our case study area is an agricultural field in the central plain on the island of Cyprus, where rainfed barley is cultivated. Observations used in our study include eddy covariance fluxes of water and carbon, meteorological variables, soil moisture at depths of 10, 30, and 50 cm, LAI and phenology, soil properties and agricultural practices. These observations span three barley growing seasons, covering the period from 2020 to 2022 and 2023 to 2024.
How to cite: Sofokleous, I., Zittis, G., Djuma, H., Christodoulou, N., and Bruggeman, A.: Modeling crop development and atmosphere interactions under changing climate conditions using the Noah-MP Model: A case study of Mediterranean rainfed barley, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10247, https://doi.org/10.5194/egusphere-egu25-10247, 2025.
Agriculture is highly vulnerable to temperature increase and variations in precipitation patterns associated with climate change. The Mediterranean region is considered a hotspot, with Italy being particularly affected by a raise in the frequency and severity of prolonged periods of drought and extreme floods. The IPCC reported that maize and wheat yields have been negatively affected by the observed climatic changes in several lower-latitude regions during recent decades. Cereal production constitutes a key asset for Italy’s agricultural sector, with wheat and maize being the main cultivated crops, reaching together the 79% of the total harvested area. However, in Italy there is still limited information on the effects of climate change and extreme weather events on maize production, particularly at very high-resolution spatial scale. Given the peculiar topography of the Italian landscape and the sudden spatial variations of weather variables due to the country’s orography, the use of very high spatial resolution climate data could significantly contribute in offering better detailed future crop yield projections. The km-scale Convection Permitting Models (CPMs), which provide a more realist representation of hourly precipitation and dry hours compared to coarser resolution models, could constitute an interesting tool to project future yield with a very fine spatial scale.
This study uses CPMs from the CORDEX-FPS on Convective Phenomena over Europe and the Mediterranean (FPS Convection) to drive the Agricultural Production System sIMulator (APSIM) crop model to project maize yield under RCP 8.5 over the 2090-2099 period.
At first, the ability of APSIM in simulating the observed maize yield at province scale in Italy over the period from 2006 to 2023 is assessed. In this phase, the APSIM crop model is initialized with weather data from the reference dataset Era5Land remapped at the same spatial resolution of CPMs. Then, the performance of nine CPMs (run with boundary conditions provided by ERA-Interim) in reproducing the simulated maize yield over the 2000-2009 period is evaluated. The APSIM crop model is subsequently run over the 1996-2005 period with weather data from CPMs with boundary conditions provided by their respective GCM. Finally, APSIM is run over the 2090-2099 period under RCP 8.5 to get projections of future maize yield.
Results have shown that the APSIM crop model is capable of simulating maize yield over Italy at province scale, with an overall correlation between observed and simulated maize yield of 0.92 (initialization) and 0.86 (testing). Moreover, maize yield simulated through the use of CPMs shows a good agreement with maize yield simulated with Era5Land, with correlation from 0.79 to 0.91 (p<0.001) depending on the considered CPM. At province level, CPMs perform better in the high-producing areas, such as the Po Valley, while the correlation decreases over provinces with significant areas located on the Alps or the Apennines. Finally, over the 2090-2099 period, maize yield will decrease up to -30% over the Po Valley provinces, while it will increase at higher altitudes.
Results demonstrated the importance of high-spatial resolution yield projections to evaluate future adaptation strategies.
How to cite: Monteleone, B., Nyabuti Ong'era, V., Mathew, M., Massano, L., and Fosser, G. and the CORDEX-FPS-CONV community: Use of Convection Permitting climate models for maize yield projection over Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10401, https://doi.org/10.5194/egusphere-egu25-10401, 2025.
Throughout recent decades climate change has significantly impacted the dynamics of plant responses during drought and recovery. This has therefore had an influence on ecosystems and the patterns of recovery that they exhibit. The purpose of this study is to analyze and quantify the alterations climate change has caused to the resilience and adaptation ability of plants to changing climate stressors. These stressors include an increase in the frequency of severe weather events, a rise in global temperatures, and changing patterns of rainfall. In this study, we investigate the temporal and spatial changes in the health of vegetation during and after drought events in a number of different climatic zones by making use of meteorological data (rainfall) and long-term satellite-derived vegetation indices (NDVI). The results of our research indicate that vegetation is becoming more susceptible to drought, displaying more significant and apparent changes in health both during and after the occurrence of droughts. This increased sensitivity indicates that an ecosystem is becoming more susceptible to the stress that is caused by climate change, which will have long-term impacts on its resilience and ability to recover. Through this approach, the study aims to uncover patterns and trends that explain how ecosystems are adapting or failing to adapt to the compounded stressors posed by a warming climate. The analysis provides a foundation for understanding the interplay between climate change, drought, and vegetation, offering critical insights into the challenges of ecosystem management in a rapidly changing world.
How to cite: Bilal, S. B. and Gupta, V.: The Influence of Climate Change on Vegetation Response Patterns During Drought and Recovery, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11450, https://doi.org/10.5194/egusphere-egu25-11450, 2025.
Bioenergy from biofuel crops will be an important tool for meeting global renewable energy demands and evolving energy mandates. As ongoing research demonstrates the environmental costs of traditional bioenergy candidates (e.g., maize bioethanol), novel perennial crops have emerged as enticing alternatives. Perennial crops offer numerous benefits over annuals, including more efficient nutrient use and enhanced soil carbon storage. Moreover, the robust rooting structures of perennials are also better equipped to extract water from deeper soils profiles, thereby buffering carbon sequestration potential and leading to more resilient yields under extreme growth conditions including heat and drought.
Though perennial bioenergy feedstocks are valued for their enhanced productivity and stress tolerance, the extent that these benefits will persist in a warming climate is uncertain. Rising temperatures are shifting hydroclimates across United States agricultural lands, uniformly intensifying vapor pressure deficit (VPD), whereas precipitation and soil moisture responses are varying regionally. While the rooting profiles of perennials confer greater advantages over annuals during soil water dry-down, the degree by which these respective life-history strategies confer differential success when faced with elevated VPD remains unresolved.
To shed light on these uncertainties, we perform a statistical decomposition of ecosystem carbon flux observations among co-located annual (e.g., maize) and perennial (e.g., miscanthus & switchgrass) bioenergy crops to evaluate the relative limitations imposed by soil water vs. VPD stress. Preliminary results suggest that gross primary productivity of annual systems is more sensitive to both soil water and VPD stress than their perennial counterparts. However, the greatest productivity declines imposed by elevated VPD on all systems occurred when soil water availability was most abundant. Collectively, these findings highlight that the enhanced carbon mitigation potential of perennial systems will persist in a future characterized by shifting drought regimes and increasingly high VPD.
How to cite: Benson, M., Pederson, T., and Bernacchi, C.: Do perennial bioenergy crops offer greater resistance to vapor pressure deficit stress than annuals?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13755, https://doi.org/10.5194/egusphere-egu25-13755, 2025.
Agriculture in Ukraine is one of the most important economic sectors which has an impact on global food security with over 600 million people dependent on the export of crops from Ukraine. It became even more obvious after Russia's full-scale invasion of Ukraine and blockage of Ukrainian seaports that triggered a global food crisis in 2022. In the last year 2024, Ukraine reached an export level of USD 24.5 billion and exported 78.3 million tonnes of agricultural products according to the Ministry of Agrarian Policy and Food of Ukraine, which accounted for 59% of total exports. At the same time, extreme events associated with climate change caused the second biggest losses for agriculture in Ukraine after the Russian war damage. One year of drought in 2020 caused two-fold more economic losses in agriculture than caused by all other hydrometeorological extreme events in the previous 10 years. Therefore, developing a strategy for adaptation to climate change in agriculture is a vital problem for Ukraine, and it should start with an assessment of possible future vulnerability and risks for the sector.
In our work, we used an updated methodological approach based on the presented in the IPCC AR6 (2021-2022). Namely, we estimate the change in 32 Climatic impact drivers (CIDs) grouped into 5 categories: heat and cold, wet and dry, snow, wind, and coastal. Those CIDs were calculated mainly from the ensembles of up to 34 Regional Climate Models (RCMs) from the Euro-CORDEX with the maximum available for Ukraine resolution of 0.1o. In addition, we used the IPCC AR6 Interactive Atlas and Copernicus database for some of the CIDs. We assessed vulnerability and risks based on projections for two scenarios (RCP 4.5 and RCP 8.5) for three future periods (2021-2040, 2041-2060, and 2081-2100) vs 1991-2010.
The methodological approach included expert judgement on sensitivity to changes in all 32 CIDs for two of the main sub-sectors in Ukrainian agriculture: crop farming and livestock. This sensitivity is a set of weighting coefficients, their sum was chosen for the convenience of comparing vulnerability between sectors and was equal to 10 units. Note that the obtained sensitivity coefficients are irrelevant to geographical location and purely attribute of assets, therefore, it could be used for all territories where such crops and animals are grown. But what is relevant to locations is the impacts of future change in CIDs which we got in all over 7300 grid points for Ukraine and categorized within obtained limits as negligible, low, medium, high, and very high. Vulnerability and risks were calculated based on the developed original procedure in the assumption that exposure and adaptive capacity are both equal to unit, that is an asset is present in all grid points and has minimum adaptive capacity.
Our results are presented in maps, diagrams and tables for 8 regions including The Carpathians and the Crimean mountains and coastal for the Black Sea and the Azov Sea. This research is a part of the project “Promoting Green Deal Readiness in the Eastern Partnership Countries” (PROGRESS)
How to cite: Krakovska, S., Kryshtop, L., Kryvobok, O., Palamarchuk, L., and Chyhareva, A.: Climate risk and vulnerability assessment for agriculture in Ukraine, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16867, https://doi.org/10.5194/egusphere-egu25-16867, 2025.
Climate change is expected to affect crop phenology and productivity. There are no long term studies on this topic in India primarily due to lack of observational data. We developed a database of 5-decade long (1970-2020) site-scale observations of rice and wheat crop parameters by digitizing archived masters and doctoral thesis from agricultural universities in India. The observed Leaf Area Index (LAI) during the growing season shows that the major growing season for rice is June to October and for wheat it is November to April. Rice and wheat have a statistically significant increasing trend in yield (38 kg/ha/yr for rice and 34 kg/ha/yr for wheat) over the study period (p<.05). However, there is no considerable difference or trend observed in the growing season length of the crops during this period. Interestingly, a statistically significant increasing trend in harvest dates of wheat crop is observed from the dataset, harvest dates extended by ~14 days over the 50-year period (p<.1). Further, a case study was conducted on the rice crop, the largest crop by harvested area in India, to attribute the increasing trends in yield and other crop parameters to mean growing season temperature and increasing CO2 levels. This case study comprised multiple site-scale data from two major agro-climatic zones: the Central India (CI) region and the Indo-Gangetic Plain (IGP) region during the wet season. The findings show increasing trends for plant height, grain yield, and straw yield in both the CI and IGP regions. In the IGP region, there is a negative correlation between mean temperature and crop variables, while a positive correlation is observed with CO2 concentration. On the contrary, in the CI region, the mean temperature is positively correlated with plant height and straw yield but negatively correlated with grain yield. However, with increased CO2 levels, all variables show a strong, significant positive correlation. These changes in crops may also be attributed to the development of hybrid crop varieties resulting from advancements in agricultural technology, which have impacted crop production and other plant variables. The growth of rice increased in elevated CO2 levels but decreased under high temperature conditions. These changes in crop behavior underscore the need for adaptive strategies to mitigate the adverse effects of climate change on agriculture in India. This study is the first work that offers site-scale observational data for two major Indian agroecosystems and further investigates the two major agro-climatic zones. This data will be invaluable for future agricultural research and model development.
How to cite: Yadav, R., Reddy, K. N., and Baidya Roy, S.: Impact of Climate Change on Indian agroecosystems: Long-Term Trends from Site-Scale Data (1970–2020), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14646, https://doi.org/10.5194/egusphere-egu25-14646, 2025.
As climate change intensifies, the frequency and intensity of multi-year La Niña events have altered, posing potential threats to East Asia's climate and agricultural production. However, the underlying atmospheric dynamics and their long-term impacts on high-value crops, such as mangoes, remain poorly understood.
This study investigates how multi-year La Niña events influence atmospheric processes in East Asia and quantifies their impacts on mango yields. Using the CESM2 climate model, we analyzed the dynamic mechanisms of multi-year La Niña events, while a Panel Data Model was employed to assess the influence of climate anomalies on mango production. The findings indicate that multi-year La Niña events significantly and oppositely affect East Asia's precipitation patterns: reduced rainfall in the first year leads to pollen washout and decreased mango yields, while increased rainfall in the second year affects yields differently. With ongoing climate change, the frequency and intensity of such anomalies are likely to shift, warranting further attention to their potential impact on mango yields.
By combining climate modeling and economic analysis, this study reveals the dynamic drivers of the "dry-wet" cycle in East Asia under multi-year La Niña events and quantifies their effects on the mango industry. These findings provide a critical basis for understanding agricultural vulnerability to extreme climate events and offer scientific support for developing agricultural adaptation strategies.
How to cite: Chang, Y.-H. and Lo, M.-H.: Impacts of Multi-Year La Niña Events on East Asia's Climate and Mango Production, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3007, https://doi.org/10.5194/egusphere-egu25-3007, 2025.
Climate is the main factor influencing winegrape production in a region, making viticulture highly sensitive to climate change. The increase in atmospheric temperature due to climate change affects both winegrape yield and composition. Given the importance of viticulture in Extremadura (southwestern Spain) both in terms of the area it covers and the socio-economic benefits it generates, it is crucial to understand the impact that climate change may have on viticulture in the region. The aim of this study is to analyze high-resolution climate projections in Extremadura under two different scenarios, considering several future periods up to the end of the 21st century. For this purpose, four temperature-based bioclimatic indices were used. Results indicate that most of the Extremaduran region will remain suitable for winegrape production during the period 2006–2035. However, projections for the mid-century (2036–2065) suggest that, depending on the index and scenario considered, between 65% and 92% of the total area of Extremadura will become too hot for viticulture. By the end of the century (2066–2095), this figure is expected to rise to between 80% and 98%. Nonetheless, under the low emissions scenario, a few areas might still be suitable for winegrape production, provided that new heat- and drought-resistant varieties and techniques are adopted.
How to cite: Moral García, F. J., Rebollo Castillo, F. J., Rebollo Moyano, L., Paniagua Simón, L. L., and García Martín, A.: Analysis of Climate Change on Viticulture in Extremadura (Southwestern Spain), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4116, https://doi.org/10.5194/egusphere-egu25-4116, 2025.
In the current context of climate change, understanding the chilling requirements necessary for breaking dormancy and flowering in fruit trees is crucial for selecting suitable cultivars for different geographical locations. Recent data is essential for precise estimates regarding the area's suitability. Temperature data from 72 weather stations, spanning the period from 1975 to 2015, were analyzed using three models to assess winter chilling accumulation (Chilling Hours, Utah Model, and Positive Utah Model). This aimed to estimate the spatial pattern across Spain. The mapping of accumulated winter chilling was achieved through an integrated geographic information system (GIS), combined with multivariate geostatistics (regression-kriging) and algebraic mapping
As chilling accumulation is greatly influenced by the elevation of each location, elevation was utilized as a secondary variable to establish linear relationships between it and each chilling model. These relationships enhanced estimates at unsampled locations when incorporated into the regression-kriging algorithm, resulting in more accurate maps.
The findings indicated a strong correlation among the measurements from the three models, facilitating visualization of the spatial variability in accumulated winter chilling for each model. Additionally, when considering a high probability of meeting chilling requirements, quantile maps can be used instead of mean value maps, allowing for the integration of uncertainty.
Finally, the potential spatial distributions of three sweet cherry cultivars were mapped using these findings. This information aids in advising farmers on which cultivars are most appropriate for their geographical area and which regions of Spain are best suited for sweet cherry production, utilizing the most recent temporal series that incorporate climate change's impacts on climatic data.
How to cite: Rebollo Castillo, F. J., Moral García, F. J., Rebollo Moyano, L., García Martín, A., and Paniagua Simón, L. L.: Spatial analysis of winter chilling accumulation in the current scenario of climate change in Spain. An application to some cherry cultivars., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4120, https://doi.org/10.5194/egusphere-egu25-4120, 2025.
The dehesa, recognised as the most extensive High Natural Value Agricultural System in Europe, is one of the main natural and economic resources of the Southwest of the Iberian Peninsula and therefore of Europe. Numerous studies indicate that an increase in temperatures (Tm) and a reduction in precipitation (P) are expected due to climate change, especially in this area. This would lead to an increase in Evapotranspiration (ETP), which represents the water requirements of plants, and a reduced rainfall. Utilizing time series data on climatic parameters spanning the period 1981-2021, a comparative analysis was conducted on the major pasture regions of Spain and Portugal (namely, Salamanca, Cáceres, Badajoz, Beja, Lisbon, and Córdoba). The analysis employed a range of statistical tests, including one-way analysis of variance (ANOVA) and Tukey's test, as well as Dunnet's test. Additionally, trends were examined through the utilization of the Mann-Kendall test and Sen's slope estimator (Q). The results obtained demonstrated significant variations between the grassland areas for the variables analyzed. Córdoba was found to have the highest mean annual temperature and ETP, while Salamanca exhibited the lowest values for temperature, precipitation and ETP. All areas demonstrated an increase in temperature, with Lisbon, Córdoba and Badajoz exhibiting particularly significant trends. However, precipitation did not exhibit a discernible trend. These findings contribute to the climatic characterization of the grassland regions and have the potential to impact the productivity and quality of pasture and woodland. This necessitates an adaptation of management techniques (stocking rate, frequency of grazing) and cultivation, as well as a change in the distribution of suitable areas.
How to cite: García Martín, A., Paniagua Simón, L. L., Moral García, F. J., Honorio Guisado, F., and Rebollo Castillo, F. J.: Trends in evapotranspiration, temperature and precipitation in the main grassland areas of the Iberian Peninsula (1981-2022)., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5813, https://doi.org/10.5194/egusphere-egu25-5813, 2025.
This study analyses the temporal trends (1981-2022) of two widely used aridity indices: the De Martonne index (IDM) and the FAO aridity index (IF) in the main grassland agroforestry areas (Dehesa) of the Iberian Peninsula, in the context of current climate change. It is essential to understand aridity and its trends in order to assess the sustainability of these agricultural systems, especially in a global warming scenario. The annual IDM and IF have been determined in the Dehesa areas of the southwest of the Iberian Peninsula (Spain and Portugal). The mean IDM in these areas was 20.2 (corresponding to a Mediterranean-type climate), ranging from 16.0 in Badajoz with a semi-arid climate to 28.0 in Lisbon with a humid climate. The coefficient of variation ranged from 21% in Salamanca to 34% in Córdoba, indicating significant variations in the data. The mean IF was 0.44, corresponding to a semi-arid climate, ranging from 0.31 in Salamanca, also classified as semi-arid, to 0.75 in Lisbon, characterised by a humid sub-humid climate. The coefficient of variation ranged from 23% in Salamanca to 37% in Córdoba, reflecting substantial variations in the data. The findings of this study have enabled the identification of two distinct trends: a decrease in the indices in the Spanish regions and an increase in the indices in the Portuguese regions. These results imply that changes in management are necessary, particularly in regions where the indices demonstrate a decrease, given the increasing aridity, which directly affects the productivity of the dehesa agroecosystems by reducing water availability.
How to cite: Paniagua, L. L., García martín, A., García García, D., Serrano3, J., Rebollo, J., and Moral, F.: Temporal analysis of aridity in the Dehesa agroecosystems of the Iberian Peninsula (1981-2022), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6134, https://doi.org/10.5194/egusphere-egu25-6134, 2025.
Fruit from tree crops such as avocados, apples, citrus and grapes are a major Australian export, generating over $1 billion in 2023. Fruit trees require specific climatic conditions for growth and fruit production, including an optimal temperature range to allow the accumulation of energy, and a winter chilling period during dormancy to encourage blossom. We examine how Australia's climate has changed over the past 80 years and is predicted to change in the future and how these changes effect areas under tree crop cultivation. This will enable farmers to make informed choices about future land use to maintain production levels and income.
We used the ERA5-Land climate reanalysis dataset to calculate the mean, minimum and maximum annual temperatures, the occurrence of extreme heat (> 35°C), and to derive agroclimatic indices relevant to some tree crops such as growing degree days and winter chill hours. Decreases in winter chill hours and increases in growing degree days are evident along the southeastern coastline around Sydney and Melbourne, and in Tasmania. Using the current and projected climatic and agroclimatic indices, we develop crop suitability maps for five major tree crops which are assessed in relation to a Western Australia tree crop map derived using Sentinel-2 data.
How to cite: Jones, S., Roberts, G., and Dash, J.: The impact of climate change on areas suitable for growing fruit tree crops in Australia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13651, https://doi.org/10.5194/egusphere-egu25-13651, 2025.
Integrating drought —a temporary state of dryness— with aridity —a permanent state of dryness — is crucial for enhancing long-term water resource management and agricultural productivity in regional planning. In this study, the Aridity Index (AI), defined as the ratio of precipitation to reference evapotranspiration, and the Evaporative Stress Index (ESI), reflecting temporal anomalies in evapotranspiration, were analyzed across the Iberian Peninsula over the past two decades (2003–2022). Results revealed a transition towards lower values of AI, indicating climate changed towards dryness, and an increase in drought intensity within the study areas. Notably, droughts were observed in both humid and dry zones, underscoring dried conditions not necessarily led to droughts. These findings highlighted that integrating the long-term variability of drought and aridity can significantly aid policymakers in planning for drought mitigation and response strategies, as well as enhance communities’ resilience to climate change.
How to cite: Bozorgi, M., Cristóbal, J., and Casadesus, J.: Integration of climate aridity changes and agricultural drought, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17169, https://doi.org/10.5194/egusphere-egu25-17169, 2025.
Climate change is exerting strong pressure on the rural areas of the Pyrenean foothills on both sides of the Pyrenees (POCTEFA area). On the one hand, it is endangering viticulture in dryland and low-altitude areas, on the other hand, it is making cultivation viable at high altitudes (southern slope) or in areas that were previously too cold and wet (northern slope). In any case, climate change is introducing a high variability on grape ripening, causing uncertainty, excessive spending on pesticides and eventually frustrating results in terms of the quality of the vintage, with the increasingly frequent appearance of aromatic problems associated with overripeness, raisining and greenness, which sometimes only appear in bottled wines. The CLIMAROMA project (2024-2027), funded by the European Commission through the POCTEFA programme and participated by four institutions from Spain and France, aims to find out which are the most significant climatic variables in the generation of aromatic defects in wine, to avoid these defects by searching for areas with favorable climates for cultivation or with treatments based on biostimulants and, lastly, to develop an action and implementation plan in new vine growing areas in the POCTEFA regions. The project consists of: 1) evaluation of the appearance of aromatic defects related to climatic factors; 2) implementing strategies for adaptation to climate change in wine-growing areas; and 3) assessment of new potential areas of vine cultivation on POCTEFA territories. Addressing the first objective, new harmonized climatic zones were derived from a comprehensive climatic analysis of the region. To this end, ten temperature-based indices and six precipitation-based indices were calculated on a 1 km2 grid covering the entire study area. A Principal Component Analysis was applied to all of them to reduce the dimensionality of the data, leaving the first two, which accounted for 92.6% of the explained variance. These two components were used to perform a clustering analysis that would define climatically homogeneous areas. The analysis showed that four groups were the most optimal classification. In parallel, two bioclimatic indices were calculated using the Köppen and TBR methods. Both showed a spatial distribution similar to that of the groups estimated in the previous calculation, so the cartography with four groups was selected as the most appropriate for the analysis. Further work will find new potential wine-growing areas based on Species Distribution Models (SDMs).
How to cite: Serrano-Notivoli, R., Bueno, M., Ontañón, I., Sáenz-Navajas, M. P., Fernández-Zurbano, P., and Geffroy, O.: CLIMAROMA: Influence of climate change on the aroma of wines from the Pyrenees, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20217, https://doi.org/10.5194/egusphere-egu25-20217, 2025.
Climate is a key factor influencing cropland suitability, hence, climate change poses a significant threat to future cropland quality and availability across the globe. The magnitude and direction of these impacts, however, vary across regions and crop types, with rainfed agriculture systems in vulnerable regions such as sub-Saharan Africa facing the greatest challenges. Here, we assessed current and future cropland suitability (CLS) for four major cereal crops (maize, teff, sorghum, and wheat) in Ethiopia (Wakjira et al., 2024). We established functional relationships between recorded crop yield and climatic factors (growing season rainfall, temperature, and solar radiation), as well as soil factors (texture, pH, and organic carbon) extracted from global datasets, to determine current suitability and to quantify changes in CLS under future climate scenarios based on multiple climate model projections.
We show that more than half of the rainfed agricultural region of Ethiopia is moderately to highly suitable for the top three cereals crops: teff (54%), maize (51%), and sorghum (63%) while only 29% of the region is currently suitable for wheat. Under the future climate, which is projected to be wetter and warmer across most of the rainfed agricultural region, major altitudinal shifts (from lowlands to highlands) in the currently suitable croplands are expected. The differences in suitability losses across lowlands and gains in highland agroecologies are relatively smaller (with losses being slightly higher) for maize and sorghum, compared to that of teff and wheat. As a result, suitable cropland areas are projected to decrease, for example by up to 25% for teff and 16% for wheat under the SSP2-4.5 emission scenario by the end of the century. Through climate sensitivity analysis, we found that changes in CLS in the lowland and highland agroecologies are primarily driven by temperature increases, while in semi-arid and hyper-humid areas, the changes in CLS are mostly driven by rainfall. These findings underscore the urgent need for adaptation actions considering the agroclimatic conditions and locations of the croplands.
Reference
Wakjira, M. T., Peleg, N., Six, J., and Molnar, P.: Current and future cropland suitability for cereal production across the rainfed agricultural landscapes of Ethiopia, Agric. For. Meteorol., 358, 110262, https://doi.org/10.1016/j.agrformet.2024.110262, 2024.
How to cite: Wakjira, M. T., Peleg, N., Six, J., and Molnar, P.: Impacts of climate change on land suitability for cereal crops in Ethiopia , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4848, https://doi.org/10.5194/egusphere-egu25-4848, 2025.
The agricultural sector, especially in rural areas dependent on rainfall, is heavily impacted by escalating droughts, which are expected to worsen in the coming years. In developing countries like Thailand, this challenge threatens the livelihoods of farmers and their quality of life. In 2023, Thailand's agricultural sector suffered over $13.45 million in damage due to a dry spell from May to September, affecting more than 25,000 farmers and over 110,000 acres of farmland. This loss was greater than the $5.7 million in damages from flooding.
Older farmers are a supreme vulnerable group at high risk from climate change. They face challenges due to physical limitations, reduced adaptive capacity from a lack of skills and resources, and difficulty learning new methods. Supporting elderly farmers in addressing drought and other climate-related challenges is essential. Such assistance aims to enhance their well-being, helping them cope effectively with these impacts and ultimately improving their quality of life
The main objective of this study was to analyze the behaviors of older farmers in selecting coping strategies in response to drought during their farming activities. Our findings revealed three significant behavior patterns among farmers aged 60-69 in drought-prone areas of Thailand.
First, self-recognition plays a crucial role in the adaptability of aging farmers. Their long-standing experience with traditional agricultural practices often leads to the belief that these methods are best suited to their local conditions. However, this mindset may limit their awareness of changing environmental factors. In this context, learning typically occurs through observation, imitation, comparison, and self-regulation.
Second, normative values are a key factor in shaping older farmers’ decisions when selecting drought coping strategies. Their choice is influenced by behavioral beliefs and attitudes, which, in turn, affect their confidence in government policies addressing these challenges. Behavioral beliefs are shaped by expectations of the outcomes of participating in programs, particularly in terms of cost-effectiveness. The key costs include labor, financial resources, and time required for participation or strategy development. If the perceived cost-benefit ratio is favorable, elderly farmers are more likely to adopt the proposed strategies to tackle drought-related challenges.
Self-protective behavior in older farmers becomes more complex during drought. Their responses may involve direct actions like protecting crops and improving resiliency, or avoidance behaviors such as accepting fate or rejecting mitigation measures. A key factor influencing their decisions is cost-benefit comparison, shaped by their experience and agricultural culture. They evaluate the costs of participating in drought response efforts, considering past threats, and weigh the expected benefits from government support, such as financial aid and skills training. These factors guide their choice of coping strategies.
Based on these findings, we emphasized the behavioral patterns that can determine the success of agricultural extension and government promotion efforts. It is crucial to ensure the synthesis of adaptive patterns and processes of older farmers in response to climate change, considering the influence of cultural, behavioral, and socio-economic factors.
Keywords: Older farmers, drought, behavioral response, coping strategies, aging agriculture
How to cite: Pechdin, W.: Behavioral Patterns of Older Farmers in Selecting Coping Strategies for Plantation in Response to Drought, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5040, https://doi.org/10.5194/egusphere-egu25-5040, 2025.
Soft wheat is one of the most important crops in the French agricultural production, making France the 6th largest producer in the world and the leading producer in Europe. However, since the 1990s, yields have stagnated. Several studies have identified adverse climatic events as a major factor in this stagnation, including drought, heat stress, early and late wet conditions. Since 2000, these phenomena have led to four years of critically low yields: 2003, 2007, 2016, and 2024. Given the increasing likelihood of agroclimatic risks due to climate change, it is crucial to investigate the frequency and future occurrence of these risks—whether isolated or combined—for bread wheat production in France. This is particularly important in the context of global food security.
Characterising the exceedance probability of adverse climatic events represents a major challenge, especially in a context marked by a disruption in climate stationarity. Furthermore, given the crucial role of phenology in risk characterisation, it is essential to consider phenological shifts influenced by rising temperatures when analyzing these risks, rather than relying on fixed periods. To address these challenges, we propose developing risk indicators tailored to the sensitive phenological phases of wheat (ecoclimatic indicators). These indicators will be employed in generalised additive models (GAMLSS) to identify their exceedance probability across various French climatic zones (provisionally identified by k-means clustering method). The analysis will encompass three distinct emissions scenarios based on climate projections from 17 coupled GCM-RCM models.
These individual risk trajectories will help determine the predominant risks within each climatic region at various time horizons. Since yield losses often result from the combination of adverse events, a probabilistic analysis of combined risks will also be conducted. This analysis will leverage copula functions to model dependencies between climatic variables. The results of this study will provide critical insights into future high-risk production zones and enable the establishment of timelines to prioritize adaptation actions based on the dominant risks identified for each region. These findings will be essential for strengthening the agricultural sector's resilience to the growing impacts of climate change. They will also provide a strategic foundation to guide breeders in identifying priority improvement pathways, while adhering to temporal constraints.
How to cite: Aubry, M., Renard, B., Le roux, R., Launay, M., García de Cortázar-Atauri, I., and Furusho-Percot, C.: Sustainability of bread wheat in France: A GAMLSS based risk and return period assessment., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10880, https://doi.org/10.5194/egusphere-egu25-10880, 2025.
China contributes approximately 25% to global cotton production, however, over 90% of cotton in China is harvested in Xinjiang, a region increasingly impacted by heat events. Despite this, the extent to which cotton cultivation is exposed to heat events, particularly during critical growth stages, remains uncharacterized. To address this gap, we employed extreme degree days (EDD) and accumulated heat stress days (AHSD) to reflect heat events during the flowering and boll development stages. We analyzed the spatiotemporal patterns of such exposure over the historical period (1961–2020) and projections under two warming scenarios (1.5 °C and 2.0 °C) that derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results revealed a modest upward trend of heat events during critical growth stages of cotton, characterized by considerable interannual variability. Specifically, EDD and AHSD increased at 0.12 d·°C/yr and 0.12 days/yr, respectively. Despite notable spatial heterogeneity, regions such as Hami, Paotai, Yuli, and Mossel were identified as the most vulnerable, with EDD exceeding 25 d·°C and AHSD surpassing 9 days. Future projections suggest a substantial intensification of heat events, with EDD and AHSD values tripling and doubling under the 2.0 °C warming scenario. The findings highlight the critical importance of optimizing growth stage windows to reduce cotton’s exposure to heat stress. Targeted adaptive measures, such as adjusting planting windows and breeding new cultivars, are urgently needed to mitigate the adverse impacts of heat stress and ensure stable cotton yield.
How to cite: Shi, T., Zhang, W., Li, T., Wang, Z., and Liu, S.: Increasing cotton heat exposure during critical growth stages under projected warming in Xinjiang, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14330, https://doi.org/10.5194/egusphere-egu25-14330, 2025.
Posters virtual: Mon, 28 Apr, 14:00–15:45 | vPoster spot 5
EGU25-544 | ECS | Posters virtual | VPS5
Quantifying Climate Impact on Tomato Production in Central India: A Process-Based Yield Simulation for Near and Mid-Future ScenariosMon, 28 Apr, 14:00–15:45 (CEST) vPoster spot 5 | vP5.1
Tomato production is vital to Central India's agricultural output and plays a significant role in the region's economy. However, the escalating impacts of climate change pose a serious threat to the sustainability and productivity of tomato farming in this region. This study assesses the effects of variations in solar radiation and temperature on tomato yields utilizing a calibrated process-based crop simulation model (CSM). Climate forecasts utilizing SSP4.5 and SSP8.5 pathways were applied to model yields in near (2010-2039) and mid-future (2040-2069) scenarios. Significant findings indicate a large reduction in yield potential, particularly under mid-future high-emission scenarios (SSP8.5), accompanied by considerable geographical variability. Regions such as Damoh and Western Nimar demonstrate enhanced resilience owing to advantageous local climatic circumstances, whilst areas like the Kymore Plateau and Bundelkhand Agro-Climatic zone display the most significant decreases. Key developmental phases, including flowering and fruit set, are especially susceptible to elevated temperatures and diminished solar radiation. This research highlights the need for region-specific adaptation techniques to alleviate climate impacts, including modifying planting schedules and adopting heat-tolerant varieties. These insights offer a crucial basis for policymakers and farmers to guarantee the sustainability of tomato production in Central India under changing climate circumstances.
Keywords: Crop Simulation Model (CSM), Tomato Yields, GCMs, Central India, policymakers
How to cite: Singh, P. N. and Srivastava, P. K.: Quantifying Climate Impact on Tomato Production in Central India: A Process-Based Yield Simulation for Near and Mid-Future Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-544, https://doi.org/10.5194/egusphere-egu25-544, 2025.
Posters virtual: Thu, 1 May, 14:00–15:45 | vPoster spot 5
EGU25-3943 | ECS | Posters virtual | VPS6
Optimizing Corn and Soybean Yield Predictions in Illinois Using the AquaCrop ModelThu, 01 May, 14:00–15:45 (CEST) vPoster spot 5 | vP5.5
Crop yield is important for agricultural productivity and country’s economy. Accurate crop yield estimation is critical for policymakers, farmers, and governments because it allows better management techniques, decision making and the implementation of practicable agricultural policies. While crop yield estimation is an essential aspect of modern agriculture, it continues to be one of the most challenging tasks to manage effectively. In this study, we used the Food and Agriculture Organization (FAO) of the United Nations developed AquaCrop model to estimate the crop yields of corn and soybean crops in Illinois, United States (US). Data of various meteorological parameters as precipitation, maximum and minimum temperature, relative humidity, wind speed, solar radiation datasets were collected from NASA Prediction of Worldwide Energy Resources (POWER), for a period of 25-years from 2000 to 2024. Whereas, reference evapotranspiration was calculated by using the modified Hargreaves method. The objective of this study is to assess the accuracy of yield estimation of corn and soybean by using the AquaCrop model. The AquaCrop model was simulated for the growing period of corn and soybean from May to September. Using the AquaCrop model, the maximum and minimum corn yields were found to be 14.49 tons/ha in the year 2022 and 7.60 tons/ha in the year 2005, respectively. Similarly, the maximum yield of soybean was found to be 4.33 tons/ha in the year 2022, while the minimum yield was 2.26 tons/ha in the year 2012. The coefficient of determination (R2) values of 0.72 for maize and 0.76 for soybean, gives a satisfactory level of model accuracy. The model's performance can be improved further by incorporating more ground-truth data and appropriate parameters. This study demonstrates the AquaCrop model's ability to estimate crop production with few input parameters, as well as suggest opportunities for improvement. To improve prediction accuracy and promote informed agricultural planning and food security, future study might use sophisticated methodologies, localized farming practices, crop phenology, and specific soil data.
Keywords: AquaCrop, Crop yield, Illinois, Yield Predictions.
How to cite: Gautam, V. and Pathak, S.: Optimizing Corn and Soybean Yield Predictions in Illinois Using the AquaCrop Model , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3943, https://doi.org/10.5194/egusphere-egu25-3943, 2025.
EGU25-14951 | ECS | Posters virtual | VPS6
Assessing Climate Change Effects on Fruit Growing Conditions in the Northwestern Himalayan Region of IndiaThu, 01 May, 14:00–15:45 (CEST) vPoster spot 5 | vP5.6
The Himalayan region of India is experiencing warmer winters and hotter summers, which are causing reduced yields and putting the production of traditional fruit species in danger. In order to gain an understanding of the thermal growing conditions, it is essential to have chill and heat accumulation monitored. In the current investigation, the Dynamic model is utilized to compute the chill accumulation, while the Growing Degree Days (GDD) method is utilized to compute the heat accumulation. In order to calculate these indices, gridded hourly temperature data from the European Centre for Medium-Range Weather Forecasts (ERA)5 dataset was utilized. The time period covered by this dataset is from 1940 to 2023. The study's findings revealed the best elevation ranges for several of the region's most significant fruits, such as citrus fruits, almond trees, and fresh fruits. Furthermore, places with elevations ranging from 1000 to 2000 are good for growing fresh fruits. This is due to the fact that 70 percent of the Chilling Portion (CP) values are high enough to be greater than 60.
How to cite: Shukla, Y. and Gupta, V.: Assessing Climate Change Effects on Fruit Growing Conditions in the Northwestern Himalayan Region of India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14951, https://doi.org/10.5194/egusphere-egu25-14951, 2025.
EGU25-19591 | ECS | Posters virtual | VPS6
Downscaling Earth Observation Operational Soil Moisture Products Using multi-sensor Satellite Data: “A Triangle Inversion Approach"Thu, 01 May, 14:00–15:45 (CEST) vPoster spot 5 | vP5.7
Monitoring key parameters that drive land-surface processes, such as surface soil moisture (SSM)), is essential for understanding global biogeochemical cycles, including those of water, energy, and carbon. While Earth Observation (EO)-based SSM products have demonstrated significant potential, their practical application is often limited by coarse spatio-temporal resolution. Therefore, downscaling these operational products is a critical scientific challenge for enabling their effective use in regional and local-scale applications.
This study’s aims at presenting an innovative approach for downscaling operational soil moisture products using a variant of the so-called “triangle” method, named the “simplified” triangle. The use of the proposed technique is demonstrated herein using the European Space Agency's (ESA) operational soil moisture product from the Soil Moisture and Ocean Salinity (SMOS) and optical data from ESA’s Sentinel-3 platform. The enhanced spatial SSM estimates are compared against near collocated reference ground data from multiple validated experimental sites across Europe. The results obtained indicate a satisfactory agreement, confirming the proposed approach's promising potential to accurately estimate key land-surface interaction parameters. Conceptually the proposed herein methodological framework is applicable to any operational product, a topic of further investigation.
How to cite: Detsikas, S. E., Petropoulos, G. P., Saviolakis, P., Lekka, C., Karymbalis, E., Katsafados, P., and Georgaki, F.: Downscaling Earth Observation Operational Soil Moisture Products Using multi-sensor Satellite Data: “A Triangle Inversion Approach", EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19591, https://doi.org/10.5194/egusphere-egu25-19591, 2025.
EGU25-21175 | Posters virtual | VPS6
Advancing our understanding of land surface interactions via the development of innovative geoinformation toolsThu, 01 May, 14:00–15:45 (CEST) vPoster spot 5 | vP5.8
Advances in geo-information technologies, including Earth Observation (EO), GIS, cloud computing and software tool development, have shown great potential towards addressing key societal challenges faced today associated with the study of land-atmosphere interactions. Accurate information on spatially explicit, distributed estimates of land-atmosphere fluxes and soil surface moisture is essential in a wide range of disciplines, including meteorology, hydrology, agriculture and ecology.
Use of simulation process models has played a key role in extending our abilities to study Earth system processes and enhancing our understanding on how different components of it interplay. A special category of such models includes the so-called Soil Vegetation Atmosphere Transfer (SVAT) models. Those are deterministic simulation models that describe the physical processes controlling energy and mass transport in the soil/vegetation/atmosphere system.
SimSphere is such a software toolkit written in Java for simulating the interactions of soil, vegetation and atmosphere layers of the Earth’s land surface. Its use is at present continually expanding worldwide both as an educational and as a research tool for scientific investigations. It is being used either as a stand-alone application or synergistically with EO data and important advancements particularly in the recent years have been implemented to the model.
Herein, we present state of the art advancements introduced recently to SimSphere SVAT model aiming at making its use more robust when integrated with EO data via the so-called “triangle” method. Use of the recently developed add-on to SimSphere is illustrated herein using a variety of examples that involve both satellite and UAV data. The presented work is of key significance to the users' community of the model and very timely, given that variants of the so-called “triangle” method being considered for deriving operationally regional estimates of energy fluxes and soil moisture from EO data provided by non-commercial vendors.
KEYWORDS: land surface interactions, geoinformation, earth observation, triangle, SimSphere Acknowledgements The research presented herein has been conducted in the framework of the project LISTEN-EO (DeveLoping new awareness and Innovative toolS to support efficient waTer rEsources man- agement Exploiting geoinformatiOn technologies), funded by the Hellenic Foundation for Research and Innovation programme (ID 15898).
How to cite: Gkatzios, G., Petropoulos, G. P., Detsikas, S. E., Lekka, C., Karymbalis, E., and Katsafados, P.: Advancing our understanding of land surface interactions via the development of innovative geoinformation tools , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21175, https://doi.org/10.5194/egusphere-egu25-21175, 2025.
