Displays

Evapotranspiration is the transfer of water from the earth's surface to the atmosphere. It comprises the sum of water losses to atmosphere due to the processes of evaporation of moisture from soil, water bodies and wet plant canopies, and the transpiration of water from plants. Forecasts of this crucial component of the hydrologic cycle can be very valuable for growers, farm managers, irrigation practitioners, water resource planners and managers, and reservoir operators for their planning, allocation, delivery and scheduling decisions, as well as to hydrologic scientists for research purposes. Verifying the reliability of models’ forecasts is among the critical tasks for development and performance evaluation of physical models. In fact, the verification allows understanding the models’ behavior, and evaluating their applicability and dependability. The US National Weather Service (NWS) has released a product that provides forecasts of reference evapotranspiration (FRET) at 2.5-km grid resolution for the entire continental US. In this study, a comparison is made between ETo estimates from FRET and ETo values calculated by the California Irrigation Management Information System (CIMIS) for 68 days during summer 2019. Both the FRET forecasts and ETo values were obtained from NWS and CIMIS, respectively, on the basis of 15 CIMIS locations that are representative of different climatic conditions in California. In addition, air temperature, dew point temperature, relative humidity, wind speed, and vapor pressure deficit (VPD) data were also collected/calculated from the NWS and CIMIS websites to analyze the sensitivity of FRET forecasts to predictions of these parameters. All FRET forecasts were performed with timescales of 1, 3, 5 and 7 days. Statistical indices were calculated to assess the dependability of FRET values. They showed a good correlation of the FRET model outputs with CIMIS ETo data, with some differences depending on the climatic characteristics of selected weather stations’ locations, suggesting that FRET data could be valuable for anticipating near-future water demand and improve irrigation management in California.

How to cite: Ben Hamouda, G., Ventura, F., Zaccaria, D., M. Bali, K., and L. Snyder, R.: Comparison between forecasts of reference evapotranspiration and ETo values calculated using data from different climatic conditions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-372, https://doi.org/10.5194/egusphere-egu2020-372, 2020.

The aim of the ADAPTER project (www.adapter-projekt.de) of the Helmholtz Association of German Research Centres is to develop products and usable information that help improve agriculture’s resilience to extreme weather conditions and climate change in Germany. One of the main hydrometeorological impacts on agriculture is the soil water budget. Here, we use the Terrestrial Systems Modelling Platform (TSMP) in forecast mode forced by ECMWF forecast data over a domain covering most of North-Rhine Westfalia (NRW, Germany). TSMP is a fully-coupled regional Earth system model with COSMO at 1km spatial resolution as the atmospheric component, with the Community Land Model (CLM) for the land surface interface, and ParFlow for the surface and sub-surface part of the water cycle, both models at 500m resolution. This allows a representation of the closed water budget, including three-dimensional sub-surface and groundwater flow. Here, we demonstrate the usefulness of the fully coupled TSMP for agriculture applications, by focussing on two fundamental parameters of the soil water budget: First, in the context of the droughts that affected Europe, and particularly Germany, over the summers 2018 and 2019, one major parameter for estimating and monitoring the water stress of plants is the fraction of plant available water (fPAW). The pressure head simulated by ParFlow is used to calculate fPAW, on the basis of soil parameters like porosity and the Van Genuchten equation. fPAW is calculated over different soil depths from 0.1m to 3m, to provide information about the water stress of plants with different rooting depths. Our results show that the succession of extremely dry summers in 2018 and 2019, when the meteorological drought evolved into an agricultural and eventually into a hydrological drought, has led to very dry soils showing a fPAW below 30-50% over most of NRW, meaning that it became stressful for plants to extract water from the soil. This did not only affect the upper soil layers, as in 2018, but also deeper layers became very dry, thus no longer only impacting shallow root crops, but also plants with a higher root depth like trees. The wetter 2019 autumn allowed a recovery of the soil water content around the field capacity for the upper layers over a major part of the domain, while the deeper soil remains abnormally dry, especially in the south-western part of the domain. Second, knowing the amount of seepage water over a given period is not only important to monitor the groundwater recharge, which has become a major issue in the context of the past two summers, but also to estimate the leakage of nutrients and pollutants from the upper soil to deeper layers or even the groundwater in the context of certain environmental compliance issues. In accordance with the results obtained for fPAW, TSMP simulated seepage water flux during autumn 2019 only for the upper soil layers; this excessive water is only gradually percolating into deeper soil layers, which still remain clearly below the field capacity over a significant part of the domain.

How to cite: Belleflamme, A., Goergen, K., Wagner, N., Bathiany, S., Rechid, D., and Kollet, S.: Forecasts of plant available and seepage water for agricultural usage during recent extreme hydrometeorological conditions in western Germany using a convection-permitting regional Earth-system model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4704, https://doi.org/10.5194/egusphere-egu2020-4704, 2020.

The global concentration of reactive nitrogen (e.g. NH₃, NO_x and N₂O) has intensely increased since the pre-industrial era. Ammonia (NH₃) is one of the main sources of reactive nitrogen in the atmosphere and plays a crucial role in the formation of inorganic particulate matter, which harms health and deteriorates air quality. In addition to that, the wet/dry deposition of ammonia derivatives affects ecosystems through acidification and eutrophication of soil and water bodies; leading to a loss in biodiversity and intensification of the response to climate change. NH₃ is mainly emitted by biomass burning and agricultural activities. Agriculture contributes to air pollution and is affected by atmospheric composition, meteorology and climate change.

Several studies proved the efficiency of the IASI instrument aboard Metop satellites in measuring ammonia from space. For the last ten years, hotspots of ammonia point sources have been identified and categorized around the world.

In this poster, we explore the interaction of atmospheric ammonia with land, meteorological, and leaf conditions. We look at the temporal variability of ammonia in different regions of the world. The relationship land-ammonia volatilization is assessed by comparing the variability of surface soil moisture and the skin temperature products from the ECMWF latest reanalysis (ERA5) with IASI NH₃ total columns. The meteorology-ammonia relation is examined, by looking at air temperature, humidity, precipitation, planetary boundary layer height, and wind speed/direction. Agricultural seasons in studied regions are detected from space in matter of leaf area per ground area. The crop-ammonia relation is assessed by looking at the Leaf Area Index (LAI) products. The regions examined have been identified as point sources and/or hotspots of ammonia of agricultural and industrial sources (mainly fertilizer industry).

The result of this work will improve our understanding of biosphere-atmosphere interactions, in particular, the relationship between ammonia on the one hand and land, meteorology and crops on the other hand, in different regions in the world.

How to cite: Abeed, R., Safieddine, S., Clarisse, L., Van Damme, M., Coheur, P.-F., and Clerbaux, C.: Ammonia-biosphere interaction from IASI and ERA5, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8495, https://doi.org/10.5194/egusphere-egu2020-8495, 2020.

The crop growth model IVINE (Italian Vineyard Integrated Numerical model for Estimating physiological values) was developed at our Dept. of Physics in FORTRAN language as a research model in order to evaluate the environmental forcing effects on vine growth, being vines generally strongly sensitive to meteorological conditions, and with the idea of using it for assessing climate change effects on grape growth. IVINE requires a set of hourly meteorological and soil data as boundary conditions. Input data that are more relevant for the model to correctly simulate the plant growth are air temperature and soil moisture. Among the principal IVINE outputs, we mention: the main phenological stages (dormancy exit, bud-break, fruit set, veraison, and harvest), the Leaf Area Index, the yield, the berry sugar concentration and the predawn leaf water potential. IVINE model requires to set some experimental parameters depending on the cultivar; at present, IVINE is optimized for Nebbiolo and other northern Italy autocthonous and common varieties. In order to use the model for forecasting purposes, the set of input data required by IVINE must be retrieved by the simulation's outputs of a mesoscale model, in turn driven by a Global Circulation Model simulation. In our Department, a voluntary meteorological forecasting service has been working for several years; for this task four daily 5-days simulations are performed over Piedmont Italian region with WRF (Weather Research and Forecast) mesoscale model driven by the GFS (Global Forecast System). Taking advantage of these runs, we have organized a system able to extract, for each simulation, the hourly values of the parameters needed by IVINE. The input dataset is updated every six hours using the values coming by the new simulation, while considering past values acquired. Since IVINE simulation must start from the previous season, in order to correctly simulate the dormancy exit, we have carried out several simulations with IVINE by starting in the same date (January 1^st 2018) and ending at the fifth day of the last available WRF simulation. In this way, we were able to made a sort of temporal ensemble meteogram for the last five days; where the results of the most recent simulation were displayed with those of previuos runs and the number of simulations was gradually decreasing from 20 to 1 with the progress of the time.

The simulations were performed for the whole 2019 year over 156 WRF grid points distributed in the Langhe, Roero and Monferrato wine areas of Piedmont. Here some pheno-physiological variables in vineyards are analyzed, relative to some significant points and events, and the main results are discussed.

How to cite: Andreoli, V., Cassardo, C., and Manfrin, M.: Predicting vineyard's evolution with the crop model IVINE driven by meteorological model forecasts: preliminary results., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8573, https://doi.org/10.5194/egusphere-egu2020-8573, 2020.

Soil moisture is a key environmental factor for plant cultivation: too little and plant growth is restricted due to drought conditions; too much and soil becomes water-logged. It is important to understand how well climate models can represent current soil moisture processes as well as how soil moisture will respond to a changing climate, to inform adaptation of plant cultivation to future climate change. We explore current and future climate soil moisture conditions alongside water cycle processes such as evaporation and run-off in the latest UK Climate Projections (UKCP). Three model ensembles are available: UKCP Global, Regional and Local, with horizontal resolutions of 60km, 12km and 2.2km respectively. These each contain the Joint UK Land Environment Simulator (JULES) model as their land surface component. This suite of models offers the opportunity to understand the effects of parameter uncertainty and spatial resolution. Firstly, we assess the performance of the Global and Regional simulations by evaluating results from the baseline period (1981-2010) in terms of soil moisture (and the overall water balance) by comparing it to observations and to JULES driven by observations. Secondly, we assess how the water balance responds to a high future greenhouse gas concentration pathway. We find that soil moisture is likely to be lower in the summer and early autumn and spends a longer time below levels optimal for plant growth. The potential drivers of this change are explored, including future changes in precipitation and evaporation.

How to cite: Pirret, J., Fung, F., Mitchell, J. F. B., and McInnes, R.: Soil moisture and the water cycle in the UK Climate Projections , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8852, https://doi.org/10.5194/egusphere-egu2020-8852, 2020.

France is the fifth largest producer worldwide of soft wheat. Every year over 35 million tons of wheat are harvested (average 2011-2017, data from France AgriMer) on the territory. Hence, the cereal sector occupies an important place in the French agricultural economy.

However, because of its high dependence on the atmospheric conditions, wheat production is vulnerable to climate change. Since the mid-1990s, a stagnation of yield has already been observed. According to the agronomists, the main cause is climate change. Water deficit during the production and days of scalding during the filling of the grains. By 2050-2100, these extreme events are expected in the most likely scenario (i.e. warmer springs and summers). Hence, it is of importance to know if the shortening of the plant cycle resulted from the rise in the global temperature could prevent these extreme events from happening and if other related impacts could occur.

This study illustrates 2 agricultural plains containing open fields in the Normandie area, located in the north-west part of France. In this region, wheat locally occupies more than 50% of the agricultural land. These two areas are the plain of Caen which is under the influence of an oceanic climate and the plain of Evreux where the climate is slightly more continental.

The aim of this communication if to present what the climatic conditions for the soft wheat in 2050 and 2100 would be and to compare these projected periods with the ones of the reference period (1976-2005). The reported results were obtained by the means of a simulation of  the phenology to which is grafted the occurrence of climatic hazards such as water deficit, thermal exhaustion, frost days, vernalization, low temperatures and radiation deficit. Indeed, those hazards are able to generate consequences to the agricultural yield. The climatic data are extracted from ALADIN-Climate (data from CNRS-2014) in the case of three RCP scenarii of IPSS, available on the website of Drias Les futurs du climat.

In the context of pronounced climate change, along with unchanged sowing dates by 2050 and 2100, the increase in temperatures would lead to shorten the crop cycle, and hence to a date shift in the plant phenology. Consequently, there would be a shorter overlap between the end of the crop cycle and the summer period and, usually characterized by heat waves and water stress events which are expected to occur more often. Thus high temperature triggered scalding would not be observed as much as expected and the cumulated water limitation would be also lower. However, because of this precocity, emerging consequences might be expected regarding deleterious effects of lower temperatures during meiosis, and decrease of solar radiation at the onset of stem elongation. Mild winters would also reduce the days of vernalization, limiting cold requirements during tillering. This study demonstrates the use of bioclimatic models to unravel the crop phenology modifications, expected to occur by the end of the century, under the main environmental climatic drivers.

How to cite: Beauvais, F., Cantat, O., Madeline, P., Le Gouee, P., Brunel-Muguet, S., and Medjkane, M.: What will be the consequences of the climate change on soft wheat in Normandy (France) in 2050-2100 ? Prospective impact study based on ALADIN-Climate model , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9780, https://doi.org/10.5194/egusphere-egu2020-9780, 2020.

Historical weather data represent an extremely precious resource for agro-meteorology for studying evolutionary dynamics and for predictive purposes, to address agronomical and management choices, that have economic, social and environmental effect. The study of climatic variability and its consequences starts from the observation of variations over time and the identification of the causes, on the basis of historical series of meteorological observations. The availability of long-lasting, complete and accurate datasets is a fundamental requirement to predict and react to climate variability. Inter-annual climate changes deeply affect grapevine productive cycle determining direct impact on the onset and duration of phenological stages and, ultimately, on the grape harvest and yield. Indeed, climate variables, such as air temperature and precipitation, affect evapotranspiration rates, plant water requirements, and also the vine physiology. In this respect, the observed increase in the number of warm days poses a threat to grape quality as it creates a situation of imbalance at maturity, with respect to sugar content, acidity and phenolic and aromatic ripeness.

A study was conducted to investigate the relationships between climate variables and harvest onset dates to assess the responses of grapevine under a global warming scenario. The study was carried out in the “Monferrato” area, a rainfed hillslope vine-growing area of NW Italy. In particular, the onset dates of harvest of different local wine grape varieties grown in the Vezzolano Experimental Farm (CNR-IMAMOTER) and in surrounding vineyards (affiliated to the Terre dei Santi Cellars) were recorded from 1962 to 2019 and then related to historical series of climate data by means of regression analysis. The linear regression was performed based on the averages of maximum and minimum daily temperatures and sum of precipitation (1962–2019) calculated for growing and ripening season, together with a bioclimatic heat index for vineyards, the Huglin index. The climate data were obtained from two data series collected in the Experimental farm by a mechanical weather station (1962-2002) and a second series recorded (2002-2019) by an electro-mechanical station included in Piedmont Regional Agro-meteorological Network. Finally, a third long-term continuous series covering the period from 1962 to 2019, provided by Italian Meteorological Society was considered in the analysis.

The results of the study highlighted that inter-annual climate variability, with a general positive trend of temperature, significantly affects the ripening of grapes with a progressive anticipation of the harvest onset dates. In particular, all the considered variables excepted precipitation, resulted negatively correlated with the harvest onset date reaching a high level of significance (up to P< 0.001). Best results have been obtained for maximum temperature and Huglin index, especially by using the most complete dataset. The change ratios obtained using datasets including last 15 years were greater (in absolute terms) than results limited to the period 1962-2002, and also correlations have greater level of significance. The results indicated clearly the relationships between the temperature trend and the gradual anticipation of harvest and the importance of having long and continuous historical weather data series available.

How to cite: Rabino, D., Biddoccu, M., Bagagiolo, G., Nigrelli, G., Mercalli, L., Cat Berro, D., Spanna, F., Capello, G., and Cavallo, E.: Effects of inter-annual climate variability on grape harvest timing in rainfed hilly vineyards of Piedmont (NW Italy), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10563, https://doi.org/10.5194/egusphere-egu2020-10563, 2020.

The numerical crop growth model IVINE (Italian Vineyard Integrated Numerical model for Estimating physiological values) was originally developed at the dept. of Physics, Univ. of Torino, as a research model with the aim to simulate grapevine phenological and physiological processes. Since vines are generally strongly sensitive to meteorological conditions, the model should be able to evaluate the environmental forcing effects on vine growth and, eventually, on its production. IVINE model requires a set of hourly meteorological and soil data as boundary conditions; the more relevant input for the model to correctly simulate the plant growth are: air temperature and soil moisture. Among the principal IVINE outputs, we mention: the main philological stages (dormancy exit, bud-break, fruit set, veraison, and harvest), the leaf development, the yield, the berry sugar concentration, and the predawn leaf water potential. The IVINE requires to set some experimental parameters depending on the cultivar; at present, IVINE is optimized for Nebbiolo and other common varieties (such as, for example, cvs. Barbera, Vermentino, Cannonau, etc for Italy), but validation experiments have been performed only for Nebbiolo variety, due to the difficulty to gather all required measurements useful to drive the model and to compare its outputs for several consecutive years in the same vineyard. In the frame of the second part of the EU JPI-FACCE project named MACSUR (Modelling European Agriculture with Climate Change for Food Security), some data relative to vineyards displaced in several European countries were made available, thus we tried to execute simulations with IVINE in those vineyards. Since input data required by IVINE were not all present, we decided to extract input data from the international GLDAS database in the nearest grid point to the experimental vineyard, and to run the trusted land surface model UTOPIA on those points in order to evaluate soil variables required by IVINE. The main results obtained by those simulations, as well as the few possible validations with experimental observations, will be shown and commented. As a summary, we can say that the simulation carried out with IVINE seems able to well account for the interannual variability of the meteorological conditions, and the used settings seems able to allow a sufficiently valid simulation of the pheno-physiological conditions of the vineyards, but the approximation in the input data causes departures larger than if local measurements would be used.

How to cite: Cassardo, C., Andreoli, V., and Spanna, F.: Validation of IVINE crop growth model using MACSUR2 project measurements in a few European vineyards., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18259, https://doi.org/10.5194/egusphere-egu2020-18259, 2020.

Environmental stress factors have far‐reaching implications on global food security and significantly impact crop production through their effects on soil fertility, carbon sequestration, plant growth, and productivity.  Several approaches have been used to assess the effects of environmental stress factors on crops and to evaluate possible solutions. One such approach is the use of crop simulation models to explore the impact of climate stresses on crop plants will be discussed in this presentation, to provide a more accurate understanding of climate change effects on agriculture in the coming decades. Crop models, based on appropriate concepts and processes, have the predictive capability under new environments and can be used either alone or with other emerging newer technologies to disseminate plant growth and development information. Crop models such as GOSSYM, a cotton simulation model was used to evaluate crop responses to factors such as weather, irrigation, and fertilization by simulating the growth and production of crops from planting to harvest. The presentation also discusses the SPAR (Soil-Plant-Atmosphere Research) system to generate data required to understand various facets of growth and developmental processes and to build process-level models for managing the cotton crop to abiotic stresses. The SPAR units are optimized for the measurement of a plant and canopy-level physiological responses such as photosynthesis and transpiration under precisely controlled, but naturally lit, environmental conditions and to relate the basic processes directly to the environment. Various validation efforts of the GOSSYM cotton simulation model and its uses in multiple applications such as climate change impacts, technology transfer, hypothesis testing in research, farm management, and policymaking decisions will be discussed.

How to cite: Reddy, V. and Jyostna Devi, M.: Crop response to climate change: SPAR facilities, capabilities and tools, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22680, https://doi.org/10.5194/egusphere-egu2020-22680, 2020.