Water management in agricultural systems is essential for optimal crop yields without incurring excessive water costs and wastage. The choice of irrigation method is crucial for better water management and distribution. The drip system appears to be among the best methods in the field of precision agriculture. In addition to the irrigation system, mulching with ridge plastic film to drain excess water is widely used to increase crop yields in terms of plant water availability.

In this study, the time-lapse Electrical Resistivity Tomography (ERT), a not-invasive geophysical technique, is proposed as a simple and reliable method to evaluate the effectiveness of the irrigation systems and to monitor the changes in water content over time and over a volume of soil. ERTs data were compared to moisture one retrieved from sensors that record continuously over time, but punctually. The ERT investigations were conducted in melon-growing lands in southern Tuscany (Italy).

The aim of the work was to evaluate, by means of volumetric measures of the soil conductivity, the effectiveness of three different drip systems and of the mulch ridge: a two-wings drip system and a three-wings drip line with the same flow rate and a three-wings drip lines with a higher flow, in two different seasonal periods (spring and summer). In both the monitored fields the ridge was created in a half portion of the field itself, while in the other part the land was left plat.

The data collected showed that the 2-wing system was particularly ineffective, and that the distribution of irrigation water favoured some areas more than others. While they led to satisfactory results for the 3-wing system and same water flow than two wings and the 3-wing system and highest water flow. The first system has shown that the same quantity of water as the classic irrigations system (two wings) distributed over three wings instead of two leads to a greater concentration of water in the root zone over time, slowly draining downwards. On the contrary, the second system distributes the water uniformly like the first system, but the quantity introduced was excessive, leading the soil to always be positioned above the field capacity and draining a lot of water downwards. The excessive accumulation of water below the root zone represents a waste of water, as this cannot be used by the root system. The tests, in addition to considering which system was optimal, also evaluated the effectiveness of the mulch ridge, leading to the deduction that during the spring season a ridge of height equal to or greater than 20 cm is to be considered better than a ridge of less than 20 cm or absent, as it allows excess water, represented by rainfall, to be drained. However, during the summer period, when rainfall is less if not absent, the presence of a much lower ridge (around 10 cm in height) is much more effective as it allows the irrigation water to be retained at the root system avoiding excessive drainage.

How to cite: Innocenti, A., Pazzi, V., Napoli, M., Ciampalini, R., Orlandini, S., and Fanti, R.: Electrical Resistivity Tomography (ERT) to monitor the efficiency of different irrigation systems in horticulture field, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10617, https://doi.org/10.5194/egusphere-egu24-10617, 2024.

Accurate soil moisture (SM) monitoring is key in climate modeling, hydrological observations and irrigation as it can greatly improve water use efficiency, the understanding of energy transfer over the land surface and ground water dynamics. Recently, Cosmic-Ray Neutron Sensors (CRNS) have been recognized as a promising tool in SM monitoring due to their large footprint of several hectares and half a meter in depth. Using this technique one can relate the flux density of albedo neutrons generated in cosmic-ray induced air showers to the amount of water in the environment. CRNS have great potential as to the non-invasive nature of the method and the low-maintenance independently operating sensors. In the last years this type of sensor has been integrated into several national and international monitoring networks like COSMOS, COSMOS-UK, ADAPTER and TERENO sites. Initially, CRNS instruments have relied on the use of a scarce material - helium-3. In order to scale up the method and to reduce costs within the CosmicSense research group recently large-scale instruments have been developed using alternative technologies including readout electronics and data acquisition systems. With a more economical operation the initial focus on hydrological research Cosmic-Ray Neutron Sensors are emerging into applied agricultural contexts, for example irrigation management and soil moisture mapping. Examples are the integration of CRNS into the SWAMP (LoRa) or the Nb-IoT network of the German Chamber of Agriculture. This project, called ADAPTER, involves the development and provision of innovative simulation-based information products for weather- and climate-resilient agriculture. These are daily (”soil”) weather and comprehensive long-term climate change information available to the agricultural community and all interested parties as easy-to-use analyses, data products with forecasts, and information interfaces. Still, challenges for CRNS are posed for scenarios especially for irrigated fields with a size smaller than the CRNS footprint or heterogeneous conditions with respect to the biomass distribution.

How to cite: Köhli, M., Weimar, J., Ney, P., Nieberding, F., Stowell, P., Torre Netto, A., Goergen, K., Bogena, H., and Schmidt, U.: Use of Cosmic-ray Neutron Sensing for soil water management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12808, https://doi.org/10.5194/egusphere-egu24-12808, 2024.

Continuous monitoring of soil moisture (SM) has become a prevalent approach in precision irrigation control. Fluctuations in SM within the root zone, whether caused by overly wet or dry conditions, can potentially diminish plant transpiration, leading to decreased productivity. Hence, ensuring a timely and appropriate supply of water is essential for effective irrigation management. Though various machine and deep learning models, along with in-situ climate data, have been examined for monitoring SM, the incorporation of gridded historical and forecast climate data into this aspect has not been explored. In this research, we assess forecasting SM by Random Forest (RF) model for the next 7 days using two approaches: A) relying on forecasted data for each day, and B) relying solely on historical data. To this end, the gridded climate data (air temperature, relative humidity, wind speed, precipitation, and reference evapotranspiration-ET₀), the soil features (lagged in-situ SM and gridded soil temperature), and vegetation features (Normalized Difference Vegetation Index-NDVI) for different land covers in Oulu, Finland. The findings suggest that using gridded data could be a promising option in places where there is limited data for the SM forecasting. The lagged SM was the most explaining variable, followed by soil temperature, NDVI, and ET₀. Furthermore, both scenarios exhibited similar trends, showing a decline in forecasting accuracy as the lead time approached 7 days, and thus scenario B can provide more efficient SM forecasts.

How to cite: Saboori, M., Jalali Shahrood, A., Ghag, K., and Klöve, B.: Soil moisture forecast based on gridded historical and forecast datasets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16802, https://doi.org/10.5194/egusphere-egu24-16802, 2024.

Eco-hydrological modeling in water resources management has a pivotal role in the assessment of physical processes at various spatial-temporal scales. However, modeling the hydrological processes intrinsically contains uncertainties. Such uncertainties need to be addressed to develop a reliable hydrological model. In this study, in-situ and remotely sensed soil moisture data are used to enhance the precision of hydrological modeling using the Soil and Water Assessment Tool (SWAT). The objectives of this study are, (i) to assess the uncertainty and their propagation in hydrological modeling using the conventional and multi-source data set, and (ii) to simulate the hydrologic parameters using soil moisture as an indicator to evaluate uncertainties in hydrological forecasting. This study is carried out in the Temmesjoki basin of northern Finland with a basin area of 1190 km². This region’s land cover is dominated by forest (61%), agricultural lands (18%), and shrubs (13%). The average annual rainfall and annual average temperature in this region are 406.21 mm, and 2.60°C respectively. The mean daily discharge ranges from 0.17 to 34.15 m³/s. The in-situ soil moisture data and Soil Water Index from the Copernicus Global Land Service are used to test the hypotheses. The Sequential Fitting Algorithm (SUFI-2) in R-SWAT was used for sensitivity and uncertainty analysis and calibration of the streamflow and ET. Two conceptual models are built to compare conventional data sources and multi-source data sets for the assessment of uncertainties in the simulation of the hydrological process. Preliminary analysis of hydrologic parameters of the basin reveals higher and non-uniform distribution of rainfall, ET, and discharge during summer months. Furthermore, the application of soil moisture data for the calibration of the SWAT model reveals higher fitness score, and, at the same time, the in-situ soil moisture data are found to reflect more accurately the soil moisture conditions in SWAT model, which results in the reduction of uncertainties. Consequently, the model conceptualized with the multi-source data sets provides a better water budget for the catchment. 

How to cite: Panchanathan, A., Ghag, K., Ahrari, A. H., Klöve, B., and Oussalah, M.: Harnessing Soil Moisture Data for Enhanced Eco-Hydrological Modeling Precision in Snow-Dominated Catchment in Finland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5764, https://doi.org/10.5194/egusphere-egu24-5764, 2024.

In the domain of hydrological modeling, accurately determining initial conditions such as soil moisture content is crucial for enhancing simulation efficiency and applying these models effectively in water resource management, flood prediction, and drought forecasting. Traditional methods often rely on a data-intensive warm-up phase to establish these conditions, which diverts valuable data from calibration and validation. Addressing this challenge, our study introduces an innovative methodology that utilizes an alternative global soil moisture dataset to optimize these initial conditions without the conventional warm-up phase, thereby aiming to improve both the accuracy and efficiency of hydrological simulations. We focused on the Block-wise use of the TOPMODEL (BTOP) and ERA5-Land reanalysis data, specifically analyzing three soil moisture variables within the Fuji and Shinano River Basin, Japan. Through a comprehensive correlation analysis, we examined the dynamics between these variables and employed a range of curve-fitting functions alongside advanced techniques, particularly Long Short-Term Memory (LSTM) networks, to establish a robust relationship between BTOP and ERA5-Land soil moisture variables. The LSTM, known for their effectiveness in handling complex time series data, were instrumental in capturing the intricate spatial and temporal correlations between the variables. To validate the efficacy of our proposed methodology, we conducted four hydrological simulation scenarios, meticulously designed to assess the benefits of incorporating ERA5-Land soil moisture data into the model's initial conditions. The results were compelling: simulations using the enhanced initial conditions significantly outperformed those without the warm-up phase and closely approximated the 'optimal' scenario typically reliant on extensive warm-up data. This study not only underscores the potential of using reanalysis soil moisture data to refine initial conditions, thereby revolutionizing water resource management and forecasting practices, but also presents a scalable solution that can be adapted to various hydrological models and scenarios. Consequently, our research contributes significantly to the ongoing discourse on improving environmental modeling and management practices, advocating for more precise, resource-efficient, and adaptable methodologies in hydrological modeling.

How to cite: Zhou, L. and Liu, L.: Enhancing Hydrological Simulation Efficiency by Improving Initial Soil Moisture Conditions through Reanalysis Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5712, https://doi.org/10.5194/egusphere-egu24-5712, 2024.

How to cite: Dichio, E., Bonzi, L., Rallo, G., Puig-Sirera, A., Remorini, D., Di Biase, R., Mininni, A. N., and Massai, R.: Irrigatmo: no-moving parts system for feed-back and feed-forward irrigation scheduling , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1840, https://doi.org/10.5194/egusphere-egu24-1840, 2024.

Surface irrigation is often described as low performing insofar as its practice is labour intensive and involves the use of large water flows that are difficult to quantify and manage. However, this method remains predominant worldwide, and modernisation towards localised irrigation systems is not always feasible or advisable. To support border irrigation management, we previously developed a low-cost sensor for surface irrigation management, which remotely informs the farmer of water arrival downstream of his or her field and therefore of the moment to stop irrigation. The objectives of this study were: i) to determine the optimal position of this sensor lengthwise in the field throughout the season, and ii) to compare the influence of management scenarios (sensor-based or time-based cutoff) on irrigation performance. To this end, an integrated agro-hydraulic model was developed to simulate surface water flow dynamics throughout the season including variations in infiltration and roughness. The model was fed using monitoring data from the border irrigation of a hay field during a whole season. The results showed that the optimal sensor position can change by 10% over the course of the season, depending on inflow rates, initial soil moisture and Manning’s roughness. Sensor-based irrigation control was found to be more efficient than actual practices, and more effective than an optimised cutoff time in limiting performance gaps induced by variability or uncertainty in the initial conditions. The methods and findings should serve as a basis for larger-scale studies integrating the adoption of sensors and real-time data for surface irrigation management.

How to cite: Vandôme, P., Berkaoui, A., Guillemin, C., and Leauthaud, C.: Revisiting border irrigation management: benefits of new in-field sensor-based control compared to conventional cutoff times, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16164, https://doi.org/10.5194/egusphere-egu24-16164, 2024.

In the Mediterranean region, agricultural water use accounts for a large share of the water demand and is key for food security and socio-economic stability in rural areas. At the same time, both managing irrigation in farms and managing water distribution to farms are not trivial tasks, since the water requirements by crops are site-specific and vary in time because of weather, agronomic management and other factors. In this context, the availability of EO data opens the opportunity to develop tools for the supervision, management and forecast of irrigation, scalable from farms to districts and basins. Time series of observed biophysical parameters of the vegetation and estimates of actual crop evapotranspiration (ETa) are promising resources for these applications. Those data can be assimilated into digital twins that integrate observations from different sources with models of crop development and soil water balance, enabling assessments of irrigation performance and management decision making. Here we describe a decision-making approach for irrigation district managers that assimilates EO data and simulates the water balance parameters of the soil-crop system at each individual plot. The goal is to obtain a dynamic view of irrigation performance scaling from individual plots to the basin, quantifying at real time the progress of crop growth and seasonal water balance, including forecasts of the forthcoming crop water demands under different meteorological scenarios. This approach has been implemented in the Catalan side of the Ebro basin (Spain), on an area of 2600 km² covering 105 municipalities. A separate digital twin was defined for each of over 130000 agricultural plots listed in the Land Parcel Identification System. For each plot, the agricultural scenario was set according to open data of EU CAP’s Single Farm Payment and a soil map of the area. This included the list of crops declared from 2015 to 2022, the irrigation system and the soil class. From these basic categoric data, more detailed parameters of the crop, soil and irrigation method were assigned according to the description of actual agricultural scenarios on the area. The development of the crop and its soil water balance at each individual plot is simulated at real time, using a customized model based in a rationale similar to FAO’s AquaCrop, but with additional adaptations to permanent crops, localized irrigation and discontinuous canopies. Simulations are updated every day, using online weather data from the Meteorological Service of Catalonia. In parallel, as soon as new Sentinel-2 images are available, fAPAR and LAI are computed through the Biophysical Processor available in the SNAP software and these parameters are assimilated in the model. The output are maps and time series with the estimated ETa, irrigation amounts and available soil water at each plot, accessible at www.irrilleida.cat. Time series cover the whole year, on a week basis, including the forecasts of crop water demands for the remaining part of the year.

How to cite: Casadesús, J., Pàmies, M., and Bellvert, J.: IrriLand, a digital twin assimilating biophysical parameters of vegetation to assess and forecast site-specific crop water requirements at irrigation district scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11546, https://doi.org/10.5194/egusphere-egu24-11546, 2024.

Accurate estimations of actual crop evapotranspiration are essential to evaluate crop water requirements, to improve water use efficiency in agriculture, and to optimize the use of available freshwater resources. To this aim, several models were developed to allow quantifying crop water requirements based on the knowledge of actual crop evapotranspiration rates, ET_a.

The objective of this research was to estimate ET_a using a simplified distributed model combining ground and remotely sensed data.

The experiment was carried out in a Mediterranean commercial citrus orchard (C. reticulata cv. Tardivo di Ciaculli) located in the Northwest of Sicily, Italy, during the whole 2019. The experimental layout consisted of: i) a WatchDog 2000 standard weather station (measuring the main climate variables and the precipitation depths, P); ii) a database of irrigation volumes, I, scheduled by the farmer; iii) an Eddy Covariance tower equipped with an open patch gas-analyzer, a three-dimension sonic anemometer, a four-component net radiometer, and a soil heat flux plate iv) a dataset of 75 Sentinel-2 multispectral images, acquired in clear sky condition.

In particular, the daily crop reference evapotranspiration, ETo, was calculated according to the FAO-56 Penman-Montheith equation using the climate variables; the crop coefficient, K_c, the Fractional Vegetation Cover, FVC, and, thus, the potential evapotranspiration, ET_p, were computed via the processing of reflectance values in the RED, NIR and SWIR spectral bands. The Available Water, AW, the short-term water stress factor, Cws, and the ET_a, were computed by analyzing cumulated ET_p and water-supplying values using moving temporal windows characterized by different sizes (from 5 to 400 days).

The validation of the model outputs was carried out by taking into account the ET_a of the pixels within the flux tower footprints estimated at each satellite acquisition day (i.e. by selecting the pixels on the basis of the footprint shape and extension). The performance of the model was evaluated for each temporal window size using the following metrics: the Root Mean Square Error, RMSE, the Mean Absolute Error, MAE, the angular coefficient of the regression line forced to the origin, b, and the determination coefficient, R².

Results suggest that the best temporal window size for this crop is around 85 days allowing to achieve an RMSE of 0.51 mm d^-1, a MAE of 0.38 mm d^-1, a b value of 0.94 and an R² of 0.96. The comparison with the model outputs over the whole field (all the pixels within the crop field) revealed that a strong decrease in all the metrics occurs if the validation of the remote sensing products is not properly carried out.

How to cite: De Caro, D., Ippolito, M., Capodici, F., and Ciraolo, G.: Testing the performance of a simplified distributed model to assess actual evapotranspiration in a Mediterranean orchard using ground and remotely sensed data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-895, https://doi.org/10.5194/egusphere-egu24-895, 2024.

Land-surface models (LSM) that simulate agricultural systems can provide key support for decision makers in precision irrigation and in the management of water resources under different climate scenarios. An accurate representation of irrigation in LSM is also crucial to understand how irrigation practices influence land-atmosphere processes from regional to global scale. Irrigation practices are increasingly integrated into LSM. However, challenges such as lack of data for model development and validation undermine the possibility to evolve current LSM into precision irrigation applications as well as into decision-making tools at the catchment scale and beyond.

In this study, we used the Community Land Model version 5 (CLM5) and assessed the representation of irrigation practices and consequent effect on crop yield in the model using a) the existing irrigation scheme of CLM5 and b) a novel irrigation data stream that allows to directly use observed irrigation data. Additionally, we used CLM5 to investigate irrigation requirements as well as the effect of deficit irrigation on crop yield and crop water use efficiency (CWUE) at the catchment scale (~45 km²). Model validation was supported by two highly instrumented apple orchards located in Agia (Greece) within the Pinios Hydrologic Observatory (PHO). From 2020, an ATMOS41 all-in-one climate station for monitoring meteorological data and a SoilNet sensor network for measuring soil moisture and matrix potential at various depths across 12 locations with SMT100 and TEROS21 sensors were used in both orchards. Additionally, a System SP cosmic-ray neutron sensor (CRNS) was installed in the centre of each field to monitor the field-averaged soil moisture, and several water meters were used to monitor irrigation rates in the orchards. Finally, one field was equipped with six SFM-1 sapflow sensors to estimate whole-tree transpiration and with six SnapShot Cloud 4G remote outdoor cameras.

We found that the novel irrigation data stream outperformed the existing scheme in terms of soil moisture simulation, even when the latter was manually adjusted to better mimic actual irrigation practices. However, both methods resulted in similar harvest predictions. Nonetheless, the fact that the existing scheme lacks the necessary flexibility to represent specific irrigation practices can have important implications for the simulation of infiltration, runoff, and sensible and latent heat fluxes. Furthermore, a 25 % irrigation reduction had negligible effect on simulated yield and CWUE at the catchment scale, while a 50 % reduction negatively affected both yield and CWUE depending on climatic conditions, soil properties, and irrigation timing (on average -30 % and -17 %, respectively). Although further process representations, such as the potential impact of deficit irrigation on crop quality, have yet to be implemented in CLM5, our results clearly show how CLM5 could be utilized for irrigation and water resources management at the field and catchment scales.

How to cite: Brogi, C., Dombrowski, O., Bogena, H. R., Hendricks-Franssen, H.-J., Swenson, S., Pisinaras, V., and Panagopoulos, A.: Novel assessment and development of land surface modelling for irrigation schemes in Mediterranean apple orchards, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17532, https://doi.org/10.5194/egusphere-egu24-17532, 2024.

Recent droughts and heatwaves have shown major impacts on the agricultural sector by inhibiting crop growth resulting in reduced crop yield. With an expected increase in the frequency and severity of droughts and heat waves due to climate change, accurate projections of crop yields under these hydroclimatic extremes are required. However, there is only limited knowledge on the accuracy of crop growth models under extreme events such as droughts and heatwaves. Understanding the accuracy of crop models under hydroclimatic extremes is a necessary first step to evaluate the significance of projections of crop yields under climate change.

To this end, our study addresses this gap by quantitatively evaluating three crop growth models— WOFOST, PCRGLOBWB2-WOFOST, and AquaCrop— in terms of their ability to simulate crop yield and hydrological fluxes under drought and heatwave conditions. The evaluation focuses on conditions of hydrological stress induced by droughts and heatwaves in the contiguous United States (CONUS) during the period 1981 to 2019. Our methodological framework utilises harmonised input data in terms of consistent climate forcing, cropping calendars and crop areas, to ensure a standardised comparison. Both rainfed and irrigated crops of three crop growth models are compared for the most abundant crop types (i.e. maize, wheat and soybean). 

The multiple output variables of these models are compared with reported data and satellite observations, most notably crop yield (reported on a county basis), irrigation water withdrawal (reported for a number of states) and leaf area index and evapotranspiration (from satellite observations). Additionally, we compare crop water requirements between the models. These methodological steps aim to discern structural differences among the models and identify key factors influencing performance variations, ensuring a thorough and rigorous evaluation. The findings and insights from this evaluation will advance our understanding of the intricate relationship between hydrological stress, crop growth, and sustainable agricultural practices under droughts and heatwaves.

How to cite: Chevuru, S., van Beek, L. P. H. (., van Vliet, M. T. H., Lamsal, G., Marston, L., and Bierkens, M. F. P.: Assessing crop growth model accuracy under droughts and heatwaves, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2920, https://doi.org/10.5194/egusphere-egu24-2920, 2024.

Water is a natural resource that is in shortage in many areas of the planet. This fact will be exacerbated in the context of the climate crisis. Agriculture is the major consumer of water in Greece but at the same time an important polluter of the environment (sea intrusion problem, pollution of aquifers with fertilizers, herbicides, pesticides). these conditions, the need to reduce water consumption and use it more efficiently is imperative, aiming at sustainable water management. Today there is technology available that allows the use of satellite images and the application of an energy balance at crop and ground level to estimate actual evapotranspiration. This method, to give values, close to reality, must be calibrated using ground data. For this reason, cotton, and maize fields in Thessaly (Central Greece) were systematically monitored for soil moisture and final yield. These water consuming plants are widely cultivated in the Thessalian plain even though the area has a negative water balance. The data collected from the monitoring together with the simulation with the AquaCrop model led to the estimation of the actual evapotranspiration. The model results are considered to correspond to real evapotranspiration since water balance application conditions were favourable (runoff and deep percolation had small or zero values). As a resiult, using the estimation of ETA in the plot we were led to improve the satellite estimation of evapotranspiration.

Key words: Evapotranspiration, satellite images, monitoring, AquaCrop

How to cite: Dercas, N., Tziatzios, G., Faraslis, I., Dalezios, N., Alpanakis, N., Spiliotopoulos, M., Sakellariou, S., Sidiropoulos, P., and Brissimis, V.: Improving satellite estimation of actual evapotranspiration using field monitoring and crop simulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20601, https://doi.org/10.5194/egusphere-egu24-20601, 2024.

The newest Earth Observation optical sensors, such as Sentinel-2, provide global biophysical products and vegetation indices at high spatial (decametric or twentimetric resolution) and temporal resolution (about 5 days retrieval). These biophysical parameters are essential for constant crop status monitoring at local scale. Optimizing the water use for irrigation, the weed mapping, quantifying ground above biomass and crop yield production, are some of the benefits of biophysical parameters in agriculture. This research investigates the crop status during the 2021’s growing season in Thessaly agricultural area in Greece. Thus, in maize, biophysical variables, and vegetation indices, that is, Leaf Area Index (LAI), fraction of absorbed photosynthetically active Radiation (FAPAR), Fraction of Vegetation Cover (FVC), Leaf Chlorophyll content (Cab), Canopy Water Content (CWC), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRedEdge) are retrieved. The PROSAIL radiative transfer model by artificial neural network approach is employed (available of the free SNAP^® software) to retrieve the biophysical parameters from Sentinel-2 multispectral imagery. The monitoring of the abovementioned biophysical variables during the growth period of maize crop shows a uniform behavior. Finally, high consistency among vegetation parameters confirms the usefulness of Sentinel-2 products in agriculture.

Keywords: Biophysical indices; phenological stages; monitoring maize crop; Mediterranean agroecosystems

How to cite: Dalezios, N., Faraslis, I., Alpanakis, N., Tziatzios, G., Spiliotopoulos, M., Sakellariou, S., Sidiropoulos, P., Dercas, N., and Brissimis, V.: Monitoring crop phenology applying biophysical indices from Sentinel-2 data: the case of Thessaly region in Greece, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20652, https://doi.org/10.5194/egusphere-egu24-20652, 2024.

In the face of climate change, Mediterranean regions, such as the South of France, are increasingly struggling with drought, water scarcity, and low groundwater levels. For agricultural regions relying on irrigation systems to guarantee summertime crop productivity, this is a central issue. Consequently, optimizing agricultural water uses and understanding the impact of irrigation systems on local and regional hydrological processes is indispensable. At larger scales, another challenge is to identify crop types as well as cropping and irrigation patterns for irrigation water management, reservoir operation, and real-time resource allocation. In this context, remote sensing provides a promising approach.  

This study focuses on combining land use - land cover (LULC) analyses based on Sentinel-1 and -2 data and in-situ measurements realized using innovative low-cost sensors, to characterize irrigation water use in two Southern French case study areas. The first of these, the Crau area in Provence, is specialized in using gravity irrigation to make the production of high-quality hay possible even during the arid summer months. The second area is a viticultural one, centred around the Canal de Gignac approximately 100 km further West, in which the majority of vines are sustained using drip irrigation, provided consistent water access is possible. In both cases, the study aimed first to identify irrigated plots, and then to further characterize the irrigation practices with regard to agricultural water use efficiency. 

The LULC analysis was carried out in Google Earth Engine, using a Gradient Tree Boosting (GTB) algorithm on combined Sentinel-1 and -2 imagery from which several spectral indices as well as Haralick texture features were calculated. The detection of irrigated grassland plots further relied on a temporal characterization of phenological stages. Subsequently, a comparative implementation of different irrigation monitoring approaches was carried out, using soil moisture estimates derived from Sentinel-1 and different optical spectral indices. Data from low-cost sensors and local water user associations was used for calibration and validation. 

Preliminary results indicate that combining these diverse approaches make an operational detection and monitoring of irrigation practices possible. For the detection of irrigated vineyard and grassland plots during the 2023 growing season, overall accuracies of 92% and 95% respectively were achieved. The comparison of different irrigation monitoring approaches showed that the Normalized Difference Moisture Index (NDMI, p=0.002), the Shortwave Infrared Water Stress Index (SIWSI, p=0.001) and the Specific Leaf Area Vegetation Index (SLAVI, p=0.001) showed the highest potential for accurate irrigation detection.

How to cite: Orieschnig, C. A. and Vandôme, P.: Combining Remote Sensing and Low-Cost Sensors for LULC and Irrigation Characterization in the South of France , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9299, https://doi.org/10.5194/egusphere-egu24-9299, 2024.

Integrated hydrologic models are useful for assessing the impact of climate change on water resources and associated risks. The performance of these models strongly depends on the quality of precipitation forcing data, where errors can significantly affect the simulation accuracy. Therefore, methods such as data assimilation (DA) bias adjustments, and data-driven (e.g., deep learning, DL) methods are in use to improve precipitation simulation data. However, given the high spatiotemporal variability of hourly precipitation, challenges such as availability of “ground truth” measurements, data imbalance, and evaluation of the methods affect the applicability and assessment of these methods. In this study, we correct precipitation data for the first 24h obtained from the  ECMWF HRES 10-day deterministic forecast using EUMETSAT H-SAF h61 satellite observations, by learning the errors using a U-Net convolutional neural network (CNN) as a DL technique. Our findings show good agreement between the corrected precipitation data (HRES-C) and the reference data (H-SAF) with roughly about 49%, 33%, and 12% improvement in mean error, root mean square error, and Pearson correlation, respectively. Additionally, we investigate the impact of original HRES, H-SAF, and HRES-C corrected products used as forcing data in high-resolution (~0.6km) integrated hydrologic simulations using ParFlow/CLM over central Europe in daily and monthly scales from April 2020 to December 2022. We choose soil moisture (SM) as a diagnostic variable for our evaluation. SM simulations produced with uncorrected HRES 24h show a better agreement with ESA CCI SM satellite data compared to SM produced with HRES-C. Further comparison of the three products with in-situ rain gauge measurements over the same period shows superiority of HRES 24h in representing the “ground truth” precipitation.  Our study highlights the need for better precipitation reference data, challenging reliance only on satellite observations (H-SAF) for DL-based correction of precipitation forcing data in hydrological simulations.

How to cite: Patakchi Yousefi, K., Belleflamme, A., Goergen, K., and Kollet, S.: DL-Driven Precipitation Correction for Enhanced Hydrological Simulations over Central Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11875, https://doi.org/10.5194/egusphere-egu24-11875, 2024.

Water scarcity is arguably a pressing issue for the 21^st century in Mediterranean areas, due to limited water resources, expansion of irrigated area to sustain food security and climate change. Water extraction for agriculture sector account about to 70% of global water use, and this demand peaks to 80% of total water withdrawal in several southern Mediterranean countries. In this study, the impact of climate change on evapotranspiration demand, crop water requirements, and crop yield losses due to water shortage, were assessed by using the Simulation of Evapotranspiration of Applied Water (SIMETAW_GIS) model. This crop-soil-water model was implemented over the Sardinia island, a region with a typical Mediterranean climate and agriculture characteristics, assuming impact of climate change for a whole range of relevant Mediterranean crops (Wheat, Barley, Sugar beet, Potato, Lentil, Almond, Maize, Wine Grape, Table Grape, Tomato, Rice, Artichoke, Alfalfa, Olives, Improved Pasture and Orange). Under present analysis, daily climate data from five Earth System Models dynamically downscaled to a spatial resolution of 0.11-degrees (~11 km) from the  EURO-CORDEX project domain and available from the Copernicus Climate Data service (https://climate.copernicus.eu/) were retrieved and ensembled. The impact of climate change on crop water requirements was evaluated under historical (1976-2005) and future (2036-2065) climate conditions following different Representative Concentration Pathways (RCPs: 2.6, 4.5 and 8.5), representing alternative mitigation policies and future emission scenarios.

In the Sardinia region, results show a variegated increase of crop water demand between future (2036-2065) and historical conditions (1976-2005) for different crops, which may pose a challenge for water resource management, especially considering water use conflicts among different sectors. On average wheat and barley will foresee the most significant increase of crop water requirements, ranging on average by 12 to 14% under different RCPs. Other crops (e.g. almond, maize, wine grape, and pasture) are projected to foresee still significant increases of crop water demand, varying between 4-8%.  This work provides information that can support farmers and decision managers to evaluate climate change adaptation strategies linked to different cropping patterns to increase use efficiency of water resources for a more sustainable agriculture production under climate change.

How to cite: Aslam, M. F., Masia, S., Spano, D., Mereu, V., Debolini, M., Snyder, R. L., Borgo, A., and Trabucco, A.: Modeling crop water demand to support adaptation strategies in Mediterranean environment under climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20336, https://doi.org/10.5194/egusphere-egu24-20336, 2024.

Groundwater is a reliable and important source for irrigated agriculture but its use has consequences. In wetter regions, overuse of groundwater can threaten the health of streams that depend on discharge from the groundwater system. In drier regions or when groundwater withdrawals exceed the available groundwater recharge for a long time, groundwater resources will be depleted, and groundwater levels drop. The result is that farmers must extract water from increasingly deeper groundwater wells and incur greater costs for well construction and for the energy required to lift the water to the surface. Ultimately, a farmer can reach the economic limit for groundwater use when the cost of pumping water is larger than the revenue that can be generated with the crop. Farmers should consider this economic limit and adapt their cropping and production methods to safeguard economically sustainable production in the future.

In order to evaluate possible adaptation strategies to avoid or postpone reaching the economic limit we developed a cost-benefit model at the local -farmer’s- level called HELGA (Hydro-Economic Limits as a Global Analysis) balancing the investment costs to deepen the well in the short term against the net present value of added profits from groundwater extraction in the long term. In HELGA, crop water requirements are calculated and satisfied with the available soil water and with irrigation from groundwater to meet the with consideration of the application losses. We include aquifer recharge and other sources of water use (surface water supply to dynamically account for the groundwater requirements of crops. Hence, we place groundwater irrigation within the context of other water resources and consider the groundwater exploitation costs in conjunction with the other costs to produce a crop. To include the impact of groundwater pumping on groundwater depth we couple HELGA to the water resource model PCR-GLOBWB, thus introducing farmer-scale hydro-economic analysis in a global-scale hydrological model with a resolution of 5 arc minutes (~10 x 10 km globally). In this manner, groundwater dynamics and surface hydrology are linked and the competition for groundwater with other sectors included. This coupling allows us to understand globally the implications of groundwater (over) use in the long term and how this defines the solution space from the aggregate farmer’s perspective.

Our results show that farmers eventually reach the economic limit. Energy cost of groundwater pumping is one of the important drivers limiting groundwater use. Additionally, the increasing costs of the water infrastructure (i.e. deeper wells) is an important factor that explains the economic limit. Also, our analysis shows that variations in the irrigation water demand and the groundwater recharge as a result of climate variability strongly influences the profitability of groundwater-fed irrigated agriculture To counteract this, adaptation strategies such as changing the crop mix and increasing irrigation efficiency are effective in increasing the time to reach the economic limit and to extend the lifespan of aquifers. Farmers’ agency towards the management of a depleting resource make a difference in keeping this resource for future generations.

How to cite: Melo Leon, S. F., Van Beek, R., Reinhard, S., and Bierkens, M.: How to avoid or postpone reaching the economic limit of groundwater-fed irrigation? Aggregated analysis for adaptation strategies from the farmer’s perspective., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2871, https://doi.org/10.5194/egusphere-egu24-2871, 2024.

Drought consists one of the most critical environmental hazards for the viability and productive development of crops. This paper is focused on the application of the Standardized Precipitation Index (SPI) for drought analysis and classification. The SPI is a commonly used drought index that calculates the difference between a given time period's precipitation and its long-term average. The objectives of the study are to conduct a spatiotemporal drought analysis, estimate drought severity using the SPI, identify both dry and wet periods, classify drought using the SPI, classify the degree of drought/wetness conditions using a classification scheme for multiple timescales, and calculate and classify SPI₁₂ for each month from 1981-2020. The study area is Thessaly, Greece, which is the country’s largest agricultural productive region facing water availability problems. The innovation of this paper is the spatiotemporal drought analysis through the use of CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) instead of conventional meteorological data, avoiding the use of a prevailed sparse weather network, and the difficulties arising from that. The study shows that the region has faced two severe years of drought in 1988 and 1989, which led to moderate and extremely drought conditions, respectively. In contrast, extremely wet conditions were observed in 2002-2003, while 2009-2010 experienced moderately wet conditions. In this context, the mapping of spatial and seasonal variability across the study area permits more targeted measures instead of horizontal policies.

Keywords: drought; SPI; CHIRPS; Thessaly; Greece; desertification

How to cite: Sakellariou, S., Spiliotopoulos, M., Alpanakis, N., Faraslis, I., Sidiropoulos, P., Tziatzios, G., Karoutsos, G., Dalezios, N., and Dercas, N.: Spatial and temporal drought analysis in susceptible agroecosystems: the case of Thessaly region, Greece, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17122, https://doi.org/10.5194/egusphere-egu24-17122, 2024.

Since 1970, South-western European regions (Iberian Peninsula and South France) have been subjected to an air temperature increase of almost 2 °C, while generally southern Europe assisted to a 20% drop in annual precipitation. Agriculture is by far the sector with the greatest freshwater withdrawals, and it is essential to perform an accurate assessment of water consumption for irrigation, in order to develop strategies to reduce water abstractions from the ecosystem. In this context, this work aims at modelling water consumption for agriculture in the Aude river basin (South-West France), in order to assess the amount of water needed during the growing season of each crop in the current conditions, and in the future scenarios of climate change, according to different climate models. This project relies on the application of SIMETAW# model (Simulation of Evapotranspiration of Applied Water), which, from a set of climatic and soil data, computes the daily reference, well-watered crop, and actual evapotranspiration (ET0, ETc, ETa), the evapotranspiration of applied water (ETaw), an irrigation schedule, and crop growth and yield for a specific site. For climate inputs, the work relies on the high-resolution data (0.11-degree resolution) supplied by Copernicus Cordex, which provides historical records and future estimations according to RCPs (Representative Concentration Pathways) 2.6, 4.5 and 8.5. In the calculation of the well-known Penman–Monteith ET0 formulation, SIMETAW# also considers the effect of the increase of atmospheric CO2 concentration on stomatal resistance, which plays as a counterbalance with the increase of temperature due to climate change, by reducing stomatal opening for transpiration in plants, determining lower water loss through stomata. The model calculates ETa in both irrigated and rainfed conditions, distinguishing the irrigation methods according to the most relevant crops of the region, namely wine grapes cultivations, forage crops, wheat, olives, vegetables and fruits. Results show that, in Aude basin, the variation of total irrigation demand between 1990 and 2050 is expected to be very low in scenario RCP 2.6 (< 1%), while in RCP 4.5 a 2.5% increase is foreseen. Differently, RCP 8.5 expects a substantial decrease of irrigation requirements (-23%), due to the large increase of CO2 concentration in the atmosphere. Low water-demanding crops, such as winter wheat and wine grapes, are less sensitive to climate variations, thus their irrigation demand is expected to remain rather stable in the future, however summer crops (fruits and vegetables) will require greater irrigation inputs. The study demonstrates that, in some climate scenarios, crop water requirements may decrease due to the reduction of stomatal conductance. Still SIMETAW#, as most of the crop water models currently applied, does not take into account other climate change effects that can be damaging for the vegetation (e.g., heat waves, floods, spread of pathogens, etc.), together with the reduced availability of water supply in the basin, which can also have a consequence on the irrigation scheduling.

How to cite: Borgo, A., Trabucco, A., Aslam, M. F., Masia, S., Spano, D., and Debolini, M.: Assessment of crop water needs and its sustainability based on future climate scenarios: the Aude Department (South-West France), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20028, https://doi.org/10.5194/egusphere-egu24-20028, 2024.