Several studies have recently explored the possibility of monitoring irrigation dynamics and by optimizing irrigation water management to achieve precision farming exploiting remote sensing information combined with ground data and/or water balance modelling.
In this session, we will focus on: the use of remote sensing data to estimate irrigation volumes and timing; management of irrigation using hydrological modeling combined with satellite data; improving irrigation water use efficiency based on remote sensing vegetation indices, hydrological modeling, satellite soil moisture or land surface temperature data; precision farming with high resolution satellite data or drones; farm and irrigation district irrigation management; improving the performance of irrigation schemes; estimates of irrigation water requirements from ground and satellite data; ICT tools for real-time irrigation management with remote sensing and ground data coupled with hydrological modelling.
Orals: Tue, 5 May, 14:00–15:45 | Room 2.31
Accurate estimation of irrigation water use and supply is essential for effective irrigation management, yet most withdrawals remain unmetered and unreported in many irrigation schemes. This study applied a remote sensing–based approach to quantify irrigation water use and supply in the Amibara Irrigation Scheme, Ethiopia. The irrigation component of crop evapotranspiration (Blue ET) was isolated using the Water Accounting Plus (WA+) framework and integrated with irrigation efficiency parameters to derive remote sensing–based irrigation supply (RbIS) estimates across multiple spatial scales. Moreover, we developed crop type maps for 2010 and 2024 and a digitized irrigation layout to evaluate irrigation performance using relative evapotranspiration (RET) and relative irrigation supply (RIS) and to compare changes between the two years.
Crop type mapping revealed a substantial decline in irrigated area, from 9,941 ha in 2010 to 4,532 ha in 2024. RbIS showed reasonable agreement with reported supply in 2010 (R² = 0.6) and measured supply in 2024 (R² = 0.8), though it consistently underestimated observed supply in both years. Irrigation distribution was relatively better in 2010, with 46% of blocks experiencing deficits compared to 70% in 2024, while excess irrigation decreased from 50% of blocks in 2010 to 26% in 2024. RET and RIS estimates were generally consistent across most irrigation blocks, reinforcing the robustness of these performance indicators. However, irrigation performance varied substantially across blocks and canals, with irrigation deficits evident in both years. Key informant interviews and focus group discussions further corroborated these irrigation water deficits, supporting the remote sensing–based assessment. Overall, the methodology of this study is scalable for data-scarce regions and offers strong potential for operational irrigation monitoring to support targeted interventions.
How to cite: Mekonnen, K., Tadesses, M., Velpuri, N. M., Abedella, M., Leh, M., Akpoti, K., Owusu, A., and Seid, A. H.: Remote Sensing-Based Estimation of Irrigation Water Use and Supply in the Amibara Irrigation Scheme, Ethiopia: A Multi-Scale Assessment , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2359, https://doi.org/10.5194/egusphere-egu26-2359, 2026.
Agriculture is the largest consumer of freshwater globally, yet reliable estimates of irrigation water requirements and withdrawals remain scarce, particularly in data-poor and rainfed-dominated regions. In sub-Saharan Africa, increasing climate variability and growing food demand are intensifying seasonal water stress and highlighting the need for improved water management and irrigation planning tools.
We present the Securing Water in Agriculture (SWAG) tool, an agro-hydrological framework that integrates remote sensing–derived evapotranspiration with land-use and crop data to quantify spatially and temporally explicit crop water demand, deficits, and irrigation surpluses. Beyond these, SWAG evaluates management interventions, including small-scale storage and water reallocation within irrigation schemes.
The framework was applied across Kenya’s central highlands from 2019 to 2023. Results indicate monthly deficit volumes are largest in the dry season from June to September, with particularly severe conditions in 2021 and 2022, when monthly deficits exceeded 150 million m³. Surplus volumes are present but generally smaller, typically remaining below 100 million m³. On a monthly basis, cropland deficit areas range from approximately 18,000 ha during wet months to up to 450,000 ha at the height of the dry season, whereas surplus areas in a given month are consistently smaller, varying between approximately 17,000 and 98,000 ha per month.
To support irrigation management and to meet the deficits, SWAG evaluates the spatial feasibility and seasonal performance of small-scale storage (e.g. 1,000 m³ farm ponds) through pond-scale water balance simulations. Results indicate that storage potential is highest in small headwater catchments, where potential pond densities locally exceed 25 ponds km⁻², while most catchments accommodate fewer than 10 ponds km⁻². On average, runoff volumes exceed 2,000 million m³ during the rainy season months (April–May and October–November), and pond water levels remain high during subsequent deficit periods, indicating that additional storage can generally offset deficits.
By coupling spatially and temporally explicit water demand analytics with storage and reallocation options, the SWAG framework helps close the agricultural water budget in data-scarce basins and provides a practical decision-support tool for improving irrigation management, water use efficiency, and climate resilience in vulnerable farming systems.
How to cite: Owusu, A., Yeboah, F., Velpuri, N. M., Adamseged, M., Leh, M., Akpoti, K., Mekonnen, K., and Schmitter, P.: From Water Deficits to Storage Solutions: A Remote Sensing–Based Water Balance Framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5675, https://doi.org/10.5194/egusphere-egu26-5675, 2026.
Irrigated agriculture is a major freshwater user in the western United States, yet field-level information on whether irrigation relies on surface water, groundwater, or both remains limited. This lack of source attribution constrains water-scarcity assessments, curtailment analysis, and evaluations of irrigation efficiency, particularly where infrastructure and governance spatially decouple water sources from field locations. We present a data-driven framework for mapping field-level irrigation source access that integrates large-scale geospatial predictors with administrative water-rights information. The approach uses a two-stage probabilistic classification pipeline: first, estimating the likelihood of groundwater and surface-water access and then inferring conjunctive use from the structure and uncertainty of these probabilities. Preliminary findings suggest that single-source irrigation can be consistently identified at the field scale across diverse settings, whereas mixed-source conditions exhibit greater sensitivity to local context. This enables irrigation source information to be incorporated at the field scale, while explicitly identifying settings where local conditions govern source use.
How to cite: Waqar, M. and Marston, L.: Field-Scale Irrigation Source Attribution , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6398, https://doi.org/10.5194/egusphere-egu26-6398, 2026.
Monitoring irrigated areas and water requirements remains a key challenge in Earth Observation (EO), especially in regions experiencing growing water stress and agricultural intensification driven by rising demand and climate change [1-2]. An emerging methodology for detecting irrigated fields at large scale uses EO-derived high-resolution surface soil moisture maps (SSM). This approach can effectively segment irrigated and non-irrigated areas early in the season. Particularly, SSM derived from Synthetic Aperture Radar (SAR) data offer the resolution required to resolve irrigated fields and detect irrigation events, even before crop canopy development [3].
This study investigates the use of high-resolution (~100 m) SSM maps to detect irrigated fields in the Apulian Tavoliere agricultural district (Southern Italy), where winter cereals and tomato are the main cultivated crops. The SSM maps are derived from Sentinel-1, SAOCOM, and Sentinel-2 time series using the SMOSAR software developed at CNR-IREA [4]. The analysed data set covers the growing season 2024 and 2025. The irrigation detection is based on the application of the Constant False Alarm Rate (CFAR) algorithm. This methodology uses a sliding-window approach to classify the central pixel by comparing its value to a threshold derived from the probability distribution function of SSM values within the window, ensuring a fixed FAR. The result is the identification of fields showing higher SSM than their surrounding area. The probability distribution function adopted is the Gaussian Mixture, and the sliding window is a 3kmx3km square. Finally, the classified pixels are aggregated at the field scale using the parcel boundary information to evaluate the classification performance metrics.
Results indicate that the main factors affecting classification accuracy are satellite revisit time, vegetation stage, and radar frequency. Specifically, satellite revisit affects accuracy as SSM contrast decreases due to evapotranspiration, making detection challenging beyond three days after the irrigation. Furthermore, dense vegetation limits C-band SAR signal penetration into the soil, thereby ensuring detection is most effective during early crop growth. Analysis of the 2024 season shows that, at the start of growth, accuracy reaches 80%. While, at C-band, as vegetation matures, the canopy may dominate the backscattered signal. In contrast, L-band frequencies, less sensitive to vegetation, enable detection during later canopy development, therefore accuracy remains above 80% even in late growth stages. Analysis of the 2025 season is underway.
Acknowledgment: This study is funded by ASI under the Agreement N. 2023-52-HH.1-2025 (addendum MyGEO to the THETIS project) in the framework of ASI’s program “Innovation for Downstream Preparation for Science” (I4DP_SCIENCE).
References:
[1] C. Massari et al., “A review of irrigation information retrievals from space and their utility for users,”, Remote Sensing, 2021.
[2] C. Corbari et al., “Estimates of Irrigation Water Volume by Assimilation of Satellite Land Surface Temperature or Soil Moisture Into a Water-Energy Balance Model in Morocco,” Water Resour Res, 61, 7, 2025.
[3] A. Balenzano et al., “Sentinel-1 and Sentinel-2 Data to Detect Irrigation Events: Riaza Irrigation District (Spain) Case Study,” Water, 14, 19, 2022.
[4] A. Balenzano et al., “Sentinel-1 soil moisture at 1 km resolution: a validation study,” Remote Sens Environ, 263, 2021.
How to cite: Rossi, S., Balenzano, A., Palmisano, D., Lovergine, F. P., Mattia, F., Rinaldi, M., Ruggieri, S., Tapete, D., Sacco, P., Ursi, A., and Satalino, G.: An adaptive and unsupervised approach for irrigation detection at field scale from high-resolution soil moisture maps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6881, https://doi.org/10.5194/egusphere-egu26-6881, 2026.
Irrigation is a major source of freshwater pressure in semi-arid regions and is increasingly constrained by water scarcity, operational inefficiencies, and user conflicts. Yet routine monitoring of irrigation withdrawals, conveyance losses, and field-level water use remains limited due to weak measurement infrastructure. This study demonstrates an integrated methodology of field observation of irrigation water use and efficiency, water accounting tool validated with field observation, and a remote sensing products to quantify irrigation performance, specifically efficiency, adequacy, and equity, in a reservoir-fed irrigation scheme in northern Ghana under a unimodal rainfall regime. During the 2025 dry season, monitoring combined daily water-level observations from standardized flow structures in the main canals, selected laterals and application of water on selected fields. Flow rates were estimated based on hydraulic empirical equations and Manning-hydraulic equations for defined concrete channels. Measurements covered upstream, midstream, downstream sections of both main canals and selected laterals. Results reveal strong spatial degradation of water delivery. In the main canals, average discharge declined from 1.80 to 1.20 m³ s⁻¹ (right canal) and 2.17 to 0.85 m³ s⁻¹ (left canal), corresponding to conveyance efficiencies of 87.1% and 83.8%, respectively. At lateral scale, losses were substantially higher, with efficiencies dropping to 78.4% in one lateral and 58.5% in another, reflecting seepage, overflow, sedimentation, and structural constraints. Application irrigation depths in selected fields varied widely (21–32 mm versus 29–77 mm), producing application efficiencies of 31% and 62%, and indicating inequities in delivery reliability and water access. Unregulated abstractions (pump and tanker withdrawals) were estimated at ~38,000–53,000 m³ over the monitoring period, contributing to instability during peak demand. Independent 30-m remote-sensing evapotranspiration (ETa) captured irrigation signals and enabled scheme-wide diagnostics that complement discharge monitoring. Relative ETa provided a proxy for adequacy across water user associations and irrigation blocks, while ETa variability highlighted inequitable allocation and inconsistent delivery. Combined indicators support actionable options, including prioritizing rehabilitation in high-loss reaches, improving rotational delivery to tail-end blocks, targeting enforcement in abstraction hotspots, and benchmarking performance across associations for adaptive irrigation management. This is done in collaboration with the Ghana Irrigation Development Authority to validate the results and create ownership of the results for decision-making for expanding the irrigation area and improving the scheme's efficiency.
How to cite: Akpoti, K., Tilahun, S., Kumah, M., Owusu, A., Velpuri, N. M., Dekongmen, B. W., Mekonnen, K., Leh, M., Haile, A. T., Tadesse, M., Prabhath, T., Kagone, S., Maduskanka, L., Perera, T., and Seid, A.: Operational irrigation monitoring in data-scarce schemes using field observations, water accounting tool and remote sensing in Northern Ghana, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8102, https://doi.org/10.5194/egusphere-egu26-8102, 2026.
Climate change and global population growth, with increased vulnerability of agricultural areas and enhanced food demand, are particularly affecting arid and densely populated regions. The decreasing availability of freshwater for agricultural use is increasing the appeal of unconventional water sources, like grey and desalinated water. Accurate knowledge of crop development and vulnerability to changed environmental conditions is critical to prepare for these future scenarios.
The objective of this work is the evaluation of irrigation water management scenarios considering water availability and quality, and the impact on crop growth, by merging satellite data and a distributed, high-resolution agro-hydrological model for crop monitoring and management. Specifically, this activity focused on an irrigation district in Morocco, which has been exposed to a prolonged drought and has seen an increase in the use of (partially) saline water for irrigation. Because of the drought, all available freshwater was reserved for civil use, causing a surge in groundwater pumping to satisfy the irrigation demand. This, in turn, has progressively increased the salinity of the groundwater reserve.
The monitoring of salinity-affected areas was performed at high spatial resolution (30m) by integrating into the crop-energy-water balance model FEST-EWB-SAFY the remote sensing data of leaf area index (LAI, from Sentinel-2) and land surface temperature (LST, from Landsat-8/9 and also from Sentinel-3, downscaled to 30m using Sentinel-2) to monitor crop development. The crop-energy-water balance FEST-EWB-SAFY model couples the distributed energy-water balance FEST-EWB model, which allows computing continuously in time and distributed in space all the components of the surface energy and water balances (without requiring LST as an input, but instead computing it internally), and the SAFY (Simple Algorithm For Yield estimates) model, for crop development. Both satellite LST and LAI data were used for the calibration and validation of the different branches of the modelling framework. The model was able to pick up information regarding soil salinity via its effect on crops visible from the satellite imagery.
The application of the FEST-EWB-SAFY model, through the synergy with satellite observations of LST and LAI, constitutes a valuable tool to evaluate the impact on the crop of mutating environmental conditions and to formulate sustainable water and food policies in areas facing the harsh consequences of climate change.
How to cite: Paciolla, N., Corbari, C., Houali, Y., Berendsen, S., Sheffield, J., and Labbassi, K.: Irrigation water management driven by agro-hydrological modelling and satellite data in drought- and salinity-affected areas in Morocco, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9576, https://doi.org/10.5194/egusphere-egu26-9576, 2026.
Irrigation strongly influences land-atmosphere interactions and the terrestrial water cycle, yet its representation in land surface models (LSMs) remains highly uncertain. These uncertainties arise from both the scarcity of reliable irrigation benchmarks and the challenge of representing heterogeneous irrigation practices within coarse model grid cells (e.g., kilometer-scale resolutions).
In this study, we examine structural limitations in the representation of irrigation within the Noah-MP LSM, implemented in the NASA Land Information System, by testing different calibration strategies. A sprinkler irrigation scheme is optimized using Sentinel-1-derived irrigation estimates and a genetic algorithm over an intensively irrigated region of northeastern Spain at a 0.01° spatial resolution. Two calibration approaches are evaluated: (i) adjusting the soil moisture threshold (Thirr) that triggers irrigation, and (ii) introducing a Scale Irrigation Coefficient (SIC) to account for sub-grid heterogeneity in irrigated area and applications’ timing.
Results show that calibrating Thirr alone provides limited flexibility, resulting in unrealistic irrigation peaks and excessive water application. By contrast, the optimized SIC-based parameterization substantially improves irrigation dynamics, reduces model errors relative to benchmark in situ observations, and better captures interannual variability in surface soil moisture. Findings demonstrate that assuming uniform, full-grid irrigation at resolutions of ~1 km or coarser is physically unrealistic due to both operational constraints on irrigation practices and the fragmented structure of agricultural landscapes. Comparisons with satellite-based evapotranspiration and gross primary production datasets also reveal inconsistencies in simulated vegetation responses, highlighting remaining limitations in vegetation parameterization.
Overall, this work underscores the importance of explicitly accounting for scaling effects in irrigation schemes and points toward future integration of satellite data assimilation to enhance representation of irrigation-water-carbon interactions.
How to cite: Modanesi, S., Busschaert, L., De Lannoy, G., De Santis, D., Natali, M., Dari, J., Quintana-Seguì, P., Castelli, M., Massimo Grasso, F., and Massari, C.: Satellite-based optimization of irrigation in a land surface model accounting for scaling effects , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10443, https://doi.org/10.5194/egusphere-egu26-10443, 2026.
Irrigation represents one of the primary anthropogenic perturbations to the global terrestrial water cycle, locally reshaping the climate-driven transition between wet and dry seasons through managed water inputs in croplands. Yet the globally consistent and temporally continuous daily irrigation estimates are still lacking. Here we interpret the persistent positive bias between remotely sensed and model-simulated soil moisture as an observable signature of irrigation and develop a global framework to quantify irrigation consumptive water use at the daily scale. We integrate multi-source inputs and construct a suite of representative scenarios to span major sources of uncertainty, improving robustness and internal consistency through observation-based constraints and fusion concepts. Independent consistency assessments and cross-region validation are further conducted to systematically evaluate the robustness, transferability, and uncertainty structure across gradients of climatic background and irrigation intensity. The global gridded daily irrigation figures more clearly delineate characteristic response patterns in major irrigated regions. In climate transition zones and strongly water-limited areas, estimates are more sensitive to climatic context and thus associated with relatively higher uncertainty. These findings provide a testable basis for interpreting regional differentiation and divergent magnitudes of irrigation impacts reported in the literature.
How to cite: Wang, W. and Zhuo, L.: Global gridded daily irrigation detection and quantification through soil moisture bias, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17059, https://doi.org/10.5194/egusphere-egu26-17059, 2026.
Flooding irrigation is a method still widely used in certain farming systems and in foothill areas. Although this traditional technique remains water-consuming, it offers significant external benefits such as groundwater recharge and biodiversity preservation. For example, on the Crau area (600 km²) in the south of France, flooding irrigation of grasslands contributes to 70% of the total recharge of the aquifer, which is strategic for a large number of human activities (irrigation of orchards, drinking water, industry). Remote sensing makes it possible to characterise the area of irrigated grasslands with a high degree of accuracy (Abubakar et al., 2022). However, the number of water cycles, which is determined by meteorological conditions and possible restrictions, remains poorly characterized as well as the irrigation dose, which depends on the length of the plot along the water flow axis. There is therefore a challenge in detecting irrigation events and the direction of flow.
In order to obtain a territorial view of flooding irrigation on grasslands, the objective of this study is to use high spatial resolution (~10m) remote sensing to characterize irrigation patterns (irrigation period, frequency and dose) at the plot scale. A previous study (Bazzi et al., 2020) based on radar imagery shows that it is possible to detect flooding irrigation, but there are still many errors, mainly when vegetation is dense. In the present study, analysis of Sentinel 1 time series in both radar configurations did not show a clear signal of irrigation. The dense vegetation of the grasslands probably masks the water layer during irrigation or the wet soil after drying. On the other hand, plots undergoing irrigation appear clearly when Sentinel 2 measurements are placed in a diagram relating the reflectance in the SWIR -band 11 (RSWIR) and the NDVI. Plots undergoing irrigation have RSWIR that deviates from the RSSWIR=f(NDVI) relationship. The distance from the RSWIR/NDVI point to this relationship can therefore be used to identify flooded pixels. With adequate thresholding of this distance, it can be shown that the plots identified as being irrigated are indeed irrigated in more than 90% of cases. Intra-plot mapping of irrigated areas makes it possible to identify the direction of irrigation and some times the direction of flow, which makes it possible to specify the water amount applied and, consequently, the amount drained, contributing to groundwater recharge. Temporal analysis of the territory allows the identification of the start and end of irrigation period. The proposed method thus makes it possible to sample a large number of irrigation events and thus enable more realistic modelling of irrigation schedules.
Abubakar, M., Chanzy, A., Pouget, G., Flamain, F., Courault, D., 2022. Detection of Irrigated Permanent Grasslands with Sentinel-2 Based on Temporal Patterns of the Leaf Area Index (LAI). Remote Sensing 14, 3056. https://doi.org/10.3390/rs14133056
Bazzi, H., Baghdadi, N., Fayad, I., Charron, F., Zribi, M., Belhouchette, H., 2020. Irrigation Events Detection over Intensively Irrigated Grassland Plots Using Sentinel-1 Data. Remote Sensing 12, 4058. https://doi.org/10.3390/rs12244058
How to cite: Chanzy, A. and Saadi, S.: FLI : a new spectral index to characterize flooding irrigation , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17110, https://doi.org/10.5194/egusphere-egu26-17110, 2026.
Accurate monitoring of water availability in the root zone is a prerequisite for generating precise irrigation recommendations and mitigating drought impacts in water-limited Mediterranean ecosystems. This work evaluates the performance and physical consistency of two distinct modelling paradigms to retrieve Root-Zone Soil Moisture (RZSM) in a vineyard located in Terra Alta (Catalonia, Spain), intended as a basis for operational decision support.
We contrast a purely data-driven method, utilizing a Multilayer Perceptron (MLP), against a process-based approach that couples a parsimonious multilayer soil model with an Ensemble Kalman Filter (EnKF) for the assimilation of Surface Soil Moisture (SSM). Both schemes are currently benchmarked using in-situ SSM observations and standard meteorological forcing.
The results highlight a clear dichotomy between predictive skill and physical interpretability. The neural network approach demonstrated excellent performance in capturing non-linear seasonal trends and rapid wetting events, yielding better Kling–Gupta Efficiency (KGE) scores during validation. Conversely, the physical model exhibited lower statistical metrics but ensured mass conservation and provided a transparent representation of vertical water transport.
We conclude that while machine learning excels in reproducing local dynamics, the physical framework offers the robustness required for consistent water accounting. Consequently, we propose a synergistic roadmap where machine learning is leveraged to regionalize model parameters, and the assimilation of high-resolution satellite Surface Soil Moisture serves to spatialize the state estimates. This integration is essential to scale up from plot-level findings to regional irrigation recommendations, supporting the next generation of Digital Twins in agriculture.
How to cite: Quintana-Seguí, P., Cid-Giménez, J., Barella-Ortiz, A., and Escorihuela, M. J.: Trade-offs between data-driven and process-based approaches for root-zone soil moisture retrieval in a Mediterranean vineyard, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21629, https://doi.org/10.5194/egusphere-egu26-21629, 2026.
Reliable identification of agro-hydrometeorological change in developing countries is hindered by sparse and declining monitoring networks as well as limited data-management capacity. Increasing access to accurate, high-resolution agro-hydrometeorological data would improve hydrological model predictions and ultimately support better decision-making. One promising strategy to address data scarcity is model inversion of crop simulation models, where time-resolved crop growth information at the field scale can act as a proxy for soil moisture and, by extension, irrigation amounts.
In this study, we evaluate a yield-based inversion approach within AquaCrop, in which the observed final crop yield is used as the inversion target to retrospectively estimate seasonal irrigation. Under uniform, continuously applied irrigation, inferred irrigation amounts were generally accurate, with errors within ±10%. Model performance was strongly affected by the soil’s available water storage capacity, which is governed by texture. Incorporating information on soil texture, irrigation pattern (continuous vs. non-continuous), and rainfall substantially improved inversion accuracy. In contrast, under non-uniform or non-continuous irrigation regimes, the method tended to overestimate irrigation substantially. These findings suggest that yield-constrained inversion can reliably estimate irrigation in controlled settings but is less robust under intermittent or spatially heterogeneous irrigation. As a next step, we will invert AquaCrop using temporally resolved vegetation data rather than final yield to better constrain soil-moisture dynamics and reduce bias under complex irrigation patterns.
How to cite: Saravanan, A., Karthe, D., Kovilpillai, B., and Schütze, N.: Analysing the Crop Model Inversion Technique in the AquaCrop model under varying levels of Rainfall, Initial Soil Moisture, and Soil Texture, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-693, https://doi.org/10.5194/egusphere-egu26-693, 2026.
Rainfed olive orchards are highly vulnerable to drought in Mediterranean regions, where climate change is intensifying water scarcity and climatic variability. This study presents a spatio-temporal assessment of drought impacts on rainfed olive groves in two traditional olive-growing areas of the Iberian Peninsula: the Trás-os-Montes (TM) agrarian region in northeastern Portugal and the province of Badajoz (BA) in southwestern Spain. These regions share Mediterranean climatic conditions but differ in drought severity, land characteristics, and agro-environmental contexts.
Vegetation dynamics were analyzed over an eight-year period (2015–2023) using satellite data from the Harmonized Landsat–Sentinel-2 (HLSL30) product. Two vegetation indices were selected to characterize olive orchard conditions: the Soil-Adjusted Vegetation Index (SAVI), which reduces soil background effects in sparsely vegetated systems, and the Normalized Difference Moisture Index (NDMI), which is sensitive to canopy water content and vegetation moisture status. These indices enabled the evaluation of seasonal and interannual variability in vegetation response to water stress.
Drought conditions were quantified using the Mediterranean Palmer Drought Severity Index (MedPDSI), a drought indicator specifically adapted to Mediterranean climates and olive tree ecophysiology. The relationship between drought severity and vegetation response was examined through correlation and lagged-response analyses, allowing the identification of delayed vegetation reactions to drought events.
The results indicate clear regional contrasts in both drought characteristics and vegetation response. BA experienced more intense, prolonged, and frequent drought episodes than TM, particularly during the warm season. Seasonal variations in SAVI and NDMI were strongly correlated with MedPDSI values in both regions, with the strongest vegetation response observed at a lag of approximately two months. This delay reflects the cumulative physiological effects of water stress on olive trees rather than immediate responses.
Extreme drought years, especially 2017 and 2022, were associated with pronounced declines in both vegetation indices, indicating increased stress and reduced canopy vigor during the dry season. Rainfed olive orchards in BA showed greater susceptibility to long-term drought impacts, whereas TM exhibited slightly higher resilience, potentially related to milder climatic conditions or local environmental and management factors.
This study demonstrates the value of integrating satellite-derived vegetation indices with drought indicators to monitor drought impacts on rainfed olive systems. The proposed approach provides useful information for drought monitoring, risk assessment, and the development of adaptive management strategies aimed at improving the resilience and sustainability of Mediterranean olive orchards under ongoing climate change.
How to cite: Moral García, F. J., Crespo-Cotrina, N., Rebollo Castillo, F. J., Padua, L., Paredes, P., Santos, J. A., and Fraga, H.: Integrating satellite vegetation indices and drought metrics for agro-hydrological monitoring of rainfed olive orchards, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3046, https://doi.org/10.5194/egusphere-egu26-3046, 2026.
Vineyards in the Iberian Peninsula are highly sensitive to water stress driven by climate variability, particularly under the increasing frequency and intensity of drought events associated with climate change. Reliable, long-term indicators of water availability are therefore essential for monitoring vineyard vulnerability and supporting agro-hydrological assessment and adaptation strategies at regional scales. This study evaluates the performance of the De Martonne Aridity Index (DMI) as a climatic indicator for long-term monitoring of drought stress in vineyards across the Iberian Peninsula over the period 1993–2022.
Monthly DMI values were computed using bias-corrected temperature and precipitation data from the ERA5-Land reanalysis, allowing for a consistent characterization of aridity conditions over three decades. Vineyard conditions were independently assessed using the Vegetation Health Index (VHI), derived from satellite observations and spatially restricted to vineyard land-cover areas. The VHI integrates information on vegetation vigor and thermal stress, providing an effective proxy for plant response to water stress. Drought severity classes based on DMI were systematically compared with VHI-derived vegetation stress classes through spatial and temporal analyses.
The results reveal a strong correspondence between low DMI values and reduced VHI, particularly during periods classified as severe and extreme drought. This agreement indicates that the DMI effectively captures major water stress conditions affecting vineyard systems, despite its simple formulation and limited data requirements. Temporal analyses show that prolonged dry periods identified by DMI are consistently associated with sustained vegetation stress signals, while spatial patterns highlight a higher recurrence and persistence of drought impacts in central and southern regions of the Iberian Peninsula. In contrast, northern areas exhibit lower drought frequency and reduced vineyard vulnerability.
Overall, the findings demonstrate that the De Martonne Aridity Index provides a robust and practical indicator for regional-scale vineyard drought monitoring. When combined with satellite-based vegetation indices, DMI contributes valuable climatic context for agro-hydrological assessment, supporting drought impact analysis, water resource evaluation, and climate adaptation studies. Its simplicity and scalability make it particularly suitable for long-term monitoring frameworks and for complementing remote sensing approaches in viticultural water management under changing climatic conditions.
How to cite: Rebollo Castillo, F. J., Crespo-Cotrina, N., Moral García, F. J., Pádua, L., Claro, A. M., Fonseca, A., Paniagua Simón, L. L., García Martín, A., Santos, J. A., and Fraga, H.: Integrating climatic aridity indices and satellite vegetation indicators for agro-hydrological monitoring of vineyard drought stress, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3047, https://doi.org/10.5194/egusphere-egu26-3047, 2026.
The use of satellite remote sensing for mapping the spatial-temporal extent of irrigation at catchment scale is a key ingredient for effective irrigation water management. A thresholding approach based on vegetation indices and evapotranspiration metrics was applied in the Namoi catchment in Australia to detect irrigated areas. The results show that irrigation in the catchment is heterogeneous, with no consistent increasing or decreasing trend over the classification period. In addition, the method identifies irrigated area conservatively with a high precision and moderate accuracy when evaluated against independent reference data. The findings highlight the potential of the thresholding approach for agricultural water management. Further work will focus on refining this method and linking it to quantifying irrigation water use at catchment scale.
How to cite: Muturi, J., Majumdar, S., Ndehedehe, C., and Kennard, M.: Detecting irrigation at catchment scale over recent years (2019-2025) , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6080, https://doi.org/10.5194/egusphere-egu26-6080, 2026.
Irrigation plays a key role in the terrestrial water cycle and agricultural production, yet it remains one of the most uncertain components of large-scale water use estimates. In recent years, several irrigation datasets based on modeling approaches and remote sensing have been developed. While these products have improved the spatial and temporal characterization of irrigation water use, they often lack an explicit quantification of uncertainty, limiting their applicability for hydrological and land surface modeling, as well as data assimilation.
This study presents high-resolution irrigation estimates for maize across Europe using ensemble simulations with the crop model AquaCrop (version 7.2) coupled to NASA’s Land Information System. Simulations are run for the period 2010-2020 at a 0.05° lat–lon resolution over European regions with irrigated maize, assuming sprinkler irrigation. The ensemble mean crop and irrigation estimates are evaluated against ground-truth and satellite observations. At the field scale, simulated irrigation amounts are compared against reported irrigation data over maize fields in the Lot and Tarn departments in southern France for the period 2016–2019. At the continental scale, simulated vegetation dynamics are evaluated using the Copernicus Land Monitoring Service fraction of canopy cover (FCOVER) product across Europe.
To explicitly represent uncertainty, ensembles are generated by perturbing meteorological forcings and a key irrigation parameter, specifically the root-zone soil moisture threshold that triggers irrigation events. Multiple ensemble configurations are tested to account for uncertainties related to irrigation management practices and meteorology. In a first experiment, shortwave radiation and precipitation are perturbed. In a second experiment, this configuration is extended by additionally perturbing air temperature, leading to a larger spread in vegetation development since crop growth stages are defined by accumulated heat units (growing degree days). In a final experiment, the ensemble is further expanded by perturbing the irrigation threshold, resulting in an increased spread in simulated irrigation amounts. An ensemble verification against field-level irrigation observations is performed to assess the ensemble uncertainty, providing a basis for future data assimilation applications.
How to cite: Busschaert, L., Bechtold, M., Kumar, S. V., Le Page, M., Massari, C., and De Lannoy, G.: High-resolution irrigation estimates for maize across Europe from ensemble AquaCrop simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7446, https://doi.org/10.5194/egusphere-egu26-7446, 2026.
Irrigation is the most impactful yet less monitored human activity altering the natural hydrological cycle. In recent years, an ever-increasing number of studies have shown the potential of Earth Observation (EO) in tracking human dynamics along with natural ones, including estimates of irrigation water use (IWU). Particularly, the SM-Inversion method, a soil-water-balance approach adapted for quantifying IWU from satellite soil moisture, proved its skills across various scales of application. In this contribution, main results from the Irrigation-EU project will be presented. Its main objective is the development of the first ever European-scale, EO-based IWU data set. To do this, the SM-inversion algorithm has been optimized and implemented as operational Python processor. Features of the resulting IWU product include spatial and temporal resolutions equal to 1 km and 14-day, respectively. The temporal coverage spans from 2016 onwards. Operational input data has been leveraged for developing IWU estimates, i.e., Sentinel-1 soil moisture estimates delivered by the CLMS (Copernicus Land Monitoring Service) and total precipitation and potential evaporation from ERA5-Land (European ReAnalysis v5 – Land). Validation against reference irrigation volumes collected in several European case studies (mainly located in Spain, Italy, Greece, and Germany) will be presented. Moreover, the validation will benefit from the recently launched initiative which invites the scientific community to collaborate in developing the first database of in-situ IWU observations.
How to cite: Dari, J., Baljeet Singh, Y. K., Ntokas, K., Fomferra, N., Brandt, G., Morbidelli, R., Saltalippi, C., Flammini, A., Rahmati, M., Filippucci, P., Fernández-Prieto, D., Volden, E., and Brocca, L.: European scale, satellite-based irrigation water use estimates at 1 km spatial resolution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7480, https://doi.org/10.5194/egusphere-egu26-7480, 2026.
Climate change and population growth are putting increased pressure on water resources and their users in Italy and the Mediterranean (IPCC 2023). Italy is among the European countries that make the greatest use of irrigation (70% of total freshwater consumption), and about 60% of the area is irrigated with low-efficiency techniques (ISTAT 2014). Despite its significance, the yearly detection of irrigated fields remains poorly quantified at national scales with 1-km spatial resolution. A multi-sensor, satellite-driven framework was developed to map irrigated areas across Italy over the past years based on a change detection algorithm for the 8-day normalized difference vegetation index (NDVI) from MODIS, soil moisture from Sentinel-1, land surface temperature (LST) from MODIS, and precipitation from ERA5-Land. The datasets are harmonized and analyzed to produce statistical maps and temporal trends, providing a detailed characterization of irrigated areas. The retrievals are intercompared and validated against reference datasets from local field knowledge, the official national statistics data, and global research datasets. Inconsistency has been found in some areas, especially due to the difficulties in differentiating between rainfed and irrigated crop areas. No significant differences in the irrigated areas were observed between the different years.
This spatially continuous, multi-decadal assessment provides a methodology applicable to Mediterranean and semi-arid regions and delivers essential insights to support sustainable water management, agricultural planning, and climate adaptation strategies.
How to cite: Joy, J. and Corbari, C.: Mapping yearly irrigation patterns across Italy using multiple satellite data from 2000 to 2025, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7656, https://doi.org/10.5194/egusphere-egu26-7656, 2026.
Irrigation uniformity is essential for the efficient use of water and depends on both the design of the irrigation system and the operation of the sprinklers. Manual monitoring of sprinklers is inefficient and prone to errors, which has driven the use of remote sensing technologies for crop monitoring.
This study explored the potential of high-resolution multispectral imagery for the detection of blocked sprinklers in maize fields during the irrigation season. The research was conducted in a field in Larraga (Navarra, Spain), irrigated with sprinklers spaced 15x18 m apart, with three sprinklers randomly blocked for 15 to 25 days during four stages of maize growth. Images captured with an unmanned aerial vehicle (UAV) were subsequently resampled to simulate a 3 m satellite resolution; this approach allowed the generation of complete time series of the Normalised Difference Vegetation Index (NDVI) without interruptions caused by cloud cover, ensuring detailed monitoring of crop development.
The study field was divided into a non-irrigated zone around the blocked sprinklers and a control zone with normal irrigation, allowing comparison of crop development through multitemporal NDVI analysis and time series incorporating daily data on irrigation applied to the field, as well as precipitation and evapotranspiration recorded at the nearest weather station, which allowed assessment of their influence on vegetation dynamics.
The results showed that the images enabled clear identification of areas affected by sprinkler blockage, with significant differences in vegetation indices between the control and non-irrigated areas. Continuously irrigated zones maintained high and stable NDVI values, whereas areas with interruptions showed marked decreases, only partially mitigated by rainfall events in early stages. These findings highlight that irrigation interruption has an adverse effect on crop health, which can be detected accurately using remote sensing tools, emphasising the importance of maintaining uniform irrigation for optimal plant development.
How to cite: Campero-Taboada, M. J., Casalí Sarasíbar, J., González-Audícana, M., and Campo-Bescós, M. A.: Identification of Sprinkler anomalies using Multispectral Remote Sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11754, https://doi.org/10.5194/egusphere-egu26-11754, 2026.
In-situ soil moisture sensors provide continuous information on soil water status and soil–water–plant interactions. Such information can be used for irrigation planning and for evaluating and optimizing irrigation strategies and systems. Wireless sensors also have the advantage that they cause minimal disruption to field operations and can therefore generate spatially explicit data. Sensor performance and practicality depend on soil properties, the moisture range, and implementation conditions. We evaluated wireless dielectric soil moisture sensors under controlled laboratory conditions and on sprinkler-irrigated field plots. Twenty wireless “SoilScout” sensors were used. A dedicated logger recorded the data and transmitted it via the GSM network to a server for processing. In the laboratory, we used fine sand of known bulk density, saturated it, and then allowed it to dry at room temperature. We determined the gravimetric water content (θg) and converted it to volumetric water content (θv) using the bulk density (ρb). In the field, the 20 sensors were installed and operated in the dams of irrigated potatoes from May 2025 to July 2025 and carrots from July 2025 to November 2025. The soil was Sandy Loam. The sensor positions followed a regular grid within the 18 x 18 m sprinkler setup, and a rain gauge was installed at each point to assess distribution uniformity. Sensor data were recorded continuously, capturing both natural conditions (evapotranspiration and rainfall) and irrigation events. After each measurement period, we collected soil samples near the sensor positions to determine and , and computed . These volumetric water contents served as reference values to analyze sensor performance. We estimated the slope and intercept of the corresponding regression lines and assessed precision and accuracy using RMSE, bias, and R2 . To compare with applied irrigation depths measured by the rain gauges, we also analyzed changes in sensor-derived water content during irrigation events (Δθv). Based on these data, we calculated the uniformity of water distribution. Results show a strong correlation between the wireless sensor and the laboratory reference (R2>0.9), indicating reliable tracking of drying in homogeneous media. In the field, agreement with gravimetric sampling converted to θv was less robust. Although absolute values differed in both settings, the dynamics of soil water status were captured very well. Under the canopy, the wireless sensors produced a spatial pattern like the rain gauge data, enabling sensor-based evaluation of distribution uniformity and a rough estimation of application efficiency (and interception losses). The study demonstrates clear advantages of wireless sensors in managed fields, supporting their use for practical irrigation management. However, retrieving the sensors before harvest proved challenging: despite marking and using a metal detector, they were difficult to locate. Further work is needed to quantify the absolute measurement accuracy of the sensors used. Overall, the results support the use of wireless sensors for planning and evaluating irrigation.
How to cite: Rizqi, F. A., Kastelliz, A., and Nolz, R.: Evaluating wireless soil moisture sensors for assessing the efficiency and uniformity of sprinkler irrigation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11846, https://doi.org/10.5194/egusphere-egu26-11846, 2026.
Irrigation profoundly alters near-surface hydrological processes, yet its representation and detectability in satellite-based surface soil moisture (SSM) products remain insufficiently understood. While SSM observations are increasingly used in irrigation-related studies and applications over managed agricultural landscapes, most existing evaluations focus on natural or rainfed conditions, leaving a critical gap in anthropogenically influenced environments.
We assess the performance of six high-resolution (1 km) SSM products, including BEC SMOS L4, UFZ-Sentinel-1, RT1, CGLS, NSIDC SMAP, and a newly developed downscaled ESA CCI product. The analysis focuses on three major European irrigation hotspots: the Ebro Valley (Spain), the Po Valley (Italy), and the Thessaloniki region (Greece).
The evaluation is structured around three complementary analyses. First, spatial and temporal consistency is examined by comparing SSM distributions over irrigated and rainfed areas using global irrigation maps, and by assessing temporal dynamics against district-scale irrigation records. Second, satellite SSM products are benchmarked against model-based ERA5-Land estimates that do not explicitly represent irrigation, in order to analyze anomalies and identify potential human-induced soil moisture signals. Third, physical consistency is assessed by examining the relationship between SSM and land surface temperature (LST), as irrigation is expected to induce surface cooling through increased evapotranspiration.
The analysis highlights marked differences between products in their ability to detect irrigation-related SSM signals and provides a basis for their evaluation and use in irrigated, human-modified environments.
How to cite: Wappis, A., Laluet, P., and Dorigo, W.: How well do high-resolution surface soil moisture products capture irrigation signals?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12064, https://doi.org/10.5194/egusphere-egu26-12064, 2026.
France is the first agricultural producer and the third most irrigated country in terms of surface area in the European Union. Agriculture weighs heavily on French water resources dynamics as it features the highest water consumptive use (i.e., most of the water applied on irrigated areas is evaporated and unavailable downstream). Water withdrawals for irrigated agriculture are usually quantified through modelling at catchment scale because direct measurements are incomplete. Models can conceptualise water withdrawals as a modulation of crop irrigation requirements (i.e., water added to rainfall to compensate crop evapotranspiration and alleviate water stress) by water availability and irrigation management constraints (e.g., yield objective, irrigation technology efficiency). Establishing these models over a large sample of catchments is challenging because this requires a large range of data at different spatial scales (plot, farm, catchment).
As a first step towards assessing water withdrawals, this study investigates the spatiotemporal dynamics of monthly irrigation water requirements at catchment scale over the past decades in France. It also evaluates the contribution of climate variability (e.g., precipitation, temperature) and changes in cropland characteristics (e.g., area, crop type) to irrigation water requirement trends. Using the soil-crop water balance models CROPWAT and Optirrig (Cheviron et al. 2020), we compute irrigation water requirements over irrigated area and total cropland to approximate the agricultural water usage and quantify the crop water deficit. We build gridded yearly maps of irrigated and cropland area in France by combining statistics at the district (“département”) level and remote sensing derived products like land cover maps. Using different models and parameter values (e.g., sowing dates, crop coefficients) enables structural and parametric uncertainty quantification. Our results show that, in spite of uncertainties, the increase and the distribution of irrigation water requirements follow the rise and expansion of irrigated area in France, while crop water deficit is highly driven by climate variability.
References:
Cheviron, Bruno, Claire Serra-Wittling, Magalie Delmas, Gilles Belaud, Bruno Molle, et Juan-David Dominguez-Bohorquez. 2020. « Irrigation Efficiency and Optimization: The Optirrig Model ». doi:10.5194/egusphere-egu2020-20547.
How to cite: Michard, J., Lemaire, B. J., Andréassian, V., Cheviron, B., and Sarrazin, F.: A spatiotemporal analysis of irrigation water requirements in French croplands over the past decades, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12461, https://doi.org/10.5194/egusphere-egu26-12461, 2026.
With the wide variety of methods using satellite soil moisture (SM) observations to identify irrigation, our study aims to recreate several of the methods with a newly developed soil moisture product that uses a downscaling algorithm to produce a 400-m resolution soil moisture product from the native 36-km Soil Moisture Active Passive (SMAP) soil moisture data (Fang et al. 2025). From this product, we can estimate deeper soil moisture (20-cm and 50-cm from the original 5-cm depth). The objectives of this project are to see which of the methods perform best in the state of Colorado, and if performance differs with crop type, irrigation type, precipitation levels, and soil moisture depth as compared with the irrigation/crop type spatiotemporal field data available through Colorado’s Decision Support System (CDSS).
Our methods for identifying irrigation include:
- Summation of SM over the growth period: Because we are studying a relatively small area, we assume that all soil is receiving an approximately equal amount of moisture from precipitation. We deduce that regions with higher SM than those around them receiving additional moisture from irrigation.
- High SM despite low precipitation: If SM is detected despite there being a lack of precipitation for 4 days or more, one can assume that detected moisture came from irrigation (Lawston et al., 2017; Shellito et al., 2016).
- Changes in mean absolute deviation (MAD): MAD is used to understand the variability of SM within each pixel- because irrigation causes frequent and significant changes in SM, higher variability is a sign of irrigation (Jalilvand et al., 2021).
- Isolating irrigation signals: To isolate irrigation signals, this method incorporates a soil water balance model that accounts for vertical fluxes such as evapotranspiration and drainage, which influence soil moisture changes independently of irrigation (Zappa et al., 2021).
- Increases in NDVI after irrigation events: The normalized difference vegetation index (NDVI) measures plant greeness and vigor and is elevated for healthy plants. Previous studies have found that irrigated crops have an NDVI value greater than 0.8, while non irrigated vegetation is below 0.75 (Brown & Pervez, 2014; Ibrahim et al., 2023).
How to cite: Turman, A., Fang, B., Smith, R., and Lakshmi, V.: Comparing Methods of Identifying Irrigation Using High Resolution SMAP Soil Moisture, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15043, https://doi.org/10.5194/egusphere-egu26-15043, 2026.
Water scarcity is increasingly emerging as a critical issue even in traditionally water-abundant regions such as the European Alps. The coexistence of multiple end users - often characterized by competing and sometimes conflicting demands, ranging from aquatic ecosystem conservation to hydropower generation - renders water management one of the most pressing socio-economic and environmental challenges in mountain catchments. Although irrigation represents the third-highest priority water use after drinking and sanitation, the volumes required and actually withdrawn for agricultural purposes remain poorly constrained in mountain environments. This knowledge gap stems from a combination of factors, including technical limitations, pronounced spatial fragmentation, and historically rooted governance. Improving the estimation of irrigation water requirements is therefore a key step toward a more informed, efficient, and climate-resilient management of water resources.
Here, we present an approach for estimating irrigation water requirements (IWR) based on Sentinel-2–derived NDVI, coupled with spatially explicit meteorological drivers, namely air temperature, precipitation, and potential evapotranspiration. Daily IWR maps at 20 m spatial resolution are produced for the Aosta Valley, an inner-Alpine valley of approximately 3,200 km² located in the western Italian Alps, covering the period 2018–2025. The analysis focuses in particular on dry years (e.g. 2022), for which anomalies are computed at multiple spatial and temporal scales in order to investigate the different dimensions of drought severity in a topographically complex setting.
A more detailed analysis is conducted for a ~81 km² sub-basin, where the coexistence of multiple surface-water uses frequently leads to substantial river depletion during the summer season. In this basin, a set of discharge measurements enables the quantification of water withdrawals for both irrigation and hydropower production, thereby allowing a quantitative assessment of the relationship between estimated water requirements and actual water use. We show that, through the optimization of water allocation strategies, the risk of water scarcity can be substantially mitigated even during exceptionally dry summers such as 2022.
Wall-to-wall products such as those presented here, characterized by adequate spatial and temporal resolution, further provide a valuable basis for planning the location, design, and sizing of multi-purpose water storage reservoirs in hydrologically critical areas.
How to cite: Filippa, G., Pogliotti, P., Galvagno, M., Vassoney, E., Isabellon, M., and Avanzi, F.: Satellite-derived irrigation water requirement as a support tool for climate-resilient water management in the Alps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17590, https://doi.org/10.5194/egusphere-egu26-17590, 2026.
Posters virtual: Thu, 7 May, 14:00–18:00 | vPoster spot A
EGU26-22992 | Posters virtual | VPS10
Comparison of Irrigation Scenarios in the Ebro Basin Using the SASER Modelling ChainThu, 07 May, 14:42–14:45 (CEST) vPoster spot A
How to cite: Barella-Ortiz, A., Quintana-Seguí, P., Cid-Giménez, J., Clavera-Gispert, R., Altés-Gaspar, V., Villar, J. M., Munier, S., Laluet, P., Olivera-Guerra, L. E., and Merlin, O.: Comparison of Irrigation Scenarios in the Ebro Basin Using the SASER Modelling Chain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22992, https://doi.org/10.5194/egusphere-egu26-22992, 2026.
