This session will focus on various ET estimation methods, including sap flow or soil heat pulse sensors, lysimeters, eddy covariance stations, scintillometers, and remote sensing-based methods. We will also explore emerging techniques such as AI, data fusion, sharpening algorithms, machine learning, and cloud computing. Additionally, we will cover detailed evaluations of scale dependencies, approaches for handling uncertainties and systematic biases, and assessment of the representativeness of the estimates.
We welcome contributions that (1) assess and compare various in-situ and remote sensing methods, (2) analyse trends and spatio-temporal patterns in ET data, including sources of error and uncertainty, (3) bridge scales between different in-situ measurements, modelled and remotely sensed ET, addressing validation, calibration, and upscaling challenges, and (4) evaluate the challenges and opportunities associated with fusing data products, improving processing pipelines, applying AI methods, cloud computing and new technologies.
Orals: Mon, 4 May, 14:00–18:00 | Room 2.15
Agriculture is confronted with multiple challenges globally and tasked with the transfer to sustainable and resilient production systems. At the same time, transfer from basic science to practice is ineffective and slow. To achieve this, understanding process dynamics of energy and matter cycle dynamics under management and Climate Change is crucial. Over the past decade, rapid technological and scientific advancements have led to unprecedented spatio-temporal resolution of (iso-)flux observations and allowing to monitor water and matter cycling continuously, automatically and simultaneously for different treatments, capturing the systems spatio-temporal heterogeneity. Novel automated and adaptive systems (such as the sensor platform AgroFlux established at ZALF) allow for the case-specific, simultaneous assessment of energy and matter cycle dynamics and their partitioning alongside a holistic set of complementing system variables. This integrated, flexible and automated approach paves the way for a process-based understanding of cross-scale carbon-water interactions as well as the development of participative, co-design researcher that includes various stakeholders in decision processes. We believe, that this actively supports the development of sustainable land management strategies in the face of Climate Change.
How to cite: Dubbert, M. and Hoffmann, M.: The AgroFlux Sensor Platform: Advancing Process-based understanding of carbon-water relations in agricultural systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3023, https://doi.org/10.5194/egusphere-egu26-3023, 2026.
Quantifying the evaporative flux from the land surface into the atmosphere is important for local irrigation management, regional water management and global weather prediction, but measuring evapotranspiration (ET) remains a challenge. Methods that directly measure ET, including eddy covariance and lysimeters, have high temporal resolution but limited spatial coverage, while indirect estimates, including remote sensing techniques, have broad spatial coverage but lower temporal frequency. Each principle for estimating ET comes with unique uncertainties, so a combined use of these methods under identical conditions, which is rarely done in field campaigns, should be promoted to assess its relative and overall performance.
In order to quantify the relative uncertainty of different commonly-used ET estimation techniques, we performed an ET method intercomparison using data from the 2021 Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) experiment (Boone et al., 2025). Fifteen ET estimation methods were evaluated across six crop types, spanning footprint extents from individual fields to the regional scale and encompassing multiple measurement principles. We also include an upscaled “mixed-agriculture” estimate from the eddy-covariance data to match the footprints of methodologies that cover more than one field (e.g. satellite remote sensing data and long-path scintillometry) We find that the total sampling uncertainty in the eddy covariance measurements is between 10% and 20% of the latent heat flux regardless of time of day. Moreover, we find that standard deviation among different ET methods for a single crop type (between 2.0 and 3.7 mm day-1) is greater than the standard deviation among the different crop types (1.9 mm day-1) in the LIAISE domain.
References:
Boone, A., Bellvert, J., Best, M., Brooke, J. K., Canut-Rocafort, G., Cuxart, J., Hartogensis, O., Moigne, P. L., Miró, J. R., Polcher, J., Price, J., Seguí, P. Q., Bech, J., Bezombes, Y., Branch, O., Cristóbal, J., Dassas, K., Fanise, P., Gibert, F., … Zribi, M. (2025). The Land Surface Interactions with the Atmosphere over the Iberian Semi-Arid Environment (LIAISE) field campaign. Journal of the European Meteorological Society, 2, 100007. https://doi.org/10.1016/j.jemets.2025.100007
How to cite: Mangan, M. R., Hartogensis, O., Bellvert, J., Boone, A., Canut, G., Cristóbal, J., Cuxart, J., Gonzalez Armas, R., Groh, J., La Page, M., Llorens, R., Martí, B., Martínez-Villagrasa, D., Miró, J. R., Skokovi, D., Sobrino, J. A., and de Boer, H. J.: Evapotranspiration Intercomparison of Field-to-Regional Scale Methods at the LIAISE Experiment , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17124, https://doi.org/10.5194/egusphere-egu26-17124, 2026.
Water supply plays a crucial role in the vitality and growth of forests. Forests are an important source of high-quality drinking water and play a key role in the landscape water balance. Rising temperatures and changes in precipitation are leading to more frequent and severe droughts, which can increase mortality and pose a challenge for forest management and planning. At the same time, this is substantially changing the water balance of forests and thus the future seepage water supply. These challenges can be met with appropriate silvicultural measures such as tree species selection and thinning, but requires reliable knowledge about the transpiration of forest stands and the factors that influence it.
The transpiration (evapotranspiration) of forest stands can be estimated using micrometeorological methods, e.g. eddy covariance measurements, or hydrological approaches, e.g. by measuring streamflow from clearly defined small catchment areas or large lysimeters with trees, or – due to their simplicity and availability often used nowadays – by measuring transpiration with sap flow sensors, with each method covering different scales and with each method including specific uncertainities. We developed a model system to improve the estimation of transpiration rates of forest stands based on sap flow and soil moisture measurements in combination with the stand water balance model LWF-Brook90 with the aim of integrating this approach into the Forest Environmental Monitoring Programme of ICP Forest https://www.icp-forests.net/. The combination of measurements and modelling aims at reducing the uncertainties and potential errors of estimates based purely on measurement data, as these alone contain a considerable degree of uncertainty in terms of their absolute values.
Based on our 2-3 years measurements at three forest stands with simultaneous eddy covariance measurements as a reference, we would like to present the methodological approach and its improvements. Temporal dynamics of sap-flow measurements agreed well with EC data minus modelled evaporation, whereas absolute values were substantially over- or underestimated in most cases, indicating a high absolute uncertainty if estimates would be based on sap flow measurements alone. Comparing LWF Brook 90 modelling results of a set of established reference parameters to sap flow measurement dynamics showed that tree eco-hydrological parameters needed to be modified to yield a better agreement of modelled transpiration with the temporal sap flow dynamics, which is the more reliable information in the sap flow measurement. Together with the results from a further 13 forest stands in Germany the characteristic differences in the annual transpiration pattern and water consumption of theses stands will be presented.
How to cite: Maier, M., Raspe, S., Mayer, C., Hartmann, A., Fichtner, T., and Seeger, S.: How and how accurately can we measure transpiration of forest stands? , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18155, https://doi.org/10.5194/egusphere-egu26-18155, 2026.
The west-central Himalayas are experiencing a major ecological shift with widespread, apparently water-conserving, Banj oak (Quercus leucotricophora) forests increasingly replaced by monoculture stands of Chir pine (Pinus roxburghii). This transition has been associated with declining streamflow and weakened hydrological services, yet the role of forest transpiration—an essential component of watershed hydrology—remains insufficiently understood.
This study reports findings from a two-year measurement campaign quantifying tree-level (Tf) and stand-level (Ts) transpiration in a Chir pine–dominated watershed. We analyze seasonal variability in transpiration, its age-related differences, and sensitivity to hydrometeorological drivers. We also compare this with a few months of transpiration data collected at a nearby Banj oak forest. At the tree scale, Tf increases markedly with tree age and peaks during the post-monsoon autumn season. At the stand scale, mean annual Ts is ~1.1 mm day⁻¹, with a maximum (~1.3 mm day⁻¹) in autumn and minimum (~0.85 mm day⁻¹) in summer, along with suppressed transpiration during the monsoon. Tsin the pine forest is consistently higher than that in the oak forest during the monsoon (~0.3 mm day-1) and post-monsoon autumn seasons (~0.2 mm day-1) with similar values in early autumn. Oaks also display a stronger regulation of transpiration post-monsoon with a sharper decline in Ts as compared to nearly stable values in the pine stand. Ts is also strongly controlled by solar insolation in the post-monsoon autumn and winter seasons, signifying energy-limitation, which may not have been earlier reported for these forests. However, pines still opportunistically utilise available water resources during this period with a stable Ts. The forests also seem to be water-limited during the dry summer season, which requires supporting evidence from direct measurements. However, again, pines have an opportunistically high transpiration rate during this period, apparently utilizing watershed storage.
These sap-flux measurements align well with complementary measurements of forest evapotranspiration (ET), made with the Bowen ratio method. Ts percentage contribution in ET varied with seasons, with the lowest in monsoon (24% and 40%) at the oak forest and pine forest, respectively. ET show stronger transipraive regulation in the oak forest, suggesting a conservative impact on regional hydrology. Annually, the pine forest evapotranspires nearly 168 mm more water as compared to the oak forest with a negative impact on watershed storage as shown by complementary discharge measurements. Overall, our study shows that the ongoing land cover change may have significant consequences for regional hydrological resources.
How to cite: Kumar, M., Mohanty, J. R., Khanna, J., Krishnaswamy, J., and Sen, S.: Comparative Transpiration Responses of Chir Pine and Banj Oak Forests in the West-Central Himalaya: Evaluation with Evapotranspiration and Hydrometeorological measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1072, https://doi.org/10.5194/egusphere-egu26-1072, 2026.
Transpiration is a critical component of the carbon-water cycle, driving water from the soil to the atmosphere through plants as sap flow and linking plants to larger climate fluctuations. While the measurement of sap flow using thermometric principles has been refined over decades, translating these in-situ measurements into generalized models remains a challenge. The availability of the SAPFLUXNET database now opens opportunities for global, data-driven modeling. Despite the sophistication of recent approaches, current models often demonstrate high accuracy within specific sites but suffer performance degradation during cross-site validation. This study aims to overcome the generalization gap by introducing a modeling framework that decouples the prediction of temporal dynamics from absolute magnitude using a dual-model approach. The first model predicts normalized temporal patterns based on the 90th percentile and nighttime sap flow for each tree using XGBoost. The second model predicts absolute magnitude using tree and site-level characteristics using Random Forest. Results show a significant improvement in overall performance, with R2 increasing from 0.47 to 0.54 compared to a single combined model. This gain is primarily driven by better performance in the temporal model. While the average Root Mean Squared Error (RMSE) showed only minor improvement, the performance gains were consistent across tree sizes, genera, and plant functional types, validating the dual-model approach. Future work could further improve this framework by incorporating memory-based temporal models and integrating trait and remote sensing datasets for better tree representation. Finally, this scalable approach can be adopted to estimate regional-scale transpiration using species and tree size distributions, helping to refine our understanding of tree water use.
How to cite: Sarkar, D. P., Poyatos, R., Hildebrandt, A., Lee, S.-C., Kraft, B., and Nelson, J. A.: A Dual Model Approach to Better Generalize Individual Tree Water Use, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19641, https://doi.org/10.5194/egusphere-egu26-19641, 2026.
Groundwater is an important water source in water-limited desert ecosystem. Less attention has been paid to how the water balance and plant performance in such ecosystems vary with the presence or absence of groundwater. Appropriate replanting strategies play a pivotal role in preventing desertification. In this study, we used twelve large-scale weighing lysimeters with different replanting configurations, ranging from bare soil to various monocultures and mixtures of shrubs and semi-shrubs, to quantify water balance components and plant growth dynamics in contrasting desert ecosystems (groundwater-dependent vs. groundwater-independent) during 2019-2023. The results indicated that actual evapotranspiration in groundwater-dependent desert ecosystems was greater than in groundwater-independent ones. Linear mixed-effects models showed that groundwater had a significant effect on the water balance components and enhanced plant growth performance. Boosted regression tree models indicated that groundwater alleviated the influence of drought and sparse rainfall on the water balance components in deserts. The water use efficiency (WUE) of the semi-shrub A. ordosica (Ao) in monoculture was 6.74 and 3.10 kg m-3 in desert ecosystems with and without groundwater, respectively. The WUE of the C. korshinskii shrub (Ck) in monoculture and in a mixture with the semi-shrub A. ordosica (Ao & Ck) in groundwater-dependent desert ecosystems was 3.05 and 2.64 kg m-3, respectively. Vegetation restoration in arid areas serves as an effective nature-based solution for desertification control, where tailored replanting strategies are key to ensuring long-term sustainability.
Nan, Y. C., Huo, J. Q., Han, G. L., et al. (2025), Groundwater altered water balance and plant water use efficiency in desert ecosystems. Water Resources Research, 61(11), 1-15. doi: 10.1029/2025WR040545
How to cite: Nan, Y.: Groundwater altered water balance and plant water use efficiency in desert ecosystems - based on large-scale weighing lysimeters, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2257, https://doi.org/10.5194/egusphere-egu26-2257, 2026.
Evapotranspiration (ET) dominates the water balance of Mediterranean dune ecosystems, yet it is tightly coupled to small but frequent non-rainfall water inputs (NRWI) that modulate near-surface moisture availability during prolonged dry periods. Here, we present a three-year (2021–2024) lysimeter-based assessment of the interplay between ET and NRWI in a coastal Mediterranean dune system in Doñana National Park (SW Spain), using two large, high-precision weighing lysimeters installed under contrasting surface conditions: bare sand and shrub vegetation.
Hourly lysimeter mass changes were analysed under rain-free conditions to quantify ET and to detect and partition NRWI into dew, fog, frost, and water-vapour adsorption (WVA) using a physically based meteorological classification. Cumulative ET strongly exceeded NRWI at both sites, reaching 844.5 mm for bare soil and 931.9 mm for shrub-covered soil over the monitoring period, confirming vegetation as a major amplifier of evaporative losses. In contrast, cumulative NRWI amounted to 174.0 mm (bare soil) and 112.0 mm (shrub), highlighting a persistent but secondary moisture input.
Despite its smaller magnitude, NRWI occurred frequently, often on more than half of all rain-free nights, and directly offset early-morning evaporative losses. Bare soil consistently accumulated more NRWI due to stronger nocturnal cooling and tighter coupling to atmospheric humidity, whereas shrub cover reduced NRWI while enhancing daytime ET through transpiration and increased turbulent exchange. Among NRWI components, WVA dominated both in frequency and cumulative contribution across all seasons and years, while dew showed strong interannual variability linked to nighttime temperature and humidity, and fog inputs were negligible.
Our results demonstrate that while ET governs the annual water balance, NRWI, particularly vapor adsorption, plays a critical buffering role by repeatedly replenishing surface moisture prior to daytime evaporation. This interaction is highly sensitive to vegetation structure and climate variability and is considered to be highly relevant for ecohydrological models for Mediterranean drylands.
How to cite: Kohfahl, C. and Ruiz Bermudo, F.: Seasonal and Interannual Dynamics of Non-Rainfall Water Inputs and Evapotranspiration in a Coastal Mediterranean Dune Ecosystem, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3037, https://doi.org/10.5194/egusphere-egu26-3037, 2026.
Two micrometeorological towers were installed at two contrasting sites characterized by different precipitation regimes. The first tower is located in Orroli, an area with a mean annual precipitation of approximately 600 mm, selected as a case study within the AQUEDUCT European project. This site is characterized by a heterogeneous landscape composed of wild olive trees interspersed with C3 herbaceous vegetation. Vegetation develops on shallow soils overlying a partially fractured basaltic bedrock, with soil depths ranging between 15 and 40 cm. Tree cover accounts for about 25% of the tower footprint.
The second tower is located in a mountainous forested area dominated by Quercus ilex, characterized by steeper slopes, frequent rocky outcrops, and higher annual precipitation, averaging about 800 mm. In this site, tree cover represents approximately 67% of the tower footprint.
At both locations, land surface energy fluxes and CO₂ exchanges were measured using the eddy covariance technique. Soil moisture was monitored using water content reflectometers, while leaf area index (LAI) was periodically estimated to capture vegetation dynamics. In addition, the tree transpiration component was quantified using sap flow sensors, allowing the separation of vegetation contributions to evapotranspiration.
Results indicate that the Orroli site is strongly influenced by rainfall seasonality. Vegetation species at this site rely on water stored within the fractured rocky substrate to maintain physiological activity during dry periods. Pronounced seasonal patterns were observed in both CO₂ fluxes and evapotranspiration (ET), with higher values during periods when both herbaceous and woody vegetation are active, and lower values following the senescence of the grass component.
In contrast, the Marganai forest site exhibited relatively stable ET rates throughout the year, highlighting the high efficiency of tree species in accessing deep water reserves. ET of the site is similar at the Orroli site during periods of active grass growth, latent heat fluxes became greater in the Marganai forest once the herbaceous layer senesced. The relationship between ET and potential ET versus soil moisture suggest that Quercus ilex in Marganai appears largely independent of surface soil moisture, emphasize the contribution of the rock water reservoir. This contribution is also present at the Orroli site, and the water balance analysis shows that it plays a key role in sustaining grass vegetation during the late spring period. Overall, the results suggest the existence of a rainfall threshold of approximately 700 mm per year, below which precipitation becomes a limiting factor for tree cover development.
How to cite: Sirigu, S., Montaldo, N., and Corona, R.: Water-Limited Evapotranspiration in Two Contrasting and Heterogeneous Mediterranean Ecosystems of Sardinia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21799, https://doi.org/10.5194/egusphere-egu26-21799, 2026.
High-resolution mapping of urban evapotranspiration (ET) is challenged by landscape heterogeneity and complex natural-anthropogenic interactions, with a critical gap in fine-scale ET products that capture these land-atmosphere dynamics. This study bridges this gap by developing a hybrid physics-based and machine learning framework to generate 10-meter, hourly urban ET maps for Wuhan, China. We first simulate hourly latent heat flux at 333-m resolution using the Weather Research and Forecasting (WRF) model to establish a physical background field. Concurrently, high-resolution (10-m) surface features, including the Normalized Difference Vegetation Index (NDVI) and urban morphological parameters, are derived from Sentinel-2 imagery. Spatial downscaling from 333 m to 10 m is achieved by leveraging eddy covariance data from Wuhan’s urban flux tower. Using flux footprint modeling, hourly tower measurements are linked to the fine-scale land-cover configuration of their source areas, establishing a physical relationship between latent heat flux and surface properties. A Random Forest model is trained on this relationship and applied citywide to generate 10-m hourly ET maps. The resulting dataset effectively resolves intra-urban variability, clearly capturing sharp ET gradients between impervious surfaces and green spaces. This work provides a scalable and physics-grounded pathway for high-fidelity urban ET mapping, offering valuable insights for urban heat mitigation and water resources management.
How to cite: zhou, Z. and Song, J.: High Spatiotemporal Mapping of Urban Evapotranspiration via a Hybrid Physics-Based and Machine Learning Framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15790, https://doi.org/10.5194/egusphere-egu26-15790, 2026.
Evapotranspiration (ET) is the dominant flux in the terrestrial water balance of Mediterranean-type climates and a primary control on groundwater recharge. In south-western Australia, a long-term decline in winter rainfall combined with increasing evaporative demand and urban growth has intensified pressure on groundwater resources that support endemic ecosystems, irrigated agriculture, and urban water supply. Reliable, spatially distributed ET estimates are therefore crucial for groundwater modelling and water resources management under a changing climate.
Despite the widespread availability of satellite-derived ET products, their direct application in regions dominated by endemic vegetation remains problematic. Banksia woodlands, which cover large parts of the Swan Coastal Plain, exhibit deep rooting systems, strong soil-vegetation feedbacks, and seasonal water use strategies that are poorly represented in global ET algorithm. Consequently, commonly used products such as MOD16 and PML show significant discrepancies in magnitude and seasonal dynamics, leading to large uncertainty in groundwater recharge estimation.
To address this limitation, we developed a locally constrained ET upscaling framework that integrates multiple satellite products with ground-based observations across the Swan Coastal Plain. Empirical regression relationships were first derived for MOD16 and PML ET estimates to characterise systematic product differences. Then, time series were used to train and apply a Random Forest (RF) model, constrained by eddy covariance observations. Finally, in Google Earth Engine (GEE) the ET was upscaled in space and time using satellite-based predictors and land-cover information.
This contribution presents a multi-year, monthly ET climatology for the Perth region and evaluates its spatial and temporal consistency across major land-cover classes, with particular emphasis on banksia woodland ecosystems. Rather than benchmarking individual products alone, we assess the implications of ET uncertainty and upscaling choices for groundwater recharge estimation and regional groundwater modelling.
The resulting ET maps reveal systematic biases in standalone MOD16 and PML products over Banksia woodlands and demonstrate that the RF-based upscaling produces more coherent seasonal patterns and spatial gradients consistent with field observations. In particular, the RF model systematically constrains the high ET values characteristic of PML while preserving the spatial structure captured by MOD16. Monthly mean ET fields show reduced inter-product variability and offer stable behaviour suitable for direct use as inputs to groundwater modelling.
These results indicate that combining satellite-derived ET products through locally informed regression and machine-learning upscaling substantially improves the representation of evapotranspiration in groundwater modelling frameworks. The derived ET climatology provides a defensible basis for recharge estimation and scenario analysis under ongoing and projected climate evolution in south-western Australia. Nevertheless, more eddy covariance sites would improve estimates.
More broadly, this approach offers a transferable framework for adapting global ET products to endemic and water-limited ecosystems, supporting more robust groundwater-resource management in regions facing increasing hydroclimatic stress.
How to cite: Miotlinski, K., Moore, C., and Thompson, S.: Mapping evapotranspiration using satellite and eddy covariance in south-western Australia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16657, https://doi.org/10.5194/egusphere-egu26-16657, 2026.
Evapotranspiration (ET) is an Essential Climate Variable (ECV) that plays a key role in the energy-water cycle, as it can influence precipitation and temperature dynamics, and it also has a direct impact on irrigation water demand in agricultural areas. However, its accurate estimation is still under debate, with a major unresolved challenge: cloudy-sky conditions. This issue arises because most evaporation satellite-based models rely on instantaneous land surface temperature (LST) as an input to solve the energy balance. As a result, capturing ET dynamics under all-sky conditions remains challenging.
Moreover, these models rely solely on daily data and fail to capture the full dynamics of ET throughout the day. This partial representation of daytime ET dynamics is closely related to the asymmetric relationship between ET and radiation, which is strongly linked to LST. This asymmetry becomes even more complex in arid environments, where environmental factors such as vapour pressure deficit and air temperature modulate vegetation behaviour, as well as in irrigated areas, where water inputs can sometimes be uncertain. To interpret and represent ET dynamics under both clear-sky and cloudy-sky conditions, it is necessary to use models capable of simulating ET without relying on data availability affected by cloud presence.
This work presents preliminary results of the hybrid version of the FEST-EWB model, which is able to compute energy fluxes under all-sky conditions, merged with LST data from the Meteosat Second Generation. Evapotranspiration estimates over the entire MSG disk will be analysed and validated against eddy covariance data. The hybrid approach combines the FEST-EWB model—which continuously simulates soil moisture and ET over time and space, resolving LST and closing the energy–water balance equations (Corbari et al., 2011), thus providing a physically based framework capable of filling gaps when satellite LST is not available due to cloud cover—and the residual version of the FEST model, which uses the available LST under clear-sky conditions. Preliminary results from the FEST-Hybrid approach highlight the strong potential of the model due to its adaptability across different spatiotemporal scales and all-sky conditions. The integration of satellite LST data when available allowed to properly represent ET dynamic also in irrigated areas.
How to cite: Torralbo Muñoz, P., Mallick, K., and Corbari, C.: All-Sky Evapotranspiration and its Diurnal Asymmetry Using Physically Based Modelling and Geostationary Land Surface Temperature Data , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12020, https://doi.org/10.5194/egusphere-egu26-12020, 2026.
Accurate mapping of evapotranspiration (ET) via remote sensing is crucial for water resource management, yet it remains challenging in water-limited environments where existing water stress proxies often lack physical robustness or spatiotemporal detail.
We here propose the Temperature-Shortwave Infrared Water Index (TSWI), a synergistic index that directly couples land surface temperature (LST) and shortwave infrared (SWIR) reflectance for ET estimation. TSWI demonstrates strong agreement with in-situ soil moisture and consistency with SMAP and MODIS products. TSWI-based ET estimates perform robustly against flux tower data (R² = 0.64) and water-balance ET (R² = 0.83). Moreover, the standardized TSWI effectively captured the 2022 Yangtze River Basin drought with finer detail than conventional indices. These results demonstrate TSWI as a robust, operational tool for regional ET estimation and drought monitoring, supporting improved water management and early warning systems.
How to cite: Yang, Y. and Yan, D.: A Novel Synergistic LST-SWIR Index for Evapotranspiration Estimation and Drought Assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15503, https://doi.org/10.5194/egusphere-egu26-15503, 2026.
Terrestrial evaporation (E) has traditionally been estimated either at coarse resolution over large domains or at high resolution over limited regions due to computational and storage constraints. Recent methodological and computational advances are bridging this gap, enabling regional- to global-scale E datasets at relatively high spatial resolutions for climate, water-management, and agricultural applications. Building on these developments and the fourth generation of the Global Land Evaporation Amsterdam Model (GLEAM4¹), we present GLEAM-HR, a 1-km E dataset for 2016–2023 covering Europe, Africa, and a portion of South America (Meteosat disk). GLEAM-HR combines precipitation from Multi-Source Weighted-Ensemble Precipitation (MSWEP) v2.8 with radiative forcing derived from merged Land Surface Analysis Satellite Application Facility (LSA SAF) and Moderate Resolution Imaging Spectroradiometer (MODIS) products.
Algorithmic enhancements in GLEAM-HR enable a more realistic representation of fine-scale E dynamics, particularly in agricultural regions, and improve the characterisation of droughts and heatwaves. A key innovation is the explicit representation of irrigation through a four-step framework that (i) identifies irrigation timing and location at a daily scale, (ii) raises soil moisture to field capacity in irrigated grid cells, (iii) assimilates 1-km Sentinel-1² soil moisture observations, and (iv) estimates evaporative stress using an XGBoost-based model driven by vegetation and atmospheric stressors. Unlike other existing approaches, GLEAM-HR does not assume potential evaporation over irrigated croplands, but constrains E using multiple environmental stress factors.
The resulting estimates show increases in annual evaporation of up to 450 mm yr⁻¹ over irrigated regions compared to simulations that neglect irrigation, with spatial patterns consistent with independent irrigation datasets. Evaluation against eddy-covariance measurements shows clear improvements at irrigated sites, with daily Kling–Gupta Efficiency (KGE) values of 0.20–0.40, while performance in non-irrigated regions ranges from 0.17 to 0.64. The dataset will be made publicly available through an interactive 3D data cube³ platform. Overall, GLEAM-HR provides a realistic high-resolution representation of irrigation effects on E, supporting applications in regional agricultural management and water-resource assessment. Future work includes global production of GLEAM-HR, development of a global 3D data cube, expansion of the record length, and propagation of algorithmic improvements to the next release of the long-term GLEAM climate record (0.1°) available via www.gleam.eu.
¹ Miralles, D.G., Bonte, O., Koppa, A., Baez-Villanueva, O.M., Tronquo, E., Zhong, F., Beck, H.E., Hulsman, P., Dorigo, W., Verhoest, N.E. and Haghdoost, S., 2025. GLEAM4: global land evaporation and soil moisture dataset at 0.1 resolution from 1980 to near present. Scientific data, 12(1), p.416
² Fan, Dong; Zhao, Tianjie; Jiang, Xiaoguang; García-García, Almudena; Schmidt, Toni; Samaniego, Luis; Attinger, Sabine; Wu, Hua; Jiang, Yazhen; Shi, Jiancheng; Fan, Lei; Tang, Bo-Hui; Wagner, Wolfgang; Dorigo, Wouter; Gruber, Alexander; Mattia, Francesco; Balenzano, Anna; Brocca, Luca; Jagdhuber, Thomas; Wigneron, Jean-Pierre; Montzka, Carsten; Peng, Jian (2025): A Sentinel-1 SAR-based global 1-km resolution soil moisture data product: Algorithm and preliminary assessment. Remote Sensing of Environment, 318, 114579
³ M. Söchting, M. D. Mahecha, D. Montero and G. Scheuermann, (2024): Lexcube: Interactive Visualization of Large Earth System Data Cubes. IEEE Computer Graphics and Applications, vol. 44, no. 1, pp. 25-37.
How to cite: Baez Villanueva, O. M., Crespo-Otero, A., Söchting, M., Laluet, P., Mahecha, M., Bonte, O., Massant, J., Schellekens, J., Massari, C., Corbari, C., Modanesi, S., Dari, J., Delbare, K., Dorigo, W., Beck, H. E., Quintana-Seguí, P., Boone, A., Clavera-Gispert, R., and Miralles, D. G.: GLEAM-HR: A 1-km terrestrial evaporation dataset with explicit representation of irrigation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14974, https://doi.org/10.5194/egusphere-egu26-14974, 2026.
Early forecasting of crop irrigation demand improves water efficiency and supports food security. Accurate forecasts allow farmers and policymakers to schedule irrigation based on crop type and climate, ensuring timely water application throughout the growing season. Traditional biophysical models rely on fixed delivery schedules and broad hydrological trends, often lacking precision for specific crop stages. While machine learning (ML) has been widely used for yield and biomass prediction, its application in forecasting crop water requirements remains limited. This study bridges that gap by integrating remote sensing with ML to align water supply with crop demand under changing climate conditions, promoting sustainable irrigation. The study focuses on Pakistan’s Indus Basin, specifically Chaj Doab, located between the Jhelum and Chenab rivers. The region features flat terrain with coarse-textured alluvial soils and high evapotranspiration with wide variety of crops. The proposed methodology for estimating irrigation demand uses actual evapotranspiration (ETact) derived from satellite-based biophysical and climatic variables. Landsat imagery with a spatial resolution of 30 m resolution from 2015 to 2025 was used to calculate normalized difference vegetation Index (NDVI), soil adjusted vegetation index (SAVI), land surface temperature (LST), and net radiation (Rn).
The dataset was split with 80% used for training and 20% for validation, to simulate a continuous forecasting scenario. The primary objective was to evaluate model performance in an unseen future period, reflecting irrigation forecasts in practice rather than re-learning from shuffled segments through temporal cross-validation. By contrast, the 80-20 split ensured a long historical record for vigorous training and a sufficiently large, continuous block of unseen data that spans entire season irrigation requirement which validation against observed evapotranspiration (ET) and local climate data from the Eddy Covariance Flux Tower, providing reliable ground-truth checks across entire cropping cycle.
The machine learning models tested included CNN, XGBoost, and Random Forest. Among these, CNN achieved the highest performance with an R² of 0.89, followed by XGBoost (R² = 0.81) and Random Forest (R² = 0.76) on the testing samples. The short-term irrigation forecasting model was evaluated across two cropping seasons Kharif and Rabi, using observed ET values and local climate data from an Eddy Covariance Flux Tower. For rice during Kharif, CNN predicted 6.798 mm/day compared to the flux tower's 6.99 mm/day. During Rabi, the model predicted wheat ET at 2.041 mm/day, closely matching the observed 1.86 mm/day. During the growth phase of wheat in Rabi season, CNN forecasted ET at 29.87 mm/day, closely matching the flux tower measurement of 33 mm/day. Similarly, during early April, the model estimated 12.12 mm/day versus an observed 13.15 m/day. The lowest deviation occurred during the week of December, with both CNN and flux tower ET values closely aligned (6.99 and 6.89 mm/day, respectively). Overall, CNN showed the highest correlation than other models across multiple crops (maize, potato, guava, and orchards), showing strong spatial accuracy and temporal relevance. The outcomes support a wide range of users including farmers, local organizations, and decision-makers by enabling proactive irrigation planning.
How to cite: Aeman, H., Hafeez, M., and Munir, S.: Early Forecasting of Crop Irrigation for Sustainable Water Use with Satellite Data and Machine Learning , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-616, https://doi.org/10.5194/egusphere-egu26-616, 2026.
Accurate evapotranspiration (ET) projections are essential for water resource management and drought prediction under a changing climate. However, ET estimates from Global Climate Models (GCMs) exhibit large uncertainties stemming from systematic biases in meteorological variables, coarse spatial resolution, and inter-model structural differences. This study addresses these limitations by developing an integrated framework combining CMIP6 projections, a proxy for observations, and a machine learning approach to improve the reliability of projected ET at a fine spatiotemporal scale (temporal: daily scale; spatial: 0.1°×0.1°) over the Krishna River Basin. To select a proxy for observed multiple ET products (ERA5-Land, GLDAS-NOAH, and GLEAM) were first validated against the water balance-based basin scale ET available from Ma et al. (2024). Results indicate superior performance of ERA5-Land for the selected river basin and was subsequently used to assess the uncertainty of seven CMIP6 GCMs (over the historical period 2015–2023). Through multi-metric analysis, EC-Earth (SSP5-8.5) outperformed all other models, showing low weighted mean absolute error (WMAE,) highest correlation and strongest 1:1 alignment. Despite this, initial evaluation of raw EC-Earth inputs revealed systematic biases in variables associated with ET like solar radiation (ssrd), thermal radiation (strd), and sensible heat flux (sshf), leading to substantial underestimation of ET. To resolve this, a Quantile Mapping (QMAP) technique was employed to bias-correct the meteorological drivers, successfully restoring their statistical distributions. Then a data-driven Bayesian Network (BN) model was developed to simulate ET using precipitation, temperature, and the bias corrected variables. The BN model demonstrated robust performance (R > 0.87) during both development and testing phases. Near-future projections (2024–2030) indicate that relying on raw GCM data dampens seasonal cycles; in contrast, the bias-corrected BN projections highlight a higher mean ET and effectively capture seasonal extremes, particularly during dry months (January–June). These findings underscore the critical role of bias correction in hydro-climatic modelling and establish this framework as a reliable tool for future hydrological assessment in the Krishna River Basin. This integrated methodology demonstrates that coupling statistical bias correction with machine learning models can substantially reduce projection uncertainty. The framework is transferable to other basins and provides reliable ET projections for improved water availability assessments and climate adaptation planning.
Keyword: - Quantile Mapping, Bayesian Network, CMIP6 downscaling ,Bias correction, Hydro-climatic modelling.
How to cite: Kumar, R. and Dutta, R.: Reliable basin-scale projection of evapotranspiration at fine spatiotemporal scale using machine learning-based techniques , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-284, https://doi.org/10.5194/egusphere-egu26-284, 2026.
Evapotranspiration (ET) is a key component of terrestrial water balance and is vital in monsoon driven river basins where strong seasonal variability in land atmosphere interactions governs water availability and agricultural productivity. This study presents a basin scale ET estimation framework that integrates Atmosphere Land Exchange Inverse (ALEXI) model with multi-sensor optical thermal remote sensing through Sentinel Landsat data fusion for the Mahanadi River Basin, India. The approach enhances spatial and temporal characterization of land surface processes while retaining the physically based foundation of the ALEXI model. ALEXI is a two-source energy balance model that partitions surface fluxes between soil and vegetation canopy components and estimates latent heat flux based on the temporal increase in land surface temperature (LST) from early morning to mid-morning. In this study, thermal information from Landsat is combined with the high resolution surface reflectance, vegetation indices, and land cover information derived from Sentinel-2 to better represent surface heterogeneity across agricultural, forested, and mixed land-use areas. Meteorological forcing, including air temperature, wind speed, humidity, and incoming solar radiation, has been used to model atmospheric boundary layer (ABL) growth and derive sensible heat flux, while latent heat flux has been computed as a residual of the surface energy balance and converted to ET. The fusion of Sentinel and Landsat data improves spatial detail in canopy soil energy partitioning, enabling more accurate ET estimation in fragmented agricultural landscapes characteristic of the Mahanadi Basin. The temperature differential nature of ALEXI reduces sensitivity to absolute LST biases and atmospheric correction uncertainties, making it particularly suitable for large, cloud prone monsoon basins. The resulting ET estimates capture seasonal water use dynamics and drought stress patterns across kharif and rabi cropping cycles. This integrated ALEXI multi sensor framework provides a scalable and physically consistent approach for basin-scale hydrological assessment, offering valuable insights for irrigation management, drought monitoring, and sustainable water resources planning in data-scarce regions.
How to cite: Patel, G. P. and Keshari, A. K.: Integrating Sentinel Landsat Fusion with ALEXI Framework for Physically Based Evapotranspiration Estimation in a Monsoon-Dominated River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3696, https://doi.org/10.5194/egusphere-egu26-3696, 2026.
Evapotranspiration (ET) governs the exchange of water and energy between the land surface and the atmosphere, accounting for over 70% of agricultural water consumption. Accurate ET estimation is therefore crucial for efficient irrigation management and sustainable water allocation. Traditional in situ methods, such as lysimeters and eddy covariance towers, provide precise measurements but are costly and spatially limited. In contrast, remote sensing–based energy balance models like the Surface Energy Balance Algorithm for Land (SEBAL) offer scalable, cost-effective solutions for large-scale ET monitoring.
A critical factor determining SEBAL’s accuracy is the appropriate selection of hot and cold anchor pixels, which represent the limiting conditions of no ET and maximum ET, respectively. However, this step remains one of the most challenging and subjective aspects of SEBAL implementation. Previous approaches, including visual selection, statistical filtering, and threshold-based rules (e.g., based on NDVI, albedo, and LST), have improved consistency but still suffer from regional dependency, random selection biases, and inconsistent parameter thresholds. Methods relying on proximity to meteorological stations or calibration with lysimeter data improve accuracy but are not universally applicable due to data limitations and landscape heterogeneity. Consequently, the same scene can yield different anchor pixels across methods, leading to divergent ET estimates and reduced reproducibility.
To address these limitations, this study proposes the development of an automated, reproducible framework for anchor pixel selection using a Genetic Algorithm (GA) optimization approach. The GA systematically identifies biophysically consistent anchor pixels by exploring multidimensional feature space (NDVI, LST) while minimizing uncertainty and eliminating subjective human bias. The method is implemented using daily MODIS (Moderate Resolution Imaging Spectroradiometer) imagery, aggregated to 250 m spatial resolution to capture field-scale variability better while maintaining high temporal fidelity.
This automated approach ensures scalability, portability, and reproducibility across diverse agro-ecological regions without heavy data requirements. It offers a simple yet robust workflow suitable for operational ET monitoring and can be integrated into regional irrigation and drought management systems
How to cite: m a, N. and Narasimhan, B.: A Scalable Genetic Algorithm Framework for Automated Anchor Pixel Selection to Improve Satellite-Based Evapotranspiration Monitoring Using SEBAL, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13206, https://doi.org/10.5194/egusphere-egu26-13206, 2026.
Climate change is accelerating at an unprecedented rate, profoundly impacting sectors, systems, individuals, and institutions worldwide. Adaptation to its effects has become a critical priority. In Austria, the consequences of climate change are particularly pronounced, with its existence, pace, and impacts clearly evidenced by extensive measurements and observations. Recent climate data indicate that the country’s annual mean temperature has risen at more than twice the global average, exacerbating challenges in urban areas, agriculture, mountainous and forest ecosystems (https://www.iea.org/articles/austria-climate-resilience-policy-indicator).
In response to these pressing challenges, this project aims to develop innovative services to support climate adaptation strategies. By leveraging existing satellite missions and in-situ data, the integration and monitoring of evapotranspiration (ET) can be used as a key indicator for assessing climate resilience and providing actionable insights to decision-making.
The tool developed for the FFG Project GET-ET is using the measurements of ECOSTRESS and Sentinel-2 imagery to perform high resolution estimation of evapotranspiration. ECOSTRESS measurements have been selected as reference for the modelling, the sensor provides 70m evapotranspiration daily maps. The input corresponds to a combination of multispectral bands and digital elevation model from Copernicus data. To fit the reference with the input, it has been decided to enhance ECOSTRESS measurements with a python implementation of Data Mining Sharpener, based on Leaf Area Index values obtained from Sentinel-2. A dataset has been generated over Austria between 2019 and 2025, considering the atmospheric perturbation and the time correlation of both sensors. The dataset has been fed into a Unet with ResNet blocks pre-trained with Sentinel-2 images. The perceptual loss has been used to increase the capabilities of producing precise estimation of the evapotranspiration. The model trained over Austria reached meaningful results in terms of metric: 0.91 of Structural Similarity Index Measurement (SSIM), letting a confident space for scale generalization. The service can then provide for each new Sentinel-2 image an estimation of evapotranspiration. In the context of the project, monthly aggregation over Austria is produced and integrated into the GTIF platform.
High resolution evapotranspiration maps are valuable tools in urban planning enabling the strategic design of green infrastructure to build climate-resilient cities. These maps allow the precise identification of urban heat islands (UHI), areas experiencing elevated heat stress due to the lack of green infrastructures (GI). By pinpointing these areas, planners can implement targeted green interventions, to enhance natural cooling mechanisms such as cooling corridors. Beyond heat mitigation, ET maps also support the ongoing monitoring of green spaces, such as green roofs and parks to ensure their vitality and long-term effectiveness in providing cooling benefits, therefore improving urban livability. Within this project, the ET maps are demonstrated through real-world use cases over Austria.
How to cite: Houël, M., Azami, W., and Bojor, A.: High resolution evapotranspiration map over Austria : leveraging AI and Sentinel-2, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18926, https://doi.org/10.5194/egusphere-egu26-18926, 2026.
The partitioning of evapotranspiration (ET) into crop transpiration (T) and soil evaporation (E) is crucial for accurate modelling of land-atmosphere processes and for assessing climate sensitivity in agricultural systems, yet it remains methodologically challenging. Here, we quantify diurnal dynamics of ET and its components in winter wheat using high-resolution in-situ water isotope flux chamber measurements in comparison to estimates from micro-lysimeters, sap flow measurements and eddy-covariance (EC) measurements.
Field campaigns were conducted on 2-5 consecutive days per month during the 2025 growing season at an agricultural experimental site “The Land-Atmosphere-Feedback Observatory” (University of Hohenheim, Germany), spanning key crop phenological stages of winter wheat. Water isotope-based chamber measurements were performed in the vicinity of the EC tower from sunrise, after morning dew evaporation, until sunset. E, T and ET chambers were measured consecutively, resulting in 8-12 measurements per flux type per day. Isotopic compositions of E, T and ET used for ET partitioning were estimated using the Keeling plot method. Independent estimates of E and T were derived from five micro-lysimeters installed in a star-like pattern around the EC tower, and five sap flow micro sensors installed on five individual plants, and compared to the water isotopic partitioning results.
Our method comparison focuses on a measurement period in June 2025, whereas chamber-based water isotope measurements are presented for the entire growing season.
In June, ET derived from EC measurements ranged from 0 to 350 W m-2, peaking around midday, while E obtained from micro-lysimeters was always less than 120 W m−2. Sap flow measurements often led to reasonable values only in the afternoon, showing a decreasing trend of similar magnitude as ET. Daily patterns varied depending on meteorological conditions. Chamber-based E, T and ET estimates were close to EC tower/micro-lysimeter/sap flow flux-based measurements, but showed larger scattering, likely due to spatial heterogeneity. Across measurement approaches, T/ET ratios predominantly ranged between 0.5 and 1, indicating that T dominated ET. The T/ET ratio showed a “U-shaped” diurnal pattern when derived from micro-lysimeter and EC tower measurements but is decreasing over the day when sap flow and lysimeter data were considered. T/ET derived from chamber fluxes did not show a clear diurnal pattern. Isotope-based ET partitioning results showed large scattering mainly due to weak isotopic contrast between T and E, but also due to spatial heterogeneity and temporal variability in isotope flux signatures. In general, hydrogen isotope-based partitioning showed better agreement with flux-based estimates than oxygen isotopes, likely due to stronger isotopic fractionation signal between fluxes.
The dominant controls on isotopic variability and flux dynamics of E, T and ET will be discussed in relation to meteorological conditions, plant physiological parameters and soil water availability. Measurement uncertainty across approaches will be evaluated and a best-estimate ET and its partitioning will be derived. These findings will help to evaluate model representation of T/ET and reduce uncertainties associated with T/ET estimates.
How to cite: Orlowski, N., Eckert, J., Voigt, C., Ingwersen, J., and Streck, T.: ET partitioning method comparison for a winter wheat stand at the Land-Atmosphere-Feedback Observatory, Hohenheim, Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19290, https://doi.org/10.5194/egusphere-egu26-19290, 2026.
Lysimeters are experimental tools for identifying water fluxes in-situ in the soil-plant-atmosphere continuum. Weighable lysimeters are especially interesting for investigating evapotranspiration as they allow quantifying mass changes. Automated water balance estimations in weighable lysimeters are available in high-temporal resolution offering detailed information about diurnal processes. Besides water balance monitoring natural tracers like stable water isotopes are useful tools to investigate ecohydrological fluxes. Due to fractionation during phase transitions stable water isotopes are especially beneficial to identifying evaporative processes. Understanding the processes affecting evapotranspiration and groundwater recharge are essential for sustainable water management.
We investigated evapotranspiration based on water balance calculations and stable water isotopes at a lysimeter site in Koblenz, Germany between 2022 and 2025 to optimize our understanding of soil-plant-atmosphere water fluxes. The study site is located at the Rhine island Niederwerth in Koblenz and consists of eight drainage lysimeters, four of which are weighable with a grassland-area of 1 m² and a depth of 2 m. The corresponding soil monoliths consist of alluvial clay, loess loam, alluvial sand, and clayed pumice sampled within a 20 km radius surrounding Niederwerth. Seepage is collected weekly for evaluating automated measurements and to sample stable water isotopes. The site further includes a water basin for evaporation measurement and a meteorological measuring set up with air temperature, humidity, precipitation amount, solar radiation and others. The site-specific precipitation from 2022 to 2025 was 2885 mm (721 mm/a) while measured evaporation from a free water surface was 2473 mm (618 mm/a) and evapotranspiration 2291 mm (573 mm/a; mean over all four weighable lysimeters). The years of 2022 and 2025 were especially dry with evaporation exceeding precipitation input.
We found that normalized solar radiation measurements showed similar trends compared to normalized evapotranspiration measurements of the lysimeters which may offer the opportunity to investigate evapotranspiration via remote sensing techniques or to optimize model predictions. Preliminary results regarding stable water isotopes indicate a hysteresis cycle in lc-excess mean of seepage for alluvial sand and clayed pumice. Alluvial clay and loess loam showed little to no seepage in autumn and loess loam seepage lc-excess exhibited low variability. The seepage of loess loam with 142 mm/a was the lowest of the four soil types while alluvial sand showed the highest seepage of 194 mm/a. This supports a general understanding of stable water isotopic mixing in soils as in fine grained soils evaporated precipitation is mixing with surrounding water mitigating the effects of evaporation until percolating to the seepage depth of 2 m while highly permeable soils allow for evaporation effects to be monitored also in deeper soil depths.
With our study we will offer further insights into the variability of evapotranspiration based on soil types and aim to investigate evapotranspiration via tracer-aided modeling. Upcoming steps will include the estimation of the young water fraction and modeling evapotranspiration based on water balance and isotopic composition.
How to cite: Landgraf, J., Goetzie, J., Wilhelmi, J., and Schmidt, A.: In-situ long-term monitoring of evapotranspiration via weighable lysimeters and stable water isotopes in seepage, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4541, https://doi.org/10.5194/egusphere-egu26-4541, 2026.
Evapotranspiration (ET) constitutes one of the most significant fluxes of matter and energy within terrestrial ecosystems and serves as a key indicator of landscape functioning. A range of in situ measurement techniques—such as lysimeters, eddy covariance systems, and sap flow sensors—are now widely employed in monitoring networks and research initiatives to obtain high-quality field observations. These datasets offer substantial potential for secondary use, including methodological intercomparisons and refinements, upscaling efforts, and analyses of large-scale or long-term patterns. However, effective and efficient data reuse—and thus scientific progress—is frequently hindered or rendered impossible by insufficient or missing metadata.
Previous sessions at EGU General Assemblies focusing on in situ and remote-sensing-based ET have highlighted the need to support comparisons of ET estimates obtained with different methods and to enable informed reuse of existing data. Given the rapid growth of available datasets and the increasing importance and potential of data reuse, there is an urgent need to reduce this persistent bottleneck caused by insufficient metadata. In response, we initiated a collaborative working group to address this issue by developing a set of standardized templates for relevant metadata and uncertainty information. These templates build upon and extend existing initiatives, including ICOS, FLUXNET, and SAPFLUXNET, and are tailored to eddy covariance, sap flow, and lysimeter measurements.
We present the templates, highlight their differences from existing initiatives and standards, and we outline our vision for future use cases, e.g. in inter-method comparisons and modelling studies enabled by the enhanced metadata descriptions. We invite feedback from the data producers whether the templates facilitate providing metadata, increasing the re-usability of the data; and from the data users on whether the proposed metadata templates meet their needs. Based on community input, we aim to further refine the templates to support useful and sustainable data description.
How to cite: Hassler, S. K., Bonal, D., Maier, M., Groh, J., Hartogensis, O., Hirschi, M., Nelson, J., Nolz, R., Paulus, S., Rebmann, C., Seeger, S., Steppe, K., and Werisch, S.: Co-Developing Metadata Standards for In-Situ ET Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1866, https://doi.org/10.5194/egusphere-egu26-1866, 2026.
Heat-pulsed based Sap flow sensors are widely used to quantify plant water uptake by employing heat as a tracer. These methods estimate sap flow using the temperature breakthrough curves measured down and/or upstream of the heat source following a heat pulse. Several established methods, such as Marshall, Tmax, Compensation Heat Pulse (CHP), and Heat Ratio (HR) methods, rely on simple analytical formulations that can be implemented using spreadsheets or basic computing tools. In contrast, physically based inverse modelling approaches such as the Sum of Squares Error (SSE) or Sapflow+ method estimate heat pulse velocity by fitting analytical solutions of the heat transport equation to measured temperature responses using nonlinear optimization. This involves more complex computing tools that may not be easy to implement in spreadsheets. This computational complexity has limited the broader adoption of these methods. To address this, here we develop an interactive, Python-based open-source Graphical User Interface (GUI) for estimating sap flow using the SSE method. The GUI can be accessed through a web browser making it platform-independent. Additionally, users have the option to deploy the computational platform locally on their computers/tablets/mobile devices or access the cloud computing services we have enabled. The tool enables users to i) upload temperature data, ii) apply data filtering and wound correction, iii) perform automated parameter estimation, and iv) visualize heat pulse velocity and sap flow rates through interactive plots with downloadable outputs. Users also have the option to compare sap flux estimates from different methods. By providing a user-friendly interface for different sapflow methods, including the computationally intensive SSE method, our GUI facilitates more consistent and reliable sap flow analysis across research studies.
How to cite: sogala balaram, S. and Srinivasan, V.: An Open-Source Graphical User Interface for Estimating Heat-Pulse Based Sap Flux, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9032, https://doi.org/10.5194/egusphere-egu26-9032, 2026.
A major concern for revegetated desert ecosystem is accounting for the evapotranspiration dynamics which is influenced by the carrying capacity of the soil moisture content. Most field observations indicate that soil moisture at certain depth varies with the stochastically occurrence of rainfall events, and the evapotranspiration at community level also varies with the total of annual precipitation. Based on a study of the long-term field observation on the revegetated desert ecosystem, we find that the evapotranspiration of the shrub community correlates closely to the availability of soil moisture, and it can be quantified by analytical description of the stationary and transient joint behavior of plant transpiration and soil moisture. The experimental results indicate that the size and diversity of plant species in water-limited ecosystem can be determined by plant transpiration, which is a comprehensive indicator for plant water resource competition. These results suggest that revegetating large sandy areas with desert shrubs could reduce soil water storage by transpiration, which could significantly change groundwater recharge conditions. However, from a viewpoint of desert ecosystem restoration, it appears that natural rainfall can sustain desert shrubs which would reduce wind erosion.
How to cite: Wang, X.: Variation among desert shrub patches in evapotranspiration, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1977, https://doi.org/10.5194/egusphere-egu26-1977, 2026.
Groundwater plays a pivotal role in regulating water consumptive use within the soil-plant-atmosphere continuum and maintaining sustainable vegetation restoration in arid areas. Nevertheless, the mechanistic understanding of how groundwater influence root distribution patterns and evapotranspiration (ET) dynamics, as well as how it is partitioned into evaporation (E) and transpiration (T), remains incomplete. In this study, we employed four large-scale weighing lysimeters in a 2×2 factorial design (vegetation: Artemisia ordosica vs. bare soil; groundwater: presence vs. absence), complemented by Hydrus-1D simulations to investigate the groundwater effects on ecohydrological processes in the desert ecosystem. From April to August 2024, we analyzed the influence of groundwater (-2.5 m) on plant growth properties, soil water, ET and its partitioning. Findings revealed that the roots of A. ordosica in desert ecosystems with groundwater extended deeper (-3.0 m) than those without groundwater (-2.8 m). The ratios of root area to leaf area of A. ordosica under conditions without and with groundwater were 5.22-15.4 and 30.1-60.5, respectively. The groundwater increased soil water contents in the middle and deep soil layers, while the higher precipitation (31 mm d-1) could influence the soil water contents at depths of 0.8-1.0 m. The performance of the model for the simulated soil water contents, E or ET of the four lysimeters with Hydrus-1D achieves satisfactory results (R2 = 0.501-0.726, RMSE = 0.0135-0.590, NSE = 0.438-0.692). The observed mean daily ET of A. ordosica was 2.18 ± 0.0973 and 1.19 ± 0.0685 mm d-1 for the treatment with and without groundwater. The simulated root water uptake (RWU) was clearly higher for the groundwater treatment (1.15 ± 0.0625 mm d-1) than for the control (0.548 ± 0.0317 mm d-1). The RWU:ET ratios of A. ordosica were 52.7 ± 1.58% and 50.1 ± 1.57% with and without groundwater. Redundancy analysis and Pearson correlation showed that the presence of groundwater alleviated the influences of low precipitation, relative humidity, and high air temperature on ET and its partitioning. This study provides robust empirical evidence to help us understand the interactions between groundwater-soil-plant-atmosphere in desert ecosystems. This has significant implications for the sustainable revegetation management practices in arid areas.
How to cite: Zhang, Z.-S.: Observed and modeled root water uptake by sand-fixing semi-shrub in response to groundwater, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4729, https://doi.org/10.5194/egusphere-egu26-4729, 2026.
Reference evapotranspiration (ET0) is a key variable for quantifying crop water requirements and for irrigation management. The FAO-56 Penman–Monteith equation is widely adopted as the standard method for estimating ET0; however, its application depends strongly on the availability, quality, and spatial representativeness of the input meteorological variables. In agricultural regions with high microclimatic variability, the heterogeneous spatial distribution of meteorological stations can introduce substantial uncertainties into ET0 estimates. Southern Brazil is characterized by a subtropical climate with relatively well-distributed precipitation throughout the year, thus allowing for rainfed cropland systems. In this region, agricultural systems exhibit a high intensity of land use, with croplands remaining under production almost continuously. As a consequence of this intensification, rainfed systems are dominated by crop rotations involving soybean, wheat, and maize, often combined with cover crops. In lowland areas, production systems are primarily based on flood-irrigated rice, alternated with soybean or pasture. This diversity of land uses, together with the resulting variability in surface conditions, poses additional challenges for the accurate estimation of crop evapotranspiration (ETc), due to the strong influence of microclimatic conditions on soil–plant–atmosphere water and energy exchanges. Within this context, this study aims to evaluate the impact of using in situ versus regional meteorological data on the estimation of ET0 and on the determination of actual crop evapotranspiration (ETc,act) and therefore actual crop coefficients (Kc,act). To this end, the SIMDualKc model will be applied to estimate actual ETc following the FAO-56 approach, and the results will subsequently be evaluated against ET measurements derived from eddy covariance flux towers. These analyses will be conducted in two agricultural areas under crop rotation in the state of Rio Grande do Sul, Brazil: one under rainfed conditions and another located in a lowland system. For the application of the SIMDualKc model, meteorological data from the Instituto Nacional de Meteorologia (INMET) station located approximately 50 km from each field will be used, as well as data from a local meteorological station installed over a reference surface at a distance of about 1 km from the monitored areas. In addition to the difference in distance relative to the croplands, the local station provides direct measurements of net radiation and soil heat flux, variables that must be estimated when using INMET station data. Therefore, it is expected that the results will demonstrate that the use of local meteorological data allows a more robust quantification of the uncertainties associated with FAO-56-based ETc estimates and, if necessary, supports adjustments of actual Kc derived from regional data, accounting for local microclimatic patterns. Finally, the study seeks to highlight that spatial discrepancies related to station distance may fail to represent the local climatic conditions relevant for ET0 estimation and, consequently, may directly affect uncertainties in actual crop evapotranspiration estimates and irrigation management.
How to cite: Pinheiro, M., Lobato, R., Petry, M., Paredes, P., Souza, V., Mergen, A., Lopes, M., Souza, E., Basso, J., and Roberti, D.: Effect of the representativeness of meteorological input data on the estimation of reference evapotranspiration (FAO-56) and crop evapotranspiration in intensive agricultural systems under a subtropical climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7080, https://doi.org/10.5194/egusphere-egu26-7080, 2026.
Advances in soil moisture monitoring techniques and sensor networks have made data assimilation a powerful approach for estimating evapotranspiration (ET). The commonly used simple water balance (SWB) model provides reliable ET estimates within data assimilation frameworks; however, it often neglects the influence of ET on vertical fluxes. While this assumption may be valid during drying periods in low-drainage soils, it is less appropriate for soils with high hydraulic conductivity. This study introduces a comprehensive water balance (CWB) model that explicitly accounts for ET-driven percolation. The model captures the effect of ET on vertical fluxes by comparing soil water depletion with and without ET, thereby highlighting the role of root water uptake (RWU) in controlling percolation. The CWB model, coupled with an ensemble Kalman filter, predicts daily ET using soil moisture sensor data across different soil types. Within this framework, RWU is treated as the observable variable for state updating. Model performance was evaluated against the conventional SWB model under varying drainage conditions. For loamy sand, the CWB model was independently validated using weighing lysimeter measurements. Results demonstrate that the CWB model outperforms the SWB model, particularly in coarse-textured soils, reducing ET estimation error by up to 45% and achieving higher accuracy (NSE = 0.918 vs. 0.727). Sensitivity analyses incorporating sensor uncertainty show that fine-textured soils exhibit lower sensitivity to measurement errors, resulting in more robust ET estimates. These findings underscore the importance of incorporating vertical flux effects to avoid ET underestimation, especially in highly permeable soils.
Keywords: Water balance model, vertical flux, evapotranspiration, Ensemble Kalman filter, root water uptake.
How to cite: Chaudhary, D. and Sonkar, I.: Improved Evapotranspiration Estimation in Coarse-Textured Soils Using a Comprehensive Water Balance Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7151, https://doi.org/10.5194/egusphere-egu26-7151, 2026.
Remote sensing based water productivity indicators are increasingly used in agricultural and ecosystem monitoring, yet their accuracy is constrained by uncertainties in evapotranspiration (ET) and net primary production (NPP) data. Here we evaluate four global ET and NPP products against eddy covariance (EC) flux tower data from AmeriFlux, ICOS, and OzFlux for 2018-2022. Tower latent heat flux is used to derive ET, while tower gross primary production (GPP) is converted to NPP. All satellite products are harmonized temporally and evaluated at dekadal scale using correlation, bias, and RMSE, with stratification by land cover classes. In cropland and forest land covers, WaPOR shows the highest overall performance against EC data for both ET and NPP, with strong correlations and low systematic bias. NPP products show much stronger site-to-site variability than ET, and no product is consistently superior at all locations. Overall, the comparison suggests a clear imbalance: ET estimates are relatively consistent across products, while NPP remains the main source of disagreement between datasets at many sites. This matters directly for any water productivity calculation based on ET and NPP, because water productivity can shift simply with the choice of NPP dataset. The next step of this work will use these results to quantify how much ET versus NPP drives uncertainty in remotely sensed water productivity over cropland and forest land covers.
How to cite: Dehati, S., Ngoc Tran, B., Mul, M., and Karimi, P.: Sources of uncertainty in remote sensing based water productivity from evapotranspiration and net primary production inputs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14176, https://doi.org/10.5194/egusphere-egu26-14176, 2026.
Evapotranspiration (ET), the largest water flux through which water returns to the atmosphere in vapor form, plays a central role in the terrestrial water and energy cycles. In the water-limited Mediterranean basin, in particular, relatively small changes in green water fluxes (i.e., ET), triggered for example by environmental factors and land cover changes, affect water resources, including runoff, groundwater, irrigation needs, and overall ecosystem functioning. Sustainable water resources management in these regions therefore requires accurate spatiotemporal characterization of ET losses. The increasingly available Earth Observations allow us to address some of these challenges by monitoring ecosystem functioning with high spatiotemporal resolution. Here, focusing on the Acheloos river basin (7531 km2), one of the most important hydrological systems of Greece with regards to water supply (domestic and irrigation uses), hydropower, and ecosystem services, we quantified the spatial variability of ET and its temporal dynamics at the seasonal, annual, and inter-annual time scales. We synthesized remotely sensed ET products together with auxiliary geospatial and environmental variables to tackle the following research questions: (1) What land cover contributes the most to the ET losses over the basin? (2) How did ET respond to recent climate extremes (i.e., droughts and heatwaves), and (3) What were the hotspots with the most sensitive land cover? By synthesizing spatially explicit historical estimates of ET across the study area, together with environmental and land use datasets, this study aims to provide constrained estimates of ET for the dominant land cover types at seasonal, annual, and inter-annual time scales. Such estimates could facilitate impact assessments of natural hazards (e.g., droughts, heatwaves, and wildfires) on the water balance via land cover change feedback (i.e., ET losses), providing valuable insights towards sustainable long-term water resources management in the Mediterranean region.
How to cite: Georgoutsou, K. F., Langousis, A., and Pappas, C.: Spatiotemporal variability of green water fluxes and their response climate extremes: pinpointing drought- and heatwave-hotspots over the Acheloos River basin, Western Greece, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19448, https://doi.org/10.5194/egusphere-egu26-19448, 2026.
The Satellite Application Facility on Climate Monitoring (CM SAF) of EUMETSAT develops satellite-derived climate data records to support climate monitoring and research. In 2024, CM SAF extended its product portfolio with a Climate Data Record (CDR) of land surface variables based on two sensors of the Meteosat suite of geostationary satellites: the Meteosat Visible and InfraRed Imager (MVIRI) and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI). The CM SAF LANDFLUX Ed. 1 provides nearly 40 years (1983–2020) of parameters describing surface states and radiation fluxes, including the Surface Radiation Balance, Cloud Fractional Cover, Land Surface Temperature, Evapotranspiration (ET), and the Latent (LE) and Sensible (H) Heat Fluxes. This dataset constitutes one of the longest satellite-based records of land surface energy and water fluxes derived from geostationary observations. Close collaboration between CM SAF, the Land Surface Analysis (LSA) SAF and the EUMETSAT Secretariat ensures the uniqueness of this CDR and the consistency among its parameters.
This contribution focuses on ET and the LE and H fluxes. These parameters are retrieved using an adapted version of the LSA SAF methodology, itself derived from the Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL). Observations from MVIRI and SEVIRI onboard Meteosat First and Second Generation (MFG and MSG) are used as inputs for all radiation components. CDR parameters are provided hourly, daily and monthly (and monthly mean diurnal cycle) at a spatial resolution of 0.05 degrees (approximately 5.5 km), covering the Meteosat disk (65° N–65° S and 65° W–65° E). The combination of high temporal resolution and multi-decadal coverage at 0.05° enables robust analyses of diurnal to interannual variability of land surface fluxes at continental scales, supporting climate monitoring and model evaluation, as well as hydrological and surface energy and water-balance applications.
The adopted methodology, validation results, and selected study case are presented, together with future perspectives focusing on the development of a quasi-global prototype (“GeoRing”). LE and H are validated against in situ observations (30 Fluxnet2015 and ICOS stations) and inter-compared with reanalysis (ERA5, GLDAS) and satellite-based products (LSA SAF, GLEAM). Errors are comparable to those reported in the literature, with daily biases of −10.8 W m⁻² (~0.38 mm/d) and −2.6 W m⁻², and daily unbiased RMSEs of 24.7 W m⁻² (~0.87 mm/d) and 34.1 W m⁻² for LE and H, respectively. The LANDFLUX Ed. 1 CDR represents a significant step toward long-term monitoring of land surface energy and water exchanges from geostationary satellites and is publicly available via the CM SAF website (https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SLF_METEOSAT_V001) to support scientific research and operational climate services.
How to cite: Moutier, W., Clerbaux, N., Barrios, J.-M., De Pue, J., Gellens-Meulenberghs, F., Sharma, V., Schröder, M., and Duguay-Tetzlaff, A.: Evapotranspiration, latent and sensible heat fluxes dataset within the CM SAF: present and future, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20202, https://doi.org/10.5194/egusphere-egu26-20202, 2026.
Evapotranspiration (ET) is a major component of the global water cycle and provides a critical nexus between terrestrial water, carbon and surface energy exchanges. Climate change influences ET through the combined effects of rising temperatures and increased net radiation, both of which tend to increase ET rates. Reliable estimation of ET across different spatial and temporal scales is therefore crucial for sustainable water resource management and for improving hazelnut productivity. This study integrates multi source satellite observations from Sentinel-2 and Sentinel-3 to estimate high-resolution ET fluxes over 40 hazelnut orchards in Viterbo Province, Italy, from 2016–2024. The objectives of this study were to: (i) estimate and compare ET fluxes in hazelnut orchards using two methods: The Priestley–Taylor approach and the Time Domain Triangle method; and (ii) examine the relationships between ET, climatic variables, and vegetation indicators. The results showed a statistically significant increasing trend in latent heat flux (λET) across 38 hazelnut fields from 2016 to 2024. λET exhibited a strong and significant relationship with temperature in all fields (with R² 0.59 to 0.85) as well as good correlation with NDVI in 13 orchards (with R² > 0.5). Notably, from June to August, NDVI values tended to be negatively correlated with ET, thus suggesting potential water stress. The relationship between λET and cumulative precipitation was generally weak across all hazelnut orchard fields. These findings demonstrate the potential of synergistic Sentinel-2 and Sentinel-3 observations for monitoring field scale ET dynamics in hazelnut orchards.
How to cite: Tauro, F. and Alem, D. M.: Evapotranspiration assessment in agriculture: a case study in the hazelnut orchards of Viterbo Province, Italy., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20407, https://doi.org/10.5194/egusphere-egu26-20407, 2026.
Mediterranean mountain ecosystems play a key role in water supply and biodiversity conservation but are highly vulnerable to climate change. In these environments, vegetation strongly regulates key hydrological fluxes such as evapotranspiration and interception. However, the complex structure and marked seasonal dynamics of Mediterranean agroforestry systems remain poorly represented in many hydrological models, which often rely on static vegetation parameterizations or climate-driven evapotranspiration formulations.
In this study, we investigate the integration of remotely sensed evapotranspiration and vegetation dynamics into the distributed, physically based hydrological model WiMMed (Watershed Integrated Model in Mediterranean regions) for a mountain catchment located in the Parque Natural de Cardeña y Montoro (southern Spain). Vegetation heterogeneity and dynamics are explicitly accounted for by combining MODIS-derived evapotranspiration estimates obtained from the Two-Source Energy Balance (TSEB) model with satellite-based fractional vegetation cover (FCV). Model performance and hydrological responses are evaluated against a conventional modelling approach based on a crop-modified Hargreaves formulation and static vegetation representation.
Differences between the two approaches are analyzed in terms of evapotranspiration patterns, soil moisture dynamics, interception, and runoff generation. The inclusion of remotely sensed ET improved the simulation of water balance components and their spatial variability. Dynamic vegetation scenarios better capture seasonal and interannual variability in ET and runoff, highlighting vegetation-water interactions that are not reproduced by climate-only ET formulations. Results show that neglecting vegetation dynamics or assuming static full cover leads to substantial biases in water flux partitioning, e.g., static vegetation scenarios “overestimate” interception (up to ~11.5% of annual precipitation), whereas incorporating dynamic vegetation reduces interception to ~3–4% and significantly alters the partitioning between infiltration and runoff, particularly during wet years. Overall, this approach provides a framework for identifying system inflection points, evaluating future climate and land-use scenarios.
How to cite: Andreu, A., Gabella, D., and Pimentel, R.: Integrating remotely sensed evapotranspiration into hydrological modelling of Mediterranean tree-grass ecosystems , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8913, https://doi.org/10.5194/egusphere-egu26-8913, 2026.
Microwave remote sensing observations provide unique information on soil–plant water status as they are directly sensitive to changes in soil moisture, internal vegetation water content, and the water content present on vegetated surfaces due to precipitation, dew, or irrigation. These variables are key drivers of terrestrial evaporation (E) and important indicators of ecosystem functioning and health. Current E models that exploit microwave observations are largely constrained to daily time scales, due to the lack of sub-daily satellite microwave data. However, water transport within the soil–plant–atmosphere continuum exhibits strong diurnal dynamics, and E shows a pronounced diurnal hysteresis, with higher rates typically occurring during the morning compared to the afternoon. This highlights the need to monitor vegetation responses to environmental stress at sub-daily scales and to model transpiration (Et) at high temporal resolution. Sub-daily resolutions are also required to accurately represent rainfall interception loss (Ei), which exhibits strong intra-day variability, particularly during and shortly after precipitation events. Sub-daily microwave observations offer the potential to resolve these fast processes, thereby advancing the understanding of E, stomatal regulation, and the coupling between water, energy, and carbon cycles. In particular, sub-daily observations of vegetation optical depth (VOD) and canopy surface wetness are expected to improve the estimation of Et and Ei, respectively.
This study is motivated by the continued development of a sub-daily SAR mission concept to enable global monitoring of vegetation water dynamics, health, and stress. One of the challenges in the early development of the SLAINTE mission, as an ESA New Earth Observation Mission Idea (NEOMI) and as a concept in response to ESA’s 12th call for Earth Explorers, was the scarcity of sub-daily observations (Steele-Dunne et al., 2024; Matar et al., 2024). Here, the potential to constrain E estimates using sub-daily VOD data is revisited using observations from a new network of in-situ GNSS-based sensors.
We assess the potential value of sub-daily microwave observations by constraining a sub-daily version of the Global Land Evaporation Amsterdam Model (GLEAM; Miralles et al., 2011), using sub-daily observations of VOD and binary wet/dry canopy state (WDCS). These observations are derived from in-situ GNSS-based sensors deployed across several European forest ecosystems. By analyzing the diurnal cycle of VOD, potential descriptors of vegetation water stress can be identified and incorporated as constraints in this sub-daily version of GLEAM. This study presents a methodology to exploit sub-daily VOD and provides a pathway to consolidate observation requirements for estimating E at sub-daily scales and for quantifying the impact of environmental stress.
Matar, J., et al. “A Concept for an Interferometric SAR Mission with Sub-daily Revisit”, EUSAR 2024; 15th European Conference on Synthetic Aperture Radar, Munich, Germany, 2024, pp. 18-22.
Miralles, D. G., et al. “Global land-surface evaporation estimated from satellite-based observations”, Hydrology and Earth System Sciences, vol. 15, no. 2, pp. 453–469, 2011.
Steele-Dunne, S. C., et al. “SLAINTE: A SAR mission concept for sub-daily microwave remote sensing of vegetation”, EUSAR 2024; 15th European Conference on Synthetic Aperture Radar, Munich, Germany, 2024, pp. 870-872.
How to cite: Tronquo, E., Van der Borght, N., Neyer, A. S., Miralles, D. G., and Steele-Dunne, S. C.: Sub-daily microwave observations to constrain evaporation modelling over forest ecosystems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14809, https://doi.org/10.5194/egusphere-egu26-14809, 2026.
Recurrent drought between 2018 and 2021 resulted in decreased vitality and productivity of Central European forest ecosystems and increased mortality rates across many tree species. With progressing climatic change, drought and heat stress events are projected to further increase, highlighting the need for monitoring approaches that allow early detection and regular interpretation. Therein, changes in evapotranspiration (ET) may indicate plant water stress earlier than visible symptoms such as canopy browning or dieback. The ongoing temp-2-stress project across six broadleaved forest regions in Germany integrates space-borne satellite observations with high-resolution uncrewed aerial vehicle (UAV) measurements to assess ET, vegetation health and derived drought and heat stress indices from stand to regional scales. For larger-scale satellite analyses, the focus lies on ET (and, e.g., ESI, Evaporative Stress Index) products from the ECOSTRESS and MODIS missions and on multispectral indices (e.g., NDVI, Normalized Difference Vegetation Index) derived from a harmonized LANDSAT/Sentinel dataset; the data coverage spans several years encompassing the 2018 to 2021 period. For high-resolution imagery over long-term forest monitoring plots located within each of the studied forest regions, we employ a UAV-based multispectral and thermal camera system (Micasense Altum PT) combined with an on-board four-component net radiation system (Apogee), which provides quality in-situ data for modelling ET with energy-balance based approaches such as the DATTUTDUT model. For a validation of derived stress indices, independent ground measurements of meteorological key variables, soil moisture and tree growth are available at the long-term forest monitoring plots, of which one is additionally equipped with an eddy covariance tower for further methodological scrutiny. Here, we present preliminary results from a unique forest irrigation experiment that is covered in the temp-2-stress UAV missions in addition to the mentioned long-term monitoring sites. Three irrigated and three non-irrigated mixed broadleaf forest plots were surveyed repeatedly during the 2025 growing season, allowing to assess temporal dynamics in surface temperature, ET and vegetation vitality and their sensitivity to irrigation and meteorological conditions. We expect increasing differences among irrigated and non-irrigated plots with increasing summer temperatures (analyses in progress). These results will serve to evaluate the potential of the UAV-based approach to detect differences in water availability and evaporative response in forests at the (sub-) stand level.
How to cite: Swatek, S. and Röll, A.: Remotely sensed surface temperatures for the analysis of evapotranspiration, drought and heat stress in Central European deciduous forests, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20498, https://doi.org/10.5194/egusphere-egu26-20498, 2026.
Evapotranspiration (ET) is the process describing the transfer of water from the land surface into the atmosphere. It includes evaporation from soil and plant surfaces, as well as transpiration through plant stomata. ET represents a central link between the terrestrial water cycle, energy cycle, and carbon cycle. Thus, an accurate estimation of ET is essential for understanding the landscape water budget and biomass production, as well as for improving agricultural water management and irrigation strategies. Additionally, more accurate ET values improve models of carbon-water interactions in land ecosystems and promote sustainable water use.
This study aims to identify an appropriate model for estimating daily ET across a west-east climate and land-use gradient in Germany, providing an effective method that accurately reflects ET variability. The main objectives of this study are to (i) estimate ET using a combination of machine learning, physics-based, and hybrid models; (ii) evaluate the performance, efficiency, and sensitivity of these models by comparing estimated ET with ET observations; and (iii) use the most accurate model to predict daily ET variability along the climate and land use gradient in Germany.
To achieve this, remote sensing data from Sentinel-2 and Landsat-8, as well as meteorological data from the German Weather Service (DWD) and the ERA-5 reanalysis, were used for model training. To assess the models' performance, eddy-covariance data from the Integrated Carbon Observation System (ICOS) and ET products from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the years 2017 to 2024 were used.
We used five different approaches to estimate daily ET, including deep learning (DL), machine learning, hybrid models, and physical models. Specifically, we employed the Optical Trapezoid Model (OPTRAM), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Random Forest (RF), and TabTransformer. We evaluated the accuracy of each approach using ICOS ET observations.
The results indicated that DL models generally performed better than RF and OPTRAM-ET models in the study area. Among all the experiments, the ANN achieved the best performance, with a root mean square error of 0.6 and a correlation coefficient of 0.91. Additionally, we observed significant variations in modeling performance across different ecosystem types. In grassland, ET estimates showed the highest accuracy, whereas in cropland ecosystems, the greatest deviations were observed.
How to cite: Ahmadpour, S. and Trachte, K.: Enhancing Daily Evapotranspiration Estimates in Germany Using Multi-Source Data and Machine Learning Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9899, https://doi.org/10.5194/egusphere-egu26-9899, 2026.
Evapotranspiration (ET) is a key component of the hydrological cycle with important implications for water management and climate modeling. Despite the robustness of physically based evapotranspiration models, their application at high spatial resolution remains limited by point-scale forcing and the coarse representation of key meteorological drivers, particularly near-surface wind speed. In this study, we present a fully satellite-based framework to estimate reference evapotranspiration (ETo) at 30 m resolution by integrating Landsat observations with a physics-informed neural network (PINN). Near-surface wind speed at 2 m is first estimated to the Landsat scale using a Random Forest model that leverages static terrain and land-cover information together with dynamically retrieved land surface temperature, net radiation, and vapor-pressure deficit. These high-resolution meteorological fields are then used to drive a PINN constrained by the FAO-56 Penman–Monteith and Priestley–Taylor formulations, which are embedded as complementary physical losses to ensure consistency with both aerodynamic and radiative controls on ETo. The approach is evaluated across eight eddy covariance flux-tower sites spanning cropland, grassland, and forest ecosystems in Asia and Europe. Results demonstrate strong agreement with tower-based Penman–Monteith ETo (R = 0.80–0.97; RMSE = 0.52–1.43 mm/d), with the highest accuracy observed over homogeneous croplands and larger, yet systematic, deviations during short-duration high-flux periods in heterogeneous and structurally complex canopies. Spatial comparison with ERA5-Land ETo further highlights the added value of high-resolution, satellite-driven estimates in capturing sub-grid variability. These results indicate that physics-informed learning provides a robust and scalable pathway for canopy-scale ETo mapping in heterogeneous landscapes.
Acknowledgment: This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as 「Innovative Talent Education Program for Smart City」. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00416443). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2022-NR070339).
How to cite: Kshetrimayum, A., Jeon, H., and Choi, M.: Integrating Landsat and Physics-Informed Neural Networks for reference evapotranspiration estimation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8548, https://doi.org/10.5194/egusphere-egu26-8548, 2026.
The semi-arid Gundar River Basin in South India is home to around two million people. Agriculture remains central to sustaining livelihoods in this region, and competition for water resources has intensified over the years owing to factors such as variability in monsoon precipitation, shifting agricultural practices, and migration to urban areas. The landscape is dotted with centuries old water storage tanks (reservoirs) built to collect runoff and recharge groundwater, and the hydrology of the region is further complicated by the widespread presence of Prosopis juliflora, a water consuming invasive species that increases competition for water. With evapotranspiration (ET) accounting for up to 60% of the water outflux in semi-arid regions, there is a pressing need to quantify ET in this region to support improved water and resource management.
The region lacks ET monitoring networks, is highly heterogeneous, and requires the use of satellite datasets and climate data products to understand catchment scale ET behaviour and its evolution over time. This study aims to intercompare ET derived from SEB methods: SEBAL (novel estimates generated using Landsat data) and METRIC (through the EEFlux product), both available at 30m resolution with global ET products such as MOD16 and GLEAM at the basin scale between 2010 and 2020. ET estimates will be aggregated to a common spatial scale, stratified using NDVI classes, and analyzed according to the study’s objectives: (1) examining consistency among datasets across different seasons, (2) investigating interannual variability using the correlation coefficient (r), root mean squared error (RMSE), anomaly assessment, and (3) assessing long term ET trends using the Mann–Kendall test and detecting breakpoints with Pettitt’s test given the basin’s recent history of frequent droughts. Physical consistency will be evaluated using reference ETo estimates, enabling an assessment of the utility of the various ET methods applied in this unique hydrological setting with significant implications for local livelihoods.
How to cite: Senthilkumaran, A. and Kelly, R.: Assessing the Suitability of Evapotranspiration Products and Surface Energy Balance Estimates in the Semi-Arid Gundar River Basin, South India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17387, https://doi.org/10.5194/egusphere-egu26-17387, 2026.
Accurate estimation of reference evapotranspiration (ETo) is fundamental for irrigation scheduling, hydrological modeling, and sustainable water-resources management, particularly in arid and semi-arid regions where strong climatic variability challenges predictive reliability. This study investigates the performance of advanced Transformer-based deep learning architectures and their hybrid extensions for daily ETo prediction at two climatically distinct inland stations—Nizwa and Rustaq in northern Oman. Three state-of-the-art single models, namely Autoformer, Informer, and FEDformer, were evaluated and further integrated with Multivariate Variational Mode Decomposition (MVMD) to develop hybrid frameworks capable of explicitly disentangling multi-scale temporal patterns and cross-variable dependencies. Meteorological data spanning 2018–2025 were used to train and test the models under five input scenarios: (i) temperature only, (ii) temperature with wind speed (U2), (iii) temperature with net radiation (Rn), (iv) temperature with vapor pressure deficit (es–ea), and (v) all available meteorological variables. Model performance was assessed using the coefficient of determination (R²), root mean square error (RMSE), and the RMSE–standard deviation ratio (RSR). Results indicate that hybrid MVMD-based models consistently outperform their single-model counterparts across all input scenarios and both stations, with the most pronounced improvements observed under multi-variable configurations. FEDformer-MVMD model demonstrated superior generalization, particularly under high evaporative demand conditions, highlighting its effectiveness in capturing long-term dependencies and non-stationary climatic signals. Scenario-based analysis further reveals that incorporating radiation and vapor pressure deficit substantially enhances prediction accuracy in inland arid environments. Overall, the findings confirm that combining Transformer architectures with multivariate signal decomposition significantly improves ETo prediction accuracy and robustness. The proposed frameworks provide a scalable and climate-adaptive solution for operational irrigation management and drought-risk assessment in data-scarce arid regions.
How to cite: Elzain, H. E., Al-Maktoumi, A., and Chen, M.: Enhancing Reference Evapotranspiration Prediction Using Deep Learning Transformer Models and Multivariate Variational Mode Decomposition in Arid Regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13975, https://doi.org/10.5194/egusphere-egu26-13975, 2026.
Posters virtual: Thu, 7 May, 14:00–18:00 | vPoster spot A
EGU26-3158 | Posters virtual | VPS10
Revisiting a riparian invasive shrub and its biocontrol in the western United States: Measured Changes in Water UseThu, 07 May, 14:24–14:27 (CEST) vPoster spot A
In 2001, the tamarisk leaf beetle (Diorhabda spp.) was released as a biological control agent for invasive tamarisk (Tamarix spp.), which dominates many floodplains in the western United States (US) and substantially alters riparian ecosystem structure and function. Since its release, the beetle has expanded across thousands of river kilometers, repeatedly defoliating tamarisk far beyond original release sites. Although biological control offers an alternative to mechanical or chemical removal, its ecological benefits and tradeoffs remain uncertain. Here, we synthesize current understanding of one of the most extensive biological control programs implemented in North America, evaluating impacts on riparian evapotranspiration (ET) and riverine hydrology. We assess ongoing challenges and opportunities associated with tamarisk biocontrol and consider how western US riparian forests may evolve under reduced tamarisk dominance.
Early management efforts were driven by the assumption that tamarisk consumed exceptionally large volumes of water, motivating legislative and large-scale removal programs. Subsequent studies, however, demonstrated that tamarisk water use is highly variable and comparable to native riparian vegetation such as cottonwood (Populus spp.) and willow (Salix spp.), as well as mixed shrub communities. Reported tamarisk ET since 2000 ranges widely (109–1456 mm yr⁻¹), with mean values near 850 mm yr⁻¹, depending on stand age, density, health, groundwater depth, soil properties, and salinity.
Defoliation by Diorhabda spp. was expected to enhance streamflow by reducing riparian ET, yet observed hydrologic responses have been inconsistent. In past research ET declines exceed 40% relative to healthy tamarisk at some locations, whereas at other sites, reductions are modest or absent, particularly where baseline ET is low. In this current study, we reassess post-defoliation dynamics by analyzing ET across 27 riparian sites from 2014–2023 using Landsat-derived Nagler ET(EVI2) estimates and gridded climate data. Approximately half of the sites exhibited sustained ET reductions averaging a loss of 18% (−142 mm yr⁻¹), while the remainder showed negligible change or increases in ET of 9% (+54 mm yr⁻¹), likely reflecting tamarisk regrowth or replacement by other vegetation. Across all sites, net water savings were modest, averaging a loss of 7% (−48 mm yr⁻¹), consistent with earlier estimates.
These findings reinforce that hydrologic benefits from tamarisk biocontrol are site-specific, often transient, and frequently offset by vegetation recovery or compositional shifts. Consequently, biological control alone is unlikely to yield substantial or reliable increases in water availability for agricultural or municipal use. Predicting future structure and function of western US riparian forests under tamarisk biocontrol requires explicit consideration of ecosystem complexity, spatial heterogeneity, and interacting drivers that will shape whether alternative states favor native vegetation recovery or secondary invasions.
How to cite: Nagler, P., Palmquist, E., Snyder, K., Jimenez-Hernandez, E., and Hultine, K.: Revisiting a riparian invasive shrub and its biocontrol in the western United States: Measured Changes in Water Use, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3158, https://doi.org/10.5194/egusphere-egu26-3158, 2026.
EGU26-12323 | ECS | Posters virtual | VPS10
Estimation of Crops Water Consumption by Remote Sensing: SEBAL Model Calculations Versus Ground Observation In The Irrigated Area of Lakhmess (Siliana, Northern Tunisia)Thu, 07 May, 14:51–14:54 (CEST) vPoster spot A
Tunisian agriculture remains a crucial component of the country’s economic development and faces considerable constraints related to increasing water demand and reducing water resources’ availability. Improving the assessment of irrigation water use is a prerequisite for sustainable water management. The present study aims to evaluate the quality of water consumption estimates in the Public Irrigated Area of Lakhmess using open-source data. High-resolution (10 m) Sentinel 2 images, combined with ERA5-land meterological data, were used to assess monthly and seasonal actual evapotranspiration (ET) and water use through the implementation of the Surface Energy Balance Algorithm for Land (SEBAL) in the Google Earth Engine (GEE) environment. The calculated water uses were combined with the seasonal supplied water to the PIA Lakhmess, collected at plot level.
This study was conducted over eight agricultural campaigns from 2015-2016 to 2022-2023. The method is validated for three sectors Sidi Jaber, Gantra and Gabel, comparing the seasonal water use estimates to water meter observations. Correlation analysis between estimated water use from open-access data and in-situ measurement yielded correlation coefficients of 0.76, 0.75 and 0.73, with corresponding RMSE values of 0.461, 0.425, and 0,391 mm/day, respectively. In addition, SEBAL-derived evapotranspiration estimates were evaluated through comparison with reference evapotranspiration computed using the FAO-56 Penman-Monteith, resulting in an R² of 0,68 and an RMSE of 0.315 mm/day. Overall, the methods were deemed satisfactory, as they facilitated the monitoring of excessive water usage by identifying areas where water losses occurred.
Key words: Evapotranspiration, Irrigation, water use, Remote sensing, GEE, SEBAL.
How to cite: Belhaj Kilani, A., Alonso, A., Bousselmi, A., Khlifi, S., and Vanclooster, M.: Estimation of Crops Water Consumption by Remote Sensing: SEBAL Model Calculations Versus Ground Observation In The Irrigated Area of Lakhmess (Siliana, Northern Tunisia), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12323, https://doi.org/10.5194/egusphere-egu26-12323, 2026.
