Terrestrial vegetation, central to water-energy-carbon interactions between land and atmosphere, such as evapotranspiration, is under severe pressure due to human disturbances and changing climate. Evapotranspiration switches from being energy to being water limited at critical soil water thresholds. Despite the importance of such soil water thresholds for terrestrial ecosystems, the key mechanisms and drivers (being them related to plants, soils or the atmosphere) controlling their values remain unclear at the ecosystem scale.

Soil water thresholds have recently been estimated from global networks of terrestrial flux measurements based on Eddy-Covariance method (FLUXNET). However, this approach does not allow to partition between soil evaporation and plant transpiration, which might have different thresholds. Therefore, we also estimated soil water thresholds from a complementary monitoring network based on sapflow measurements (SAPFLUXNET), which provides the actual flow velocity along the xylem being closely related to transpiration rate. Besides comparing the two measurements approach, we aimed to explain the key mechanisms controlling soil water thresholds.

We found that the two monitoring approaches provide similar values of soil water thresholds. These thresholds, expressed as either soil moisture θ_crit or soil matric potential ψ_crit, are function of soil texture globally. By applying a soil-plant hydraulic model (considering the key soil, plant, and atmospheric parameters) at plant and ecosystem scale, we show that at both scales, θ_crit and ψ_crit are determined by the abrupt decrease of soil hydraulic conductivity with decreasing soil moisture content, causing a loss in leaf water potential that triggers stomatal closure. For soils with a moderate decrease of hydraulic conductivity (loam), atmospheric conditions and vegetation properties become more relevant, resulting in a higher variability of soil water thresholds compared to sandy soils (sharpest decrease of hydraulic conductivity).

Overall, our results show that soil texture modulates land-atmosphere exchange globally across scales, biomes, and climates, highlighting the importance of soil water flow for predicting and understanding evapotranspiration dynamics.

How to cite: Wankmüller, F. J. P., Delval, L., Lehmann, P., Baur, M. J., Wolf, S., Or, D., Javaux, M., and Carminati, A.: Soil hydraulic conductivity determines the onset of water-limited evapotranspiration across scales, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17116, https://doi.org/10.5194/egusphere-egu24-17116, 2024.

Evaporation from water (PE_w) is generally considered equivalent to evaporation from saturated bare soils (PE_s) as a starting point to estimate actual evaporation E_a. This simplification considers that the PE value is mainly determined by meteorological variables. The influences of the surface type on PE, as well as the energy and vapour transfer in evaporation processes over saturated soil textures and water surface have so far received little attention. In this research, evaporation over two saturated sandy soils including coarse sand (PE_coarse), fine sand (PE_fine) and water were assessed for lysimeters installed in the Guanzhong Basin, China. Evaporation from Class A Pan (PE_pan), meteorological variables and temperatures in soil and water were also captured at a high temporal resolution (5 min.) for more than 14 consecutive months. Observed PE rates demonstrated evident differences in both absolute values and diurnal dynamics between saturated soils and water. PE_s is ~12% higher than PE_w on a yearly scale. Annual PE_fine exceeded PE_coarse by 7.3%, with the differences more obvious during daytime in spring and summer. The cumulative evaporation rates over water column and Class A Pan showed minor differences. PE_s is higher than PE_w at day but smaller at night, with the peak value of PE_w lagging ~4 hours behind PE_s. Compared with PE_w-curve, the PE_pan-curve resembles more the PE_s-curves over a sub-daily scale.

Our research revealed that these observed PE dynamics and energy transfer processes can be quantitatively explained with detailed calculations of the surface energy balance. It is found that differences in PE are governed by differences in available energy (related to different albedos, different thermal properties and different surface temperature T) between soils and water. Moreover, the observed differences in PE and vapour transfer processes were reproduced and described by improving the vapour diffusion equation, with considering the influence of different surfaces and boundary layer thicknesses. PE dynamics were mainly characterized by the surface temperature T, which further determined the vapour gradients between the evaporation surfaces and airflow. Previous research considered surface temperature T to be an independent external forcing that determines ‘wet surface’ evaporation. Our research suggests that T is a significant internal forcing for both energy and vapour transfer during the evaporation process since it influences the redistribution of energy fluxes at the surface (the ground heat flux G and the variation in water heat storage N), the outgoing longwave radiation (R_lu), as well as the vapour gradients above the surface (Δe).

How to cite: Li, W., Wang, W., Wang, Y., Lu, X., Fu, C., Wang, Z., Ma, S., Sun, Z., Peng, J., Wang, J., and Gu, D.: Energy and vapour transfer in evaporation processes over saturated soil textures and water surface, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8549, https://doi.org/10.5194/egusphere-egu24-8549, 2024.

The forested area is growing in Italy. The eco-hydrological monitoring of such an ecosystem is not trivial, because of canopy height, deep root system and soil heterogeneity. Hence, it is important to merge multiple measurement approaches to quantify the ecohydrological dynamics at the sites. In addition, it is also important to consider multiple temporal and spatial scales from point measurements to areal measurements of the soil-atmosphere interactions. At the Bussoleno - Grangia dell’Alpe forest site (Piedmont, Northwest Italy), we monitored two years, and in particular, two growing seasons (2021 and 2022, with a severe drought in Italy) with areal measures in the atmosphere of actual evapotranspiration (ETa) estimated via eddy covariance technique overcanopy (25 m mast) and areal estimates of soil water content measured continuously with cosmic ray sensors. Moreover, the soil resistivity was measured at the plot scale with Electrical Resistivity Tomography (ERT) technique with several campaigns in which two measurement transects were explored. The point scale with continuous measurements was monitored via soil water content and matric potential probes installed at several depths between 0.1 m and 2 m. In addition, during the ERT campaigns, the soil water content of the first 30 cm profile was also measured via TDR probes in different locations of the experimental site. All this effort allows the reconstruction of a forest volume from about 3 m of soil depth to 23 m of height (height of the eddy covariance setup), including the whole canopy effect. Results highlight the consistency of the soil water content estimation with different approaches (cosmic ray sensors, ERT technique, TDR and capacitive probes). Moreover, using different soil moisture measurements, the ETa regimes can be correctly and well identified. Furthermore, the drought effects are explored also using eddy covariance technique, highlighting that, despite a very low water content above 2 m of soil depth, the vegetation is not severely stressed, likely because of its resilience (the site is characterized by low precipitation, usually below 600 mm/year).

This publication is part of the project NODES which has received funding from the MUR –M4C2 1.5 of PNRR with grant agreement no. ECS00000036.

How to cite: Gisolo, D., Canone, D., Comina, C., Vagnon, F., Gentile, A., and Ferraris, S.: Drought effects investigation of a forested site at different spatial scales with eddy covariance technique, cosmic ray sensors, electrical resistivity tomography and 2 meters deep soil moisture and matric potential profile, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11292, https://doi.org/10.5194/egusphere-egu24-11292, 2024.

Urban hydrological processes are mainly influenced by anthropogenic activities, such as the expansion of impermeable surfaces which reduces infiltration of rainwater, increases runoff generation processes, and reduces evapotranspiration. Urban trees contribute positively to moving the urban water cycle closer to a natural one through their ecohydrological processes (e.g. transpiration). However, high heterogeneity within urban environments and increasing drought periods create multiple stressors to trees’ ecophysiology. We conducted intensive field measurements on Norway maple (Acer platanoides) and small-leaved lime (Tilia cordata) in the city of Freiburg, Germany, to advance our process understanding of transpiration behaviour of urban trees at contrasting growing and microclimatic environments and to determine the main hydrometeorological factors influencing transpiration. Soil water content, sap flux, crown solar radiation transmissivity, and meteorological measurements within the crown were carried out on 11 trees per species on sites with different degrees of surface sealing underneath tree crowns, in particular parks, parking lots, grass verges and tree pits.

Throughout the investigation period (2021-2022), average daily transpiration rates during the growing season were higher for small-leaved lime (1.76 ± 0.53 mm) than for Norway maple (1.53 ± 0.51 mm). We observed significantly reduced daily transpiration rates (1.13 ± 0.31 mm) at tree planting sites (e.g. tree pits) with 90% impermeable surface underneath the tree crowns. On average, the main hydrometeorological drivers for day-to-day transpiration dynamics were solar radiation (39.7%), followed by vapour pressure deficit (22.4%), and soil water content (4.8%). Additionally, tree morphological traits, such as leaf area index (LAI) and leaf area density (LAD), as well as the degree of surface sealing affected transpiration significantly (p-value < 0.05) among sites. Furthermore, LAI is significantly correlated with the proportion of surface sealing within the crown projection area. With this study, we created a highly needed dataset for the main urban tree species in Central European cities and provided a solid knowledge base for transpiration processes of trees in various urban environments. The study revealed that long-term field measurements with multiple tree species under contrasting urban growing conditions are a necessity to quantify tree transpiration dynamics and their contribution to the urban water cycle and to the cooling potentials of urban trees. In addition, relevant factors to plan resilient urban ecosystems can be extracted with such datasets and analysis.

How to cite: Anys, M. and Weiler, M.: Urban and meteorological factors controlling transpiration dynamics of two common deciduous tree species in the city of Freiburg, Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7816, https://doi.org/10.5194/egusphere-egu24-7816, 2024.

For the purpose of managing irrigation water and raising agricultural water yield, the daily transpiration (T_d) monitoring is essential. The estimation of evapotranspiration (ET) and its components, which include crop transpiration (T) and soil evaporation (E), for a variety of crops has shown to be robust when using remote sensing energy balance models. However, as measurements from remote sensing are instantaneous, daily upscaling methods are required in order to estimate T_d from remote sensing models. Although upscaling methods for daily ET have been validated by multiple studies, those techniques have not been validated for estimating T_d separately in woody crops. The purpose of this study is to assess upscaling methods for recovering T_d in almond crops with varying water status and production systems. Sap flow sensors were used to monitor the T in-situ (T-SF), allowing for a continuous measurement for each plant every 15 minutes. The stem water potential (Ψs), stomatal conductance (g_s) and leaf transpiration (E_leaf) were also measured at 7:00, 9:00, 12:00, 14:00 and 16:00 solar time for two days in the same trees where sap flow sensors were installed. High-resolution images were used to estimate hourly T using the two-source energy balance model (TSEB). The upscaling methods were evaluated with in situ sap flow data and then implemented to the TSEB estimations. The evaluated upscaling methods were the simulated evaporative fraction variable (EFsim), irradiance (Rs) and potential evapotranspiration (ETp) methods. As a results, the EFsim and ETp methods were more correlated with T-SF, reducing the observed potential underestimation using the Rs method. The improvement was especially important at midday in the tress subjected to severe water stress, where the EFsim reduced the error by 17.61% and the ETp reduced it by 10.6% compared to the Rs method, respectively. Nevertheless, the daily T-SF revealed significant differences across production systems that the daily upscaling methods used in the TSEB were unable to identify. One issue in determining T_d on surfaces with discontinuous architectural features was the insufficient sensitivity of daily TSEB between production systems. This issue might be resolved by applying more sophisticated ETp models or enhanced ETp as an upscaling parameter, since ETp can account for variations in canopy structures that have an impact on daily T curves.

How to cite: Quintanilla-Albornoz, M., Bellvert Rios, J., Nieto Solana, H., Pelecha, A., and Miarnau Prim, X.: Deriving Daily Transpiration from Instantaneous Measurements in Almond Orchards with Varied Water Stress and Production Systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5593, https://doi.org/10.5194/egusphere-egu24-5593, 2024.

Accurate estimation of sap flow is vital for plant models and significantly impacts existing management policies, particularly when scaling from the plant level to plot levels. The heat pulse method (HPM) is widely used for measuring sap flow in plants because of its added benefits compared to other traditional methods. In HPM, there are many approaches, all of which follow the Marshall theory. The existing HPM fails to measure a wide range of sap flow rates in a single approach. The literature suggests that those limitations may be because of factors such as wounding, sensor resolution, and others. However, these reasons apply only within specific heat pulse velocity ranges. These methods typically rely on 1-3 data points for sap flow estimation. In some methods, the data points for particular flow rates may be susceptible to noise, resulting errors in sap flow estimates. While a combination of different methods could potentially address this issue, they often require different probe configurations, additional probes, and complex switching algorithms. However, none of the existing techniques have successfully measured the full range of sap flow rates. In this study, we present a new approach capable of measuring a wide range of sap flow rates by minimizing the sum of square errors between modeled and observed temperature data points, utilizing 180 data points. Additionally, we demonstrate that the signal-to-noise ratio as an explanatory framework shows the limitations of existing methods within specific heat pulse velocity ranges. We show that the signal-to-noise ratio can be increased by utilizing all available data points. The Sum of Square Errors Minimization method can accurately measure a wide range of sap flow rates without the need to change probe configurations, contributing to improved scaling from plant level to plot levels.

How to cite: s b, S. and srinivasan, V.: A new technique of measuring sap flow that accurately measures a wide range of sap flow rates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14542, https://doi.org/10.5194/egusphere-egu24-14542, 2024.

In this presentation, we explore the application of Long Short-Term Memory networks (LSTMs) to predict hourly tree-level sap flow across Europe, utilizing the comprehensive SAPFLUXNET database. This study emphasizes the potential of deep learning in estimating transpiration and understanding forest water use dynamics and plant-climate interactions. By developing LSTM models with varied training sets, we assess their capability to perform in previously unencountered conditions. Our research reveals that these models achieve an average Kling-Gupta Efficiency of 0.77 when trained on 50% of the time series across all forest stands, and 0.52 for models trained on 50% of the forest stands without prior gauging. These continental-scale models not only meet but often exceed the performance of specialized and baseline models across all tree genera and forest types. In this submission, we will discuss the methodologies employed, the challenges faced, and the insights gained from this research. The presentation will also highlight the broader implications of this study for ecohydrological investigations, particularly the enhanced capacity of deep learning models to generalize sap flow data, thereby improving our understanding of ecohydrology from individual trees to a continental scale.

How to cite: Loritz, R., Wu, C. H., Klotz, D., Gauch, M., Kratzert, F., and Bassiouni, M.: Generalising Tree–Level Sap Flow Across the European Continent using LSTMs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15285, https://doi.org/10.5194/egusphere-egu24-15285, 2024.

Terrestrial evapotranspiration (ET) is the nexus of the water, energy and carbon cycles, and therefore accurate quantification of ET is important for understanding the climate and the Earth system. However, current ET estimates derived from process-based models and remotely sensed observations are subject to significant uncertainty, a major reason for which is the limited availability and quality of ground validation data. The eddy covariance (EC) technique provides an excellent opportunity for continuous ET measurements at ecosystem scales with high temporal resolution (half-hourly or hourly resolution), and nowadays eddy towers are deployed in almost all types of terrestrial ecosystems and climatic conditions. Most EC-based ET estimates, however, suffer from an energy imbalance: the sum of sensible and latent heat fluxes is often lower than the available energy (i.e. the difference between net radiation and soil heat flux). The general consensus on the causes includes instrumental bias, missing stored fluxes, different footprints for different variables, and imperfect assumptions in the eddy covariance approach.

In this presentation, we propose a generalised correction method (Zhang et al., 2023, 2024) across the site network. The method, statistical in nature, can improve the energy imbalance from ~80% to ~98% across the site network. The results are markedly better than those by the standard correction method implemented in the dataset processed by the ONEFlux pipeline, which tends to over-correct turbulent flux measurements. We further evaluate the corrected ET by comparing it with independent regional measurements in terms of spatial patterns and temporal variations, after upscaling the ecosystem-level data to the global scale using the latest FLUXCOM framework. The results show that the corrected ET-based upscaled estimates are closer to the ET derived from the water balance perspective and from the balloon-sounding observations. Our method provides a state-of-the-art alternative to improve the energy balance closure by correcting the site-level ET, and the improved global ET estimates can be of great value for water cycle studies and for model development.

References:

Zhang, W., Jung, M., Migliavacca, M., Poyatos, R., Miralles, D. G., El-Madany, T. S., . . . Nelson, J. A. (2023). The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation. Agric. For. Meteorol., 330, 109305. doi:10.1016/j.agrformet.2022.109305

Zhang, W., Nelson, J. A., Miralles, D. G., Mauder, M., Migliavacca, M., Poyatos, R., Reichstein, M., Jung, M. (2024). A new post-hoc method to reduce the energy imbalance in eddy covariance measurements. Geophys. Res. Lett., (accecpted)

How to cite: Zhang, W., Nelson, J., Miralles, D., Mauder, M., Migliavacca, M., Poyatos, R., Reichstein, M., and Jung, M.: A new post-hoc method to improve the eddy-covariance-based evapotranspiration measurements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14185, https://doi.org/10.5194/egusphere-egu24-14185, 2024.

Quantification of evapotranspiration over complex terrain is still challenging because all the known methods are indirect and rely on strong assumptions. The bichromatic scintillometry method, combining optical/near-infrared and microwave scintillometers, is probably one of the closest methods to the turbulent theoretical framework as it measures turbulent parameters for temperature and moisture fluctuations. However, since its description in the 90s, very few works have been published using this method, mainly because of the availability of manufactured instruments but also because of technical and methodological issues.

In this study we present evapotranspiration series from two different campaigns with the combination of two scintillometers operating, one in the near infra-red domain and the other in the radiofrequency domain (94GHz), a prototype developed in collaboration with the Rutherford Appleton Laboratory (UK). The first 18-month time series has been measured over a crop mosaic These instruments have been installed in the Critical Zone observatory Oracle, located east of Paris in the Seine Catchment, and have run continuously since May 2016 to the end of year 2017 on a 4.5km pathlength. The data processing has been developed from raw received intensity data logged at 1kHz for both scintillometers. Turbulent fluxes have been processed from Cn² measurements using the bichromatic method. The data processing toolbox has been fully developed at IGE. Fluxes are then compared with aggregated fluxes from Eddy-Covariance stations representative of the different land cover within the footprint. Results are very encouraging with very good energy balance closure on short periods. However an underestimation of fluxes during summer times suggests some possible saturation impacts. To address this issue we installed a renewed scintillometry setup on a shorter 600m path length at the P2OA facility site near Lannemezan (South of France). The presentation will focus on these new results. 

How to cite: Cohard, J.-M., Barral, H., Coulaud, C., Mercier, B., Regneau, D., Arrivé, J., and Lohou, F.:  evapotranspiration measurements at the landscape scale using a Micro-Wave scintillometer prototype: Evaluation from two field campaigns., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18879, https://doi.org/10.5194/egusphere-egu24-18879, 2024.

Evapotranspiration (ET) is the combined result of two highly dynamic processes: transpiration, which is the water loss from plants through their stomata, and evaporation, which is the conversion of water on the surface into water vapor. Thus, ET is a key factor in crop growth and yield. To efficiently estimate ET with high temporal and spatial coverage, satellite data provide essential input, such as input to the well-developed models utilizing the energy balance method. Spaceborne thermal infrared data allow the derivation of land surface temperature (LST), a vital component for many ET models that utilize the energy balance theory.

Spaceborne-based modelling of ET using energy balance approaches requires input of atmospheric and surface variables with biophysical parameters of the plants covering the surface. Latent heat flux is a critical component that is modelled, as it is the transfer of energy from the surface to the atmosphere that results from evaporation and the transpiration of water from plants. With the lack of validation data worldwide, estimating ET with more than one model has allowed the identification of suitable input, improvement of assumptions, detection of outliers, and assessment of uncertainty.

In this research, two models are used to estimate ET: (1) two-source energy balance (TSEB), (2) Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL). They are two-source models; thus, they consider vegetation and soil to be independent regarding heat flux estimation, yet with distinct characteristics.  PT-JPL uses empirical environmental constraints to scale an equilibrium ET to the actual ET, yet it can have bias when there is a saturated evaporating front (i.e., after a heavy rainfall event or irrigation). TSEB attempts to iteratively estimate soil and canopy temperatures. Yet, it tends to overestimate the latent heat flux and underestimate the sensible heat flux in certain cases.

This research aims to assess the ET estimation characteristics of the two models throughout several years of full crop growth periods. It aims to understand the impact of specific parametrization on their output and the added value of utilizing a next generation of high resolution LST data, the constellr LST30 data product. constellr LST30 is used as precursor data of the upcoming constellr HiVE thermal satellite constellation. This LST dataset has a spatial resolution of 30m and is utilized as input data for the ET estimation. The modelled latent heat flux is compared to flux tower measurement for this purpose. Flux tower footprints are calculated using the two-dimensional parameterisation Flux Footprint Prediction approach that is based on a scaling of the crosswind distribution of the flux. The outcomes of the research bring information about the suitability of each model to certain environmental and crop conditions and highlights the importance of high quality LST to ET modelling.

How to cite: Pregel Hoderlein, A., Ibrahim, E., Berhin, J., Spengler, D., and Taymans, M.: Comparative analysis of two evapotranspiration models: Unveiling insights into their dynamics over crop growth cycles and the contribution of land surface temperature to their performance, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22493, https://doi.org/10.5194/egusphere-egu24-22493, 2024.

The tropics play a pivotal role in the terrestrial energy and water cycles, as well as regulating the carbon cycle. The increasing pressures over the remaining natural vegetation areas in Brazilian tropical forests, allied to climate change, are likely expected to alter these cycles. Despite the existence of studies that have already observed changes on water and energy fluxes, questions regarding heat and mass exchange mechanisms and the biophysical processes in tropical ecosystems and crops for food and energy production remain. Studies involving carbon exchanges and water fluxes in the Cerrado ecoregion are mostly related to agricultural land uses (e.g., pasture, eucalyptus, and sugarcane). Thus, empirical answers from undisturbed areas of this ecoregion are important to understand the whole of pristine vegetation in carbon and water flux related processes in tropical ecosystems, which generally lacks on-site observations. Here, the complex land use pattern (contrasting land use in the footprint area) of an experimental site challenges the data processing and the representativeness of a dataset obtained using eddy covariance technique. However, these challenges may also create scientific opportunities to obtain responses from contrasting land uses at the same measurement tower if a consistent data processing along fluxes calculations is performed. The purpose of this study is to compare contrasting land uses responses concerning the water and carbon dioxide fluxed observed from an eddy covariance experiment deployed in a complex site, which measures an undisturbed tropical woodland and a mixed agricultural site (pasture, sparse trees, and sugarcane). This complex landscape created methodological challenges concerning the flux footprint representativeness for data filtering to allow modelling water and carbon dioxide fluxes. Thus, this study also evaluated a workflow to calculate fluxes considering a dynamic metadata that varied canopy height, displacement height, and roughness length binned by the wind direction. The evapotranspiration in wooded Cerrado is higher than the agricultural land along the entire year, mainly due to the increased transpiration along the whole year including the dry season. In addition, this remarkable plant activity difference between the observed land covers can also be seen in the carbon dioxide flux, as its absorption tend to be higher in the wooded Cerrado than what was observed in the agricultural site. Thus, through the sampling context of this site-specific studies, it is possible to assume that the plants water-use strategies are driven by vegetation height, and the ecosystem carbon flux is controlled by vegetation structure and water availability.

How to cite: Ayach Anache, J. A., Holl, D., Kobayashi, A., Zhao, Y., Pessoa de Souza, P. B., and Wendland, E.: Water and carbon dioxide fluxes in contrasting land covers typical from Brazilian Cerrado: Modeling and methodological challenges using eddy covariance data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13995, https://doi.org/10.5194/egusphere-egu24-13995, 2024.

Climate change poses fundamental challenges to viticulture, such as more frequent droughts in Central Europe. This development requires precise, site-specific methods to determine plant water status. Especially in steep sloped vineyards, the spatial variability of drought stress can be high and depends on different factors such as slope, aspect and soil characteristics.      

Most established methods for determining plant water status are destructive, labor-intensive, or provide point-in-time measurements, or e.g. non-destructive modeling approaches need to be well referenced. UAV campaigns using thermal and multispectral imagery, as well as in-field sensor networks, provide non-destructive solutions with high spatio-temporal resolution. This study aims to combine both solutions to measure the high spatial and temporal variability of drought stress in a steep sloped vineyard. The goal is to develop a continuous, cross-scale, and resource-efficient method that can be used directly for irrigation scheduling or as a reference method for cross-scale modeling approaches at high spatial resolution.  

During the growing season of 2022, UAV campaigns were conducted every two weeks to generate  thermal and multispectral imagery over a vineyard of 1 ha in Saxony, Germany. The vineyard was divided into five management zones (MZ), which differ in terms of slope, aspect, soil characteristics and grape varieties. A monitoring system has been established in each management zone to continuously collect data on local climate, as well as soil and plant water properties. Simultaneously with the UAV campaigns, the water status and physiological stage of the vines were determined as reference measurements. Therefore, predawn leaf water potential (Ψ_pd) was measured using a Scholander pressure chamber. Based on the processed aerial images and the in-situ sensor-based measurements the Crop Water Stress Index (CWSI) was computed and then validated by comparing it’s values to in-field reference measurements such as soil water status and Ψ_pd. Weather and plant physiological in-situ measurements were also integrated into a grapevine water balance model to derive quantitative information on plant and soil water status.   

In-situ measurements of plant and soil water potentials correlated well with the results of the modeling approach. This was a good representation of the spatial heterogeneity of the vineyard, especially the differences in plant water availability between MZs. CWSI values from the UAV campaigns will be compared with the in-situ measurements in terms of spatial  variability, and also temporal variability to reproduce drought and other seasonal events. 

The combination of sensor data, simulation modeling and UAV-based thermal and multispectral imagery offers great potential to provide site-specific information with high spatio-temporal resolution about the plant water status. In particular, the inclusion of UAV campaigns can help to optimize the implemented sensor network and minimize the number of in-situ reference measurements. However, this cross-scale method also depends on a large number of influencing factors that need to be considered and discussed in depth in order to allow a valid assessment of drought stress dynamics and to set thresholds for irrigation or other management measures.  

How to cite: Boedeker, H., Graß, R., Mollenhauer, H., and Ohnemus, T.: Site-specific determination of plant water status in a steep sloped vineyard using a microclimatic monitoring system in combination with a water balance model and UAV-based thermal and multispectral imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12533, https://doi.org/10.5194/egusphere-egu24-12533, 2024.

This study compares evapotranspiration (ET) estimates obtained from 42 hours (7 – 9 July 2023) of continuous eddy covariance measurements with ET estimates derived from UAV thermal and multispectral imagery collected in 12 flight missions completed in the same period. Meteorological conditions were stable during the measurement campaign with mostly clear sky, weak wind and air temperatures fluctuating diurnally between +8 to +30°C. The eddy covariance estimates averaged over 30 minute intervals corresponded to the source area covered mostly by oat field and freshly cut meadow. The algorithm for ET estimation was based on the Priestley-Taylor scheme applied in the ECOSTRESS Level-3 Evapotranspiration product, however, with a number of variables (components of radiative energy balance, relative humidity, ground heat flux) obtained from direct measurements. The NDVI and SAVI indices obtained from multispectral UAV images were used to estimate fractions of absorbed and intercepted photosynthetically active radiation. UAV-based ET estimates obtained at ca. 8 cm spatial resolution were averaged over the eddy covariance footprint area and interpolated for the times of EC-based ET measurements. Our results show that the approach combining UAV-based thermal and multispectral imagery with point measurements of meteorological variables and energy balance components might provide robust spatial ET estimates for agricultural areas of the size covered by one UAV mission, this is of the order of up to tens of hectares.

This research was funded by National Science Centre, Poland, project WATERLINE (2020/02/Y/ST10/00065), under the CHISTERA IV programme of the EU Horizon 2020 (Grant no 857925) and the "Excellence Initiative - Research University" programme at AGH University of Kraków.

How to cite: Wachniew, P., Szostak, R., Jasek-Kamińska, A., and Zimnoch, M.: Evapotranspiration estimates from eddy covariance and from UAV imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14382, https://doi.org/10.5194/egusphere-egu24-14382, 2024.

Evaporation (E) plays a key role in the terrestrial water, energy, and carbon cycles, and modulates climate change through multiple feedback mechanisms. Its accurate monitoring is thus crucial for water management, meteorological forecasts, and agriculture. However, traditional in situ measurements of E are limited in terms of availability and spatial coverage. As an alternative, global monitoring of E using satellite remote sensing, while indirect, holds the potential to fill this need. Today, different models exist that yield E estimates by combining observable satellite-based drivers of this flux, but typically work at daily or oven monthly time scales. As natural evaporation processes occur at sub-daily resolution, there is a need to estimate evaporation at finer temporal scales to capture the diurnal variability of this flux and to monitor water stress impacts on transpiration. Likewise, interception loss shows high intra-day variability, mainly concentrated during precipitation events and shortly after. Moreover, the moisture redistribution within the soil–plant–atmosphere continuum as a consequence of transpiration is highly non-linear and has a strong daily cycle.

Sub-daily microwave data could inform about these short-term processes, and as such improve process understanding and monitoring of E and its different components, while providing all-skies retrievals. The Sub-daily Land Atmosphere INTEractions (SLAINTE) mission, a mission idea submitted in response to ESA’s 12^th call for Earth Explorers, will aim to provide sub-daily SAR observations of soil moisture, vegetation optical depth (VOD) and wet/dry canopy state, enabling a more accurate estimation of E and the potential to advance E science beyond its current boundaries.

This study investigates the potential value of future SLAINTE observations for improving the estimation of E at four eddy covariance sites. In this regard, Observing System Simulation Experiments (OSSEs) are assembled. In total, three experiments using synthetic microwave observations are implemented, focusing on the role of (1) sub-daily soil moisture in improving bare soil evaporation and transpiration estimates, (2) sub-daily VOD in improving transpiration estimates, and (3) sub-daily microwave observations that inform about the wetness state of the canopy, to address the uncertainties related to rainfall interception loss. The Global Land Evaporation Amsterdam Model (GLEAM; Miralles et al., 2011) is used for the simulations. GLEAM is a state-of-the-art E model that estimates the different E components (mainly transpiration, soil evaporation, and interception loss) using satellite data, including microwave observations of surface soil moisture and VOD. The model is here adapted to work at sub-daily resolution. The results of the OSSEs illustrate that prospective sub-daily microwave data would lead to improvements in the estimation of evaporation and its separate components, even if based on current-generation evaporation models, and highlight the need for missions like SLAINTE to better comprehend the flow of water in ecosystems.

How to cite: Tronquo, E., Steele-Dunne, S. C., Lievens, H., Verhoest, N. E. C., and Miralles, D. G.: Assessing the potential of future sub-daily microwave observations for estimating evaporation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15876, https://doi.org/10.5194/egusphere-egu24-15876, 2024.

Evapotranspiration (ET) is the loss of water from both the soil and plants, and it is an important component of the hydrologic cycle. In the recent decades, ET estimation has improved due to developments in remote sensing technologies, particularly in the agricultural domain. ET is affected by a variety of factors, including weather and crop conditions, which are difficult to estimate for larger regions at fine resolution. Therefore, the current study intends to employ the Surface Energy Balance Algorithms for Land (SEBAL) model using satellite images to estimate and provide spatial ET variation using crop growth biophysical parameters such as land surface temperature, albedo, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Leaf Area Index (LAI), net radiation, sensible heat flux, and soil heat flux. The pixel-based SEBAL technique was used for the Haridwar district (area = 2360 sq. km) of Uttarakhand, India. The study utilized 5 cloud-free harmonized Landsat 8 and sentinel 2 satellite data for winter wheat crop at the beginning, middle, and end of the season. The area of cultivated wheat fields was initially identified using a machine learning support vector machine technique based on time series-threshold values of NDVI. This showed a wheat area of 526.86 sq. km, while the observed wheat acreage was 446.44 sq. km. The results showed that, for the research region, the support vector machine produced a significantly accurate assessment, with a kappa coefficient of 0.89, producer accuracy of 0.89, user accuracy of 0.82, and overall accuracy of 0.84. The estimated mean actual ET values were found to be 3.7 mm/day, 3.0 mm/day,  4.1 mm/day, 0.6 mm/day, 0.8 mm/day, and potential ET calculated by FAO-56 Penman-Monteith method were 4.4 mm/day, 4 mm/day, 4.1 mm/day, 3.7 mm/day, 2.1 mm/day dated 14^th and 6^th March, 2023, 26^th and 18^th February 2023, 17^th January 2023, respectively.  Based on the findings, ET maps and NDVI maps showing spatial variation were developed for the study area. These maps can be helpful for hydrological modeling, drought management, crop yield estimation, and irrigation scheduling.

How to cite: Singh, P. and Kothari, K.: Integrating Satellite-Derived Data and Machine Learning Algorithm for Assessing Winter Wheat Evapotranspiration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-927, https://doi.org/10.5194/egusphere-egu24-927, 2024.

The Sahel region, identified as a "hot spot" for climate change, is characterized by a water scarcity and an inter-annual variability of water resources. Indeed, ongoing climate changes intensify the evaporative demand which could lead to more frequent period of droughts. Therefore, an important issue in these countries is to provide accurate estimation of evapotranspiration (ET) in a spatially distributed manner. The growing number of spatial ET products, including simple empirical equations (e.g., Penman-Monteith), land surface models (LSM), energy balance models, interpolated in-situ measurements, neural network approaches, or data fusion, form an interesting alternative in these areas scarcely gauged. However, until recently, there is no product combining simultaneously good spatiotemporal resolution (i.e., <1km, <daily) and good performances. Remote Sensing (RS) data in the thermal infrared domain, used in energy balance models, is particularly useful because it allows for spatial ET estimates at various space-time resolutions. A well-adapted method for the Sahelian context was proposed based on an ensemble contextual energy balance model combining thermal and visible satellite information (EVASPA S-SEBI Sahel method; E3S, Allies et al, 2020, 2022). This contextual method is based on the thermal contrast (hot/dry and cold/wet pixels) observed in a given thermal image to provide an ensemble of instantaneous estimation of evapotranspiration conditions. The applicability and accuracy of this approach suppose: (1) The presence of sufficient heterogeneity between dry and wet pixels within the same image and (2) the correct identification of the driest and wettest pixels, also known as dry and wet boundaries. These two hypotheses are rarely checked before computation within contextual models, leading to high uncertainties in ET estimation. Therefore, the aim of this study is firstly to allow for a systematic detection of the heterogeneity conditions and a dynamic selection of adapted methods for the determination of wet and dry boundaries by using only the image information without prior knowledge of local conditions. Secondly, our aim is also to assess the added value of using a thermal information from high spatial resolution (Landsat or Ecostress data) compared to medium resolution (Modis data) on the image heterogeneity and consequently on ET estimation. The proposed method shows higher performance in comparison with reference ET products in our study area in central Senegal, with a lower RMSE value (around 0.5 mm.day^-1) compared to eddy-covariance measurements. Moreover, it reduces significantly structural uncertainties by around 0.6 mm.day^-1 in dry season and around 0.4 mm.day^-1 in wet season. Thermal information from higher resolution data are expected to further improve ET simulation due to a higher perceived heterogeneity in satellite images. It could lead to more accurate estimates of surface water deficit in semi-arid areas. The use of high-resolution data also makes this study a good demonstrator for the upcoming thermal earth observation missions like TRISHNA (CNES/ISRO), which this work is part of, LSTM (ESA) and SBG (NASA).  

How to cite: Farhani, N., Etchanchu, J., Dezetter, A., Thiam, P. B., Allies, A., Bodian, A., Boulet, G., Chahinian, N., Diop, L., Mainassara, I., Ndiaye, P. M., Ollivier, C., Olioso, A., Roupsard, O., and Demarty, J.: Spatially remotely sensed evapotranspiration estimates in Sahel regions using an ensemble contextual model : structural uncertainty estimation and reduction , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10616, https://doi.org/10.5194/egusphere-egu24-10616, 2024.

Drought, as an extreme climatic event with a growing presence worldwide, plays an important role in forestry as well as in the management and conservation of natural areas, especially in the Mediterranean basin. Currently, the abandonment of primary activities because of the lack of economic profitability and the lack of generational relief is detrimental to agroforestry mosaics. The expansion of the forest mass due to the afforestation of former crops and pasture fields favors naturalization, but also leads to an alteration of the water balance at both local and regional levels. In addition, one of the effects of climate change will be an increase in the demand for atmospheric water for natural vegetation caused by the increase in temperatures and the decrease in precipitation and water reserves. This will have an important effect on the increase in wildfire risk as well as water flow decrease to maintain fauna and flora, especially in riparian habitats. Thus, in a global change scenario, the next challenge in the 21st century for the conservation and management of biodiversity and natural resources will be on how to adopt a set of technologies that allow monitoring and estimating water resources at regional scales. Evapotranspiration (ET) plays a significant role in the hydrologic cycle of Mediterranean basins, where surface-atmosphere exchanges due to ET may be more than 70% of annual precipitation. Together with precipitation (P), the surface water balance (P-ET), key parameter in the management and conservation natural resources, can be estimated. Even though ET is a significant component of the hydrologic cycle in this region, bulk estimates do not accurately account for spatial and temporal variability due to vegetation type or topography. The main objective of this study is to estimate the surface water balance at a regional scale on a mountainous protected area, the Montseny Biosphere Reserve, through the analysis of ET remote sensing estimates from 2017 to 2022 in a long-term gauged catchment. To estimate ET, the SEN-ET modelling framework (http://esa-sen4et.org) based on the Two-Source Energy Balance model that allows estimating high-resolution ET daily estimates at a spatial resolution of 20 m by sharpening thermal observations from Sentinel-3 satellites (1km, daily) and optical observations from Sentinel-2 satellites (20m, every 5 days) was applied. To estimate the surface water balance, daily precipitation was obtained by multiple regression analysis of meteorological stations. Preliminary evaluation of ET remote sensing estimates with ET derived from the long-term gauged catchment yielded an RMSE of around 1.5 mm·day^-1 that allowed computing a reasonable surface water balance for this period. Due to a severe drought within the study period, the annual surface water budget showed a decrease pattern. A water balance anomaly analysis showed that 80% of the reserve forest were under a negative anomaly for three consecutive years, pointing out that surface water balance derived from ET remote sensing estimates can be used to improve forest management by focusing on those areas that will become more affected by drought episodes.

How to cite: Cristóbal, J., Latron, J., Bozorgi, M., Bellvert, J., and Pàmies, M.: Surface water balance estimation in a mountainous and forested Mediterranean protected area using remote sensing estimates of evapotranspiration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16071, https://doi.org/10.5194/egusphere-egu24-16071, 2024.

Evapotranspiration (ET) plays a major role in climate processes by facilitating water redistribution between continental surfaces and the atmosphere. Accurately quantifying ET remains a challenge due to the scarcity of direct ET measurements, particularly in some regions with poor climate monitoring like Madagascar. Moreover the island has a very different climate, from semi-arid in the southwest to humid in the east. Estimating ET from remote sensing and climate reanalysis appears as a relevant way to provide spatially distributed data at a regional scale. Several operational or pre-operational usually, they providing quite different results. How to choose the most relevant product for a study area is a key question for any hydrological study.

Our study focused on evaluating five popular evapotranspiration products over Madagascar: GLDAS-NOAH, ERA5, ERA5-LAND, WAPOR, and GLEAM. The data covers years from 2009 to 2021. The analysis aims to provide a comprehensive understanding of their utility and accuracy in estimating ET over the different climatic zone.

Our initial findings involved a comprehensive assessment of various datasets, focusing on their differences and evaluating their validity in maintaining water and energy balance. This comprehensive analysis encompassed (i) analyzing jointly evapotranspiration estimates, potential evapotranspiration, and precipitation used by each ET dataset and (ii) validating ET estimates on the few catchments where streamflow data are available. The results indicate significant differences in ET estimates, as well as in each climate zone in Madagascar (in average 550 mm/year in semi-arid area and 1050 mm/year in humid area). The observed differences warrant a deeper exploration of the factors contributing to these differences and a careful assessment of the strengths and limitations of each datasets.

How to cite: alimohammad nejad, R., D. Carrière, S., Olioso, A., and Oudin, L.: Exploring the physical consistency of evapotranspiration estimates over Madagascar using remote sensing., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3899, https://doi.org/10.5194/egusphere-egu24-3899, 2024.

Evapotranspiration (ET) is a fundamental element of the hydrological cycle which plays a major role on surface water balance and surface energy balance. At local scale, ET can be estimated from detailed ground observations, for example using flux towers, but these measurements are only representative of very limited homogeneous area. When regional information is required, e.g.  for monitoring ground water resources, ET can be mapped using thermal infrared and spectral reflectance data. Various ET models have been developed but there was no competitive evaluation of them over a large range of situations, so that it is not possible to evaluate the intrinsic performance of one model compared to another. In such situation, ensemble model averaging may provide a coherent estimation of ET with an increased overall accuracy. In this work the ensemble modelling approach is extended to a multi-model – multi-data framework that provides ET estimations together with an uncertainty of estimation.

We developed the EVASPA framework for estimating ET through ensemble averaging with the objective of providing estimates of ET together with an estimation uncertainty. In this presentation we present a full analysis of the uncertainties of ET estimation in relation to uncertainties in input variables and models. Airborne remote sensing data were acquired over the Grosseto area in Italy in the frame of the ESA SurfSense experiment (high spatio-temporal Resolution Land Surface Temperature Experiment) in support of the LSTM mission project (Copernicus Land Surface Temperature Monitoring). Evapotranspiration was computed using two different types of models considering: -1) the evaporative fraction (EF) computed from the variability of surface temperature versus vegetation amount (fraction cover) or albedo over the investigated areas ('triangle' approach) and -2) the residual aerodynamic equation. Two types of uncertainties were computed: the ‘novice user’ uncertainty and the ‘expert user’ uncertainty which differed by the previous knowledge on the accuracy of input data and on the performances of models that was available to users. Evapotranspiration uncertainties ranged between 0.8 mm.d^-1 (EF model, expert case) and 2.7 mm.d^-1 (aerodynamic model, novice case). The analysis showed that the main uncertainty sources were related to model formulations (evaporative fraction calculation and ground heat flux calculation for both types of models) and to solar radiation (both types of models), wind speed and air temperature (aerodynamic model).

The EVASPA framework is presently used for the definition of the ET product in the frame of the TRISHNA thermal infrared space mission (CNES/ISRO).

How to cite: Olioso, A., Mwangi, S., Desrutins, H., Sobrino, J., Skoković, D., Carrière, S., Farhani, N., Etchanchu, J., Demarty, J., Hu, T., Mallick, K., Jia, A., Buis, S., Weiss, M., Ollivier, C., and Boulet, G.: Multimodel – multidata simulations for mapping evapotranspiration and its uncertainty of estimation from remote sensing data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13113, https://doi.org/10.5194/egusphere-egu24-13113, 2024.

ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) has been providing high spatio-temporal thermal infrared (TIR) observations (~70 m, 1-5 days) since August 2018. Land surface temperature (LST) retrieval obtained from TIR observations indicates the thermal status of the surface as a consequence of the land-atmosphere exchange of energy and water. It carries the imprint of vegetation water use and stress, thus serving as a pivotal lower boundary condition for retrieving evapotranspiration (ET). Taking advantage of the ECOSTRESS observations, the European ECOSTRESS Hub (EEH) funded by the European Space Agency (ESA) retrieves high-resolution ET for terrestrial ecosystems.

In EEH Phase 1 (2020-2022), instantaneous ET data between 2018 and 2021 were generated from three models with different structures and parameterization schemes over Europe and Africa, including the Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the analytical Surface Temperature Initiated Closure (STIC) model. The evaluation by comparing against ground measurements at 19 eddy covariance sites for 6 different biomes over Europe showed that the physically based STIC model had relatively better consistency and higher accuracy across varying aridity and diverse biomes. Also, an advantage of STIC was found as compared to the official ECOSTRESS ET product obtained using the PT-JPL model, especially over arid and semiarid regions due to the weak LST control in PT-JPL.

Taking advantage of the recalibrated ECOSTRESS Collection 2 data, EEH Phase 2 (2023-2025) analyses the impacts of LST estimates from different algorithms on ET retrieval and related biophysical conductances over different biomes. It is found that ET estimates of STIC driven by LST retrieved from the two most commonly used algorithms (i.e., split window, SW, and temperature and emissivity separation, TES) have comparable accuracies. The sensitivity of ET to LST over savannas is almost three times of those over biomes over lower aridity. Surface-canopy conductance is more sensitive to surface temperature as compared to aerodynamic conductance.

Overall, the EEH is promising to provide quality assured ET estimates for monitoring terrestrial ecosystem water use and stress. Furthermore, it will facilitate the preparation for the next generation high-resolution thermal missions by investigating surface energy balance modeling, including TRISHNA (CNES/ISRO), SBG (NASA), and LSTM (ESA).

How to cite: Hu, T., Mallick, K., Hitzelberger, P., Didry, Y., Szantoi, Z., Boulet, G., Olioso, A., Roujean, J.-L., Gamet, P., and Hook, S.: High-resolution Mapping of Terrestrial Evapotranspiration using ECOSTRESS: Insights into Surface Energy Balance Modeling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9508, https://doi.org/10.5194/egusphere-egu24-9508, 2024.

High resolution accurate evaporation (E) estimates are crucial for large-scale agricultural, ecological, and hydrological applications. However, field observations are sparse, traditional satellite-based datasets based on thermal and optical imagery are unavailable during cloudy times, and most continuous global records are too coarse in terms of spatial resolution.  One of the latter, the Global Land Evaporation Amsterdam Model (GLEAM)¹ dataset, has been widely used in climate studies in recent years, but the realm of hydrological and agricultural applications was prohibited until recently due to its coarse spatial resolution². Ongoing developments have led to the development of high-resolution (1-km) E estimates over the Mediterranean region covering the period 2015–2021. The Mediterranean region, characterised by diverse hydroclimatic conditions and seasonal rainfall, experiences challenges related to droughts, floods, and landslides, making it an ideal testbed for GLEAM datasets at a high spatial resolution (GLEAM-HR).

This work summarises current activities and future plans for GLEAM-HR. Our ongoing efforts include extending coverage from the Mediterranean to embrace the entire Meteosat disk (including Europe and Africa). This expansion involves incorporating modifications in the interception module³, addressing groundwater effects⁴, and using deep learning for transpirational stress estimation⁵. These advancements enhance the utility of GLEAM-HR for addressing water-related challenges, supporting sustainable water management practices, and contributing to evidence-based decision-making.

¹Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.

²Koppa, A., Rains, D., Hulsman, P., Poyatos, R., Miralles, D. G., 2022: A deep learning-based hybrid model of global terrestrial evaporation. Nature Communications, 13 (1), 1912.

³Zhong, F., Jiang, S., van Dijk, A. I. J. M., Ren, L., Schellekens, J., and Miralles, D. G.: Revisiting large-scale interception patterns constrained by a synthesis of global experimental data, Hydrol. Earth Syst. Sci., 26, 5647–5667, https://doi.org/10.5194/hess-26-5647-2022, 2022.

⁴Hulsman, P., Keune, J., Koppa, A., Schellekens, J., and Miralles, D. G: Incorporating plant access to groundwater in existing global, satellite-based evaporation estimates, Water Resources Research, https://doi.org/10.1029/2022WR033731, 2023.

⁵Koppa, A., Rains, D., Hulsman, P., Poyatos, R., and Miralles, D. G.: A deep learning-based hybrid model of global terrestrial evaporation, Nat. Commun., 13, 1912, https://doi.org/10.1038/s41467-022-29543-7, 2022.

How to cite: Baez-Villanueva, O. M., Koppa, A., Bonte, O., and Miralles, D. G.: Towards a continuous, multiyear, high resolution dataset of evaporation  over Europe and Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8972, https://doi.org/10.5194/egusphere-egu24-8972, 2024.

The Satellite Application Facility on Land Surface Analysis Programme (LSA SAF, http://lsa-saf.eumetsat.int/) has developed an operational service that delivers satellite-based information on the land’s surface. The portfolio of the LSA SAF comprises estimates of evapotranspiration (ET) and surface energy fluxes (SEF) on the basis of observations by the geostationary Meteosat Second Generation (MSG) satellite. Those estimates are generated every 30 minutes across Europe, Africa and Eastern South America at the spatial resolution of the SEVIRI instrument of MSG. The time step and timeliness of these products are seldom found in operational and ET/SEF products in spite of the relevance of accounting for the variability in energy exchange between land surface and atmosphere in the course of the day.

The operational character of the LSA SAF programme has ensured the generation of a nearly 20 years-long archive of ET and SEF estimates (Barrios et al., 2024). The archive keeps growing as the ET/SEF estimates are generated in near-real time. The near real time operational data is freely available through the LSA SAF internet portal (https://landsaf2.ipma.pt/geonetwork/).

The recent launch of the Meteosat Third Generation (MTG) satellite will bring improvements in the spatial detail of the ET/SEF products of the LSA SAF programme while remaining compatible with the existing archive. The imager onboard MSG (SEVIRI) delivers observations at ~3 km spatial resolution at sub-satellite position whereas the spatial detail derived from Flexible Combined Imager (FCI) onboard the MTG exhibits a spatial resolution of 1-2 km. 

This contribution will discuss the expected advances  in the LSA SAF ET/SEF products as a consequence of the operational ingestion of MTG-based observations in the forcing of the LSA SAF algorithm. Other synergies with spatial missions to further improve ET/SEF estimates will be discussed as well.

Reference:

Barrios, J. M., Arboleda, A., Dutra, E., Trigo, I., Gellens-Meulenberghs, F. 2024: Evapotranspiration and surface energy fluxes across Europe, Africa and Eastern South America throughout the operational life of the Meteosat second generation satellite. Geoscience Data Journal (accepted).

How to cite: Barrios, J. M., Arboleda, A., and Gellens-Meulenberghs, F.: The Meteosat Third Generation satellite, advantages for an enhancement of evapotranspiration and surface energy fluxes estimates in the LSA SAF Programme, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12126, https://doi.org/10.5194/egusphere-egu24-12126, 2024.

In the pursuit of optimizing use of limited water resources in agriculture, leveraging high-resolution aerial imagery to estimate ETa (actual crop evapotranspiration) is of interest to farmers and water managers. However, there remains a dearth of information regarding the efficacy of energy balancing algorithms—initially developed for satellite remote sensing for estimating ETa from aerial imagery. This study presents an approach that estimates ETa for processing tomatoes employing high-resolution aerial data and the pySEBAL (Surface Energy Balance Algorithm for Land) remote sensing algorithm. During the 2021 growing season, an aircraft captured multispectral and thermal imagery over a processing tomato farm near Esparto, California, USA. Simultaneously, low-frequency biometeorological data essential for energy balance assessment, along with high-frequency turbulent fluxes, were measured by an eddy covariance flux tower installed within the field. Extensive evaluation of ETa and other energy balance components showed that pySEBAL produced accurate, high-resolution estimates of ETa. The root mean square error (RMSE) for the energy balance components were as follows: 33 Wm^-2 for the latent heat flux, 29 Wm^-2 for the sensible heat flux, 24 Wm^-2 for the net radiation, and 10 Wm^-2 for the soil heat flux. Moreover, the RMSE for ETa was 0.26 mm d^-1. Notably, each component exhibited an R² value exceeding 0.92. Furthermore, the ETa mapping of the processing tomato field delineated spatial variability linked to irrigation schedules, crop development, areas affected by disease, and soil heterogeneity, visually representing these aspects. This research underscores the pivotal role of high-resolution spatial aerial imagery and the pySEBAL algorithm in estimating ETa variability within fields, demonstrating high potential for improving precision irrigation management and maximizing the judicious utilization of water resources in agriculture.

How to cite: Peddinti, S. R. and Kisekka, I.: Estimating Crop Evapotranspiration Variability in Processing Tomatoes Using High-Resolution Aerial Imagery and pySEBAL Algorithm, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14187, https://doi.org/10.5194/egusphere-egu24-14187, 2024.

Global evaporation modeling faces challenges in understanding the combined biophysical controls imposed by aerodynamic and canopy-surface conductance, particularly in water-scarce environments. We addressed this by integrating a machine learning (ML) model estimating surface relative humidity (RH₀) into an analytical model (Surface Temperature Initiated Closure - STIC), creating a hybrid model called HSTIC. This approach significantly enhanced the accuracy of modeling water stress and conductance regulation. Our results, based on the FLUXNET2015 dataset, showed that ML-RH₀ markedly improved the precision of surface water stress variations. HSTIC performed well in reproducing latent and sensible heat fluxes on both half-hourly/hourly and daily scales. Notably, HSTIC surpassed the analytical STIC model, particularly in dry conditions, owing to its more precise simulation of canopy-surface conductance (g_Surf) response to water stress. Our findings suggest that HSTIC g_Surf can effectively capture physiological trait variations across ecosystems, reflecting the eco-evolutionary optimality of plants. This provides a fresh perspective for process-based models in simulating terrestrial evaporation.

How to cite: Bai, Y., Mallick, K., Hu, T., Zhang, S., Yang, S., and Ahmadi, A.: Integrating machine learning with analytical surface energy balance model improved terrestrial evaporation through biophysical regulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5661, https://doi.org/10.5194/egusphere-egu24-5661, 2024.