Integrating GLEAM Earth Observation data strategy usage within GEOframe

To better understand the previous discussions, let's examine the specific case of GLEAM 3.0 (Martens et al., 2017) and 4.0. GLEAM (Miralles et al., 2025) is a global evapotranspiration product built on multiple Earth Observation (EO) resources. Since evapotranspiration (ET) cannot be measured directly, it must be inferred or modeled from available data. AS you know, GEOframe is our system for doing hydrology. For GEOframe methodologies in catchments application, please see this previous post.

Actually GLEAM operates at 0.1-degree spatial resolution (approximately 11 km grid cells), which is adequate for global analyses but insufficient for our purposes. Our objective requires information at 1km (approximateli 0.01-degree resolution), particularly for applications in complex terrain such as the Alps, where significant topographic variation occurs within very small areas.

From Miralles et al. 2025. References can be recovered there

Earth Observation Resources in GLEAM

According to Miralles et al. (2025), GLEAM utilizes the EO and reanalysis resources listed in Table 1. Below is an analysis of how each resource could be integrated with GEOframe:

Radiation

Current GLEAM approach: 0.1-0.5 degree resolution GEOframe implementation: Point-wise calculations using local solar radiation, filtered through cloud interception and atmospheric scattering models, then topographically corrected using digital elevation models.

Limitations: The coarse satellite resolution is inadequate for rugged terrain. Additionally, GEOframe's current empirical methods lack reliability, often requiring radiation estimates from randomly selected points within catchments, reducing representativeness.

Potential improvements: Satellite data could be fused with ground measurements to enhance overall accuracy.

Air Temperature

GEOframe implementation: Kriging interpolation with drift using ground station data.

Assessment: Generally reliable since temperature varies gradually across space, though canopy effects may introduce complications.

Precipitation

GEOframe implementation: Ground station measurements interpolated using kriging (typically without drift, as drift was found insignificant). Event-specific variograms are employed.

Key challenge: Determining whether kriging interpolation accurately captures storm spatial patterns. Satellite and radar data could provide valuable validation and improvement opportunities. See also the last post here.

Wind Speed

Application: Required for Penman-Monteith formulations.

Potential integration: ERA5 reanalysis data could supplement ground station measurements through data fusion/assimilation approaches, pending reliability validation (see Azimi et al., 2025)

Vapor Pressure Deficit (VPD)

Current use: Input for Penman-Monteith solutions (D'Amato and Rigon, 2025).

Technical note: VPD represents the temperature difference between emitting surfaces and air. Understanding EO estimation methods could enable valuable comparative analyses with the Prospero model (Bottazzi et al., 2021; D'Amato et al, 2025), where VPD emerges from energy budget calculations.

Carbon Dioxide Concentration

Application: Controls transpiration conductance in both Jarvis and Ball-Berry-Leuning parameterizations.

Current status: Available as input parameter in GEOframe for Penman-Monteith and Prospero models but not utilized in Priestley-Taylor formulations.

Snow Water Equivalent (SWE)

GEOframe approach: Calculated from precipitation, temperature, and snowpack evolution models. It should not be confused with Snow Covered Area (SCA).

Improvement opportunities: MODIS snow products offer superior resolution compared to GLEAM's 25 km resolution. For mountainous terrain with 5000 m elevation changes within 25 km, higher-resolution products could provide significant improvements.

Surface Soil Moisture

GEOframe implementation: Prognostic variable within root zone compartment.

Integration potential: Could enable GEOframe calibration if EO resolution and reliability improve. GEOframe soil moisture could be upscaled to match EO data resolution for comparative analysis.

Vegetation Optical Depth (VOD)

Definition: Proxy for vegetation biomass and cumulative transpiration (assuming linear correlation).

Current status: Not implemented in GEOframe.

Integration potential: Could be connected to Leaf Area Index (LAI), which is used in both interception and transpiration calculations.

Fraction of Absorbed Photosynthetic Radiation (fPAR)

Current status: Not used in GEOframe, which employs total radiation instead.

Advantage: Available at appropriate spatial scales for potential integration.

Leaf Area Index (LAI)

Applications: Useful for both interception and transpiration calculations (when using Penman-Monteith or Prospero models).

Integration potential: High, given its direct relevance to existing GEOframe processes.

Vegetation Height

Value: Excellent spatial resolution and direct application in aerodynamic resistance calculations.

Integration status: Not currently used but could be easily incorporated into GEOframe.

Land Cover Fraction

Current status: Not implemented in GEOframe.

Potential application: Could enhance transpiration estimations.

Soil Properties

Current status: Not utilized, as no GEOframe parameters currently depend on soil characteristics.

Future applications: Could become relevant if replacing reservoir-based root zone approaches with simplified versions of WHETGEO (Tubini and Rigon, 2022) or GEOSPACE (D'Amato and Rigon, 2025b).

GLEAM Methodological Components and GEOframe Integration

Rainfall Interception

GLEAM approach: Utilizes the van Dijk-Bruijnzeel model (van Dijk et al., 2001), developed from global experimental datasets.

GEOframe current implementation: Uses the Gash model.

Integration opportunity: Adding a van Dijk-Bruijnzeel component to GEOframe could enhance model compatibility and performance. Notably, Zhong et al. (2022) successfully constrained interception estimates using fPAR, providing valuable insights for EO integration.

Potential Evapotranspiration

GLEAM approach: Employs the Penman equation for potential ET estimation.

GEOframe compatibility: This methodology is already available in GEOframe, enabling direct reproduction of GLEAM's approach. However, D'Amato et al. (2025) implement a more sophisticated Penman-Monteith formulation than GLEAM, allowing for comparative analyses.

Aerodynamic conductance: GLEAM uses Thom's equation, which differs from GEOframe's current formulation but could be easily implemented. Both approaches require roughness length and zero displacement height parameters that can be derived from EO vegetation retrievals.

Soil Moisture Integration

GLEAM4 advancement: Incorporates data assimilation using European Space Agency (ESA) Climate Change Initiative (CCI) surface soil moisture data through a Newtonian Nudging scheme. The method decomposes soil moisture into anomalies and computes uncertainties using triple collocation (Miralles et al., 2025).

GEOframe current approach: Relies solely on root zone reservoirs for ET sources.

Enhancement opportunities:

• Adding ET sources from groundwater reservoirs through minor modifications to GEOframe's groundwater component
• Implementing GLEAM4's multi-layer running water balance approach, which considers constant root depth per land cover fraction

Physical realism considerations: While GLEAM4 moved from reservoir models (similar to GEOframe) to multi-layer approaches, GEOframe could implement GEOSPACE (D'Amato et al., 2025) to achieve superior physical realism compared to GLEAM4. However, the reliability of satellite-derived soil moisture estimates requires careful validation.

Stress Function Formulations

GEOframe current approach: GEOET (GEOframe's transpiration component) employs empirical schemes following Jarvis or Ball-Berry-Leuning (BBL) parameterizations.

GLEAM4 innovation: Introduces an innovative deep neural network approach replacing traditional semi-empirical stress computations. As described by the authors: "GLEAM4 replaces the original semi-empirical computation based on soil moisture and vegetation optical depth (VOD) with the deep neural network approach presented in Koppa et al., 2022."

Neural network advantages: The approach recognizes that actual-to-potential transpiration ratios are controlled by numerous environmental variables with non-linear interactions, including:

• Soil moisture and VOD
• Vapor pressure deficit (VPD)
• Incoming solar radiation (SWi)
• Air temperature (Ta)
• CO2 concentration
• Wind speed (u)
• Leaf Area Index (LAI)

Training methodology: The neural network learns universal transpiration stress functions using global eddy-covariance and sap flow data, with separate parameterizations for tall and short vegetation.

Implementation potential: Incorporating this neural network approach could represent a significant alternative for GEOframe, moving beyond traditional empirical formulations to data-driven, physically-informed methods.

Implementation Considerations

Full understanding and implementation of these methodological improvements requires careful examination of Miralles et al. (2025) and its supporting literature. The integration of these approaches could substantially enhance GEOframe's capabilities while maintaining compatibility with global EO datasets.

Conclusions

While GLEAM provides a comprehensive framework using multiple EO resources, significant opportunities exist for improving spatial resolution and integrating these datasets with process-based models like GEOframe. The main challenges involve resolution limitations and the need for validation of empirical methods against ground-truth data. The methodological advances in GLEAM4, particularly the neural network-based stress functions and improved data assimilation schemes, offer promising directions for enhancing GEOframe's predictive capabilities. Overall GLEAM should not be considered as a EO dataset but a modeling product. Describing it as an EO product makes it more objective that it is actually. Other global products on ET are available. Ecostress (Pierrat et al., 2025) is a recent notable example. A scrutiny similat to the one applied to GLEAM can be made with that platform too, but I let you as an exercise.

References

• Azimi, Shima, Christian Massari, Gaia Roati, Silvia Barbetta, and Riccardo Rigon. 2025. “A New Tool for Correcting the Spatial and Temporal Pattern of Global Precipitation Products across Mountainous Terrain: Precipitation and Hydrological Analysis.” Journal of Hydrology 660 (133530): 133530. https://doi.org/10.1016/j.jhydrol.2025.133530.
• Bottazzi, M., M. Bancheri, M. Mobilia, and G. Bertoldi. 2021. “Comparing Evapotranspiration Estimates from the Geoframe-Prospero Model with Penman–Monteith and Priestley-Taylor Approaches under Different Climate Conditions.” WATER. https://www.mdpi.com/2073-4441/13/9/1221.
• D’Amato, Concetta, and Riccardo Rigon. 2025. “Elementary Mathematics Helps to Shed Light on the Transpiration Budget under Water Stress.” Ecohydrology: Ecosystems, Land and Water Process Interactions, Ecohydrogeomorphology 18 (2). https://doi.org/10.1002/eco.70009.
• D’Amato, Concetta, Niccolò Tubini, and Riccardo Rigon. 2025. “A Component Based Modular Treatment of the Soil-Plant-Atmosphere Continuum: The GEOSPACE Framework (v.1.2.9).” https://doi.org/10.5194/egusphere-2024-4128.
• D’Amato, Concetta, Niccolò Tubini, and Riccardo Rigon. 2025. “A Component Based Modular Treatment of the Soil-Plant-Atmosphere Continuum: The GEOSPACE Framework (v.1.2.9).” https://doi.org/10.5194/egusphere-2024-4128.
• Dijk, A. I. J. M. van, and L. A. Bruijnzeel. 2001. “Modelling Rainfall Interception by Vegetation of Variable Density Using an Adapted Analytical Model. Part 1. Model Description.” Journal of Hydrology 247 (3–4): 230–38. https://doi.org/10.1016/s0022-1694(01)00392-4.
• Koppa, Akash, Dominik Rains, Petra Hulsman, Rafael Poyatos, and Diego G. Miralles. 2022. “A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation.” Nature Communications 13 (1): 1912. https://doi.org/10.1038/s41467-022-29543-7.
• Martens, Brecht, Diego G. Miralles, Hans Lievens, Robin Van Der Schalie, Richard A. M. De Jeu, Diego Fernández-Prieto, Hylke E. Beck, Wouter A. Dorigo, and Niko E. C. Verhoest. 2017. “GLEAM v3: Satellite-Based Land Evaporation and Root-Zone Soil Moisture.” Geoscientific Model Development 10 (5): 1903–25. http://www.geosci-model-dev-discuss.net/gmd-2016-162/.
• Pierrat, Zoe Amie, Adam J. Purdy, Gregory Halverson, Joshua B. Fisher, Kanishka Mallick, Madeleine Pascolini-Campbell, Youngryel Ryu, et al. 2025. “Evaluation of ECOSTRESS Collection 2 Evapotranspiration Products: Strengths and Uncertainties for Evapotranspiration Modeling.” Water Resources Research 61 (6). https://doi.org/10.1029/2024wr039404.
• Miralles, Diego G., Olivier Bonte, Akash Koppa, Oscar M. Baez-Villanueva, Emma Tronquo, Feng Zhong, Hylke E. Beck, et al. 2025. “GLEAM4: Global Land Evaporation and Soil Moisture Dataset at 0.1 Resolution from 1980 to near Present.” Scientific Data 12 (1): 416. https://doi.org/10.1038/s41597-025-04610-y.
• Zhong, Feng, Shanhu Jiang, Albert I. J. M. van Dijk, Liliang Ren, Jaap Schellekens, and Diego G. Miralles. 2022. “Revisiting Large-Scale Interception Patterns Constrained by a Synthesis of Global Experimental Data.” https://doi.org/10.5194/hess-2022-155.

Integrating Earth Observation Data in Hydrological Modeling: Challenges and Opportunities

 Some time ago, I wrote about the need for remote sensing experts to collaborate more closely with hydrologists to improve both hydrological forecasting and remote sensing applications. While the communities have largely continued working separately (and who am I to challenge such a fruitful paradigm?), my involvement in several projects with ASI (the Italian Space Agency) and ESA (the European Space Agency) has made this integration an unavoidable priority for me. This means I must become an Earth Observation (EO) scientist myself to cover the gap.  First question actually would be:, is the EO data resolution enough for hydrologic simulations ? (The answer here is usually no, it is usually not, but we have, as usual,  to survive with this and understand how the coarser resolution can be useful. Paraphrasing a well know motto: EO are usually wrong but can be useful).

Two Categories of Earth Observation Data

Earth Observation (EO) data can be broadly divided into two categories: quantities we routinely observe on the ground and those we typically do not (or could but usually don't) measure directly.

Ground-Measured Quantities

For simplicity, let's assume proximal data are correct, subject to verification and cross-checking with EO and modeled data. The first category includes rainfall, air temperature, point discharge measurements, surface water levels, groundwater storage (though less frequently measured), solar radiation, soil moisture (rarely measured), wind speed and evapotranspiration if we fully trust eddy covariance towers, which I don't take for granted.

EO-Derived Quantities

The second category encompasses snow cover, depth, and water content, along with soil moisture, vegetation water content, and evapotranspiration. It also includes various ancillary data such as albedo, leaf area index, land cover and land use classifications, surface temperature (which is distinct from the air temperature we typically measure), groundwater storage variations, precipitable water content, cloud cover, atmospheric liquid water content, and other quantities I'm not recalling at the moment. (Yes evapotranspiration is also on this side)

Challenges with Ground-Measured Quantities

For ground-measured quantities, comparing data sources is essential, acknowledging that ground measurements are generally more accurate but come with important caveats. There exists a fundamental scale mismatch where ground measurements are point observations, while EO measurements represent areal averages derived from radiation or gravity observations. Following Rodriguez-Iturbe and Mejia (1974), connecting point values to areal values requires careful assumptions about spatial scaling. Additionally, any systematic differences between ground and EO measurements must be identified and eliminated through proper bias correction procedures. How EO data are biased if compared to ground stations ?

However, ground data are spatially sparse. Obtaining geographic information requires interpolation, typically using methods like Kriging that preserve spatial correlation structure. Yet hydrological processes may be spatially nonlinear, raising the question: Do EO data, even if biased, better reproduce the spatial structure of hydrological variables?

If so, merging ground and EO data could yield superior spatial fields—an approach we pursued in Azimi et al. (2025). These quantities share another important characteristic: they can be produced by hydrological models and serve as inputs, validation data, or assimilation targets to improve model performance (which raises questions about how we evaluate "improvement").

Challenges with EO-Derived Quantities

The second category presents different challenges. These are primarily ancillary quantities that help estimate hydrological variables—leaf area index being the classic example for estimating transpiration, since plants without leaves don't transpire. Each quantity must find its proper role in hydrological models to be truly useful, requiring them to become model parameters.

The key question becomes: How can we effectively use these data in models?

While leaf area index integration seems straightforward, the data become useless if transpiration is estimated using temperature-only formulas. Similarly, how do we use land use data in models? What model characteristics connect to "land use" if it's not an explicit parameter?

Dal Molin et al. (2020) provide an interesting answer: geological setting relates to model structure within the framework of Hydrological Dynamical Systems (reservoir-based hydrological models). This expands possibilities but complicates the connections.

Another crucial question: Are these data actually necessary for the modeler's purpose?

Perhaps not, if the goal is simply discharge forecasting, since calibration procedures have proven effective at compensating for missing data and model's structure (as shown in the 4DHydro project outcomes). However, if the objective is reproducing realistic water budgets, these data may be essential, making assessment of all intermediate steps necessary, as shown in the 4DHydro Science Case 5. 

Conclusions

This blogpost does not aim to be comprehensive but just an aid to start the discussion. Two clear conclusions emerge from this analysis. First, EO ancillary data are required only if models have mechanisms to utilize them effectively. Second, they may not be necessary for all modeling purposes, particularly when the primary objective is discharge forecasting rather than comprehensive water budget reproduction.

Ultimately, new data should either increase the precision and accuracy of predictions or enhance our understanding of hydrological process mechanisms. The challenge lies in determining when and how to integrate these diverse data streams effectively.  The final goal is what stated in the last paragraph of this post, which I invite you to read again. 

Cited References (an absolutely insufficient sample of the possible ones)

Azimi, Shima, Christian Massari, Gaia Roati, Silvia Barbetta, and Riccardo Rigon. 2025. “A New Tool for Correcting the Spatial and Temporal Pattern of Global Precipitation Products across Mountainous Terrain: Precipitation and Hydrological Analysis.” Journal of Hydrology 660 (133530): 133530. https://doi.org/10.1016/j.jhydrol.2025.133530.

Molin, Marco Dal, Mario Schirmer, Massimiliano Zappa, and Fabrizio Fenicia. 2020. “Understanding Dominant Controls on Streamflow Spatial Variability to Set up a Semi-Distributed Hydrological Model: The Case Study of the Thur Catchment.” Hydrology and Earth System Sciences 24 (3): 1319–45. https://doi.org/10.5194/hess-24-1319-2020.

Rodriguez‐Iturbe, I., and J. M. Mejía. 1974. “On the Transformation of Point Rainfall to Areal Rainfall.” Water Resources Research. https://doi.org/10.1029/WR010i004p00729.

Methodology and tools for analyzing the hydrology of catchments: four papers and a set of slides and videos.

Recently I recommended 5 papers of mine, which I consider  representative of my recent work. However, they are on the side of the theory/numerics/informatics work. Not less important are those that could be erroneously classified as applications. The four papers presented here, in fact, represent more than a straightforward run of models to individual catchments. They deploy a comprehensive methodology that integrates traditional surface water systems with new features and methods. Their approach combines mixed-resolution spatial discretization through strategic Hydrological Response Unit (HRU) refinement with accurate pre-analysis of the input multi-source validation using neutron probes (as representative of local field measurements), satellite data, and conventional discharge observations.

The modular GEOframe implementation provides flexible model configuration while preserving physical consistency and enabling validation of individual modeling components. A key finding across these papers is that careful analysis of input data can guide model organization and improve  predictions. Each paper targets complete water budget estimation, identifying inconsistencies and providing more robust assessments of catchment hydrology than traditional modeling approaches than the traditional simulation based on discharge alone.

These studies introduce  analytical tools that should become standard practice for catchment hydrology modelers and gently use Earth Observations in their specific contexts. The collective work establishes a framework where data-driven model organization, multi-source validation, and comprehensive water budget analysis modelling try to converge to advance our understanding of hydrological processes at the catchment scale.  Part of the lesson learned from these papers has been also summarized in the set of "Seven Steps in Modeling a catchment", a series of slides and videos that can be considered complementary to reading the papers.  

Much more can be done using the flexibility of the GEOframe system which remains undisclosed.

References

Abera, Wuletawu, Giuseppe Formetta, Luca Brocca, and Riccardo Rigon. 2017. “Modeling the Water Budget of the Upper Blue Nile Basin Using the JGrass-NewAge Model System and Satellite Data.” Hydrology and Earth System Sciences 21 (6): 3145–65. https://doi.org/10.5194/hess-21-3145-2017.

Andreis, D; Formetta, G.;Bancheri, M. and Rigon R., Multiple Resolution Analysis of an Alpine Basin. submitted to Water Resources Research, 2025. Preprint

Abera, Wuletawu, Giuseppe Formetta, Marco Borga, and Riccardo Rigon. 2017. “Estimating the Water Budget Components and Their Variability in a Pre-Alpine Basin with JGrass-NewAGE.” Advances in Water Resources 104 (June): 37–54. https://doi.org/10.1016/j.advwatres.2017.03.010.

Azimi, Shima, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon. 2023. “On Understanding Mountainous Carbonate Basins of the Mediterranean Using Parsimonious Modeling Solutions.” Hydrology and Earth System Sciences 27 (24): 4485–4503. https://doi.org/10.5194/hess-27-4485-2023.