Mwangi et al. (2026) Uncertainties in long-term ensemble estimates of contextual evapotranspiration over southern France

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Identification

• Journal: Repository for Publications and Research Data (ETH Zurich)
• Year: 2026
• Date: 2026-02-26
• Authors: Samuel Mwangi, Albert Olioso, Jordi Etchanchu, Kanishka Mallick, Aolin Jia, Jérôme Demarty, Nesrine Farhani, Emmanuelle Sarrazin, Philippe Gamet, Jean-Louis Roujean, Gilles Boulet
• DOI: 10.3929/ethz-c-000796435

Research Groups

Researchers associated with the development and application of the EVapotranspiration Assessment from SPAce (EVASPA) contextual tool.

Short Summary

This study applies the EVASPA ensemble contextual tool over southern France (2004–2024) to estimate evapotranspiration (ET) using MODIS data, demonstrating that ensemble-based modelling provides reliable ET estimates and a meaningful uncertainty spread, with land surface temperature (LST) and evaporative fraction (EF) formulations being the dominant sources of uncertainty.

Objective

• To apply and evaluate the EVapotranspiration Assessment from SPAce (EVASPA) contextual tool for continuous evapotranspiration (ET) retrieval over southern France, and to quantify the sources of uncertainty within its ensemble framework.

Study Configuration

• Spatial Scale: Regional (southern France), larger spatial scales.
• Temporal Scale: 20 years (2004–2024), yielding 972 instantaneous ET estimates; continuous ET monitoring; daily ET estimates; seasonal dependence of uncertainties.

Methodology and Data

• Models used: EVapotranspiration Assessment from SPAce (EVASPA) contextual tool, an ensemble modelling framework integrating multiple member outputs from alternative formulations of evaporative fraction (EF) and ground heat flux (G), and different LST and radiation inputs.
• Data sources: MODIS-derived land surface temperature/emissivity (LST/E), Normalized Difference Vegetation Index (NDVI), and albedo products. Flux tower data for evaluation.

Main Results

• The EVASPA ensemble average provides reasonable agreement with flux tower data, despite substantial variation in individual member performance.
• Land surface temperature (LST) inputs and evaporative fraction (EF) formulations are the dominant sources of variability and uncertainty in modelled ETs.
• Absolute uncertainties in ET estimates peak during summer, following the annual cycle of radiation and partly influenced by satellite characteristics.
• The satellite's overpass time introduces more uncertainty to gap-filled daily ET estimates compared to LST/LSE separation methods.
• Radiation inputs contribute to ensemble variations, while ground heat flux (G) methods exert comparatively minor influence, especially for estimates derived from TERRA morning overpasses.
• Ensemble-based contextual modelling provides both reliable flux estimates and a meaningful uncertainty spread.

Contributions

• Demonstrates the robustness of an ensemble-based contextual modelling approach (EVASPA) for reliable ET estimation and comprehensive uncertainty quantification at regional scales.
• Identifies the primary drivers of uncertainty in remote sensing-based ET estimates (LST and EF formulations), highlighting their seasonal dependence.
• Proposes that ensemble modelling, by allowing optimal member selection based on surface and climatic conditions, enhances ET retrieval robustness and provides more resilient and informative estimates.
• Provides a valuable framework for future high-resolution ET monitoring missions, such as TRISHNA, crucial for operational water and ecosystem management.

Funding

• Not specified in the provided text.

Citation

@article{Mwangi2026Uncertainties,
  author = {Mwangi, Samuel and Olioso, Albert and Etchanchu, Jordi and Mallick, Kanishka and Jia, Aolin and Demarty, Jérôme and Farhani, Nesrine and Sarrazin, Emmanuelle and Gamet, Philippe and Roujean, Jean-Louis and Boulet, Gilles},
  title = {Uncertainties in long-term ensemble estimates of contextual evapotranspiration over southern France},
  journal = {Repository for Publications and Research Data (ETH Zurich)},
  year = {2026},
  doi = {10.3929/ethz-c-000796435},
  url = {https://doi.org/10.3929/ethz-c-000796435}
}