EGU General Assembly 2020
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Evapotranspiration and soil moisture indexes derived from remote sensing data to identify and investigate the mechanisms of the spatio-temporal patterns of drought in the Ebro-Basin (NE Spain).

Jaime Gaona1, Pere Quintana-Seguí1, and Maria José Escorihuela2
Jaime Gaona et al.
  • 1Observatori de l'Ebre (CSIC-URL), Climate change Dept., Roquetes, Spain ( (
  • 2IsardSAT, Barcelona, Spain (

The Mediterranean climate of the Iberian Peninsula defines high spatial and temporal variability of drought at multiple scales. These droughts impact human activities such as water management, agriculture or forestry, and may alter valuable natural ecosystems as well. An accurate understanding and monitoring of drought processes are crucial in this area. The HUMID project (CGL2017-85687-R) is studying how remote sensing data and models (Quintana-Seguí et al., 2019; Barella-Ortiz and Quintana-Seguí, 2019) can improve our current knowledge on Iberian droughts, in general, and in the Ebro basin, more specifically.

The traditional ground-based monitoring of drought lacks the spatial resolution needed to identify the microclimatic mechanisms of drought at sub-basin scale, particularly when considering relevant variables for drought such as soil moisture and evapotranspiration. In situ data of these two variables is very scarce.

The increasing availability of remote sensing products such as MODIS16 A2 ET and the high-resolution SMOS 1km facilitates the use of distributed observations for the analysis of drought patterns across scales. The data is used to generate standardized drought indexes: the soil moisture deficit index (SMDI) based on SMOS 1km data (2010-2019) and the evapotranspiration deficit index (ETDI) based on MODIS16 A2 ET 500m. The study aims to identify the spatio-temporal mechanisms of drought generation, propagation and mitigation within the Ebro River basin and sub-basins, located in NE Spain where dynamic Atlantic, Mediterranean and Continental climatic influences dynamically mix, causing a large heterogeneity in climates.

Droughts in the 10-year period 2010-2019 of study exhibit spatio-temporal patterns at synoptic and mesoscale scales. Mesoscale spatio-temporal patterns prevail for the SMDI while the ETDI ones show primarily synoptic characteristics. The study compares the patterns of drought propagation identified with remote sensing data with the patterns estimated using the land surface model SURFEX-ISBA at 5km.  The comparison provides further insights about the capabilities and limitations of both tools, while emphasizes the value of combining approaches to improve our understanding about the complexity of drought processes across scales.

Additionally, the periods of quick change of drought indexes comprise valuable information about the response of evapotranspiration to water deficits as well as on the resilience of soil to evaporative stress. The lag analysis ranges from weeks to seasons. Results show lags between the ETDI and SMDI ranging from days to weeks depending on the precedent drought status and the season/month of drought’s generation or mitigation. The comparison of the lags observed on remote sensing data and land surface model data aims at evaluating the adequacy of the data sources and the indexes to represent the nonlinear interaction between soil moisture and evapotranspiration. This aspect is particularly relevant for developing drought monitoring aiming at managing the impact of drought in semi-arid environments and improving the adaptation to drought alterations under climate change.

How to cite: Gaona, J., Quintana-Seguí, P., and Escorihuela, M. J.: Evapotranspiration and soil moisture indexes derived from remote sensing data to identify and investigate the mechanisms of the spatio-temporal patterns of drought in the Ebro-Basin (NE Spain)., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5564,, 2020

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Display material version 1 – uploaded on 04 May 2020
  • CC1: Comment on EGU2020-5564, Carmelo Cammalleri, 05 May 2020

    Thanks for your really interesting and detailed presentation. Do you think that your lag analysis results may be explained by a residual seasonality in the signal of the two drought indices? In semi-arid area, it is known that similar indicators may have recurring "droughts" during the dry season, hence the seasonality in the correlation. 

    • AC1: Reply to CC1, Jaime Gaona, 06 May 2020

      Thanks for your insightful comment, Mr. Cammalleri. Yes, in those plots of lags at monthly and weekly scale some residual seasonality seems to appear at 6,12,18... months, with higher signals than expected. We will explore alternatives for filtering the seasonality of the indexes. Nonetheless, realizing that this lag assessment requires careful evaluation, we mainly focused on the correlations of the few weeks or months around the reference, where signals look more consistently related to the SMDI-ETDI interaction.