Characterisation of extreme events through satellite and model-based soil moisture products over Europe and Africa

Records of soil moisture dynamics are the land register of the convolution of footprints of the multiple water fluxes that intervene in the soil. Therefore, the variability of soil moisture changes occurring at a specific location expresses not only the stochastic nature of soil moisture but, more importantly, differences in the statistics of their distribution in space and time, which can be used to classify patterns of soil moisture regime across space and time. 

The analysis of hydrological extremes often deals with the scarcity of data (i.e. insufficient extreme events contained in the series, which biases the range of values from reality). Fortunately, earth observation data is increasingly providing reliable distributed datasets whose spatial detail can compensate for the limitation in time length. This is particularly true for soil moisture data whose ground records rarely surpass decades but whose distributed data is achieving resolutions that, despite persistent applicability limits below certain resolutions, can ease the analysis of soil moisture variability. 

Accordingly, this study analyses the temporal changes in soil moisture of various types of soil moisture products across all cells of the adopted 5 x 5 Km grid across Europe and Africa, in both the rewetting and drying signs of change, with the aim of finding distinct ranges and frequency-magnitude characteristics along the distribution of soil moisture changes. Special attention is devoted to the soil moisture changes distribution’s upper tail (extreme events like floods (positive change) and flash drought (negative change)) and lower tail (detection limit, product sensitivity).  

Three types of soil moisture products are used (remote sensing passive: ESA CCI passive subset; remote sensing active: EUMETSAT ASCAT; and model-based: LISFLOOD model integrated in the European Copernicus Emergency Monitoring System (CEMS)) to evaluate their ability to show consistency across ranges of the distribution of soil moisture changes so that it can ensure the efficacy of monitoring systems integrating earth observation and modelling data. 

The analyses to extremes applied to soil moisture change data show results consistent and complementary to those published for rainfall and runoff generation, identify the areas where soil moisture mediation of the water cycle is more relevant in relation to hydrological regime classification and map thresholds of impactful events.  

But more importantly, results reveal notable disparity in the estimation of the relevance of an event (magnitude (expressed as intensity of change) and occurrence (expressed in frequency or return period), particularly in the most impactful cases of extreme events, entailing gaps between the dynamics detected by current soil moisture products and their true dynamics. Such disparities among datasets must be prevented from propagating to monitoring systems. 

Therefore, the approach provides insights for the continuous upgrading of the products’ consistency (i.e. remote sensing and model-based datasets), while encourages adopting metrics of distribution consistency in the early warning system pipelines, particularly across impactful ranges of soil moisture value change, to improve the monitoring accuracy according to the regional characteristics, with subsequent benefits to the efficacy of responses to the impacts.