6533b828fe1ef96bd1288de6

RESEARCH PRODUCT

Nonlinear Complex PCA for spatio-temporal analysis of global soil moisture

Gustau Camps-vallsDiego BuesoMaria Piles

subject

State variable010504 meteorology & atmospheric sciencesFOS: Physical sciences020206 networking & telecommunications02 engineering and technology15. Life on landAtmospheric sciences01 natural sciencesPhysics::GeophysicsKernel (linear algebra)Nonlinear systemVariable (computer science)Physics - Atmospheric and Oceanic Physics13. Climate actionPrincipal component analysisAtmospheric and Oceanic Physics (physics.ao-ph)0202 electrical engineering electronic engineering information engineeringEnvironmental scienceWater cycleTime seriesWater contentPhysics::Atmospheric and Oceanic Physics0105 earth and related environmental sciences

description

Soil moisture (SM) is a key state variable of the hydrological cycle, needed to monitor the effects of a changing climate on natural resources. Soil moisture is highly variable in space and time, presenting seasonalities, anomalies and long-term trends, but also, and important nonlinear behaviours. Here, we introduce a novel fast and nonlinear complex PCA method to analyze the spatio-temporal patterns of the Earth's surface SM. We use global SM estimates acquired during the period 2010-2017 by ESA's SMOS mission. Our approach unveils both time and space modes, trends and periodicities unlike standard PCA decompositions. Results show the distribution of the total SM variance among its different components, and indicate the dominant modes of temporal variability in surface soil moisture for different regions. The relationship of the derived SM spatio-temporal patterns with El Ni{\~n}o Southern Oscillation (ENSO) conditions is also explored.

https://dx.doi.org/10.48550/arxiv.2012.04996