Rainfall estimation from in situ soil moisture observations at several sites in Europe: an evaluation of the SM2RAIN algorithm

Abstract

Rain gauges, weather radars, satellite sensors and modelled data from weather centres are used operationally for estimating the spatial-temporal variability of rainfall. However, the associated uncertainties can be very high, especially in poorly equipped regions of the world. Very recently, an innovative method, named SM2RAIN, that uses soil moisture observations to infer rainfall, has been proposed by Brocca et al. (2013) with very promising results when applied with in situ and satellite-derived data. However, a thorough analysis of the physical consistency of the SM2RAIN algorithm has not been carried out yet. In this study, synthetic soil moisture data generated from a physically-based soil water balance model are employed to check the reliability of the assumptions made in the SM2RAIN algorithm. Next, high quality and multiyear in situ soil moisture observations, at different depths (5-30 cm), and rainfall for ten sites across Europe are used for testing the performance of the algorithm, its limitations and applicability range.SM2RAIN shows very high accuracy in the synthetic experiments with a correlation coefficient, R, between synthetically generated and simulated data, at daily time step, higher than 0.940 and an average Bias lower than 4%. When real datasets are used, the agreement between observed and simulated daily rainfall is slightly lower with average R-values equal to 0.87 and 0.85 in the calibration and validation periods, respectively. Overall, the performance is found to be better in humid temperate climates and for sensors installed vertically. Interestingly, algorithms of different complexity in the reproduction of the underlying hydrological processes provide similar results. The average contribution of surface runoff and evapotranspiration components amounts to less than 4% of the total rainfall, while the soil moisture variations (63%) and subsurface drainage (30%) terms provide a much higher contribution. Overall, the SM2RAIN algorithm is found to perform well both in the synthetic and real data experiments, thus offering a new and independent source of data for improving rainfall estimation, and consequently enhancing hydrological, meteorological and climatic studies.

Rainfall estimation by inverting SMOS soil moisture estimates: A comparison of different methods over Australia

“…To calculate the rainfall at a given time step, the method inverts a single‐layer soil water balance model using soil moisture observations at current and previous time steps and assumes that during a rainfall event evapotranspiration and surface runoff contributions are negligible. Previous global applications of the model under varying precipitation regimes [ Brocca et al ., , ] have provided evidence for the validity of those assumptions. That is, where P SM2RAIN [ L / T ] is the estimated rainfall, s [−] is the relative saturation of the soil (between 0 and 1), t [ T ] is the time, Z * [ L ] is the soil water capacity (soil layer depth multiplied by porosity), and a [ L / T ] and b [−] are two parameters expressing the nonlinearity between the loss rate (including both the drainage and the evapotranspiration components) and soil saturation.…”

Soil Moisture for Hydrological Applications: Open Questions and New Opportunities

“…More recently, the number of publications on this topic is remarkably increasing likely due to the work by Brocca et al [137,138] who developed a "bottom-up" approach, called SM2RAIN, for directly estimating precipitation rates from soil moisture observations only. The method has been applied on a local scale with in situ observations [137,139] and on a regional/global scale with satellite data [138,140,141]. Moreover, the "bottom-up" approach was integrated with state-of-the-art rainfall products (i.e., "top-down" approach) for obtaining a superior rainfall product by Brocca et al [141] and Ciabatta et al [142] in Australia and Italy.…”

SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture

“…SM2RAIN has also the main limitation of not being able to estimate rainfall if the soil is close to saturation, since no SM variations can be observed after rainfall events in such conditions. The algorithm has proved to accurately estimate rainfall both on a regional (Abera et al, 2016;Brocca et al, , 2015Brocca et al, , 2016Ciabatta et al, 2015Ciabatta et al, , 2017 and on a global scale (Brocca et al, 2014;Koster et al, 2016). For further details about the SM2RAIN formulation, the reader is referred to Brocca et al ( , 2014.…”