Song, S., Brocca, L., Wang, W., Cui, W., 2020, Testing the potential of soil moisture observations to estimate rainfall in a soil tank experiment, Journal of hydrology (Amst.) (2020). doi_10.1016/j.jhydrol.2019.124368,
URL: http://www.cnr.it/prodotto/i/410408

An experimental loamy sand soil tank (12m long × 1.5m wide × 1.5m deep) with a slope of 3 ° was designed to conduct artificial rainfall-runoff experiments and continuously monitor soil moisture (SM), rainfall, surface runoff (SR) and subsurface runoff (SSR). A total of 28 rainfall-runoff events are analysed to investigate the capability of estimating rainfall from SM observations through SM2RAIN algorithm which is then adjusted in SM2RAIN-exp according to the conditions of the artificial rainfall-runoff experiment, i.e., including the SR and SSR component. The main purpose of this study is to test the underlying hypotheses of SM2RAIN algorithm in a controlled experiment to better understand the most important processes driving the relationship between SM and rainfall. Results suggest that SM2RAIN-exp demonstrates satisfactory performance in estimating rainfall with respect to the observed data. When the aggregation intervals of rainfall increase, the SM2RAIN-exp performances improve. The integration of 10cm and 30cm SM data yields performance scores better than using the sensor at 10cm and 30cm depth alone. The lagged response of 30cm SM sensor has a slight influence on the results. In addition, the major contribution to the estimated rainfall is provided by the term incorporating the SM variations (~85%), followed by the SR (12%), which is different from the hypothesis of neglecting SR made in the classical SM2RAIN. By comparing the results obtained by SM2RAIN and SM2RAIN-exp, SM2RAIN-exp is proved more stable and accurate. The recognition of the more suitable soil depth needed for obtaining good results, and of the important SR contribution, could help to clarify some important aspects related to the usage of satellite surface SM data for estimating rainfall.

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