Speaker
Description
Rainfall is one of the significant meteorological variables governing soil moisture variability. Rain gauges (RGs) provide reliable point measurements of rainfall but suffer from limited spatial coverage, whereas commercial microwave links (CMLs) offer accurate spatially integrated rainfall information, when they have been calibrated to correct the bias produced by Wet Antenna Attenuation (WAA). This study evaluates how rainfall estimates derived from WAA-corrected CMLs, RGs, and a merged CML+RG dataset correlate with soil moisture product from ERA5 (layer 1: 0–7 cm) within the Seveso river basin (~150 km2, Northern Italy). CML rainfall is obtained using WAA-correction framework, based on RGs as ground truth and using a slightly modified version of the WAA model proposed by Valtr, Fencl, and Bareš (VFB), with model parameters adjusted on our dataset. Rainfall from 100 CMLs is subsequently merged with RG observations to generate a spatially-gridded hourly rainfall product covering the Seveso basin. The different rainfall datasets (RG, CML, CML+RG) are evaluated against independent soil moisture observations from ERA5, including an analysis of hourly rainfall–soil moisture time lags. Results show that rainfall derived from CML+RG exhibits significantly better correlations with the soil moisture than the one retrieved from either of the two sensor networks. The merged CML+RG rainfall product consistently yields the highest soil moisture skill, demonstrating a reduced bias and improved temporal dynamics. Furthermore, the lagged analysis reveals improved correlations of soil moisture with both CML and CML+RG rainfall estimates. These findings highlight the benefits of jointly exploiting opportunistic CML observations and conventional rain gauges, demonstrating the added value of integrated rainfall products for soil moisture estimation and hydrological modeling.