Evaluating Soil Moisture–Precipitation Interactions Using Remote Sensing: A Sensitivity Analysis

Abstract

The complex interactions between soil moisture and precipitation are difficult to observe, and consequently there is a lack of consensus as to the sign, strength, and location of these interactions. Inconsistency between soil moisture–precipitation interaction studies can be attributed to a multitude of factors, including the difficulty of demonstrating causal relationships, dataset differences, and precipitation autocorrelation. The purpose of this study is to explore these potential confounding factors and determine which are most important for consideration when assessing statistical coupling between soil moisture and precipitation. Soil moisture is assessed via three remote sensing datasets: the Advanced Microwave Scanning Radiometer for Earth Observing System, the Tropical Rainfall Measuring Mission Microwave Imager, and the Essential Climate Variable Soil Moisture. Estimates of soil moisture are coupled with afternoon thunderstorm events identified by the Thunderstorm Observation by Radar (ThOR) algorithm, and dry soil or wet soil preferences for convection initiation are determined for over 16 000 thunderstorm events between 2005 and 2007. Differences in soil moisture datasets were found to have the largest impact with regard to determining wet or dry soil preferences. Precipitation autocorrelation is prevalent in the data; however, precipitation autocorrelation did not influence the results with regard to dry or wet soil preferences. Consideration of the convective environment (i.e., weakly or synoptically forced) did result in significant differences in wet/dry soil preference, but only for certain soil moisture datasets. The results suggest that observation-driven soil moisture–precipitation interaction studies should both consider the convective environment and implement multiple soil moisture datasets to assure robust results.

Soil Moisture Effects on Afternoon Precipitation Occurrence in Current Climate Models

“…Using multiple observational data sets allows to consider observational uncertainty in identifying the SMP feedback (Ford et al, 2018;Guillod et al, 2015). Using multiple observational data sets allows to consider observational uncertainty in identifying the SMP feedback (Ford et al, 2018;Guillod et al, 2015).…”

“…In this study, three precipitation data sets and four soil moisture data sets are used, which resulted in 12 combinations of observational estimates of the SMP feedback metrics. Using multiple observational data sets allows to consider observational uncertainty in identifying the SMP feedback (Ford et al, 2018;Guillod et al, 2015). All observational data are sets commonly available over the period 2002-2011 at 0.25 • × 0.25 • resolutions.…”

Summer temperature response to extreme soil water conditions in the Mediterranean transitional climate regime

“…The top panels shows results for the CMCC-SPS3 model, the bottom panels for the CNRM-CM6 model. Note that NOAA-20C precipitation is different in c-f, because it is averaged over two MedTCR that are not the same in the two models water is scarcely sensitive to precipitation, and it is hard to say whether this low responsiveness is accurate, since observational-based studies still struggle to establish strength and location of the soil moisture response to rain (Ford et al 2018). Observational studies report reaching soil moisture contents below the wilting point after few months of very little rain (e.g.…”

Soil Moisture Monitoring in Iran by Implementing Satellite Data into the Root-Zone SMAR Model

“…In recent years, the surface-reflected Global Navigation Satellite System (GNSS) signals have also been evaluated for SM estimations, which applies a different source of signals from the active/passive microwave sensors to observe the Earth's surface [14]. Moreover, the Advanced Scatterometer (ASCAT), which is an active microwave remote sensing instrument, provides global SM data sets derived from the backscatter measurements [15,16].SM products obtained from active/passive microwave remotely-sensed data have been applied in wide spectra of contexts [17][18][19][20][21][22][23][24][25][26]. However, SM data derived from most of the satellite sources provide the near surface moisture that needs to be converted in Root Zone Soil Moisture (RZSM) estimations [27,28].…”

“…SM products obtained from active/passive microwave remotely-sensed data have been applied in wide spectra of contexts [17][18][19][20][21][22][23][24][25][26]. However, SM data derived from most of the satellite sources provide the near surface moisture that needs to be converted in Root Zone Soil Moisture (RZSM) estimations [27,28].…”