Abstract
The interpretation of in situ or remotely sensed soil moisture data for drought monitoring is challenged by the sensitivity of these observations to local soil characteristics and seasonal precipitation patterns. These challenges can be overcome by standardizing soil moisture observations. Traditional approaches require a lengthy record (usually 30 years) that most soil monitoring networks lack. Sampling techniques that combine hourly measurements over a temporal window have been used in the literature to generate historical references (i.e., climatology) from shorter-term datasets. This sampling approach was validated on select U.S. Department of Agriculture Soil Climate Analysis Network (SCAN) stations using a Monte Carlo analysis, which revealed that shorter-term (5+ years) hourly climatologies were similar to longer-term (10+ year) hourly means. The sampling approach was then applied to soil moisture observations from the U.S. Climate Reference Network (USCRN). The sampling method was used to generate multiple measures of soil moisture (mean and median anomalies, standardized median anomaly by interquantile range, and volumetric) that were converted to percentiles using empirical cumulative distribution functions. Overall, time series of soil moisture percentile were very similar among the differing measures; however, there were times of year at individual stations when soil moisture percentiles could have substantial deviations. The use of soil moisture percentiles and counts of threshold exceedance provided more consistent measures of hydrological conditions than observed soil moisture. These results suggest that hourly soil moisture observations can be reasonably standardized and can provide consistent measures of hydrological conditions across spatial and temporal scales.
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