Long‐Term Variability of Soil Moisture in the Southern Sierra: Measurement and Prediction

Abstract

Core Ideas An algorithm for predicting soil‐water storage was evaluated for 6 yr. Algorithm accuracy was high during wet‐up, snow cover, and early recession. Accuracy declined during late recession and during dry periods. Accuracy declined and relative independent‐variable rank changed in dry years. Transient snow cover in dry periods altered soil‐water storage patterns. Using 6 yr (Water Year [WY] 2009–WY 2014) of hourly in situ measurements from a spatially distributed water‐balance cluster, we quantified the long‐term accuracy of an algorithm used to predict spatial patterns of depth‐integrated soil‐water storage within the rain–snow transition zone of the southern Sierra Nevada. The algorithm—the multivariate, non‐parametric regression‐tree estimator Random Forest—was used to predict soil‐water storage based on a combination of attributes at each instrument cluster (soil texture, topographic wetness index, elevation, northness, and canopy cover). Out‐of‐bag R2 (similar to cross‐validation for Random Forest) was used to quantify the accuracy of the estimator for unobserved data. Accuracy was consistently high during the wet‐up, snow‐cover, and early recession periods of average and wet years. The accuracy declined at the end of a 3‐yr dry period, and the relative rank of the independent variables in the model shifted. Soil texture was the highest‐ranked independent variable across all years, followed by elevation and northness. Topographic wetness increased in importance during dry periods. Northness exhibited high importance during the wet‐up and early recession periods of most water years. During dry years, the importance of elevation declined. In dry years, notable differences in soil‐water storage at each depth include lower‐than‐average storage in the deeper regolith at the beginning of the water year and lower storage in near‐surface layers during the winter resulting from transient snow cover.