A soil moisture monitoring network to assess controls on runoff generation during atmospheric river events

Abstract

AbstractSoil moisture is a key modifier of runoff generation from rainfall excess, including during extreme precipitation events associated with Atmospheric Rivers (ARs). This paper presents a new, publicly available dataset from a soil moisture monitoring network in Northern California's Russian River Basin, designed to assess soil moisture controls on runoff generation under AR conditions. The observations consist of 2‐min volumetric soil moisture at 19 sites and 6 depths (5, 10, 15, 20, 50, and 100 cm), starting in summer 2017. The goals of this monitoring network are to aid the development of research applications and situational awareness tools for Forecast‐Informed Reservoir Operations at Lake Mendocino. We present short analyses of these data to demonstrate their capability to characterize soil moisture responses to precipitation across sites and depths, including time series analysis, correlation analysis, and identification of soil saturation thresholds that induce runoff. Our results show strong inter‐site Pearson's correlations (>0.8) at the seasonal timescale. Correlations are strong (>0.8) during events with high antecedent soil moisture and during drydown periods, and weak (<0.5) otherwise. High event runoff ratios are observed when antecedent soil moisture thresholds are exceeded, and when antecedent runoff is high. Although local heterogeneity in soil moisture can limit the utility of point source data in some hydrologic model applications, our analyses indicate three ways in which soil moisture data are valuable for model design: (1) sensors installed at 6 depths per location enable us to identify the soil depth below which evapotranspiration and saturation dynamics change, and therefore choose model soil layer depths, (2) time series analysis indicates the role of soil moisture processes in controlling runoff ratio during precipitation, which hydrologic models should replicate, and (3) spatial correlation analysis of the soil moisture fluctuations helps identify when and where distributed hydrologic modelling may be beneficial.