The relationship between seasonal catchment water storage and discharge is typically nonunique due to water storage that is not directly hydraulically connected to streams. Hydraulically disconnected water volumes are often ecologically and hydrologically important but cannot be explicitly estimated using current storage–discharge techniques. Here, we propose that discharge is explicitly sensitive to changes in only some fraction of seasonally dynamic storage that we call “direct storage,” whereas the remaining storage (“indirect storage”) varies without directly influencing discharge. We use a coupled mass balance and storage–discharge function approach to partition seasonally dynamic storage between these 2 pools in the Northern California Coast Ranges. We find that indirect storage constitutes the vast majority of dynamic catchment storage, even at the wettest times of the year. Indirect storage exhibits lower variability over the course of the wet season (and in successive winter periods) than does direct storage. Predicted indirect storage volumes and dynamics match field observations. Comparison of 2 neighbouring field sites reveals that indirect storage volumes can occur as unsaturated storage held under tension in soils and weathered bedrock and as near‐surface saturated storage that remains on hillslopes (and is eventually evapotranspired). Indirect storage volumes (including moisture in the weathered bedrock) may support plant transpiration, and our method indicates that this important water source could be quantified from precipitation and stream discharge records.

The falling limb of the hydrograph—the streamflow recession—is frequently well approximated by power law functions, in the form dq/dt = −aqb, so that recessions are often characterized in terms of their power law parameters (a, b). The empirical determination and interpretation of the parameter a is typically biased by the presence of a ubiquitous mathematical artifact resulting from the scale‐free properties of the power law function. This reduces the information available from recession parameter analysis and creates several heretofore unaddressed methodological “pitfalls.” This letter outlines the artifact, demonstrates its genesis, and presents an empirical rescaling method to remove artifact effects from fitted recession parameters. The rescaling process reveals underlying climatic patterns obscured in the original data and, we suggest, could maximize the information content of fitted power laws.

Abstract. The study of single streamflow recession events is receiving increasing attention following the presentation of novel theoretical explanations for the emergence of power law forms of the recession relationship, and drivers of its variability. Individually characterizing streamflow recessions often involves describing the similarities and differences between model parameters fitted to each recession time series. Significant methodological sensitivity has been identified in the fitting and parameterization of models that describe populations of many recessions, but the dependence of estimated model parameters on methodological choices has not been evaluated for event-by-event forms of analysis. Here, we use daily streamflow data from 16 catchments in northern California and southern Oregon to investigate how combinations of commonly used streamflow recession definitions and fitting techniques impact parameter estimates of a widely used power law recession model. Results are relevant to watersheds that are relatively steep, forested, and rain-dominated. The highly seasonal mediterranean climate of northern California and southern Oregon ensures study catchments explore a wide range of recession behaviors and wetness states, ideal for a sensitivity analysis. In such catchments, we show the following: (i) methodological decisions, including ones that have received little attention in the literature, can impact parameter value estimates and model goodness of fit; (ii) the central tendencies of event-scale recession parameter probability distributions are largely robust to methodological choices, in the sense that differing methods rank catchments similarly according to the medians of these distributions; (iii) recession parameter distributions are method-dependent, but roughly catchment-independent, such that changing the choices made about a particular method affects a given parameter in similar ways across most catchments; and (iv) the observed correlative relationship between the power-law recession scale parameter and catchment antecedent wetness varies depending on recession definition and fitting choices. Considering study results, we recommend a combination of four key methodological decisions to maximize the quality of fitted recession curves, and to minimize bias in the related populations of fitted recession parameters.

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Seasonally dry ecosystems exhibit periods of high water availability followed by extended intervals during which rainfall is negligible and streamflows decline. Eventually, such declining flows will fall below the minimum values required to support ecosystem functions or services. The time at which dry season flows drop below these minimum values (Q * ), relative to the start of the dry season, is termed the ''persistence time'' (T QÃ ). The persistence time determines how long seasonal streams can support various human or ecological functions during the dry season. In this study, we extended recent work in the stochastic hydrology of seasonally dry climates to develop an analytical model for the probability distribution function (PDF) of the persistence time. The proposed model accurately captures the mean of the persistence time distribution, but underestimates its variance. We demonstrate that this underestimation arises in part due to correlation between the parameters used to describe the dry season recession, but that this correlation can be removed by rescaling the flow variables. The mean persistence time predictions form one example of the broader class of streamflow statistics known as crossing properties, which could feasibly be combined with simple ecological models to form a basis for rapid risk assessment under different climate or management scenarios.