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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.

In seasonally dry climates, a distinct rainy season is followed by a pronounced dry season during which rainfall often makes a negligible contribution to soil moisture. Using stochastic analytical models of soil moisture to represent the effects of this seasonal change has been hindered by the need to mathematically represent the stochastic influence of wet season climate on dry season soil water dynamics. This study presents a simple process-based stochastic model for soil moisture dynamics, which explicitly models interseasonal transient dynamics while accounting for carry over soil moisture storage between the wet and dry seasons, and allows a derivation of an analytical expression for the dry season mean first passage time below a soil moisture threshold. Such crossing times pose controls on both vegetation productivity and water stress during dry summers. The new model, along with an existing model that incorporates nonzero dry season rainfall but not variability in the soil moisture condition at the start of the dry season, are tested against data from the Tonzi Ranch Ameriflux site. Both models predict first passage times well for high soil moisture thresholds, but the new model improves prediction at lower thresholds. The annual soil moisture probability distribution function (PDF) from the new model also compares well with observations.

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" (). 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.

Flow duration curves (FDC) display streamflow values against their relative exceedance time. They provide critical information for watershed management by representing the variation in the availability and reliability of surface water to supply ecosystem services and satisfy anthropogenic needs. FDCs are particularly revealing in seasonally dry climates, where surface water supplies are highly variable. While useful, the empirical computation of FDCs is data intensive and challenging in sparsely gauged regions, meaning that there is a need for robust, predictive models to evaluate FDCs with simple parameterization. Here, we derive a process-based analytical expression for FDCs in seasonally dry climates. During the wet season, streamflow is modeled as a stochastic variable driven by rainfall, following the stochastic analytical model of Botter et al. (2007a). During the dry season, streamflow is modeled as a deterministic recession with a stochastic initial condition that accounts for the carryover of catchment storage across seasons. The resulting FDC model is applied to 38 catchments in Nepal, coastal California, and Western Australia, where FDCs are successfully modeled using five physically meaningful parameters with minimal calibration. A Monte Carlo analysis revealed that the model is robust to deviations from its assumptions of Poissonian rainfall, exponentially distributed response times and constant seasonal timing. The approach successfully models period-of-record FDCs and allows interannual and intra-annual sources of variations in dry season streamflow to be separated. The resulting median annual FDCs and confidence intervals allow the simulation of the consequences of interannual flow variations for infrastructure projects. We present an example using run-of-river hydropower in Nepal as a case study. Key Points Probabilistic derivation of flow distribution in seasonally dry climate Successfully applied in Nepal, California, and Western Australia Disentangles inter- and intra-annual streamflow variations © 2014. American Geophysical Union. All Rights Reserved.

The linearized hillslope Boussinesq equation, introduced by Brutsaert (1994), describes the dynamics of saturated, subsurface flow from hillslopes with shallow, unconfined aquifers. In this paper, we use a new analytical technique to solve the linearized hillslope Boussinesq equation to predict water table dynamics and hillslope discharge to channels. The new solutions extend previous analytical treatments of the linearized hillslope Boussinesq equation to account for the impact of spatiotemporal heterogeneity in water table recharge. The results indicate that the spatial character of recharge may significantly alter both steady state subsurface storage characteristics and the transient hillslope hydrologic response, depending strongly on similarity measures of controls on the subsurface flow dynamics. Additionally, we derive new analytical solutions for the linearized hillslope-storage Boussinesq equation and explore the interaction effects of recharge structure and hillslope morphology on water storage and base flow recession characteristics. A theoretical recession analysis, for example, demonstrates that decreasing the relative amount of downslope recharge has a similar effect as increasing hillslope convergence. In general, the theory suggests that recharge heterogeneity can serve to diminish or enhance the hydrologic impacts of hillslope morphology.

In the past several decades, field studies have shown that woody plants can access substantial volumes of water from the pores and fractures of bedrock 1-3 . If, like soil moisture, bedrock water storage serves as an important source of plant-available water, then conceptual paradigms regarding water and carbon cycling may need to be revised to incorporate bedrock properties and processes 4-6 . Here we present a lower-bound estimate of the contribution of bedrock water storage to transpiration across the continental United States using distributed, publicly available datasets. Temporal and spatial patterns of bedrock water use across the continental United States indicate that woody plants extensively access bedrock water for transpiration. Plants across diverse climates and biomes access bedrock water routinely and not just during extreme drought conditions. On an annual basis in California, the volumes of bedrock water transpiration exceed the volumes of water stored in human-made reservoirs, and woody vegetation that accesses bedrock water accounts for over 50% of the aboveground carbon stocks in the state. Our findings indicate that plants commonly access rock moisture, as opposed to groundwater, from bedrock and that, like soil moisture, rock moisture is a critical component of terrestrial water and carbon cycling., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

A growing empirical literature associates climate anomalies with increased risk of violent conflict. This association has been portrayed as a bellwether of future societal instability as the frequency and intensity of extreme weather events are predicted to increase. This paper investigates the theoretical foundation of this claim. A seminal microeconomic model of opportunity costs-a mechanism often thought to drive climate-conflict relationships-is extended by considering realistic changes in the distribution of climate-dependent agricultural income. Results advise caution in using empirical associations between short-run climate anomalies and conflicts to predict the effect of sustained shifts in climate regimes: Although war occurs in bad years, conflict may decrease if agents expect more frequent bad years. Theory suggests a nonmonotonic relation between climate variability and conflict that emerges as agents adapt and adjust their behavior to the new income distribution. We identify 3 measurable statistics of the income distribution that are each unambiguously associated with conflict likelihood. Jointly, these statistics offer a unique signature to distinguish opportunity costs from competing mechanisms that may relate climate anomalies to conflict., Competing Interests: The authors declare no competing interest.

The liberalization of marijuana policies, including the legalization of medical and recreational marijuana, is sweeping the United States and other countries. Marijuana cultivation can have significant negative collateral effects on the environment that are often unknown or overlooked. Focusing on the state of California, where by some estimates 60%-70% of the marijuana consumed in the United States is grown, we argue that (a) the environmental harm caused by marijuana cultivation merits a direct policy response, (b) current approaches to governing the environmental effects are inadequate, and, ((c) neglecting discussion of the environmental impacts of cultivation when shaping future marijuana use and possession policies represents a missed opportunity to reduce, regulate, and mitigate environmental harm.)