Multi-year droughts in Mediterranean climates may shift the water balance, that is, the partitioning rule of precipitation across runoff, evapotranspiration, and sub-surface storage. Mechanisms causing these shifts remain largely unknown and are not well represented in hydrologic models. Focusing on measurements from the headwaters of California's Feather River, we found that also in these mixed rain–snow Mediterranean basins a lower fraction of precipitation was partitioned to runoff during multi-year droughts compared to non-drought years. This shift in the precipitation–runoff relationship was larger in the surface-runoff-dominated than subsurface-flow-dominated headwaters (−39 % vs. −18 % decline of runoff, respectively, for a representative precipitation amount). The predictive skill of the Precipitation Runoff Modeling System (PRMS) hydrologic model in these basins decreased during droughts, with evapotranspiration (ET) being the only water-balance component besides runoff for which the drop in predictive skill during drought vs. non-drought years was statistically significant. In particular, the model underestimated the response time required by ET to adjust to interannual climate variability, which we define as climate elasticity of ET. Differences between simulated and data-driven estimates of ET were well correlated with accompanying data-driven estimates of changes in sub-surface storage (ΔS, r=0.78). This correlation points to shifts in precipitation–runoff relationships being evidence of a hysteretic response of the water budget to climate elasticity of ET during and after multi-year droughts. This hysteresis is caused by carryover storage offsetting precipitation deficit during the initial drought period, followed by vegetation mortality when storage is depleted and subsequent post-drought vegetation expansion. Our results point to a general improvement in hydrologic predictions across drought and recovery cycles by including the climate elasticity of ET and better accounting for actual subsurface water storage in not only soil, but also deeper regolith that stores water accessible to roots. This can be done by explicitly parametrizing carryover storage and feedback mechanisms capturing vegetation response to atmospheric demand for moisture.

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km ${}^2$ network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.

An inconsistent relationship between precipitation and runoff has been observed between drought and non-drought periods, with less runoff usually observed during droughts than would be expected based solely on precipitation deficit. Predictability of these shifts in the precipitation–runoff relationship is still challenging, largely because the underlying hydrologic mechanisms are poorly constrained. Using 30 years of data for 14 basins in California, we show how the Budyko framework can be leveraged to decompose shifts in precipitation versus runoff during droughts into “regime” shifts, which result from changes in the aridity index along the same Budyko curve, and “partitioning shifts”, which imply a change in the Budyko parameter ω and thus in the relationship among water balance components that governs partitioning of available water. Regime shifts are primarily due to measurable interannual changes in precipitation or temperature, making them predictable based on drought conditions. Partitioning shifts involve further nonlinear and indirect catchment feedbacks to drought conditions and are thus harder to predict a priori. We show that regime shifts dominate changes in absolute runoff during droughts but that gains or losses due to partitioning shifts are still significant. Low aridity, high baseflow, a shift from snow to rain, and resilience of high-elevation runoff correlate with higher annual runoff during droughts than would be predicted by the precipitation–runoff ratio during non-drought years. Differentiating between these shifts in the precipitation–runoff relationship using a Budyko approach will help water resource managers, particularly in arid, drought-prone regions, to better project runoff magnitudes during droughts based on available climate data and, furthermore, understand under what circumstances and to what extent their forecasts may be less reliable due to nonlinear basin–climate feedbacks.

Forests across the Western U.S. face unprecedented risk due to historic fire exclusion, environmental degradation, and climate change. Forest management activities like ecological thinning, prescribed burning, and meadow restoration can improve landscape resilience. Resilient forests are at a lower risk of high-intensity wildfires, drought, insects, and other disturbances and provide a wide range of benefits to ecosystems and communities. However, insufficient funding limits implementation of critically needed management. To address this challenge, we propose a multi-benefit framework that leverages the diverse benefits of forest management to engage a suite of stakeholders in sharing project costs. We take a three-pronged approach to develop our conceptual model: examining existing frameworks for environmental project implementation, conducting a literature review of forest management benefits, and evaluating case studies. Through our framework, we describe the steps to engage partners, starting by identifying benefits that could accrue to potential public and private beneficiaries, and moving through an iterative and collaborative process of valuing benefits, which can accrue over different spatial and temporal scales, in close consultation with potential beneficiaries themselves. The aim of this approach is to stack funding streams associated with each valued benefit to fully fund a given forest management project. The multi-benefit framework has the potential to unlock new sources of funding to meet the exceptional challenges of climate and wildfire disturbances. We apply the framework to dry forests of the Western U.S., but opportunities exist for expanding and modifying this approach to any geography or ecosystem where management provides multiple benefits.