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Water conservation in California has been a major subject of concern for agencies in their efforts to satisfy their residential demand while coping with frequent shortfalls, especially in periods of drought. During the 2012–2016 severe drought in California, the state enacted a conservation mandate that imposed specific conservation targets of 4% up to 36% for water utilities. While the utilities met those targets in 2015, water use, on average, has slowly crept up or rebounded subsequently, although not to pre‐drought levels. Understanding the manner and degree to which water use rebounds can be critically important for water utilities in their planning and investment decisions. Using a unique panel dataset on single‐family residential water use by nearly 20,000 customers of a Northern California water agency from 2013 to 2019, this paper explores the magnitude and character of the rebound effect that occurred after the cessation of a statewide conservation mandate that was imposed on water use in response a severe drought enveloping California from 2014 through 2016. Our results suggest the presence of a significant rebound in water use—of approximately 9% on average—after the conservation mandate ended. Yet, and novel to our research, we find significant heterogeneity in the rebound effects across seasons and water users, with a greater rebound in the warm season months relative to other months and among lower water use households relative to higher water use households. Our results also suggest a significant shift in water use to earlier periods of the day once the mandate was lifted. Understanding the magnitude and variation in rebound effects both temporally and across different types of water users can be useful to water agencies in their efforts to make informed decisions surrounding investments, management, and messaging in response to drought, conservation, and water scarcity. Plain Language Summary: Given the prominence of demand‐side management in the water utilities toolbox for addressing increasing water shortages driven by drought and climate change, understanding how water use changes during a drought along with how and to what extent it increases, or "rebounds," afterward can be useful if not necessary in making informed policy decisions and cost‐effective investments. This research develops a unique data set consisting of household‐level daily and hourly water use by nearly 19,500 residential accounts from a California water utility from 2013 through 2019. It explores how water use changed during and post‐drought relative to pre‐drought levels. During this period, California entered a significant drought. Its governor requested a voluntary cutback, imposed a conservation mandate, and implemented a self‐certification requirement lasting until the drought eased. We find that residential water use decreased by 26% during the conservation mandate relative to pre‐drought levels but rebounded by approximately 9% post‐mandate. The "rebound" effect varied across the season (greatest in summer months) and water user type (lower‐end water users showed a larger percentage rebound than higher‐end water users). Our results suggest that due to the mandate, the peak hour water consumption shifted to the earlier hours of the day. Key Points: Our results suggest that the 2015 California water mandate resulted in temporary and permanent reductions in water useRebound effects are more prevalent in the warm season than in other months and for lower‐end water users than high‐end water usersOur results also show that, due to the mandate, the peak hour water consumption shifted to the earlier hours of the day [ABSTRACT FROM AUTHOR]

Salt accumulations in aquifers significantly affect and transform the conjunctive use of surface water and groundwater supporting irrigated agriculture. Salt accumulates in aquifers under many semi‐arid irrigated lands where pumping has lowered water levels enough to prevent drainage of saline groundwater from the basin. This paper provides new insights into optimal conjunctive management of groundwater pumping, recharge, surface water, and cropping patterns with groundwater salinity and hydrologic variability in an irrigated semi‐arid region, such as California's western San Joaquin Valley, reducing agricultural crop yields and revenues. A two‐stage stochastic quadratic model explores this problem to prescribe economically optimal crop mix and conjunctive water operation policies over a 10‐year period with probabilistic annual surface water availability, considering groundwater salinity's harm to crop yields. At low groundwater salinity, agricultural conjunctive use usually pumps most groundwater in drier years, supplied by additional recharge in wetter years. In contrast, at higher groundwater salinity, optimal conjunctive use pumps less in drier years while pumping more in wetter years, when more surface water allows more dilution of saltier groundwater. Reduced pumping in drier years substantially reduces a region's ability to support higher‐value perennial crops and reduces or eliminates lower‐value annual crops in dry years. Artificial recharge with fresh surface water in wetter years can have economic value from slowing groundwater salination which allows more groundwater use in drier years. Key Points: A two‐stage stochastic quadratic model estimates the economic impact of groundwater salinity on agriculture, especially with perennial cropsEconomically optimized groundwater management fundamentally changes with groundwater salinationArtificial recharge for aquifers with severe salt accumulation can reduce both loss of agricultural production and groundwater salinity [ABSTRACT FROM AUTHOR]

Reservoir operators face pressures on timing releases of water. Releasing too much water immediately can threaten future supplies and costs, but not releasing enough creates immediate economic hardship downstream. This paper examines how the economic valuation of end‐of‐year carryover storage can lead to optimal amounts of carryover storage in complex large water resource systems. Economic carryover storage value functions (COSVFs) are developed to represent the value of storage in the face of interannual inflow uncertainty and variability within water resource optimization models. The approach divides a perfect foresight optimization problem into year‐long (limited foresight) subproblems solved sequentially by a within‐year optimization engine to find optimal short‐term operations. The final storage state from the previous year provides the initial condition to each annual problem, and end‐of‐year COSVFs are the final condition. Here the COSVF parameters that maximize the interannual benefits from river basin operations are found by evolutionary search. This generalized approach can handle nonconvexity in large‐scale water resources systems. The approach is illustrated with a regional model of the California Central Valley water system including 30 reservoirs, 22 aquifers, and 51 urban and agricultural demand sites. Head‐dependent pumping costs make the optimization problem nonconvex. Optimized interannual reservoir operation improves over more cautious operation in the historical approximation, reducing the average annual scarcity volume and costs by 80% and 98%, respectively, with more realistic representation of hydrologic foresight for California's Mediterranean climate. The economic valuation of storage helps inform water storage decisions. Key Points: This paper introduces an approach to estimating the economic value of interannual reservoir storageThe approach uses an evolutionary search algorithm linked to a hydroeconomic optimization modelA regional model of the California Central Valley water resource system illustrates the approach [ABSTRACT FROM AUTHOR]

Growing water stress has led to emerging interest in protecting fresh and brackish groundwater as a potential supplement to water supplies and raised questions about factors that could affect the future quality of fresh and brackish aquifers. Limited well infrastructure, particularly in regions where elevated salinity has led to limited historical groundwater development, hinders traditional mapping of salinity distributions through groundwater sampling. This paper presents a quantitative salinity mapping approach of the upper 300 m using high‐resolution, regionally comprehensive resistivity models derived from Bayesian inversion of an airborne electromagnetic survey adjacent to the Lost Hills and Belridge oil fields in the southwestern San Joaquin Valley of California. Using local water quality observations as an interpretational foundation, a probabilistic approach yields maps of fresh, saline, and brackish groundwater while quantifying joint uncertainty inherited from the geophysical data and interpretational relations. Saline and fresh regions are mapped with relatively high confidence in many locations, while areas of lower confidence, particularly at depth, can be mapped as their most probable salinity category while reflecting the relative uncertainty in the interpretation. These maps identify a stratified salinity structure, where saline water commonly occurs in the surficial aquifer overlying fresher groundwater in the Tulare aquifer, separated by regional confining clay layers. Downgradient of unlined surface water diversions, recharge of imported surface water results in relatively fresh groundwater throughout the depth of investigation. Plain Language Summary: Salinity, a measure of salt concentration, can impact the potential use of groundwater. Salinity can be difficult to map using wells alone, especially where groundwater development has been limited in the past. Dissolved salts are excellent conductors of electricity, and resistivity measurements are commonly used to estimate salinity where wells are not available. Uncertainty in estimating salinity from resistivity data can be introduced into the interpretation by two sources: (1) how well we can estimate salinity from resistivity and (2) how accurately we can measure resistivity. We present a method for mapping salinity that accounts for both sources of uncertainty using regional‐scale, high‐resolution airborne geophysical data, leading to spatially comprehensive 3‐D maps of likely fresh (low salt) and saline (high salt) groundwater. The method is applied in the southwestern San Joaquin Valley of California adjacent to the Lost Hills and Belridge oil fields. Shallow groundwater is shown to have relatively high salinity in much of the study area, and a clay layer protects lower salinity water in the underlying Tulare aquifer. Leaking surface water canals have imported relatively fresh water into the area, resulting in fresher groundwater downgradient of the canals. Key Points: Fresh, brackish, and saline groundwater are mapped using airborne electromagnetic‐derived regional resistivity dataCategorical probability developed from Bayesian inversion and local wells reflect geophysical and interpretational uncertaintySurface water diversions and clay units strongly influence groundwater quality in the southwestern San Joaquin Valley [ABSTRACT FROM AUTHOR]

The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others. [ABSTRACT FROM AUTHOR]

This paper used a data assimilation framework to estimate spatially and temporally continuous snow water equivalent (SWE) from a 27 year reanalysis (from water year 1985 to 2011) of the Landsat-5 record for the Kern River watershed in the Sierra Nevada, California. The data assimilation approach explicitly treats sources of uncertainty from model parameters, meteorological inputs, and observations. The method is comprised of two main components: (1) a coupled land surface model (LSM) and snow depletion curve (SDC) model, which is used to generate an ensemble of predictions of SWE and fractional snow cover area (FSCA) for a given set of prior (uncertain) inputs, and (2) a retrospective reanalysis step, which updates estimation variables to be consistent with the observed fractional snow cover time series. The final posterior SWE estimate is generated from the LSM-SDC using the posterior estimation variables consistently with all postulated sources of uncertainty in the model, inputs, and observations. A reasonable agreement was found between the SWE reanalysis and in situ SWE observations and streamflow data. The data set was studied to evaluate factors controlling SWE spatial and temporal variability. Elevation was found to be the primary control on spatial patterns of peak-SWE and day-of-peak. The easting coordinate had additional explanatory power, which is hypothesized to be related to rain shadow effects due to the prevailing storm track directions. The spatial patterns were found to be interannually inconsistent. However, drier years and lower elevations were found more variable than wetter years and higher elevations, respectively. Only a very small percentage of the Kern River watershed had a significant trend in peak-SWE and day-of-peak. Trends deemed to be significant were found to be positive (peak-SWE is increasing and day-of-peak occurs later) at higher elevations, but negative (peak-SWE is decreasing and day-of-peak occurs earlier) at lower elevations. The reanalysis approach proved to be useful in terms of identifying subwatershed variability and trends, and could be extended to larger regions and areas where in situ data are sparse or unavailable. [ABSTRACT FROM AUTHOR]

Managed aquifer recharge (MAR) can provide long‐term storage of excess surface water for later use. While decades of research have focused on the physical processes of MAR and identifying suitable MAR locations, very little research has been done on how to consider competing factors and tradeoffs in siting MAR facilities. This study proposes the use of a simulation‐optimization (SO) framework to map out a cost‐effectiveness frontier for MAR by combining an evolutionary algorithm with two objective functions that seek to maximize groundwater storage gains while minimizing MAR cost. We present the theoretical framework along with a real‐world application to California's Central Valley. The result of the SO framework is a Pareto front that allows identifying suitable MAR locations for different levels of groundwater storage gain and associated MAR project costs, so stakeholders can evaluate different choices based on cost, benefits, and tradeoffs of MAR sites. Application of the SO framework to the Central Valley shows groundwater can be recharged from high‐magnitude (95th percentile) flows at a marginal cost of $57 to $110 million per km3. If the 10 percent largest flows are recharged the total groundwater storage gain would double and the marginal costs would drop to between $30 and $50 million per km3. If recharge water is sourced from outside local basins (e.g., the Sacramento‐San Joaquin Delta), groundwater storage gain is approximately 25%–80% greater than can be achieved by recharging local flows, but the total cost is about 10%–15% higher because of additional lift cost. Key Points: Combining a simulation‐optimization framework with a groundwater model can identify suitable managed aquifer recharge locationsRecharging water from local rivers within the same basin is the most cost‐efficient managed aquifer recharge approach per unit of groundwater storage gainDiverting more water may not yield the most efficient groundwater storage gain due to higher transportation cost [ABSTRACT FROM AUTHOR]

Low streamflows can increase vulnerability to warming, impacting coldwater fish. Water managers need tools to quantify these impacts and predict future water temperatures. Contrary to most statistical models' assumptions, many seasonally changing factors (e.g., water sources and solar radiation) cause relationships between flow and water temperature to vary throughout the year. Using 21 yr of air temperature and flow data, we modeled daily water temperatures in California's snowmelt‐driven Scott River where agricultural diversions consume most summer surface flows. We used generalized additive models to test time‐varying and nonlinear effects of flow on water temperatures. Models that represented seasonally varying flow effects with intermediate complexity outperformed simpler models assuming constant relationships between water temperature and flow. Cross‐validation error of the selected model was ≤1.2°C. Flow variation had stronger effects on water temperatures in April–July than in other months. We applied the model to predict effects of instream flow scenarios proposed by regulatory agencies. Relative to historic conditions, the higher instream flow scenario would reduce annual maximum temperature from 25.2° to 24.1°C, reduce annual exceedances of 22°C (a cumulative thermal stress metric) from 106 to 51 degree‐days, and delay onset of water temperatures >22°C during some drought years. Testing the same modeling approach at nine additional sites showed similar accuracy and flow effects. These methods can be applied to streams with long‐term flow and water temperature records to fill data gaps, identify periods of flow influence, and predict temperatures under flow management scenarios. Plain Language Summary: Warm water temperatures threaten culturally and economically important salmon in Pacific Northwest Rivers, causing chronic stress and direct mortality. Climate change and agricultural water use have reduced summer river flows in recent decades, intensifying water scarcity. Years with deep mountain snowpack and resulting high groundwater levels extend the high flow season and keep water temperatures cool through the end of July, whereas in drought years the river warms sooner. We used 21 yr of river flow and air temperature data from the Scott River, California, to create computer models that simulate water temperatures. Our models allow the effect of flow on water temperatures to vary by season (i.e., stronger cooling effects in spring and summer), improving accuracy of the simulated temperatures. We used the Scott River model to simulate water temperatures under two alternative flow scenarios considered in local water management plans. Our simulations indicate that relative to current conditions, the higher flow scenario would lower the summers' highest temperatures and decrease the number of days that river temperatures exceed a biological threshold. Testing the same modeling approach at nine additional Klamath Basin sites showed similar accuracy and flow effects. Our model is freely available for public use. Key Points: In this snowmelt and groundwater‐influenced river, water temperatures stayed cool later into summer in high‐flow years than low‐flow yearsStatistical water temperature model predictions became more accurate when the influence of river flow was allowed to vary seasonallyThese accessible models can be applied to other rivers or streams with daily, long‐term flow and water temperature records [ABSTRACT FROM AUTHOR]

Large wood (LW) influences geomorphic and ecological processes. However, most field data sets describing LW dynamics are conducted over the span of only a few years. We present a unique long‐term data set from northern California, USA of LW volumes, inputs, and transport in two small headwater streams, the North Fork (NF) and South Fork (SF) of Caspar Creek. We used data collected approximately every 2 years from 1998 to 2018 to assess how LW budgets change over time between catchments, what factors influence inputs from standing trees to the streams, and controls on LW piece mobility. We find that the SF, which experienced stream‐side logging and instream wood removal in the 1970s, continued to have lower LW volumes (range of 101–153 m3 km−1 over the 20‐year period) compared to the NF (range of 297–313 m3 km−1), which experienced logging in the 1980s but with buffer strips left near the channel. If current trends continue, it will likely take ∼100–200 years from the time of logging for the SF to reach NF instream wood volumes. We show that windstorms combined with precipitation events, very high winds, and hillslope steepness are strongly associated with LW piece inputs from standing trees. LW moves infrequently in the channel, with piece characteristics, the number of wood pieces per meter surrounding an LW piece, and discharge metrics influencing LW entrainment and displacement lengths. Our data set provides strong support to previously identified controls on LW dynamics in streams while also capturing variability over a 20‐year period. Plain Language Summary: Wood in rivers affects both physical processes and ecological processes, such as habitat creation. Long‐term field data sets are important for understanding how the amount of wood in streams changes over time, how standing trees enter the channel, and how wood moves. However, most field data on wood in streams were collected in the span of just a few years. We present a field data set of repeat wood surveys in the channel for two streams in northern California, USA. Wood and wood inputs were measured every 2 years over a 20‐year period. With these data, we demonstrate that intensive logging near the stream on the South Fork of Caspar Creek from 1968 to 1973 is still resulting in lower wood volumes in that channel compared to the North Fork of Caspar Creek, which was logged further from the channel from 1988 to 1991. The frequency of winter storms and the steepness of adjacent hillslopes also influence inputs of wood from standing trees. Wood moves relatively infrequently, but the size and shape of wood pieces and the frequency of high flow events impact whether a wood piece moves and how far it moves. Our results can inform management decisions about wood in rivers. Key Points: A 20‐year instream wood study of two headwater streams demonstrates that near‐channel logging significantly reduced instream wood loadsWet windstorms and steep slopes were associated with wood inputs from standing trees, but rates varied between streams and over timePiece characteristics, the surrounding density of wood pieces, and discharge metrics influence wood entrainment and displacement lengths [ABSTRACT FROM AUTHOR]

This study reports on a blending approach using snowpack measurements from a wireless‐sensor network, gauge precipitation, and atmospheric‐moisture data to estimate mountain precipitation amount and phase. We applied the approach in California's American River basin, using dense measurements from a network consisting of over 130 sensor nodes distributed across the upper, more snow‐dominated part of the basin (≥1,500 m elevation). Analysis of 60 precipitation events in water years 2014–2017 showed that the approach provides estimates of precipitation and orographic enhancement that reduce uncertainty from apparent snow undercatch by limited gauges. This approach also infers total precipitation based on snow measurements during rain‐on‐snow events. The sensor network and blending approach yielded median upper‐basin orographic precipitation gradients (OPGs) of 0.57 km−1, smaller than the also‐positive lower‐basin (<1,500 m) medians OPGs from precipitation gauges and a gauge‐based gridded data set of 1.23 and 1.00 km−1, respectively. However, during 73% of the events, both gauges and the gridded product showed negative OPGs in the upper basin, inconsistent with typically positive values from the distributed sensor network. Upper‐basin OPGs from gauges and the gridded product were more negative (p‐values < 0.03) during heavy events related to atmospheric rivers and Sierra barrier jets than during milder events, revealing the challenges for gauges to reliably measure precipitation from large moisture transport by strong winds. In snow‐dominated headwater areas, precipitation from the blending approach is recommended as being more accurate for decision support, providing critical rain‐versus‐snow amounts and complementing precipitation‐gauge data. Key Points: Blending spatial snow data from a ground network with gauge precipitation and atmospheric moisture improves mountain rain and snow estimatesThe blending approach better represented orographic precipitation gradients, compared to gauge‐based resultsThe blending approach performed well in snow‐dominated elevations during heavy events related to atmospheric rivers and Sierra barrier jets [ABSTRACT FROM AUTHOR]

Working with airborne electromagnetic (AEM) data acquired in the Kaweah Subbasin in the Central Valley of California, USA, we developed a new approach for imaging the bedrock surface and the confining Corcoran Clay layer. Our approach, which incorporated the prior knowledge of the targets for the improved imaging, included multiple L2‐norm and Lp‐norm inversions as well as an interpolation process. The major improvement in imaging the targets was made in the Lp‐norm inversion step by incorporating prior knowledge. For the Corcoran Clay, pairs of resistivity and driller's logs at two wells guided the selection of the primary resistivity model and were used to increase the accuracy of the estimated Corcoran Clay thickness. The bedrock surface was poorly constrained by well data in the existing groundwater model, appearing as a flat surface. Acquired AEM data covered most of the area, so we had higher confidence in the obtained map of the bedrock surface at depths ranging from 15 to 160 m. There was relatively good agreement between the location of the Corcoran Clay in the AEM data (depth ranging from 50 to 130 m and thickness ranging from 3 to 25 m) and the existing groundwater model, with both depth and thickness showing ∼15% relative difference. The AEM data provided information about the continuity of the Corcoran Clay that is challenging to capture in the well data. The locations of the bedrock and Corcoran Clay were used in a structurally constrained inversion to improve the imaging of the smaller‐scale resistivity structure. Plain Language Summary: A groundwater model is a representation of a groundwater system used to both understand the properties and processes of the subsurface, and to manage the groundwater resource. An important step in developing a groundwater model is describing the large‐scale structure, the "plumbing system" that controls the groundwater flow. Given the size of many groundwater models, obtaining an accurate description of the large‐scale structure can be a challenging task. The traditional approach has involved the drilling of wells. While wells can provide accurate information at the location of the wells, the spatial sampling of the subsurface that can be accomplished through the drilling of wells is limited. An airborne electromagnetic (AEM) method provides an alternative by using a geophysical measurement to map out the electrical resistivity to depths of hundreds of meters. Working with AEM data acquired in the Kaweah Subbasin in the Central Valley of California, we were able to image the large‐scale structure—the bedrock surface and a thick confining clay layer. The key improvement was made by incorporating prior knowledge into the inversion of the data. Once we had obtained the large‐scale structure, we found that this could be used to improve the imaging of the smaller‐scale structure. Key Points: Airborne electromagnetic (AEM) data were used to map out the large‐scale structure in the Kaweah subbasin in California's Central ValleyA new approach to the inversion of AEM data improved the accuracy in estimates of the bedrock surface and the confining Corcoran Clay layerThe defined large‐scale structure was used in a structurally‐constrained inversion to improve the imaging of smaller‐scale structure [ABSTRACT FROM AUTHOR]

The NASA LIS/WRF‐Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi‐scale surface hydrological modeling capabilities of the WRF‐Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub‐surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF‐Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open‐loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF‐Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications. Plain Language Summary: Land surface models are useful because of their ability to resolve surface‐atmosphere feedbacks, including those with vegetation. Land surface models also have the capability to assimilate surface observations, usually measured through remote sensing techniques, into the model. Hydrologic models have the strength of resolving horizontal movements of water both on the surface and through the sub‐surface. In the present study, we combine the data‐assimilation capabilities of the NASA‐LIS land surface model with the WRF‐Hydro hydrologic model to combine the utility of both systems. We use this new system to demonstrate the impact of assimilating snow water equivalent, measured from an aircraft, on both the land surface and streamflow from the model in the Tuolumne River basin in Central California. Results show that assimilation of snow water equivalent into the coupled model corrects snow errors and improves the streamflow in both wet and dry years. We find that hydrologic processes that are now added to the land surface model impact simulated soil moisture and evapotranspiration. These findings are important because the ability for a model to better resolve streamflow, from snow assimilation, could be beneficial for water management. Key Points: Snow water equivalent data‐assimilation improves hydrologic response in the coupled LIS/WRF‐Hydro model for a case study in the Tuolumne River basinHorizontal surface routing increases soil moisture and evapotranspiration downstream near channels in LIS/WRF‐Hydro, compared to an LSM‐only simulation [ABSTRACT FROM AUTHOR]

Pool-riffle dynamics is governed by complex time and spatial interactions between water and sediment flows. In the last few decades, significant advances have been made in characterizing and modeling the hydrodynamics of pool-riffle sequences, and this information has been extensively used as the basis of conceptual models to describe or infer pool-riffle morphodynamics. A lot less attention, however, has been paid to the coupled dynamics of flow and sediment, which is essential to fully understand these complex geomorphic systems. This paper uses an unsteady 1-D flow-morphology and bed-sorting model to analyze pool-riffle dynamics. The model is first applied to a pool-riffle sequence on a 1.1 km reach of the lower Bear Creek, Arkansas, United States. After showing the model's ability to describe the general reach hydrodynamics and morphological evolution over 1 year, the detailed sediment and flow information is used to investigate pool-riffle dynamics in terms of self-maintenance mechanisms. Two effects that have been only marginally explored in the past, i.e., bed sediment sorting and downstream riffle control, are explained and quantified with the help of the model's outputs. The results show that self-maintenance occurs more frequently than previously thought as a result of grain sorting and that erosion or deposition of contiguous riffles also constitutes a self-maintenance mechanism. These findings provide the support for a physically based, integral description of pool-riffle morphodynamics and highlight the importance of flow and sediment variability on pool-riffle self-maintenance. The morphodynamic analysis bridges the gap between observations and current theories based mainly on hydrodynamic information. [ABSTRACT FROM AUTHOR]

Intermittent streams comprise much of the global river network, and are expected to become more prevalent with climate change. Characterizing the expansion and contraction of intermittency in stream networks, and understanding how sensitive these dynamics are to climatic variability, is critical for predicting the trajectory of hydrologic regimes in a changing climate. Here, we consider the spatial patterns of stream intermittency, focusing on wetted channel conditions at the end of the dry season, and identify land cover, physiographic, and climate variables that influence surface water presence and variability across years. We trained statistical models with wetted channel mapping data from 25 streams over 7 years to predict both the spatial and interannual variability of the wetted channel network. We then used the models to assess intermittent stream dynamics across the Russian River watershed in northern California, USA. We found that an average of 3.7% of the stream network was reliably dry, while 16.1% was reliably wet at the end of the dry season, with the remainder of the network exhibiting variability in wetted conditions in response to antecedent precipitation. Both climatic and landscape characteristics controlled the extent of the wetted network, particularly antecedent precipitation at seasonal and annual time scales, highlighting the role of hydrologic memory in this system. Given predictions of increased climate volatility, an improved understanding of the spatial patterns and stability of dry season conditions in intermittent streams can inform climate risk assessments and strategies for protecting biodiversity and the ecosystem services that intermittent streams support. Key Points: We evaluate patterns of intermittent stream drying and identify variables that influence surface water presence and variability across yearsWe developed models to characterize streams based on their sensitivity to climate variabilityPhysical watershed characteristics and precipitation at multiple time scales controlled the spatial and temporal variation of intermittency [ABSTRACT FROM AUTHOR]

Groundwater depletion is a major threat to agricultural and municipal water supply in California's Central Valley. Recent droughts during 2007–2009 and 2012–2016 exacerbated chronic groundwater depletion. However, it is unclear how much groundwater storage recovered from drought‐related overdrafts during post‐drought years, and how climatic conditions and water management affected recovery times. We estimated groundwater storage change in the Central Valley for April 2002 through September 2019 using four methods: GRACE satellite data, a water balance approach, a hydrologic simulation model, and monitoring wells. We also evaluated the sensitivity of drought recovery to different climate scenarios (recent climate ± droughts and future climate change scenarios: 20 GCMs and 2 RCPs) using water balance method and statistical sampling of historical climate data. Estimated Central Valley groundwater loss during the two droughts ranged from 19 km3 (2007–2009) to 28 km3 (2012–2016) (median of four methods). Median aquifer storage recovery was 34% and 19% of the overdraft during the 2010–2011 and 2017–2019 post‐drought years, respectively. Numerical experiments show that recovery times are sensitive to climate forcing, with longer recovery times for a future climate scenario that replicate historical climatology relative to historical forcing with no droughts. Overdraft recovery times decrease by ∼2× with implementation of pumping restrictions (30th to 50th percentiles of historical groundwater depletion) to constrain groundwater depletion relative to no restrictions with a no‐drought future climatology. This study highlights the importance of considering water management implications for future drought recoveries within the context of climate change scenarios. Plain Language Summary: California's Central Valley has experienced chronic groundwater depletion over the past few decades, the rate of which was amplified by droughts in 2007–2009 and 2012–2016. There is limited knowledge as to how much of the drought‐caused groundwater depletion has recovered during post‐drought years and how climate and water management affect overdraft recovery times. We address these issues by estimating groundwater storage changes using four methods and conducting numerical experiments with varying climatic conditions and water management options. We find that less than one‐third of the groundwater overdraft from the most recent droughts was recovered during post‐drought years. Projected overdraft recovery times vary greatly depending on the climate scenarios and water management strategies, and future droughts are likely to cause overdrafts from which recovery is unlikely given the current level of groundwater extractions. However, management measures such as capping groundwater pumping could reduce recovery times by a factor of two or more depending on the groundwater extraction cap and post‐drought climate. Key Points: Groundwater storage recovery during post‐drought periods ranged from 34% (2007–2009 drought) to 19% (2012–2016 drought)Projected drought recovery times decrease by a factor of 3.6–7.8 with post‐drought periods containing no drought years or wet years onlyOverdraft recovery time decreased by a factor of 2 with implementation of modest pumping restrictions under no‐drought post‐drought climate [ABSTRACT FROM AUTHOR]

Airborne electromagnetic (AEM) data can be inverted to recover models of the electrical resistivity of the subsurface; these, in turn, can be transformed to obtain models of sediment type. AEM data were acquired in Butte and Glenn Counties, California, USA to improve the understanding of the aquifer system. Around 800 line‐kilometers of high‐quality data were acquired, imaging to a depth of ∼300 m. We developed a workflow designed to obtain, from the AEM data, information about the large‐scale structure and heterogeneity of the aquifer system to better understand the vertical connectivity. Using six different inversions incorporating various forms of available information and posterior sampling of the recovered resistivity models, we produced 6,006 resistivity models. These models were transformed to models of sediment type and estimates of percentage of sand/gravel. Exploring the model space, containing the resistivity models and the derived models, allowed us to delineate the large‐scale structure of the aquifer system in a way that captures and communicates the uncertainty in the identified sediment type. The uncertainty increased, as expected, with depth, but also served to indicate, as areas of high uncertainty in sediment type, the location of both large‐scale and small‐scale interfaces between sediment types. A plan view map of the integrated percentage of sand/gravel, when compared to existing hydrographs, revealed the extent of lateral changes in vertical connectivity within the aquifer system throughout the study area. Plain Language Summary: In studying and managing groundwater systems, it can be very difficult to get the information needed about the subsurface. The airborne electromagnetic (AEM) method uses a helicopter to move a geophysical system over the land surface to collect this needed information. In this study we acquired ∼800 line‐kilometers of high‐quality AEM data in an area of Butte and Glenn Counties in the Central Valley of California, USA. Acquisition of these data allowed us to obtain three‐dimensional (3D) resistivity models covering the region from the ground surface to a depth of about 300 m. Working with descriptions from wells, we were able to transform the 3D resistivity models into 3D sediment‐type models. These models allowed us to map out the large‐scale structure of the groundwater system and better understand the vertical connectivity within the system. Because of fundamental limitations in the AEM method, we obtained many different resistivity models and corresponding sediment‐type models. Exploring these models allowed us to quantify the uncertainty in our interpretation of the data. This not only assisted in our interpretation, but it also communicated, to the local water agency, our confidence in our interpretation. Key Points: By incorporating various forms of available information in the inversion of airborne electromagnetic data, we designed a workflow that reduces and quantifies uncertainty in the derived sediment‐type modelsModels of sediment type reveal the large‐scale architecture of the aquifer systemModels of the percentage of sand/gravel, when combined with hydrographs, provide information about the lateral variability in vertical connectivity within the aquifer system [ABSTRACT FROM AUTHOR]

Determining wildland fire impacts on streamflow can be problematic as the hydrology in burned watersheds is influenced by post‐fire weather conditions. This study presents a quantile‐based analytical framework for assessing fire impacts on low and peak daily flow magnitudes, while accounting for post‐fire weather influences. This framework entails (a) the bootstrap method to compute the relative change in the post‐fire annual flow and weather statistics, (b) double mass analysis to detect if post‐fire baseflow and quick‐flow yield ratios are significantly altered, and (c) a quantile regression method to parse fire effects on flow at a specific quantile. We illustrate the applicability of this analytical framework using 44 western US streams with at least 5% of their watershed area burned. Results indicate that large, high‐severity burns in upland watersheds can raise the streamflow magnitude at the 0.05th and 0.95th quantiles for at least the five post‐fire years. Quantile regression results show that the median fire‐related increase in flow for the five post‐fire years can be up to 5,000% (Standard Error; S.E.< 2%) at the 0.05th quantile and 161% (S.E.< 10%) at the 0.95th quantile. The fire‐related increase in flow was often pronounced at the 0.05th quantile for streams in the Pacific Northwest and California regions. The difference in fire effects on flow (at both quantiles) across streams was related to post‐fire weather, pyrology, physiography, and land cover. The proposed analytical framework can be useful for detecting and quantifying fire effects on the low and peak stream flows in burned watersheds without overlapping disturbances. Key Points: Evaluating the impact of watershed burning on streamflow can be problematic due to the confounding effect of post‐fire weather influencesA quantile‐based analytical framework is presented for assessing fire effect on low & peak flow while accounting for weather influencesThis analytical framework can be useful for inferring fire effect on low & peak flows in burned watersheds without overlapping disturbances [ABSTRACT FROM AUTHOR]

Extreme precipitation and consequent floods are some of California's most damaging natural disasters, but they are also critical to the state's water supply. This motivates the need to better understand the long‐term variability of these events across the region. This study examines the possibility of reconstructing extreme precipitation occurrences in the Sacramento River Watershed (SRW) of Northern California using a network of tree‐ring based moisture proxies across the Western US. We first develop a gridded reconstruction of the cold‐season standardized precipitation index (SPI) west of 100°W. We then develop an annual index of regional extreme precipitation occurrences in the SRW and use elastic net regression to relate that index to the gridded, tree‐ring based SPI. These regressions, built using SPI data across the SRW only and again across a broader region of the Western US, are used to develop reconstructions of interannual variability in extreme precipitation frequency back to 1400 CE. The SPI reconstruction is skillful across much of the West, including the Sacramento Valley and Central Oregon. The reconstructed SPI also captures historical interannual variations in extreme SRW precipitation, although individual events may be under‐ or over‐estimated. The reconstructions show more SRW extremes from 1580 to 1700 and 1850 to 1915, a dearth of extremes prior to 1550, and a 2–8 year oscillation after 1550. Using tree‐ring proxies beyond the SRW often dampens the reconstructed extremes, but these data occasionally help to identify known extreme events. Overall, reconstructions of SRW extreme precipitation can help to understand better the historic variability of these events. Key Points: Tree‐ring based moisture proxies were used to reconstruct extreme precipitation in the Sacramento River Watershed back to 1400 CETree‐ring based proxies in the Intermountain West can improve reconstructions in some years, but can also dampen the magnitude of extremesExtreme precipitation was prevalent from 1580 to 1700 and 1850 to 1915, very rare from 1400 to 1550, and exhibited a 2–8 year oscillation after 1550 [ABSTRACT FROM AUTHOR]

Surface deformation in California's Central Valley (CV) has long been linked to changes in groundwater storage. Recent advances in remote sensing have enabled the mapping of CV deformation and associated changes in groundwater resources at increasingly higher spatiotemporal resolution. Here, we use interferometric synthetic aperture radar (InSAR) from the Sentinel‐1 missions, augmented by continuous Global Positioning System (cGPS) positioning, to characterize the surface deformation of the San Joaquin Valley (SJV, southern two‐thirds of the CV) for consecutive dry (2016) and wet (2017) water years. We separate trends and seasonal oscillations in deformation time series and interpret them in the context of surface and groundwater hydrology. We find that subsidence rates in 2016 (mean −42.0 mm/yr; peak −345 mm/yr) are twice that in 2017 (mean −20.4 mm/yr; peak −177 mm/yr), consistent with increased groundwater pumping in 2016 to offset the loss of surface‐water deliveries. Locations of greatest subsidence migrated outwards from the valley axis in the wetter 2017 water year, possibly reflecting a surplus of surface‐water supplies in the lowest portions of the SJV. Patterns in the amplitude of seasonal deformation and the timing of peak seasonal uplift reveal entry points and potential pathways for groundwater recharge into the SJV and subsequent groundwater flow within the aquifer. This study provides novel insight into the SJV aquifer system that can be used to constrain groundwater flow and subsidence models, which has relevance to groundwater management in the context of California's 2014 Sustainable Groundwater Management Act (SGMA). Key Points: The timing, magnitude, and spatial pattern of deformation in the San Joaquin Valley changes between dry and wet water yearsSeasonal amplitudes and peak seasonal uplift timing of deformation indicate possible recharge locations and pathways for groundwater flowGeodetic observations have the potential to provide insight into aquifer dynamics at policy relevant scales [ABSTRACT FROM AUTHOR]

The success of water conservation restrictions depends in part on governments' capacity to monitor water use. Inviting the general public to report instances of water waste is one means of expanding government capacity to monitor and enforce water use. Why are people in some communities more frequently engaged in such "participatory surveillance" than in other communities? Does participatory surveillance help achieve conservation goals? We explore the social correlates and conservation outcomes of participatory surveillance regimes with an analysis of water conservation in the U.S. state of California. During California's historic 2014–2017 drought, the state established hotlines and websites for the public to report water waste anonymously. Californians responded by reporting more than 485,000 water waste complaints over the course of the emergency. Analysis shows that besides the water supply characteristics and drought severity, several social and institutional factors correlate strongly with complaint volume. Specifically, we find that municipal and special district utilities received more complaints than investor‐owned utilities, and that complaints correlated positively with communities' level of education and partisan competition. We do not find strong evidence that communities' income, income distributions, and racial/ethnic compositions correlate with overall reporting. We further find that complaints are positively associated with conservation outcomes. Taken together, results indicate that social contexts can influence participation in participatory surveillance, and that participatory surveillance can be a potent means of enhancing water restrictions during a drought emergency. Key Points: Participatory surveillance is a way for governments to increase water waste monitoring capacity and achieve conservation goalsSocial and institutional aspects of community water systems correlate with water waste reporting in response to drought regulationsIncreased participatory surveillance is correlated with improved conservation outcomes [ABSTRACT FROM AUTHOR]

The southern Central Valley of California is one of the most productive agricultural regions in the world. Yet, decades of groundwater use beyond sustainable yield have left rural communities highly vulnerable to shortages and contamination of their drinking water supply. As state regulation begins to address these issues, a need exists to design adaptive and appropriate management systems to increase resilience of rural communities. Targeted managed aquifer recharge on agricultural land (Ag‐MAR) near rural communities is one such strategy that could potentially stabilize groundwater tables and maintain or improve groundwater quality in domestic supply wells. Here we present a geographic information system‐based multicriteria decision analysis that combines biophysical data (soils, land use, and surface water conveyance) with groundwater modeling and particle tracking to identify suitable agricultural land parcels for multibenefit groundwater recharge within well capture zones of 288 rural communities. Parcels are prioritized using a vulnerability index to change in groundwater supply, derived from well reliance and failures, pesticide applications, land subsidence, and socio‐economic data. Our analysis identifies 2,998 suitable land parcels for Ag‐MAR within the well capture zones of 149 of the 288 communities, of which 144 rely mainly on groundwater for drinking water. The majority of identified Ag‐MAR parcels serve communities ranked as having extreme or very high vulnerability to changes in groundwater supply. Our research produces new understanding of factors contributing to community vulnerability and resilience to changes in drinking water supply and can be used to discuss actions to help achieve a stable and high‐quality water supply. Key Points: Determined vulnerability to changes in groundwater supply for 288 rural communities in southern Central Valley, CaliforniaCombined geographic information system‐based multicriteria decision analysis with groundwater modeling and particle tracking to identify recharge locationsIdentified 2,998 agricultural land parcels for managed aquifer recharge within domestic well capture zone of 149 communities [ABSTRACT FROM AUTHOR]

With aquifers around the world stressed by over‐extraction, water managers are increasingly turning to managed aquifer recharge (MAR), directly replenishing groundwater resources through injection wells, recharge basins, or other approaches. While there has been progress in understanding the geological and infrastructure‐related considerations to make MAR more effective, critical evaluations of its institutional design and implementation are limited. This study assesses MAR projects, using a case study of projects proposed by groundwater sustainability agencies (GSAs) in California to comply with the state's Sustainable Groundwater Management Act of 2014; these projects will almost double the number of MAR projects in the United States. We draw on content analysis of groundwater sustainability plans that propose these projects. We first assess the types of recharge projects proposed and the stated aims of the projects, to assess when and why agencies are turning to MAR as a solution. We find that recharge basins are by far the most common approach, and that GSAs hope these basins will improve water table levels, reduce subsidence, and improve water quality. We then analyze potential barriers to project implementation and assess the projects' ability to achieve the stated goals. Primary concerns identified include a potential lack of available water, a potentially challenging legal framework, and minimal consideration of funding and cumulative land needs. To conclude, we discuss broader considerations for ensuring that MAR is an effective water management tool. Key Points: 233 Managed aquifer recharge (MAR) projects are proposed in California, almost doubling the number of MAR projects in the United StatesWe identify multiple feasibility concerns, including inconsistent legal requirements and water, funding, and land availabilityMAR's ability to help reverse decades of over‐extraction in California can only be realized if implemented effectively [ABSTRACT FROM AUTHOR]

Climate change impacts on hydroclimatology are becoming increasingly apparent around the world. It is unknown how annual variations in precipitation and air temperature alter the model‐inferred importance of hydrological processes and how this varies across watersheds. To examine this, we used parsimonious rainfall‐runoff model and applied time‐varying sensitivity analysis across 30 Californian watersheds for a 33‐year period (1981–2014). We calculated annual total order sensitivity indices for five performance metrics: Kling‐Gupta Efficiency (KGE) and model error in simulating hydrologic signatures (runoff ratio, slope of flow duration curve, baseflow index, and timing of streamflow centroid). Sensitivity of hydrological signatures to the parameters differed by signature, while parameter importance with respect to KGE was much more spatially and temporally variable. Variations in parameter sensitivity with respect to KGE were either correlated with air temperature (snow‐dominated sites in the Sierra Nevada) or precipitation (lower elevation sites < 1,300 m). Across error metrics and signatures, parameter sensitivity strongly differed between wet and dry years for a subset of our study sites. While parameter importance varied through time, parameter sensitivity variations across watersheds were much more pronounced. This suggests that parameter controls on model performance are much more a reflection of watershed properties as opposed to being dominantly shaped by shifts in precipitation and air temperature. These findings emphasize the importance of understanding simulated watershed responses to fluctuating annual conditions, as this conceptual knowledge is necessary to anticipate similarities and differences in response across even relatively proximal watersheds in the face of growing extreme conditions. Plain Language Summary: Computer models are useful tools that can be used to analyze what inputs matter most to ensuring that model output is similar to observed conditions. In order to understand how very wet years versus very dry years impact which model inputs (called parameters) are most important to matching good predictions, we simulated streamflow using a simple mathematical model many thousands of times. We used these outputs to determine how variations in predictions of streamflow (output from the model) are related to variations in model parameters, to identify which parameters are most important for ensuring reasonable model predictions. We focused on watersheds that are close together, but that may experience different average conditions and interannual variability. We found that which parameters are important varied across different watersheds and also varied with total annual precipitation and watershed elevation. In addition, we found that drought impacted the importance of processes in the model, especially in mountainous watersheds. Our results emphasize the importance of understanding how watersheds respond to varying annual conditions, as this type of knowledge is essential to anticipate the future of streamflow availability and inform the management style that needs to be implemented to predict water availability in these systems. Key Points: Annual time‐varying sensitivity analysis revealed unique patterns of parameter importance across 21 proximal study watershedsLow elevation parameter importance to Kling‐Gupta Efficiency varied with precipitation, while shifts at high elevations varied with elevationParameter sensitivity to select hydrological signatures was generally static through time, despite annual variations in observed values [ABSTRACT FROM AUTHOR]

Precipitation is a key input variable in distributed surface water‐groundwater models, and its spatial variability is expected to impact watershed hydrologic response via changes in subsurface flow dynamics. Gridded precipitation data sets based on gauge observations, however, are plagued by uncertainty, especially in mountainous terrain where gauge networks are sparse. To examine the mechanisms via which uncertainty in precipitation data propagates through a watershed, we perform a series of numerical experiments using an integrated surface water‐groundwater hydrologic model, ParFlow.CLM. The Kaweah River watershed in California, USA, is used as our virtual catchment laboratory to characterize watershed response to variable precipitation forcing from headwaters to groundwaters. By applying the three‐cornered hat method, we quantify the spatially distributed uncertainty in four publically available precipitation forcing data sets and their simulated hydrology. Simulations demonstrate that uncertainty in the simulated groundwater storage is primarily a result of topographic redistribution of uncertainty in precipitation forcing. Soil water redistribution is the primary pathway that redistributes uncertainty downslope. We also find that topography exerts a larger impact than variable subsurface parameters on propagating uncertainty in simulated fluxes. Finally, we find that improvement in model performance metrics is higher for a single simulation forced with the mean precipitation from the available data sets than the averaged simulated results of separate simulations forced with each data set. Results from this study highlight the importance of topography‐moderated flow through the critical zone in shaping the groundwater response to climate variability. Key Points: Uncertainty in simulated groundwater storage change is a result of topographic redistribution of uncertainty in precipitation forcingSubsurface flow pathways in both soil and deeper bedrock control watershed response to variable precipitation inputMerging multiple precipitation data sets provides better results than merging simulated fluxes, due to high model sensitivity to changes in precipitation [ABSTRACT FROM AUTHOR]

The spatiotemporal dynamics of plant water sources are hidden and poorly understood. We document water source use of Quercus garryana growing in Northern California on a profile of approximately 50 cm of soil underlain by 2–4 m of weathered bedrock (sheared shale mélange) that completely saturates in winter, when the oaks lack leaves, and progressively dries over the summer. We determined oak water sources by combining observations of water stable isotope composition, vadose zone moisture and groundwater dynamics, and metrics of tree water status (potential) and use (sapflow). During the spring, oak xylem water is isotopically similar to the seasonal groundwater and shallow, evaporatively enriched soil moisture pools. However, as soils dry and the water table recedes to the permanently saturated, anoxic, low‐conductivity fresh bedrock boundary, Q. garryana shifts to using a water source with a depleted isotopic composition that matches residual moisture in the deep soil and underlying weathered bedrock vadose zone. Sapflow rates remain high as late‐summer predawn water potentials drop below −2.5 MPa. Neutron probe surveys reveal late‐summer rock moisture declines under the oaks in contrast to constant rock moisture levels under grass‐dominated areas. We therefore conclude that the oaks temporarily use seasonal groundwater when it occupies the weathered profile but otherwise use deep unsaturated zone moisture after seasonal groundwater recedes. The ample moisture, connected porosity, and oxygenated conditions of the weathered bedrock vadose zone make it a key tree water resource during the long summer dry season of the local Mediterranean climate. Plain Language Summary: What are the water sources that allow oaks to transpire through extended dry periods? Oaks in California are thought to tap into groundwater to meet dry season water demand. Here, we show that oaks growing in a savanna woodland instead use tightly held moisture within deep soil and weathered bedrock above the saturated zone. We determined this by matching stable isotopes in the trees' water to these moisture pools. Deep soil and rock moisture were isotopically lighter than shallow soil moisture, which was affected by evaporative enrichment, and the groundwater, which looked more like average wet season rainfall. Monitored moisture declines within the weathered bedrock corroborate the isotopic interpretation that rock moisture sustained oak transpiration. Although the saturated zone was only a few meters below the ground surface in late summer, the trees did not use it due to its low oxygen content and residence in low permeability fresh bedrock. Our findings matter to forest management because of the need to determine when and where trees use groundwater, which regulates ecologically critical baseflow. Moisture extraction from the weathered bedrock unsaturated zone is likely important globally, as forests are widespread on hillslopes with thin soils overlying weathered bedrock. Key Points: Isotopic, physiologic, and hydrologic observations were combined to identify oak water sourcesRock and deep soil moisture rather than groundwater sustain transpiration in summer dry seasonDeep unsaturated zone moisture sources and oak water were isotopically depleted [ABSTRACT FROM AUTHOR]

The atmospheric freezing level (ZFL) determines the rain‐snow transition zone at the surface, how much rainfall is available for runoff, and the flood risk during a precipitation event. An accurate ZFL forecast is thus critical for reservoir operation, especially in mountain watersheds with narrow elevation bands like the Feather and North Fork Yuba in California, where a 500‐m elevation gain can amount to >50% of the watershed area. Using a ±350‐m ZFL forecast error, we find inflow volume uncertainties of <10% to >50% of the flood pool storages at Lake Oroville and New Bullards Bar reservoirs, depending on the ZFL, antecedent moisture condition, and the precipitation event magnitude. The uncertainties can increase by up to >3% per inch (25.4 mm) of precipitation, depending on the ZFL and antecedent moisture condition. This result substantiates the significant impact of ZFL forecast error and the critical need of ZFL forecast accuracy to support reservoir flood control operations in the two watersheds. Plain Language Summary: How much rain falls over California's Sierra Nevada mountains during winter storms is influenced by the atmospheric freezing level. Above this level, air temperature is below freezing, and precipitation falls as snow. Below this level, precipitation generally falls as rain. Thus, freezing level forecasts are critical for reservoir operation, particularly flood control, which must account for the fraction of event precipitation that will become runoff and that which will contribute to snowpack. However, as with all weather forecast products, freezing level prediction carries errors and uncertainties. In this study, we evaluate how sensitive the flood control storages of Lake Oroville and New Bullards Bar reservoirs are to the magnitude of freezing level forecast error. We find that even a conservative forecast error estimate of 350 m can result in the uncertainties of rainfall‐runoff estimates that exceed half of either reservoirs' flood control capacity during heavy precipitation events. This result emphasizes the impact of freezing level forecast uncertainty on reservoir operations and the importance of reducing these forecast errors. Key Points: The narrow elevation bands make the hydrology of Feather and Yuba River Watersheds especially susceptible to freezing level forecast errorA freezing level forecast error of 350 m is equivalent to a runoff volume uncertainty up to over a half of the reservoir flood pool storageWith the same forecast error, every inch of precipitation can amount to more than 3% of the reservoir flood pool storage [ABSTRACT FROM AUTHOR]

Land subsidence caused by groundwater extraction has numerous negative consequences, such as loss of groundwater storage and damage to infrastructure. Understanding the magnitude, timing, and locations of land subsidence, as well as the mechanisms driving it, is crucial to implementing mitigation strategies, yet the complex, nonlinear processes causing subsidence are difficult to quantify. Physical models relating groundwater flux to aquifer compaction exist but require substantial hydrological data sets and are time consuming to calibrate. Land deformation can be measured using interferometric synthetic aperture radar (InSAR) and GPS, but the former is computationally expensive to estimate at scale and is subject to tropospheric and ionospheric errors, and the latter leaves many temporal and spatial gaps. In this study, we apply for the first time a machine learning approach that quantifies the relationships of various widely available input data, including evapotranspiration, land use, and sediment thickness, with land subsidence. We apply this method over the Western United States and estimate that from 2015 to 2016, ~2.0 km3/yr of groundwater storage was lost due to groundwater pumping‐induced compaction of sediments. Subsidence is concentrated in the Central Valley of California, and the state of California accounts for 75% of total subsidence in the Western United States. Other significant areas of subsidence occur in cultivated regions of the Basin and Range province. This study demonstrates that widely available ancillary data can be used to estimate subsidence at a larger scale than has been previously possible. Key Points: Remote sensing and geological data sets are integrated to estimate land subsidence due to groundwater extractionThe estimates are integrated using a machine learning method called random forestsUsing this method we estimate that from 2015 to 2016, ~2.0 km3/yr of groundwater storage was lost due to land subsidence in the western United States [ABSTRACT FROM AUTHOR]

The contributions and composition of baseflow sources across an extended recession period were quantified for six subwatersheds of varying size in a structurally complex watershed in coastal California using endmember mixing analysis and related to catchment characteristics (e.g., topography, geology, land use, and soil characteristics). Both shallow subsurface and deep groundwater reservoirs were important contributors for streamflow during low flow periods, and the composition of baseflow sources across subwatersheds was directly related to geologic indices. A binary classification of underlying bedrock permeability (e.g., low vs. high) best explained the changes in shallow subsurface water and deeper groundwater inputs through the seasonal recession. Dissolved inorganic carbon (DIC), dissolved organic carbon (DOC), and specific UV absorbance at 254 nm (SUVA254) were used to provide additional insight into endmember characteristics and their contributions to baseflow. Stream water DIC concentrations were broadly controlled by mixing of groundwater and shallow subsurface water endmembers with relatively constant DIC concentrations, while stream water DOC concentrations reflected both spatial and temporal changes in shallow subsurface water DOC. Results from this study show (1) the importance of considering baseflow as a dynamic mixture of water from multiple sources, (2) the effect of geology on source composition at the subwatershed scale during low flow conditions, and (3) the impact of shifting baseflow sources on stream water dissolved carbon concentrations and the utility of using dissolved carbon concentrations to obtain additional insight into temporal variability in baseflow sources. Key Points: Both deep and shallow subsurface flow paths contribute to baseflow during the summer dry down periodDifferences in contributions of shallow and deep subsurface flow paths to baseflow across subwatersheds are related to local geologyStream water dissolved carbon geochemistry fluctuates in line with shifting sources during the summer dry down period [ABSTRACT FROM AUTHOR]

We developed a technique for reconstructing annual gravel yields and generated a 55‐year record of gravel transport for the North Fork catchment of the Caspar Creek Experimental Watersheds in Northern California. The technique relies on field data collection including annual surveys of weir pond volumes and suspended sediment measurements, as well as an accounting for settling of suspended sediment and organic matter in the pond. We compared these annual yields to gravel yields predicted by the Wilcock two‐fraction bed load transport model, which we calibrated from measured values at Caspar Creek. We considered three velocity‐discharge relationships and found that values of hydraulic variables measured during storms produced the best fit between reconstructed and predicted annual gravel yields when years with large disturbances were excluded. We also compared predicted gravel transport rates to bed load transport rates measured from 1988 to 1995 with bed load pit samplers. We found that the calibrated model predictions agreed well with the field‐measured bed load transport rates. To investigate the role of supply and storage on gravel transport, we compared the reconstructed gravel yields to predicted gravel yields and found that increased occurrence of landslides and headcut erosion in the 1990s and early 2000s did not lead to an increase in gravel yields. Instead, input of large downed wood in the 1990s created storage space and decreased bed load delivery to the weir pond. Plain Language Summary: Bed load sediment yields are important for understanding catchment responses to natural and human caused disturbances, but are difficult to measure. We demonstrate a method that uses pond sediment accumulations to quantify annual bed load yields at Caspar Creek, CA. We also use these data and data collected during more focused storm‐based sampling to show that a common bed load model provides relatively accurate estimates of gravel transport at Caspar Creek. Comparing annual gravel yields to predicted gravel yields reveals that gravel transport decreased in the 1990s and 2000s after a large number of trees blew down and increased the wood content in the channel. The approach presented here can be applied to other sites where similar data are available, such as other ponds or small reservoirs. Key Points: We reconstructed a 55‐year record of annual gravel yields from weir pond deposits at Caspar Creek, CAReconstructed gravel yields compare well to modeled gravel yields and to a continuous record of bed load transport ratesComparing reconstructed yields to predictions reveals a decrease in transport associated with increased downed wood and sediment storage [ABSTRACT FROM AUTHOR]

Airborne light detection and ranging is an emerging measurement tool for snowpack estimation, and data are now emerging to better assess multiscale snow depth patterns. We used airborne light detection and ranging measurements from four sites in the southern Sierra Nevada to determine how snow depth varies with canopy structure and the interactions between canopies and terrain. We processed the point clouds into snow depth rasters at 0.5×0.5‐m2 resolution and performed statistical analysis on the processed snow depth data, terrain attributes, and vegetation attributes, including the individual tree bole locations, canopy crown area, and canopy height. We studied the snow depth at such a fine scale due in part to the spatial heterogeneity introduced by canopy interception and enhanced melting caused by tree trunks in forested areas. We found that the dominant direction of a tree well, the area around the tree bole that has shallower snowpack, is correlated with the local aspect of the terrain, and the gradient of the snow surface in a tree well is correlated with the tree's crown area. The regression‐tree based XGBoost model was fitted with the topographic variables and canopy variables, and about 71% of snow depth variability can be explained by the model. Key Points: Lidar point density affects snow depth estimates in dense forest with a warmer snowpack, over runoff‐producing elevations in the SierraIn a tree well, more snow accumulates in the downslope direction from the tree boleCompared to the dense forest spanning the rain‐snow transition elevations, the same forest with half trees will have about 10% deeper snow [ABSTRACT FROM AUTHOR]

Groundwater aquifers provide an important "insurance" against climate variability. Due to prolonged droughts and/or irrigation demands, groundwater exploitation results in significant groundwater storage depletion. Managed aquifer recharge (MAR) is a promising management practice that intentionally places or retains more water in groundwater aquifers than would otherwise naturally occur. In this study, we examine the possibility of using large irrigated agricultural areas as potential MAR locations (Ag‐MAR). Using the California Central Valley Groundwater‐Surface Water Simulation Model we tested four different agricultural recharge land distributions, two streamflow diversion locations, eight recharge target amounts, and five recharge timings. These scenarios allowed a systematic evaluation of Ag‐MAR on changes in regional, long‐term groundwater storage, streamflow, and groundwater levels. The results show that overall availability of stream water for recharge is critical for Ag‐MAR systems. If stream water availability is limited, longer recharge periods at lower diversion rates allow diverting larger volumes and more efficient recharge compared to shorter diversion periods with higher rates. The recharged stream water increases both groundwater storage and net groundwater contributions to streamflow. During the first decades of Ag‐MAR operation, the diverted water contributed mainly to groundwater storage. After 80 years of Ag‐MAR operation about 34% of the overall diverted water remained in groundwater storage while 66% discharged back to streams, enhancing base flow during months with no recharge diversions. Groundwater level rise is shown to vary with the spatial and temporal distribution of Ag‐MAR. Overall, Ag‐MAR is shown to provide long‐term benefits for water availability, in groundwater and in streams. Key Points: Agricultural managed aquifer recharge at low recharge rates over large areas offers benefits to groundwater storage and seasonal base flowLonger recharge periods allow for more recharge than short, fixed windows of recharge that accommodate high recharge ratesMaximum groundwater level rise is sensitive to recharge timing and recharge locations [ABSTRACT FROM AUTHOR]

Headwater catchments in the mixed-conifer zone of the American and Merced River basins were selectively thinned in 2012 to reduce the risk of high-intensity wildfire. Distributed observations of forest vegetation thinning, precipitation, snowpack storage, soil water storage, energy balance, and stream discharge from 2010 to 2013 were used to calculate the water balance and constrain a hydroecologic model. Using the spatially calibrated RHESSys model, we assessed thinning effects on the water balance. In the central-Sierra American River headwaters, there was a mean-annual runoff increase of 14% in response to the observed thinning patterns, which included heterogeneous reductions in leaf area index (-8%), canopy cover (-3%), and shrub cover (-4%). In the southern-Sierra Merced River headwaters, thinning had little impact on forest structure or runoff, as vegetation growth in areas not thinned offset reductions from thinning. Observed thinning effects on runoff could not be confirmed in either basin by measurements alone, in part because of the high variability in precipitation during the measurement period. Modeling results show that when thinning is intensive enough to change forest structure, low-magnitude vegetation reductions have greater potential to modify the catchment-scale water balance in the higher-precipitation central Sierra Nevada versus in the more water-limited southern Sierra Nevada. Hydrologic modeling, constrained by detailed, multiyear field measurements, provides a useful tool for analyzing catchment response to forest thinning. [ABSTRACT FROM AUTHOR]

Evapotranspiration ( ET), an essential process in biosphere-atmosphere interactions, is highly uncertain in cities that maintain cultivated and irrigated landscapes. We estimated ET of irrigated landscapes in Los Angeles by combining empirical models of turfgrass ET and tree transpiration derived from in situ measurements with previously developed remotely sensed estimates of vegetation cover and ground-based vegetation surveys. We modeled irrigated landscapes as a two-component system comprised of trees and turfgrass to assess annual and spatial patterns of ET. Annual ET from vegetated landscapes ( ETveg) was 1110 ± 53 mm/yr and ET from the whole city (vegetated and nonvegetated areas, ETland) was three times smaller, reflecting the fractional vegetation cover. With the exception of May and June, monthly ETland was significantly higher than predicted by the North American Land Data Assimilation System. ETveg was close to potential ET, indicating abundant irrigation inputs. Monthly averaged ETveg varied from 1.5 ± 0.1 mm/d (December) to 4.3 ± 0.2 mm/d (June). Turfgrass was responsible for ∼70% of ETveg. For trees, angiosperm species (71% of all trees) contributed over 90% to total tree transpiration, while coniferous and palm species made very small contributions. ETland was linearly correlated with median household income across the city, confirming the importance of social factors in determining spatial distribution of urban vegetation. These estimates have important implications for constraining the municipal water budget of Los Angeles and improving regional-scale hydrologic models, as well as for developing water-saving practices. The methodology used in this study is also transferable to other semiarid regions for quantification of urban landscape ET. [ABSTRACT FROM AUTHOR]

Using a multicentury reconstruction of drought, we investigate how rare the 2012-2015 California drought is. A Bayesian approach to a nonstationary, bivariate probabilistic model, including the estimation of copula parameters is used to assess the time-varying return period of the current drought. Both the duration and severity of drought exhibit similar multicentury trends. The period from 800 to 1200 A.D. was perhaps more similar to the recent period than the period from 1200 to 1800 A.D. The median return period of the recent drought accounting for both duration and severity, varies from approximately 667-2652 years, if the model parameters from the different time periods are considered. However, we find that the recent California drought is of unprecedented severity, especially given the relatively modest duration of the drought. The return period of the severity of the recent drought given its 4 year duration is estimated to be nearly 21,000 years. [ABSTRACT FROM AUTHOR]

Although most freshwater resources are used in agriculture, residential water use is a much more energy intensive user. Based on this, we analyze the increased willingness to adopt water conservation strategies if energy cost is included in the customers' utility function. Using a Water-Energy-CO2 emissions model for household water end uses and probability distribution functions for parameters affecting water and water-related energy use in 10 different locations in California, this research introduces a probabilistic two-stage optimization model considering technical and behavioral decision variables to obtain the most economical strategies to minimize household water and water-related energy bills and costs given both water and energy price shocks. Results can provide an upper bound of household savings for customers with well-behaved preferences, and show greater adoption rates to reduce energy intensive appliances when energy is accounted, resulting in an overall 24% reduction in indoor water use that represents a 30% reduction in water-related energy use and a 53% reduction in household water-related CO2 emissions. Previous use patterns and water and energy rate structures can affect greatly the potential benefits for customers and so their behavior. Given that water and energy are somewhat complementary goods for customers, we use results of the optimization to obtain own-price and cross-price elasticities of residential water use by simulating increases in water and energy prices. While the results are highly influenced by assumptions due to lack of empirical data, the method presented has no precedent in the literature and hopefully will stimulate the collection of additional relevant data. [ABSTRACT FROM AUTHOR]

A 11 year data set of spatially distributed snow water equivalent (SWE) was used to inform a quantitative, near-optimal sensor placement methodology for real-time SWE estimation in the American River basin of California. Rank-based clustering was compared with geographically based clustering (subbasin delineation) to determine the existence of stationary covariance structures within the overall SWE data set. The historical SWE data, at 500 x 500 m resolution, were split into 8 years of training and 3 years of validation data. Within each cluster, a quantitative sensor-placement algorithm, based on maximizing the metric of mutual information, was implemented and compared with random placement. Gaussian process models were then built from validation data points selected by the algorithm to evaluate the efficacy of each placement approach. Rank-based clusters remained stable interannually, suggesting that rankings of pixel-by-pixel SWE exhibit stationary features that can be exploited by a sensor-placement algorithm, yielding a 200 mm average root-mean-square error (RMSE) for 20 randomly selected sensing locations. This outperformed geographic and basin-wide placement approaches, which generated 460 and 290 mm RMSE, respectively. Mutual information-based sampling provided the best placement strategy, improving RMSE between 0 and 100 mm compared with random placements. Increasing the number of rank-based clusters consistently lowered average RMSE from 400 mm for 1 cluster to 175 mm for 8 clusters, for 20 total sensors placed. To optimize sensor placement, we recommend a strategy that couples rank-based clustering with mutual information-based sampling design. [ABSTRACT FROM AUTHOR]

Water year indices and drought indices are helpful for categorizing water years into similar types, allowing water managers and policymakers to quantify years, visualize variability, and guide water operations. Many water management decisions, such as environmental flow requirements and water supply allocations, are based on water year type designations. They vary by region and index, but are defined by runoff in the current water year compared to average historical runoff, with numerical thresholds categorizing year types. California's Sacramento Valley and San Joaquin Valley Indices are used as case studies to examine how climate change affects indices. Modeled streamflow for 1951--2099 from the climate-forced Variable Infiltration Capacity hydrologic model estimate potential changes in runoff and water year type frequency. We show that the frequency of water year types changes significantly with climate change and different strategies to adapt water year classification indices to climate change affect water allocations as much as the impacts from changing hydroclimatic conditions. [ABSTRACT FROM AUTHOR]

The western United States is marked by limited water resources and a fast-growing population. Increasing climate variability as well as a growing demand on water resources highlights the need for improved understanding of connections between regional climate, surface water dynamics, and groundwater recharge. The current study focuses on the linkages between climate variability and groundwater levels in Calleguas Creek watershed located in southern California. The Calleguas Creek groundwater system serves as a critical source of water supply for agricultural and industrial use. Precipitation time series and groundwater levels were analyzed throughout the Calleguas Creek watershed for the period 1975-2004. Water level variability was analyzed for over 311 individual wells with a subset of 20 wells selected for further analysis. A correlation matrix was computed to establish well locations (or groups) with similar hydrologic behavior. Prewhitening methods were used to evaluate the effect of time series autocorrelation on the test statistics for trend detection using the Mann-Kendall test. Both climate and selected groundwater level (well) data were subjected to frequency analysis using fast Fourier transform. The time series of precipitation, the El Niño-Southern Oscillation (ENSO) index, and well levels were analyzed. A strong persistence was observed in the groundwater level time series, ranging from 66% to 99%. Results suggest the existence of significant periodicities between 2.0 and 7.0 years in both the precipitation and the well level data that are partially coincident with ENSO modes. A decadal oscillation was also observed in the well level data, which partially corresponds with the Pacific Decadal Oscillation. Assessment of the complex interactions between climate variability and groundwater levels will facilitate improved water resources planning and management in water-stressed regions where marginal changes in hydrologic budgets have significant implications. [ABSTRACT FROM AUTHOR]