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We used compound-specific isotope analysis of carbon isotopes in amino acids (AAs) to determine the biosynthetic source of AAs in fish from major tributaries to California's Sacramento-San Joaquin river delta (i.e., the Sacramento, Cosumnes and Mokelumne rivers). Using samples collected in winter and spring between 2016 and 2019, we confirmed that algae are a critical component of floodplain food webs in California's Central Valley. Results from bulk stable isotope analysis of carbon and nitrogen in producers and consumers were adequate to characterize a general trophic structure and identify potential upstream and downstream migration into our study site by American shad Alosa sapidissima and rainbow trout Oncorhynchus mykiss, respectively. However, owing to overlap and variability in source isotope compositions, our bulk data were unsuitable for conventional bulk isotope mixing models. Our results from compound-specific carbon isotope analysis of AAs clearly indicate that algae are important sources of organic matter to fish of conservation concern, such as Chinook salmon Oncorhynchus tshawytscha in California's Central Valley. However, algae were not the exclusive source of energy to metazoan food webs. We also revealed that other sources of AAs, such as bacteria, fungi and higher plants, contributed to fish as well. While consistent with the well-supported notion that algae are critical to aquatic food webs, our results highlight the possibility that detrital subsidies might intermittently support metazoan food webs.

Inter- and intra-annual water availability is naturally highly variable in Mediterranean regions, with swings between extremes costing nations potentially billions of dollars in damages and threatening lives. In California, future projections foresee an increase in the bimodal distribution of hydrological extremes, leading to greater hydroclimatic whiplash. Here, we quantify the relative impact of hydroclimatic whiplash on hydropower systems, flood control and water deliveries in the Central Sierra Nevada, California. We aim to explore at what point these services become less resilient to drought, and if wet whiplash years can re-establish an ‘average’ system state. To represent a wide range of wet, dry and dry-to-wet transitions, we sampled water years from upper (floods) and lower (droughts) quintiles, with replacement, across 30 years of future streamflow projections (2030-2060) from 10 global circulation models. Synthetic hydrological sequences of 2 to 5 dry years, followed by 1 to 2 wet years form a total sample of 200 whiplash sequences for the Stanislaus, Tuolumne, Merced and Upper San Joaquin River basins. This stress test indicates that the intensification of whiplash cycles would seriously challenge existing hydropower production, water storage and flood control operating rules. Compared to baseline averages, all basins had negative impacts on hydropower generation, with losses varying from 6% in the Merced to almost around 67% in the Upper San Joaquin, depending on the whiplash sequence. Agricultural and/or urban demands are most impacted in the Tuolumne and the Upper San Joaquin, in particular for all sequences. Historically, this basin has had more than 70% of outflows delivered to irrigation districts, therefore whiplash sequences tend to disrupt these services more easily. Meanwhile, carryover storage is negatively affected in all basins, but more noticeably in the Merced and Stanislaus basins, with losses of 7-60% and 15-31%, respectively, due to their small overall storage capacity. The small reservoirs in the upper watersheds and inflexible operating flood control rules constitute a challenge to accommodate whiplash impacts in the region. These results show heterogenous sensitivities of flood control releases, environmental flows and agricultural/urban deliveries with projected climate whiplash conditions, with varying degrees of annual, time and volumetric reliability. These services compete for scarce water supply within the low-elevation terminal dams in each basin. This analysis identifies perspectives on the challenges and risks of regional climate whiplash effects and adaptation strategies to include extremes and their impacts on water allocation to human and environmental purposes.

California’s San Joaquin Valley is both drought-prone and water-scarce but relies on high-intensity agriculture as its primary economy. Climate change adaptation strategies for high-intensity agriculture require reliable and highly resolved land use classification data to accurately account for changes in crop water demand, greenhouse gas (GHG) emissions, and farmgate revenue. Understanding direct and indirect economic impacts from potential changes to high-intensity agriculture to reduce groundwater overdrafts, such as reductions in the cultivated area or switching to less water-intensive crops, is unachievable if land use data are too coarse and inconsistent or misclassified. This study quantified the revenue, crop water requirement, and GHG emission discrepancies resulting from land use misclassification in the United States’ most complex agricultural region, California’s San Joaquin Valley. By comparing three commonly used land use classification datasets—CropScape, Land IQ, and Kern County Agriculture—this study found that CropScape led to considerable revenue and crop water requirement discrepancies compared to other sources. Crop misclassification across all datasets resulted in an underestimation of GHG emissions. The total revenue discrepancies of misclassified crops by area for the 2016 dataset comparisons result in underestimations by CropScape of around USD 3 billion and overestimation by LIQ and Kern Ag of USD 72 million. Reducing crop misclassification discrepancies is essential for crafting climate resilience strategies, especially for California, which generates USD 50 billion in annual agricultural revenue, faces increasing water scarcity, and aims to reach carbon neutrality by 2045. Additional investments are needed to produce spatial land use data that are highly resolved and locally validated, especially in high-intensity agriculture regions dominated by specialty crops with unique characteristics not well suited to national mapping efforts.

Solar power development over canals is an emerging response to the energy–water–food nexus that can result in multiple benefits for water and energy infrastructure. Case studies of over-canal solar photovoltaic arrays have demonstrated enhanced photovoltaic performance due to the cooler microclimate next to the canal. In addition, shade from the photovoltaic panels has been shown to mitigate evaporation and potentially mitigate aquatic weed growth. However, the evaporation savings and financial co-benefits have not been quantified across major canal systems. Here we use regional hydrologic and techno-economic simulations of solar photovoltaic panels covering California’s 6,350 km canal network, which is the world’s largest conveyance system and covers a wide range of climates, insolation rates and water costs. We find that over-canal solar could reduce annual evaporation by an average of 39 ± 12 thousand m3 per km of canal. Furthermore, the financial benefits from shading the canals outweigh the added costs of the cable-support structures required to span the canals. The net present value of over-canal solar exceeds conventional overground solar by 20–50%, challenging the convention of leaving canals uncovered and calling into question our understanding of the most economic locations for solar power. Over-canal solar photovoltaic arrays are likely to reduce water evaporation and carry financial co-benefits, but estimates are lacking. With hydrologic and techno-economic simulations of solar panels covering California’s canal network, this study shows the advantages of covering canals with solar panels.

Environmental flow management in watersheds with multi-objective reservoirs is often presented as an additional constraint to an already strained and over-allocated stream system. Nevertheless, environmental flow legislation and regulatory policies are increasingly being developed and implemented globally. In California, USA, recent legislative and regulatory policies place environmental flows at the forefront of the state’s water management objectives; however, the increased reliance on hydropower to support climate change mitigation goals may complicate efforts on both issues. This study modelled alternative environmental flow strategies in the major tributaries to the San Joaquin River in California. Strategies included detailed water management rules for hydropower production, flood control, and water deliveries, and three methodological approaches to environmental flow releases: minimum instream flows (“baseline”) year-round, 40% of full natural flow (FNF) during the spring runoff season and minimum releases the remainder of the year, and functional flows year-round. Results show that environmental flow strategies affect downstream flow releases in each of the San Joaquin’s four sub-basins differently depending on infrastructure capacity, water management objectives, and hydrologic year types. While hydropower production was comparable or declined in the Stanislaus, Tuolumne, and Merced basins, functional flow and 40% FNF strategies increased hydropower production in the Upper San Joaquin basin by 11%. Uncontrolled spill of high flow events decreased when high flow releases were based on hydrologic cues rather than exclusively on flood storage capacity. Water deliveries were reduced in all years regardless of environmental flow strategy. The 40% FNF and functional flow strategies both increased water released to the river relative to baseline, but in different ways. The functional flow strategy allocated water in a holistic approach that enhanced ecological functions in all years, but particularly in moderate and wet years. In contrast, the 40% FNF strategy provided increased flows relative to baseline and some ecological benefit in dry years, but less ecological benefit in other years. This study shows that alternative environmental flow strategies will have different and important trade-offs for integrated water management, and may mutually benefit seemingly conflicting objectives.

Background Organic viticulture can generate a range of ecosystem services including supporting biodiversity, reducing the use of conventional pesticides and fertilizers, and mitigating greenhouse gas emissions through long-term carbon (C) storage. Here we focused on aboveground C storage rates and accumulation using a one-year increment analysis applied across different winegrape varietals and different-aged vineyard blocks. This produced a chronosequence of C storage rates over what is roughly the productive lifespan of most vines (aged 2–30 years). To our knowledge, this study provides the first estimate of C storage rates in the woody biomass of vines. Additionally, we assessed C storage in wildland buffers and adjacent oak-dominated habitats over a 9-year period. Results Carbon storage averaged 6.5 Mg/Ha in vines. We found the average annual increase in woody C storage was 43% by mass. Variation correlated most strongly with vine age, where the younger the vine, the greater the relative increase in annual C. Decreases in C increment rates with vine age were more than offset by the greater overall biomass of older vines, such that C on the landscape continued to increase over the life of the vines at 18.5% per year on average. Varietal did not significantly affect storage rates or total C stored. Carbon storage averaged 81.7 Mg/Ha in native perennial buffer vegetation; we found an 11% increase in mass over 9 years for oak woodlands and savannas. Conclusions Despite a decrease in the annual rate of C accumulation as vines age, we found a net increase in aboveground C in the woody biomass of vines. The results indicate the positive role that older vines play in on-farm (vineyard) C and overall aboveground accumulation rates. Additionally, we found that the conservation of native perennial vegetation as vineyard buffers and edge habitats contributes substantially to overall C stores. We recommend that future research consider longer time horizons for increment analysis, as this should improve the precision of C accumulation rate estimates, including in belowground (i.e., soil) reservoirs.

Advances in water resources research in China has blossomed in recent decades. Although historically many Chinese scientific publications had been limited to regional, language-specific outlets, the more recent emergence of Chinese scholarship in water resources sustainability and engineering suggests that broader recognition within the scientific community is needed. In this study, we explore the recent evolution of Chinese environmental flows research through the bibliometric analysis of two prominent databases. This paper highlights trends in environmental flow publication rates, emerging areas of research, and citation networks for prominent authors in the Chinese context as compared to the global network of environmental flows research. Results indicate that China’s environmental flows research developed rapidly over the past two decades (2005–2019) and that Chinese authors are among the most productive in the field (60% of the top 20 authors). However, based on the number of citations, few Chinese articles are among the most highly cited (5 out of 800). The field of environmental flows research has identified the need to minimize parochial boundaries, and this analysis suggests a rich, yet untapped, literature to promote scientific integration. Differences in journal selection, keyword choice, and lesser-known authors to readers outside of China were identified as potential limitations to broader global integration. This approach to systematic bibliometric analysis can be used to identify prominent authors and important studies such that new ideas in environmental flows research can flow across continental boundaries, from East to West, to accelerate global understanding of prevailing trends and best practices in river management

This study investigates the ability of machine learning models to retrieve the surface soil moisture of a grassland area from multispectral remote sensing carried out using an unoccupied aircraft system (UAS). In addition to multispectral images, we use terrain attributes derived from a digital elevation model and hydrological variables of precipitation and potential evapotranspiration as covariates to predict surface soil moisture. We tested four different machine learning algorithms and interrogated the models to rank the importance of different variables and to understand their relationship with surface soil moisture. All the machine learning algorithms we tested were able to predict soil moisture with good accuracy. The boosted regression tree algorithm was marginally the best, with a mean absolute error of 3.8 % volumetric moisture content. Variable importance analysis revealed that the four most important variables were precipitation, reflectance in the red wavelengths, potential evapotranspiration, and topographic position indices (TPI). Our results demonstrate that the dynamics of soil water status across heterogeneous terrain may be adequately described and predicted by UAS remote sensing and machine learning. Our modeling approach and the variable importance and relationships we have assessed in this study should be useful for management and environmental modeling tasks where spatially explicit soil moisture information is important.

Describing the specific details and textures implicit in real-world hydro-climatic data sets is paramount for the proper description and simulation of variables such as precipitation, streamflow, and temperature time series. To this aim, a couple of decades ago, a deterministic geometric approach, the so-called fractal-multifractal (FM) method,1,2 was introduced. Such is a holistic approach capable of faithfully encoding (describing)3, simulating4, and downscaling5 hydrologic records in time, as the outcome of a fractal function illuminated by a multifractal measure. This study employs the FM method to generate ensembles of daily precipitation and temperature sets obtained from global circulation models (GCMs). Specifically, this study uses data obtained via ten GCM models, two sets of daily records, as implied from the past, over a year, and three sets projected for the future, as downscaled via localized constructed analogs (LOCA) for a couple of sites in California. The study demonstrates that faithful representations of all sets may be achieved via the FM approach, using encodings relying on 10 and 8 geometric (FM) parameters for rainfall and temperature, respectively. They result in close approximations of the data's histogram, entropy, and autocorrelation functions. By presenting a sensitivity study of FM parameters' for historical and projected data, this work concludes that the FM representations are useful for tracking and foreseeing the records' complexity6 in the past and the future and other applications in hydrology such as bias correction. References

Climate signals have been consistently changing over the past century, together with increased population and human activities. Consequently, notable shifts in timing and magnitude of floods and drought and declining surface and subsurface water storage have been seriously posing effects on water supply and demand throughout the planet. Hence, it becomes relevant to understand the optimal water allocation to different water users such as agriculture, urban, environmental, and wildlife refuge and manage water infrastructure projects accordingly to support optimal water allocation. In the past, we have shown the successful application of the statewide hydro-economic model, also known as CALVIN (California Value Integrated Network)1,2, to minimize water allocation costs and optimize water utility under the policy, operational, and environmental constraints.This study utilizes economic and water allocation output from the CALVIN model historical run (1921 to 2003; monthly scale), and it explores the opportunity cost of water storage and conveyance expansion in California (economic data based on 2050 projected water use3,4). This study performs a time series analysis on the marginal economic value of expansion to characterize the correlation between historical climatic factors with water allocation capacity extension to characterize how climate events such as droughts or floods can affect the profitability of water infrastructure expansion projects. The result provides useful information for statewide planners and decision-makers in setting coping strategies for the future under climate change conditions5,6. Additionally, this study uses the historical run expansion cost results to identify the most profitable water infrastructure expansion locations using spatial analysis. This study concentrates on agriculture and urban water demands from surface and groundwater sources and categorizes the water allocation over different water years dictated by the California Department of Water Resources (DWR). This study offers a holistic approach to elucidate responses of existing water supply-demand nexus, and the results will be useful for the Sustainable Groundwater Management Act (SGMA) of California.References

We developed machine learning models to retrieve surface soil moisture (0–4 cm) from high resolution multispectral imagery, terrain attributes, and local climate covariates. Using a small unmanned aircraft system (UAS) equipped with a multispectral sensor we captured high resolution imagery in part to create a high-resolution digital elevation model (DEM) as well as quantify relative vegetation photosynthetic status. We tested four different machine learning algorithms. The boosted regression tree algorithm provided the best accuracy model with mean absolute error of 3.8 % volumetric water content. The most important variables for the prediction of soil moisture were precipitation, reflectance in the red wavelengths, potential evapotranspiration, and topographic position indices (TPI). Our results demonstrate that the dynamics of soil water status across heterogeneous terrain may be adequately described and predicted by UAS remote sensing data and machine learning. Our modeling approach and the variable importance and relationships we have assessed in this study should be useful for management and environmental modeling tasks where spatially explicit soil moisture information is important.

We developed machine learning models to retrieve surface soil moisture (0–4 cm) from high resolution multispectral imagery using terrain attributes and local climate covariates. Using a small unmanned aircraft system (UAS) equipped with a multispectral sensor we captured high resolution imagery in part to create a high-resolution digital elevation model (DEM) as well as quantify relative vegetation photosynthetic status. We tested four different machine learning algorithms. The boosted regression tree algorithm gave the best prediction with mean absolute error of 3.8 % volumetric water content. The most important variables for the prediction of soil moisture were precipitation, reflectance in the red wavelengths, potential evapotranspiration, and topographic position indices (TPI). Our results demonstrate that the dynamics of soil water status across heterogeneous terrain may be adequately described and predicted by UAS remote sensing data and machine learning. Our modeling approach and the variable importance and relationships we have assessed in this study should be useful for management and environmental modeling tasks where spatially explicit soil moisture information is important.

Active floodplains can putatively store large amounts of organic carbon (SOC) in subsoils originating from catchment erosion processes with subsequent floodplain deposition. Our study focussed on the assessment of SOC pools associated with alluvial floodplain soils that are affected by human-induced changes in floodplain deposition and in situ SOC mineralisation due to land use change and drainage. We evaluated depth-dependent SOC contents based on 23 soil cores down to 3 m and 10 drillings down to 7 m in a floodplain area of the lower Cosumnes River. An estimate of 266 Mg C ha-1 or about 59% of the entire SOC stored within the 7 m profiles was found in the upper 2 m. Most profiles (n = 25) contained discrete buried A horizons at depths of approximately 0.8 m. These profiles had up to 130% higher SOC stocks. The mean δ13C of all deep soil profiles clearly indicated that arable land use has already altered the stable isotopic signature in the first meter of the profile. Radiocarbon dating showed that the 14C age in the buried horizon was younger than in overlaying soils indicating a substantial sedimentation phase for the overlaying soils. An additional analysis of total mercury contents in the soil profiles indicated that this sedimentation was associated with upstream hydraulic gold mining after the 1850s. In summary, deep alluvial soils in floodplains store large amounts of SOC not yet accounted for in global carbon models. Historic data give evidence that large amounts of sediment were transported into the floodplains of most rivers of the Central Valley and deposited over organically rich topsoil, which promoted the stabilization of SOC, and needs to be considered to improve our understanding of the human-induced interference with C cycling.

Conservation scientists have adapted conservation planning principles designed for protection of habitats ranging from terrestrial to freshwater ecosystems. We applied current approaches in...

Water desalination is a proposed solution for mitigating the effects of drought, soil salinization, and the ecological impacts of agricultural drainage. In this study, we assess the public and private costs and benefits of distributed desalination in the Central Valley (CV) of California. We employ environmental and economic modeling to estimate the value of reducing the salinity of irrigation water; the value of augmenting water supply under present and future climate scenarios; and the human health, environmental, and climate change damages associated with generating power to desalinate water. We find that water desalination is only likely to be profitable in 4% of the CV during periods of severe drought, and that current costs would need to decrease by 70–90% for adoption to occur on the median acre. Fossil-fuel powered desalination technologies also generate air emissions that impose significant public costs in the form of human health and climate change damages, although these damages vary greatly depending on technology. The ecosystem service benefits of reduced agricultural drainage would need to be valued between $800 and $1200 per acre-foot, or nearly the full capital and operational costs of water desalination, for the net benefits of water desalination to be positive from a societal perspective.

Soils account for the largest terrestrial pool of carbon and have the potential for even greater quantities of carbon sequestration. Typical soil carbon (C) stocks used in global carbon models only account for the upper 1 meter of soil. Previously unaccounted for deep carbon pools (>1 m) were generally considered to provide a negligible input to total C contents and represent less dynamic C pools. Here we assess deep soil C pools associated with an alluvial floodplain ecosystem transitioning from agricultural production to restoration of native vegetation. We analyzed the soil organic carbon (SOC) concentrations of 87 surface soil samples (0–15 cm) and 23 subsurface boreholes (0–3 m). We evaluated the quantitative importance of the burial process in the sequestration of subsurface C and found our subsurface soils (0–3 m) contained considerably more C than typical C stocks of 0–1 m. This deep unaccounted soil C could have considerable implications for global C accounting. We compared differences in surface soil C related to vegetation and land use history and determined that flooding restoration could promote greater C accumulation in surface soils. We conclude deep floodplain soils may store substantial quantities of C and floodplain restoration should promote active C sequestration.

The transport of water and sediment from rivers to adjacent floodplains helps generate complex floodplain, wetland, and riparian ecosystems. However, riverside levees restrict lateral connectivity of water and sediment during flood pulses, making the re-introduction of floodplain hydrogeomorphic processes through intentional levee breaching and removal an emerging floodplain restoration practice. Repeated topographic observations from levee breach sites along the lower Cosumnes River (USA) indicated that breach architecture influences floodplain and channel hydrogeomorphic processes. Where narrow breaches ( 250 m) enabled multiple modes of water and sediment transport onto graded floodplains. Advective sediment transport along multiple flow paths generated overlapping crevasse splays, while turbulent diffusion promoted the formation of lateral levees through large wood and sediment accumulation in near-bank areas. Channel incision (>2 m) upstream from a wide levee breach suggests that large flow diversions through such breaches can generate water surface drawdown during flooding, resulting in localized flow acceleration and upstream channel incision. Understanding variable hydrogeomorphic responses to levee breach architecture will help restoration managers design breaches that maximize desired floodplain topographic change while also minimizing potential undesirable consequences such as levee breach closure or channel incision.

Groundwater is a common pool resource that supports agriculture, human communities, and the environment. Public participation in common pool natural resources management can be affected by media representation of stakeholders and perceptions of identity as a stakeholder. Newspaper media has an outsized influence on framing subject matter, expertise, organizations, and who should participate. Media shapes individual, local, and regional perspectives around resource management and defines potential solutions to natural resources management. This study analyzes media coverage about California's new Sustainable Groundwater Management Act (SGMA) to understand impacts on public participation in common pool natural resources management and to identify represented stakeholders and solutions involved in groundwater sustainability. A total of 365 newspaper articles were collected from California newspapers in three readership locations. We also searched for representation of SGMA in Spanish-language publications. Article characteristics were analyzed through qualitative content analysis and quantitative nonparametric analysis. Results indicate bias for featuring agricultural industry, politician, and water managers' voices. Solutions for managing water resources were focused on new supply, demand reduction and infrastructure investment, though novel solutions were also presented. Most newspaper articles included few stakeholders and solutions, illustrating isolated, short narratives about a common pool resource. The trends and gaps in representation in California media coverage may contribute to the public's low levels of engagement in groundwater planning.

Assessing the hydrologic processes over scales ranging from single wetland to regional is critical to understand the hydrologically-driven ecosystem services especially nutrient buffering of wetlands. Here, we present a novel approach to quantify the multiscale hydrologic regulation of multipond systems (MPSs), a common type of small, scattered wetland in humid agricultural regions, because previous studies have stopped in commending the catchment scale flood and drought resilience of these waters, and contemporary models do not adequately represent the corresponding intra-catchment fill-spill relationships. A new version of Soil and Water Assessment Tool (SWAT) was developed to incorporate improved representation of: (1) perennial or intermittent spillage connections of pond-to-pond and pond-to-stream, and (2) bidirectional exchange between pond surface water and shallow groundwater. We present SWAT-MPS, which adopts rule-based artificial intelligence to model the possibilities of different spillage directions and GA-based parameter optimization over the two simulation years (June 2017 to May 2019), with successfully replicated streamflow and pond water-level variations in a 4.8 km2 test catchment, southern China. Water balance analysis and scenario simulations were then executed to assess the hydrologic regulation at single pond, single MPS, and entire catchment scales. Results revealed (1) the presence of 9 series- or series-parallel connected MPSs, in which pond overflow accounted for as much as 59% of the catchment water yield; (2) seasonally- and MPS-independent baseflow support and quickflow attenuation, with ranked level of pond water storage for baseflow support across different landuse types: forest > farm > village, and inversed correlation of pond spillage to baseflow and quickflow variations in the farmland; and (3) MPS-aggregated catchment flood peak reduction (>20%) and baseflow increment (26%) in the following dry days. Meteorological data analysis and simulated average daily values indicated these hydrologic patterns are credible even if extending to a 5-year period. As a first modelling attempt to explore the intra-catchment details of MPSs, our study underscores the water storage and connectivity in their hydrologic regulation, and suggests inventories, long-term field monitoring, and several research directions of the new model for integrated pond management in watersheds and river basins. These findings can inform refined assessment of similar small, scattered wetlands elsewhere, where restoration efforts are required.

To study the automation of plant-level precision irrigation, specifically learning-based irrigation controllers, we present a modular, open-source testbed that enables real-time, fine-grained data collection and irrigation actuation. RAPID-MOLT costs USD $600 and has floor space of 0.37m2. The functionality of the platform is evaluated by measuring the correlation between plant growth (Leaf Area Index) and water stress (Crop Water Stress Index) with irrigation volume. In line with biological studies, the observed plant growth is positively correlated with irrigation volume while water stress is negatively correlated. Construction directions, experimental data, CAD models, and related software are available at github.com/BerkeleyAutomation/RAPID-MOLT.

The Mediterranean climate region of central Chile is rich in biodiversity and contains highly productive agricultural lands, which creates challenges for the preservation of natural habitats and native biodiversity. Ecological data and studies for the region are also limited, making informed conservation in agricultural landscapes difficult. The increasing availability of remotely sensed data provide opportunities to relate species occurrences to measures of landscape heterogeneity even when field measures of habitat structure are lacking. When working with such remotely sensed data, it’s important to select appropriate measures of heterogeneity, including common metrics of landscape composition as well as frequently overlooked shape metrics. In this contribution we combine bird surveys with multispectral satellite imagery to develop boosted regression tree models of avian species richness, and of habitat use for 15 species across a mixed vineyard-matorral landscape in central Chile. We found a range of associations between individual species and land cover types, with the majority of species occurring most frequently in remnant habitats and ecotones rather than the interiors of large vineyard blocks. Models identified both metrics of landscape composition and patch shape as being important predictors of species occurrence, suggesting that shape metrics can complement more commonly used metrics of landscape composition. Vineyards that include corridors or islands of remnant habitat among vine blocks may increase the amount of area available to many species, although some species may still require large tracts of intact natural habitat to persist.

The agro-ecosystem is under enormous pressure due to rapid population growth, increasing global food demand, increasing fresh water withdrawals and energy consumption, excessive food waste, inefficient use of agrochemicals, environmental degradation and climate change. Nanotechnology offers opportunities to make food production more sustainable by providing better sensors for monitoring physical, chemical, or biological properties and processes; technologies for controlling pathogens to increase food safety and minimize food waste; improved membranes and sorbents for distributed water treatment and resource recovery; novel materials for timed and targeted delivery of agrochemicals; and, new materials for monitoring and improving animal health. This tutorial review provides an overview of the nanotechnology opportunities of greatest potential determined through an NSF-funded interdisciplinary workshop of ∼50 experts from the U.S. and the EU in the areas of nanotechnology, energy, water, agriculture, systems engineering, data integration and analysis, and social science. This paper also presents examples of selected specific opportunities and the remaining scientific and engineering challenges that must be overcome to realize the benefits of nanotechnology across the farm to fork continuum.

Population growth and increasing water-use pressures threaten California's freshwater ecosystems and have led many native fishes to the brink of extinction. To guide fish conservation efforts, we provide the first systematic prioritization of river catchments and identify those that disproportionately contribute to fish taxonomic diversity. Using high-resolution range maps of exceptional quality, we also assess the representation of fish taxa within the state's protected areas and examine the concordance of high-priority catchments with existing reserves and among distinct taxonomic groups. Although most of the state's native fishes are found within protected areas, only a small proportion of their ranges are represented. Few high-priority catchments occur within protected areas, suggesting that fish conservation will require active management and targeted river restoration outside of reserves. These results provide the foundation for systematic freshwater conservation planning in California and for prioritizing where limited resources are allocated for fish recovery and protection.

We assessed the potential value of hydrologic forecasting improvements for a snow-dominated high-elevation hydropower system in the Sierra Nevada of California, using a hydropower optimization model. To mimic different forecasting skill levels for inflow time series, rest-of-year inflows from regression-based forecasts were blended in different proportions with representative inflows from a spatially distributed hydrologic model. The statistical approach mimics the simpler, historical forecasting approach that is still widely used. Revenue was calculated using historical electricity prices, with perfect price foresight assumed. With current infrastructure and operations, perfect hydrologic forecasts increased annual hydropower revenue by $0.14 to $1.6 million, with lower values in dry years and higher values in wet years, or about $0.8 million (1.2%) on average, representing overall willingness-to-pay for perfect information. A second sensitivity analysis found a wider range of annual revenue gain or loss using different skill levels in snow measurement in the regression-based forecast, mimicking expected declines in skill as the climate warms and historical snow measurements no longer represent current conditions. The value of perfect forecasts was insensitive to storage capacity for small and large reservoirs, relative to average inflow, and modestly sensitive to storage capacity with medium (current) reservoir storage. The value of forecasts was highly sensitive to powerhouse capacity, particularly for the range of capacities in the northern Sierra Nevada. The approach can be extended to multireservoir, multipurpose systems to help guide investments in forecasting.

Recent advances in unmanned aerial vehicles suggest that collecting aerial agricultural images can be cost-efficient, which can subsequently support automated precision irrigation. To study the potential for machine learning to learn local soil moisture conditions directly from such images, we developed a very fast, linear discrete-time simulation of plant growth based on the Richards equation. We use the simulator to generate large datasets of synthetic aerial images of a vineyard with known moisture conditions and then compare seven methods for inferring moisture conditions from images, in which the “uncorrelated plant” methods look at individual plants and the “correlated field” methods look at the entire vineyard: 1) constant prediction baseline, 2) linear Support Vector Machines (SVM), 3) Random Forests Uncorrelated Plant (RFUP), 4) Random Forests Correlated Field (RFCF), 5) two-layer Neural Networks (NN), 6) Deep Convolutional Neural Networks Uncorrelated Plant (CNNUP), and 7) Deep Convolutional Neural Networks Correlated Field (CNNCF). Experiments on held-out test images show that a globally-connected CNN performs best with normalized mean absolute error of 3.4%. Sensitivity experiments suggest that learned global CNNs are robust to injected noise in both the simulator and generated images as well as in the size of the training sets. In simulation, we compare the agricultural standard of flood irrigation to a proportional precision irrigation controller using the output of the global CNN and find that the latter can reduce water consumption by up to 52% and is also robust to errors in irrigation level, location, and timing. The first-order plant simulator and datasets are available at https://github.com/BerkeleyAutomation/RAPID.