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Urban groundwater supply planning is often achieved at the regional scale while the effects of aquifer exploitation are experienced on a local scale. In the Mexico City metropolitan area, land and water use changes have caused massive overdraft and subsidence, threatening water supply in marginalized communities. While spatially distributed groundwater models have been developed to test model uncertainty and policy interventions, there is little understanding of how regional aggregation of planning objectives can exacerbate existing social inequities. This study develops socially informed spatial analysis to evaluate differential impacts of groundwater pumping policies at multiple scales. We compare four planning objectives at three spatial resolutions to determine if policy preference shows a relationship with socioeconomic indicators. We find that the multiobjective performance of pumping policies can neglect marginalized communities according to multiple indicators. Further, clustering model grid cells according to socioeconomic indicators allows for more accurate analysis of this effect. Finally, multiscale analysis of policy preference considering subregional socioeconomic characteristics can aid in equitable selection of aquifer management alternatives. [ABSTRACT FROM AUTHOR]

Dynamic planning of water infrastructure requires identifying signals for adaptation, including measures of system performance linked to vulnerabilities. However, it remains a challenge to detect projected changes in performance outside the envelope of natural variability, and to identify whether such detections can be attributed to one or more uncertain drivers. This study investigated these questions using a combination of ensemble simulation, nonparametric tests, and variance decomposition, which were demonstrated for a case study of the Sacramento–San Joaquin River Basin, California. We trained a logistic regression classifier to predict future detections given observed trends in performance over time. The scenario ensemble includes coupled climate and land-use change through the end of the century, evaluated using a multireservoir simulation model to determine changes in water supply reliability and flooding metrics relative to the historical period (1951–2000). The results show that the reliability metric is far more likely to exhibit a significant change within the century, with the most severe scenarios tending to be detected earlier, reflecting long-term trends. Changes in flooding often are not detected due to natural variability despite severe events in some scenarios. We found that the variance in detection times is attributable largely to the choice of climate model, and also to the emissions scenario and its interaction with the choice of climate model. Finally, in the prediction model for both cases, reliability and flooding, the model learns to associate more-recent observations of system performance with nonstationarity detection. These findings underscore the importance of differentiating between long-term change and natural variability in identifying signals for adaptation. [ABSTRACT FROM AUTHOR]

The recently launched California water price index (NASDAQ: NQH2O) and corresponding futures market provide an opportunity for water users to hedge against seasonal drought risk. While the volume of futures trading remains low, the index can be analyzed as a spatially aggregated price of physical water trades that responds to hydrology and management. This study investigates the extent to which the NQH2O index can be predicted from a combination of reservoir storage anomalies and inflow forecasts throughout the state. Over the available record (November 2013–June 2023), the daily hydrologic time-series are reduced to a set of principal components, which are shown to be nonlinearly correlated with the current and season-ahead price index. The PCs are then used as features in an exponential regression to predict the forward six-month average price. The most accurate model in cross-validation performs with R2=0.81 using four PCs that contain 85% of the total variance in the features. Predictions generally fall within $100/AF of the observed value, with larger errors associated with hydrologic forecast uncertainty during the winter. A threshold-based hedging strategy is developed to analyze the potential cost savings of buying during the winter (November–March) when the forecasted six-month average price is greater than the current index. This strategy shows an average cost reduction of 17% compared with the six-month average price, with reductions above 35% at the onset of dry water years. This study contributes a statistical modeling approach to support the development of more advanced hedging strategies in water portfolios. [ABSTRACT FROM AUTHOR]

Highlights • We extract typical behaviors of water reservoir operators from observational data. • We cluster the reservoir operators based on their behavioral profile. • We discover behavioral profiles that are vulnerable to drought conditions. Abstract The large number of dammed rivers worldwide emphasizes the need to couple models of natural processes with models describing human behaviors. However, such behavioral models are often simplistic and lack proper validation against observational data. In this work, we contribute a new approach to infer the typical operations of water reservoirs from historical observations, using data-driven behavioral modeling based on eigenbehavior analysis. The approach is demonstrated using monthly storage data from 172 reservoirs in California, USA. Results show that the proposed method identifies four typical behavioral profiles, which are strongly linked to key features of the reservoirs. Moreover, we show how the identified models can be used for discovering behavioral profiles, and associated reservoir characteristics, that are vulnerable to drought conditions. [ABSTRACT FROM AUTHOR]

We show that the classical results on the existence and uniqueness of moment maps in symplectic geometry generalize directly to weak homotopy moment maps in multisymplectic geometry. In particular, we show that their existence and uniqueness is governed by a Lie algebra cohomology complex which reduces to the Chevalley–Eilenberg complex in the symplectic setup. [ABSTRACT FROM AUTHOR]

The uncertainty of a changing climate raises challenges for water infrastructure planning and design. Not accounting for nonstationarity may result in under-designed structures that fail too frequently, or over-designed structures that are economically inefficient. This concern is magnified by uncertainty in the long-term frequency and magnitude of future extreme events. Planning strategies that allow adaptations over a structure's life could improve both reliability and economic efficiency. This study develops a method to inform adaptive water infrastructure planning with uncertain hydrologic and other forms of nonstationarity, applied to levee system planning. A stochastic dynamic programming model including a Markov process is developed for infrastructure planning with uncertain nonstationarity in flood frequency. Bayes' theorem is used to update peak flow probabilities conditioned on observed past peak flows and to update expected residual flood damages over time. A levee system planning problem with a numerical example from California illustrates the approach to derive optimal levee heights over time, and economic values of adapting to uncertain nonstationary flood risk. The projected range of probabilistic hydrology scenarios affects the optimal results, particularly in later planning stages as hydrology scenarios diverge with time. Adaptive planning strategies allowing more levee upgrades over time slightly lowers the overall cost and provides better flood protection than one-time construction under nonstationary hydrology for any climate in the example. Compared to a known future nonstationary hydrology, incorporating uncertain nonstationary climate results in higher levees being planned for observed severe hydrology scenarios in later stages. The overall present value cost with uncertain nonstationary climate depends on rates of change in peak flow distribution parameters in future hydrology scenarios. [ABSTRACT FROM AUTHOR]

We extend Noether's theorem to the setting of multisymplectic geometry by exhibiting a correspondence between conserved quantities and continuous symmetries on a multi-Hamiltonian system. We show that a homotopy co-momentum map interacts with this correspondence in a way analogous to the moment map in symplectic geometry. We apply our results to generalize the theory of the classical momentum and position functions from the phase space of a given physical system to the multisymplectic phase space. We also apply our results to manifolds with a torsion-free G 2 structure. [ABSTRACT FROM AUTHOR]

Water resources systems face irreducible uncertainty in supply and demand, requiring policies to respond to changing conditions on multiple timescales. For both short-term operation and long-term adaptation, thresholds or “decision triggers”, where a policy links observed indicators to actions, have featured prominently in recent studies. There remains a need for a general method to conceptualize threshold-based policies in an easily interpretable structure, and a corresponding search algorithm to design them. Here we propose a conceptual and computational framework where policies are formulated as binary trees, using a simulation-optimization approach. Folsom Reservoir, California serves as an illustrative case study, where policies define the thresholds triggering flood control and conservation actions. Candidate operating rules are generated across an ensemble of climate scenarios, incorporating indicator variables describing longer-term climate shifts to investigate opportunities for adaptation. Policy tree optimization and corresponding open-source software provide a generalizable, interpretable approach to policy design under uncertainty. [ABSTRACT FROM AUTHOR]

This article casts a contemporary scholarly eye on the Jewish icon Martin Buber's lifelong engagements with Chinese religious thought, which were surprisingly extensive, but have remained relatively obscure in both Sinological and Buberian intellectual circles. I argue here that Buber's Chinese studies can be divided into three distinct phases--romantic, dialogical, and Israeli--which each provide insight into Buber's broader intellectual narrative and demonstrate extended moments of Buber's cultural border-crossing. I also offer an implicit challenge to normative presentations of Chinese traditions. [ABSTRACT FROM AUTHOR]

Population genetic strategies that leverage association, selection, and linkage have identified drug-resistant loci. However, challenges and limitations persist in identifying drug-resistance loci in malaria. In this review we discuss the genetic basis of drug resistance and the use of genome-wide association studies, complemented by selection and linkage studies, to identify and understand mechanisms of drug resistance and response. We also discuss the implications of nongenetic mechanisms of drug resistance recently reported in the literature, and present models of the interplay between nongenetic and genetic processes that contribute to the emergence of drug resistance. Throughout, we examine artemisinin resistance as an example to emphasize challenges in identifying phenotypes suitable for population genetic studies as well as complications due to multiple-factor drug resistance. [ABSTRACT FROM AUTHOR]

Exploratory simulation allows analysts to discover scenarios in which existing or planned water supplies may fail to meet stakeholder objectives. These robustness assessments rely heavily on the choice of plausible future scenarios, which, in the case of drought management, requires sampling or generating a streamflow ensemble that extends beyond the historical record. This study develops a method to modify synthetic streamflow generators by increasing the frequency and severity of droughts for the purpose of exploratory modeling. To support management decisions, these synthetic droughts can be related to recent observed droughts of consequence for regional stakeholders. The method approximately preserves the spatial and temporal correlation of historical streamflow in drought-adjusted scenarios. The approach is demonstrated in a bottom-up planning context using an urban water portfolio design problem in North Carolina, a region whose water supply faces both climate and population pressures. Synthetic scenarios are used to simulate the implications for reliability and cost if events with similar severity to the recent 2007–2008 drought become more frequent under climate change, and in general, the system-level consequences of increasingly frequent and/or severe droughts. Finally, synthetically generated drought extremes are compared with runoff projections derived from downscaled climate model output, serving to support bottom-up robustness methods in water systems planning. [ABSTRACT FROM AUTHOR]

This study introduces a new open source software framework to support bottom-up environmental systems planning under deep uncertainty with a focus on many-objective robust decision making (MORDM), called OpenMORDM. OpenMORDM contains two complementary components: (1) a software application programming interface (API) for connecting planning models to computational exploration tools for many-objective optimization and sensitivity-based discovery of critical deeply uncertain factors; and (2) a web-based visualization toolkit for exploring high-dimensional datasets to better understand system trade-offs, vulnerabilities, and dependencies. We demonstrate the OpenMORDM framework on a challenging environmental management test case termed the “lake problem”. The lake problem has been used extensively in the prior environmental decision science literature and, in this study, captures the challenges posed by conflicting economic and environmental objectives, a water quality “tipping point” beyond which the lake may become irreversibly polluted, and multiple deeply uncertain factors that may undermine the robustness of pollution management policies. The OpenMORDM software framework enables decision makers to identify policy-relevant scenarios, quantify the trade-offs between alternative strategies in different scenarios, flexibly explore alternative definitions of robustness, and identify key system factors that should be monitored as triggers for future actions or additional planning. The web-based OpenMORDM visualization toolkit allows decision makers to easily share and visualize their datasets, with the option for analysts to extend the framework with customized scripts in the R programming language. OpenMORDM provides a platform for constructive decision support, allowing analysts and decision makers to interactively discover promising alternatives and potential vulnerabilities while balancing conflicting objectives. [ABSTRACT FROM AUTHOR]

Water systems planners have long recognized the need for robust solutions capable of withstanding deviations from the conditions for which they were designed. Robustness analyses have shifted from expected utility to exploratory bottom-up approaches which identify vulnerable scenarios prior to assigning likelihoods. Examples include Robust Decision Making (RDM), Decision Scaling, Info-Gap, and Many-Objective Robust Decision Making (MORDM). We propose a taxonomy of robustness frameworks to compare and contrast these approaches based on their methods of (1) alternative generation, (2) sampling of states of the world, (3) quantification of robustness measures, and (4) sensitivity analysis to identify important uncertainties. Building from the proposed taxonomy, we use a regional urban water supply case study in the Research Triangle region of North Carolina to illustrate the decision-relevant consequences that emerge from each of these choices. Results indicate that the methodological choices in the taxonomy lead to the selection of substantially different planning alternatives, underscoring the importance of an informed definition of robustness. Moreover, the results show that some commonly employed methodological choices and definitions of robustness can have undesired consequences when ranking decision alternatives. For the demonstrated test case, recommendations for overcoming these issues include: (1) decision alternatives should be searched rather than prespecified, (2) dominant uncertainties should be discovered through sensitivity analysis rather than assumed, and (3) a carefully elicited multivariate satisficing measure of robustness allows stakeholders to achieve their problem-specific performance requirements. This work emphasizes the importance of an informed problem formulation for systems facing challenging performance tradeoffs and provides a common vocabulary to link the robustness frameworks widely used in the field of water systems planning. [ABSTRACT FROM AUTHOR]

In this response to Knut Melvær's discussion of "The Spiritual Illusion: Constructive Steps Toward Rectification and Redescription," I clarify some of the theoretical and methodological background that informed the original article, redress places where Melvær appears to have misread or miscast my positions, and offer some suggestions as to what may really be most at stake in this "spiritual" dispute. [ABSTRACT FROM AUTHOR]

This paper presents a mission analysis comparison of human missions to asteroids using two distinct architectures. The objective is to determine if either architecture can reduce launch mass with respect to the other, while not sacrificing other performance metrics such as mission duration. One architecture relies on chemical propulsion, the traditional workhorse of space exploration. The second combines chemical and electric propulsion into a hybrid architecture that attempts to utilize the strengths of each, namely the short flight times of chemical propulsion and the propellant efficiency of electric propulsion. The architectures are thoroughly detailed, and accessibility of the known asteroid population is determined for both. The most accessible asteroids are discussed in detail. Aspects such as mission abort scenarios and vehicle reusability are also discussed. Ultimately, it is determined that launch mass can be greatly reduced with the hybrid architecture, without a notable increase in mission duration. This demonstrates that significant performance improvements can be introduced to the next step of human space exploration with realistic electric propulsion system capabilities. This leads to immediate cost savings for human exploration and simultaneously opens a path of technology development that leads to technologies enabling access to even further destinations in the future. [ABSTRACT FROM AUTHOR]

This article takes constructive steps toward redescribing an ernie folk category that enjoys widespread use in contemporary Western discourse, but has proven problem-atic for the academic study of religion. On close examination, the term "spirituality" is demonstrated to be vague, essentialist, and narcissistic, though consistent enough in its usage to reveal a cluster of specific associations, assumptions, and normative judg-ments. Moreover, the purported dichotomy between "spirituality" and "religion" can be rendered more intelligible through an ironic and imaginative juxtaposition with the contrast between male and female reproduction organs, with regard to physical appearance and location, social dynamics, and ethical implications. [ABSTRACT FROM AUTHOR]

One of three short companion essays to Terry Shoemaker's "World Religion and Fake News: A Pedagogical Response in an Age of Post‐Truth," published in this issue of the journal. [ABSTRACT FROM AUTHOR]

Explores the mystical dimensions of the Neo-Confucianism works of Chinese philosopher Chu Hsi. Identification of the human heart and mind with both the heart-mind of heaven and earth and the heart-mind of the sage; Resistance of writing to purely analytical scrutiny; Emphasis on the primacy of rootedness and social responsibility.

Mountain regions in arid and semi-arid climates, such as California, are considered particularly sensitive to climate change because global warming is expected to alter snowpack storage and related surface water supply. It is therefore important to accurately capture snowmelt processes in watershed models for climate change impact assessment. In this study we use the Soil and Water Assessment Tool (SWAT) to estimate projected changes in snowpack and streamflow in four alpine tributaries to the agriculturally important but less studied southern Central Valley, California. Watershed responses are evaluated for four CMIP5 climate models (HadGEM_ES, CNRM-CM5, CanESM2 and MIROC5) and two emission scenarios (RCP 4.5 and RCP 8.5) for 2020–2099. SWAT models are calibrated following a dual-objective, lumped calibration approach with an automatic calibration against observed streamflow (stage 1) and a manual calibration against reconstructed Parallel Energy Balance (ParBal) snow water equivalent (SWE) data (stage 2). Results indicate that under a warming climate, peak streamflow is expected to increase 0.5–4 times in magnitude in the coming decades and to arrive 2–4 months earlier in the year because of earlier snowmelt. In the foreseeable future, snow cover will reduce gradually in the lower elevations and diminish at higher rates at higher elevation towards the end of the 21st century. Surface water supply is predicted to increase in the southern Central Valley under the evaluated scenarios but increased temporal variability (wetter wet seasons and drier dry seasons) will create new challenges for managing supply. The study further highlights that the use of remote sensing based, reconstructed SWE data could fill the current gap of limited in-situ SWE observations to improve the snow calibration of SWAT to better predict climate change impacts in semi-arid, snow-dominated watersheds. Unlabelled Image • SWAT models are calibrated with both streamflow and ParBal SWE reference data. • Four climate models (HadGEM_ES, CNRM-CM5, CanESM2 and MIROC5) and two RCPs were compared. • Rainfall-runoff season is expected to peak 2–4 months earlier under the warming climate. • High flows are expected to be 3–8 times of historic flows in the future. • Snow diminishes quicker at high elevations under the RCP 8.5 scenario. [ABSTRACT FROM AUTHOR]

Reservoirs balance multiple conflicting objectives, including flood control and water supply. In California, shifts in seasonal hydrologic patterns under climate change will amplify the difficulties in balancing flood control with water supply. Current flood control policies are based on fixed seasonal rule curves determined by the observed timing and magnitude of floods in the record. These rule curves generally require the release of wet season inflows, reducing the available stored water for use during the dry season. Here we investigate the potential for forecast-informed reservoir operations (FIRO) to increase water supply availability while minimizing additional flood risk at 14 reservoirs in the Sacramento, San Joaquin, and Tulare river basins. We use a differential evolution algorithm to train risk-based reservoir operation policies with an ensemble of historical forecasts over the period 2013–2023. Results show an average 8.1% increase in storage normalized by capacity, though this varies across reservoirs. The forecast-informed policies also reduce the occurrence of high-magnitude releases throughout the system. The accumulation of benefits is sensitive to the timing and magnitude of flood events, and most of the cumulative benefit is obtained during a few years. Under cross-validation, we find that large floods are needed in the training data to avoid overfitting the policy. We further examine the relationship between reservoir properties and FIRO benefits, finding that the ratio of peak inflow magnitude to maximum safe release correlates with increased storage under the FIRO policy, while the ratio of mean inflow to capacity correlates to the reduction of high-magnitude releases. This study highlights how adaptive reservoir management policies can yield water supply benefits without an increase in flood risk, given adequate historical data for policy training. These policies may be a valuable adaptation to climate change but require careful validation and out-of-sample testing. [ABSTRACT FROM AUTHOR]

The adaptation of water resources infrastructure and operations to climate change can be framed as a control problem in which actions are chosen dynamically in response to observations and projections. Recent studies have developed adaptive policies based on tipping points or "signposts", which are threshold values of observed variables that indicate a future vulnerability and/or trigger a change in policy. Here we present a related approach, policy tree optimization, as a heuristic policy search method for control problems with discrete actions. Adaptive policies are represented as binary trees, making them highly interpretable, and they can be trained without predefining the tree structure. We demonstrate this method for an illustrative planning problem focused on reservoir expansion under climate change. In the process, a number of significant challenges are identified related to long-term uncertainty in ensemble streamflow projections, namely: (1) the combined roles of sample uncertainty and ensemble uncertainty in the estimation of extreme events at multiple timescales; (2) reduced performance in out-of-sample hydrologic scenarios, underscoring the need for cross-validation of optimized policies; and (3) the irreversibility of infrastructure decisions, which leads to a risk of incorrectly identifying or failing to identify a vulnerable scenario (false positives and false negatives, respectively). These challenges apply generally to any control method used for climate adaptation, and also apply to related application areas such as sea level rise. While this study demonstrates the policy tree optimization framework, the choice of optimization method is arguably less important than the choice of forcing scenarios in which policies are trained. The presentation will conclude with opportunities going forward to reconcile infrastructure and operations planning with long-term streamflow ensembles, which remain the best available projections of the future despite their severe uncertainty. [ABSTRACT FROM AUTHOR]

Forecasts and observations of hydrologic and climatic states have significant potential to improve water supply and flood control operations in multi-purpose reservoirs, which largely lack formal rules describing how to incorporate this dynamic information. This study develops reservoir control policies trained on a broad set of forecasts and observations that span short-term (1-3) and medium-range (1-15 day) timescales, including: an ensemble of synthetic short-term precipitation and temperature forecasts based on state-of-the-art dynamical hindcast products; hydrologic response, based on a rainfall-runoff model; and binary forecasts of regional storm occurrence to compensate for potential spatial bias. A simulation-optimization approach is used to train reservoir policies structured as trees, which are highly interpretable and provide insight into the value of information provided by different sources of observed and forecasted data. The approach is demonstrated on a case study of Folsom Reservoir in California, using a simulation model of reservoir operations combined with a simplified groundwater model to explore the potential for conjunctive use to improve the utility of forecasts. Results show that forecasts and current-state observations need only indicate the potential occurrence of a storm, rather than its intensity, to effectively inform management decisions in regions like California where wintertime, synoptic-scale atmospheric rivers dominate the flood regime. Further, increased conjunctive use capacity reduces the importance of forecast skill, because surface storage can be moved to groundwater during the flood season without sacrificing future water supply. Finally, the policy trees trained with this approach are validated against an out-of-sample hydrologic period to ensure their robustness. This analysis serves to better link hydroclimatic observations and predictions with water resources management decisions to improve the tradeoff between water supply and flood control objectives in large, multi-purpose reservoirs in California and other regions with similar flood hydroclimatology. [ABSTRACT FROM AUTHOR]

BACKGROUND. Sanaria PfSPZ Vaccine, composed of attenuated Plasmodium falciparum (Pf) sporozoites (SPZ), protects against malaria. We conducted this clinical trial to assess the safety and efficacy of PfSPZ Vaccine in HIV-positive (HIV+) individuals, since the HIV-infection status of participants in mass vaccination programs may be unknown. METHODS. This randomized, double-blind, placebo-controlled trial enrolled 18- to 45-year-old HIV-negative (HIV-) and wellcontrolled HIV+ Tanzanians (HIV viral load <40 copies/mL, CD4 counts >500 cells/μL). Participants received 5 doses of PfSPZ Vaccine or normal saline (NS) over 28 days, followed by controlled human malaria infection (CHMI) 3 weeks later. RESULTS. There were no solicited adverse events in the 9 HIV- and 12 HIV+ participants. After CHMI, 6 of 6 NS controls, 1 of 5 HIV- vaccinees, and 4 of 4 HIV+ vaccinees were Pf positive by quantitative PCR (qPCR). After immunization, anti-Pf circumsporozoite protein (anti-PfCSP) (isotype and IgG subclass) and anti-PfSPZ antibodies, anti-PfSPZ CD4+ T cell responses, and Vδ2+ γδ CD3+ T cells were nonsignificantly higher in HIV- than in HIV+ vaccinees. Sera from HIV- vaccinees had significantly higher inhibition of PfSPZ invasion of hepatocytes in vitro and antibody-dependent complement deposition (ADCD) and Fcγ3B binding by anti-PfCSP and ADCD by anti-cell-traversal protein for ookinetes and SPZ (anti-PfCelTOS) antibodies. CONCLUSIONS. PfSPZ Vaccine was safe and well tolerated in HIV+ vaccinees, but not protective. Vaccine efficacy was 80% in HIV- vaccinees (P = 0.012), whose sera had significantly higher inhibition of PfSPZ invasion of hepatocytes and enrichment of multifunctional PfCSP antibodies. A more potent PfSPZ vaccine or regimen is needed to protect those living with HIV against Pf infection in Africa. [ABSTRACT FROM AUTHOR]

Long-term snowpack decline is among the best-understood impacts of climate change on water resources systems. This trend has been observed for decades and is projected to continue even in climate projections in which total runoff volumes do not change significantly. For basins in which snowpack has historically provided intra-annual water storage, snowpack decline creates several issues that may require adaptation to infrastructure, operations, or both. This study develops an approach to analyze vulnerabilities and adaptations specifically focused on the challenge of snowpack decline, using the northern California reservoir system as a case study. We first introduce an open-source daily time-step simulation model of this system, which is validated against historical observations of operations. Multiobjective vulnerabilities to snowpack decline are then examined using a set of downscaled climate scenarios to capture the physically based effects of rising temperatures. A statistical analysis shows that the primary impacts include water supply shortage and lower reservoir storage resulting from the seasonal shift in runoff timing. These challenges identified from the vulnerability assessment inform proposed adaptations to operations to maintain multiobjective performance across the ensemble of plausible future scenarios, which include other uncertain hydrologic changes. To adapt seasonal reservoir management without the cost of additional infrastructure, we specifically propose and test adaptations that parameterize the structure of existing operating policies: a dynamic flood control rule curve and revised snowpack-to-streamflow forecasting methods to improve seasonal runoff predictability given declining snowpack. These adaptations are shown to mitigate the majority of vulnerabilities caused by snowpack decline across the scenario ensemble, with remaining opportunities for improvement using formal policy search and dynamic adaptation techniques. The coupled approach to vulnerability assessment and adaptation is generalizable to other snowmelt-dominated water resources systems facing the loss of seasonal storage due to rising temperatures. [ABSTRACT FROM AUTHOR]

This short communication describes a new open-source implementation of the CALVIN model (CALifornia Value Integrated Network), a large-scale network flow optimization model of California's water supply system. The model is cross-platform, uses common data formats, and connects to several freely available linear programming solvers. Given inputs including hydrology, urban/agricultural demand curves, and variable operating costs, the model minimizes the systemwide cost of water scarcity and operations including surface and groundwater reservoirs, wastewater reuse, desalination, environmental flow requirements, and hydropower. Key outputs include water shortage costs and marginal economic values of water and infrastructure capacity. We benchmark the scalability of different solvers up to roughly 5 million decision variables, using shared-memory parallelization on a high performance computing cluster. Runtimes are reduced by two orders of magnitude relative to the original model when no initial solution is provided, in addition to the benefits such as accessibility and transparency that come with an open-source platform. While this model is specific to California, the data and model structure are separated, so a similar framework could be used in any system where water allocation has been formulated as a network flow problem. [ABSTRACT FROM AUTHOR]

A letter to the editor and a response by Karen H. Lu on her article in the May 2010 issue regarding the patterns of referral of women with ovarian cancer, who are at risk of mutations, are presented.

Background Studies have demonstrated the protective role of antibodies against malaria. Young children are known to be particularly vulnerable to malaria, pointing to the evolution of naturally acquired clinical immunity over time. However, whether changes in antibody functionality track with the acquisition of naturally acquired malaria immunity remains incompletely understood. Methods Using systems serology, we characterized sporozoite- and merozoite-specific antibody profiles of uninfected Malian children before the malaria season who differed in their ability to control parasitemia and fever following Plasmodium falciparum (Pf) infection. We then assessed the contributions of individual traits to overall clinical outcomes, focusing on the immunodominant sporozoite CSP and merozoite AMA1 and MSP1 antigens. Results Humoral immunity evolved with age, with an expansion of both magnitude and functional quality, particularly within blood-stage phagocytic antibody activity. Moreover, concerning clinical outcomes postinfection, protected children had higher antibody-dependent neutrophil activity along with higher levels of MSP1-specific IgG3 and IgA and CSP-specific IgG3 and IgG4 prior to the malaria season. Conclusions These data point to the natural evolution of functional humoral immunity to Pf with age and highlight particular antibody Fc-effector profiles associated with the control of malaria in children, providing clues for the design of next-generation vaccines or therapeutics. [ABSTRACT FROM AUTHOR]

The ability to adapt to social and environmental change is an increasingly critical feature of environmental governance. However, an understanding of how specific features of governance systems influence how they respond to change is still limited. Here we focus on how system features like diversity, heterogeneity, and connectedness impact stability, which indicates a system's capacity to recover from perturbations. Through a framework that combines agent-based modeling with "generalized" dynamical systems modeling, we model the stability of thousands of governance structures consisting of groups of resource users and non-government organizations interacting strategically with the decision centers that mediate their access to a shared resource. Stabilizing factors include greater effort dedicated to venue shopping and a greater fraction of non-government organizations in the system. Destabilizing factors include greater heterogeneity among actors, a greater diversity of decision centers, and greater interdependence between actors. The results suggest that while complexity tends to be destabilizing, there are mitigating factors that may help balance adaptivity and stability in complex governance. This study demonstrates the potential in applying the insights of complex systems theory to managing complex and highly uncertain human–natural systems in the face of rapid social and environmental change. [ABSTRACT FROM AUTHOR]

Globally, the pressures of expanding populations, climate change, and increased energy demands are motivating significant investments in re-operationalizing existing reservoirs or designing operating policies for new ones. These challenges require an understanding of the tradeoffs that emerge across the complex suite of multi-sector demands in river basin systems. This study benchmarks our current capabilities to use Evolutionary Multi-Objective Direct Policy Search (EMODPS), a decision analytic framework in which reservoirs’ candidate operating policies are represented using parameterized global approximators (e.g., radial basis functions) then those parameterized functions are optimized using multi-objective evolutionary algorithms to discover the Pareto approximate operating policies. We contribute a comprehensive diagnostic assessment of modern MOEAs’ abilities to support EMODPS using the Conowingo reservoir in the Lower Susquehanna River Basin, Pennsylvania, USA. Our diagnostic results highlight that EMODPS can be very challenging for some modern MOEAs and that epsilon dominance, time-continuation, and auto-adaptive search are helpful for attaining high levels of performance. The ϵ-MOEA, the auto-adaptive Borg MOEA, and ϵ-NSGAII all yielded superior results for the six-objective Lower Susquehanna benchmarking test case. The top algorithms show low sensitivity to different MOEA parameterization choices and high algorithmic reliability in attaining consistent results for different random MOEA trials. Overall, EMODPS poses a promising method for discovering key reservoir management tradeoffs; however algorithmic choice remains a key concern for problems of increasing complexity. [ABSTRACT FROM AUTHOR]

The ability to adapt to social and environmental change is an increasingly critical feature of environmental governance. Yet, an understanding of how specific features of governance systems influence how they respond to change is still limited. Here we focus on how system features like diversity, heterogeneity and connectedness impact stability, which indicates the system's capacity to recover from perturbations. Through a framework that combines agent-based modeling with "generalized" dynamical systems modeling, we model the stability of thousands of governance structures consisting of groups of resource users and non-government organizations interacting strategically with the decision centers that mediate their access to a shared resource. Stabilizing factors include greater effort dedicated to venue shopping, and a greater fraction of non-government organizations in the system. Destabilizing factors include greater heterogeneity among actors, a greater diversity of decision centers, and greater interdependence between actors. The results suggest that while complexity tends to be destabilizing, there are mitigating factors that may help balance adaptivity and stability in complex governance. This study demonstrates the potential in applying the insights of complex systems theory to managing complex and highly uncertain human-natural systems in the face of rapid social and environmental change. [ABSTRACT FROM AUTHOR]

Water-resources planners use regional water management models (WMMs) to identify vulnerabilities to climate change. Frequently, dynamically downscaled climate inputs are used in conjunction with land-surface models (LSMs) to provide hydrologic streamflow projections, which serve as critical inputs for WMMs. Here, we show how even modest projection errors can strongly affect assessments of water availability and financial stability for irrigation districts in California. Specifically, our results highlight that LSM errors in projections of flood and drought extremes are highly interactive across timescales, path-dependent, and can be amplified when modeling infrastructure systems (e.g., misrepresenting banked groundwater). Common strategies for reducing errors in deterministic LSM hydrologic projections (e.g., bias correction) can themselves strongly distort projected climate vulnerabilities and misrepresent their inferred financial consequences. Overall, our results indicate a need to move beyond standard deterministic climate projection and error management frameworks that are dependent on single simulated climate change scenario outcomes. [ABSTRACT FROM AUTHOR]

Reservoir control policies provide a flexible option to adapt to the uncertain hydrologic impacts of climate change. This challenge requires robust policies capable of navigating scenarios that are wetter, drier, or more variable than anticipated. While a number of prior studies have trained robust policies using large scenario ensembles, there remains a need to understand how the properties of training scenarios impact policy robustness. Specifically, this study investigates scenario properties including annual runoff, snowpack, and baseline regret—the difference between baseline policy and perfect foresight performance in an individual scenario. Results indicate that policies trained to scenario subsets with high baseline regret outperform those generated with other training sets in both wetter and drier futures, largely by adopting an intra-annual hedging strategy. The approach highlights the potential to improve the efficiency and robustness of policy training by considering both the hydrologic properties and baseline regret of the training ensemble. • Baseline regret quantifies maximum improvement in reservoir policies in individual climate scenarios. • Scenarios are clustered based on baseline regret and hydrologic properties. • We identify training scenario properties that yield robust policy generalization on test sets. [ABSTRACT FROM AUTHOR]

Adapting water resources systems to climate change requires identifying hydroclimatic signals that reliably indicate long-term transitions to vulnerable system states. While recent studies have classified the conditions under which vulnerability occurs (i.e., scenario discovery), there remains an opportunity to extend such methods into a dynamic planning context to design and assess early warning signals. This study contributes a machine learning approach to classifying the occurrence of long-term water supply vulnerability over lead times ranging from 0 to 20 years, using a case study of the northern California reservoir system. Results indicate that this approach predicts the occurrence of future vulnerabilities in validation significantly better than a random classifier, given a balanced set of training data. Accuracy decreases at longer lead times, and the most influential predictors include long-term monthly averages of reservoir storage. Dynamic early warning signals can be used to inform monitoring and detection of vulnerabilities under a changing climate. • Machine learning approaches to predict future occurrence of long-term water supply vulnerability. • Significantly outperforms benchmark random classifier over lead times ranging from 0 to 20 years. • The most influential predictors include long-term monthly averages of reservoir storage. • Dynamic early warning signals can be used to inform monitoring and detection of environmental change. [ABSTRACT FROM AUTHOR]

Watersheds at the western margin of the Sierra Nevada mountains in California are regulated by large dams providing crucial water supply, flood control, and electricity generation. Runoff in these basins is snowmelt dominated and therefore vulnerable to alteration due to climate change. Regional climate models coupled to land surface models can be used to study the hydrologic impacts of climate change, but there is little evidence that they accurately simulate watershed‐scale runoff in complex terrain. This study evaluates capabilities of the Weather Research and Forecasting (WRF) regional climate model, coupled to the Noah‐multiparameterization (MP) land surface model, to simulate runoff into nine Sierra Nevada reservoirs over the period 2007–2017. Default parameterizations lead to substantial inaccuracy in results, including median bias of 61%. Errors can be traced to process representations; specifically, we modify the representation of snowflake formation in the Thompson microphysics scheme and subsurface runoff generation in the Noah‐MP land surface model, including a correction representing effects of groundwater storage. The resulting parameterization improves Nash‐Sutcliffe efficiency to above 0.7 across all basins and reduces median bias to 21%. To assess capabilities of the modified WRF/Noah‐MP system in supporting analysis of human‐altered hydrology, we use its streamflow projections to force a reservoir operations model, results of which maintain high accuracy in predicting reservoir storage and releases (mean Nash‐Sutcliffe efficiency > 0.41). This diagnostic analysis indicates that coupled climate and land surface models can be used to study climate change effects on reservoir systems in mountain regions via dynamical downscaling, when adequate physical parameterizations are used. Plain Language Summary: In California, reservoirs are a critical component of hydroelectric power generation and flood control, in addition to storing water used by farms and cities. Much of the water that flows into the reservoirs comes as snowmelt from the Sierra Nevada mountains each spring. Wintertime snow accumulation and springtime snowmelt are likely to be impacted by rising temperatures and shifting precipitation patterns. However, little research has addressed whether regional climate models (RCMs) can accurately simulate runoff for California's reservoirs. Here we make multiple changes to a commonly used RCM, including improvements to how water is processed in the soil and how groundwater is represented. These changes improve the modeled runoff estimates when compared to values based on observations. We use 11 years of modeled runoff to drive a reservoir operations model. Though the modeled water system behavior using RCM runoff is not as realistic as a version of the model using observed runoff, the results predict many of the same variations in reservoir storage. The combined RCM and reservoir operations model can be used to project how reservoir inflows and storage may be impacted by future climate change. Key Points: When suitably parameterized, the WRF regional climate model skillfully simulates mountain runoffInclusion of a simple aquifer correction improves accuracy of runoff into 10 key California reservoirsWRF runoff provides skill when driving an operational reservoir model, though errors remain [ABSTRACT FROM AUTHOR]

Simulation-optimization frameworks, such as multiobjective evolutionary algorithms (MOEAs), are increasingly used for realworld water resources problems. Constraints in MOEA optimization commonly represent decision maker preference, which differs from their role in classical optimization. As a result, constraints are often considered an optional aspect of the problem formulation. However, the impact of including constraints on optimization search has not been rigorously examined. This study explores how constraints impact the effectiveness, efficiency, and consistency of MOEA optimization for two water resources problems. For each problem, algorithm performance metrics are compared for two cases: (1) with constraints included during search, eliminating solutions that do not meet preference requirements, and (2) with constraints applied a posteriori to filter the full set of solutions. Results show that constraints aid in the search process by favoring solutions that meet decision maker preferences, despite the increased difficulty of finding feasible solutions. This study highlights the importance of constraints in the problem formulation for simulation-optimization applications in water resources, balancing the performance of search algorithms with the decision relevance of the solution set. [ABSTRACT FROM AUTHOR]

This paper outlines the strategic approach to realize a human mission to an asteroid, focusing specifically on the proximity operations. The risks and challenges posed by asteroid surfaces to in-situ investigations force the proximity operations to be done by the intermediary of robotic explorers. In this architecture, a precursor is sent years in advance to a potential target asteroids. Its main goals are the characterization of the gravity field and of the surface behavior. If the target is found suitable, the manned mission then proceeds. With their main spacecraft stationed on a stable orbit around the asteroid, the astronauts are transported to the surface via a small, unpressurized spacecraft. Hovering a few meters above the surface, they deploy and command small robotic landers that perform scientific operations at the surface. [ABSTRACT FROM AUTHOR]

This study introduces the California Food-Energy-Water System (CALFEWS) simulation model to describe the integrated, multi-sector dynamics that emerge from the coordinated management of surface and groundwater supplies throughout California's Central Valley. The CALFEWS simulation framework links the operation of state-wide, interbasin transfer projects (i.e., State Water Project, Central Valley Project) with coordinated water management strategies abstracted to the scale of irrigation/water districts. This study contributes a historic baseline (October 1996–September 2016) evaluation of the model's performance against observations, including reservoir storage, inter-basin transfers, environmental endpoints, and groundwater banking accounts. State-aware, rules-based representations of critical component systems enable CALFEWS to simulate adaptive management responses to alternative climate, infrastructure, and regulatory scenarios. Moreover, CALFEWS has been designed to maintain interoperability with electric power dispatch and agricultural production models. As such, CALFEWS provides a platform to evaluate internally consistent scenarios for the integrated management of water supply, energy generation, and food production. • Rules-based simulation model of water storage and conveyance in California's Central Valley. • State-aware adaptation enables representation of key institutional decision-making. • Supports Monte Carlo exploratory modelling results. • Interoperable with power dispatch and agricultural production models. [ABSTRACT FROM AUTHOR]

• Groundwater security in urban areas is threatened by land and water use trends. • Implementing managed aquifer recharge (MAR) may improve urban groundwater security. • Groundwater recharge alternatives create unique spatial signatures of influence. • Aquifer management objectives must consider the spatial distribution of MAR. Groundwater-dependent cities face increasing population and changes in urban land use, threatening long-term aquifer sustainability by simultaneously increasing pumping demand while modifying recharge rates. Aquifer management involves a portfolio of alternatives to balance the sustainability of multiple water supply sources, including novel recharge alternatives to supplement traditional groundwater and surface water sources. However, potential managed aquifer recharge solutions can lead to unintended consequences as modern infrastructure and management alter natural hydrology, changing the underlying groundwater paradigm. This study uses a multiobjective analysis approach to evaluate the performance of spatially distributed aquifer management alternatives for the Mexico City Metropolitan Area (MCMA), a region where the population has increased by 48% and urban land use by 35% in the last 30 years. Recharge alternatives are evaluated using a physically based groundwater model that incorporates land and water use trends, and include: repairs to the water supply distribution network, increased wastewater treatment and infiltration, and increased infiltration of imported water supplies. These alternatives are compared according to planning objectives aggregated over three overlapping subregions within the model to minimize energy use for pumping, minimize water quality risks in subsidence-prone areas, and minimize groundwater flooding in urban areas. Results indicate that management alternatives resulting in equivalent changes in storage can exhibit spatially distinct reductions in drawdown over the historical period. Additionally, combining multiple alternatives results in a more than an additive risk of groundwater flooding, demonstrating the need for mitigation of rising water tables. This model will serve as the basis for future spatial optimization studies under future climate and land use scenarios. These approaches have broad applicability in other rapidly urbanizing regions dependent on groundwater supplies. [ABSTRACT FROM AUTHOR]