Future Uncertainty Research Articles

Deepening the Case for Egalitarianism in the International Order: A Comparative Perspective

Abstract This essay appraises the commemoration of the 75th anniversary of the Universal Declaration of Human Rights in 2023 presents a watershed to reflect on the promises and problems of implementing the international human rights agenda. It posits that the time is ripe to accelerate progress on this fundamental global governance technology by reaping historic lessons that are reshaping international relations in the contemporary arena. This is with a view to mitigate complex ambiguities and contradictions which hamstring the mobilization of meaningful solidarity and will power that are necessary for cooperation among states to help evolve a more secure, peaceful, and prosperous world. In this light, the essay articulates the institutionalization of the international human rights regime as approximating a proxy for equality as a core value to strengthen the global common good. Building on this formulation, it proffers inequality as a warning system that is useful to radically rethink conventional pathways that compromise the international security architecture. Zeroing in on Africa as an instructive frame of reference, the essay explores how unfolding opportunities for urgent multilateral action might nurture innovative solutions to wage a sustainable war against cross-pollinating vulnerabilities that are decisively fueling insecurities and escalating future uncertainties.

Covid-19 pandemic and mental health among adolescents and young adults: results from two studies conducted in North of Italy

IntroductionThe northern region of Italy had been the epicenter of the first wave of Covid-19. The youth population residing in this area experienced an extended period of restrictive measures implemented to curb the spread of the virus. Given the adverse effects of social distancing and lockdown measures, there is a concern regarding the mental health of young individuals in this region. However, there remains a notable scarcity of studies exploring the long-term impact of the pandemic on the mental health of this vulnerable population.ObjectivesTo assess psychopathological symptoms among adolescents and young adults in order to evaluate the extent of their impact in the context of the Covid-19 pandemic. To explore potential risk factors and resilience factors in youth who have experienced the effects of the pandemic.MethodsTwo studies are performed. Study n1 has observational design and includes 7,146 adolescents and young adults (age range 14–25) evaluated during the fourth wave of the COVID-19 through standardized measures for depression, anxiety, anger, somatic symptoms, resilience, loneliness and post-traumatic growth. Study n2 has prospective design and includes 153 students (mean age 16.1±0.49), evaluated before the Covid-19 pandemic (November 2019–January 2020) and 1 year later (April–May 2021) to measure anxiety, depression, stress, emotional dysregulation, maladaptive behaviours.ResultsStudy 1. Clustering methods identifed two groups of students with different psychological features, that we further defined as “poor mental health” and “good mental health”. Those with poor mental health were characterized by higher scores of loneliness and self-harm, followed by being of female gender, presenting binge eating behaviors and, finally, having unsatisfying family relationships.Study 2. Over the course of one year, significant changes in various psychological parameters were observed: an increase in anxiety, stress for future uncertainty, and higher frequency of maladaptive behaviours.Stress related to social domains (i.e., school attendance, romantic relationships, peer pressure) decreased over the year. Cluster analysis identified three distinct groups of youths based on their changes in psychopathological symptoms over time: those who worsened (N=23; 15%), improved (N=55; 34%), or remained stable (N=75; 46%). Furthermore, adolescents who reported an increase in self-harm (OR=2.61; p<0.001), binge-drinking (OR=3.0; p=0.007), aggressiveness (OR 1.92; p=0.004), and binge-eating (OR 2.55; p=0.003) were more likely to be associated with a worsened mental health condition.ConclusionsThe findings from these studies substantiated the significant psychological distress caused by the COVID-19 pandemic. Furthermore, they yielded valuable further insights regarding into the factors linked to distinct patterns of mental health outcomes.Disclosure of InterestNone Declared

Understanding the Dynamics of Oil Price Shocks and Stock Market Volatility in Pakistan: A Non-Linear Approach during COVID-19

Purpose: This article examines the complex relationship between oil price shocks and stock market volatility in Pakistan during the COVID-19 outbreak. Design/Methodology/Approach: The study used a Non-Linear Autoregressive Distributed Lag (NARDL) model to meticulously explore how oil price fluctuations, specifically supply and demand shocks, affect Pakistan's stock market. Monthly data sets from 2020 to 2022 were utilized for the analysis. Research Findings: The study's significant findings reveal that oil price shocks, except for a demand shock specific to the oil industry, have varying long- and short-term effects on stock market volatility, leading to asymmetry. This asymmetrical impact of oil supply and demand shocks on stock market volatility is confirmed, with no asymmetry observed from oil-specific demand shocks in the short term. These findings hold crucial implications for policymakers and investors in understanding and managing the economic dynamics of Pakistan post-COVID-19. Theoretical Contribution/Originality: This research stands out for its originality in understanding Pakistan's economic dynamics post-COVID-19. It provides unique insights for policymakers and investors to navigate future uncertainties related to oil price changes and market volatility, thereby sparking new avenues of thought and action in the field. Implications/Originality/Value: The practical implications of this study's findings empower policymakers and investors to make informed decisions to stabilize Pakistan’s economy after the pandemic. Specifically, the research provides guidance on managing the behaviour of stakeholders and banks in response to oil shocks, thereby contributing to the economic resilience of the country.

The impact of the tail risk of demand on corporate investment: Evidence from Chinese manufacturing firms

The tail risk of demand over the past certain periods not only affect entrepreneurs' expectations on future demand uncertainty, increase the option value of “waiting an see” for delay investment, but also affect entrepreneurs' confidence and risk appetite by adjusting the worst-case scenario in the “demand belief set”, thereby further inhibiting investment. Utilizing the year-on-year sales growth data of various manufacturing industries in China, and the General Pareto Distribution method of Extreme Value Theory to estimate tail-index parameter and tail threshold as proxies for the tail risk of industry-level demand. Combined with the firm level data, our empirical results show that the tail risk indeed affect firm investment negatively, especially after the outbreak of global financial crisis, but such focal effect is stronger for firms with non-state ownership, small size, lower productivity and stricter financing constraints. Expansionary macroeconomic policies such as monetary and fiscal policy in periods of high tail risk have a higher incentive effect on firm investment, which provides more evidence for the effectiveness of macroeconomic policies during the economic recessions.

Planning for wastewater infrastructure adaptation under deep uncertainty

Infrastructure in low-lying coastal areas faces challenges from climate change, sea level rise, and the impact of compound hazards. Dynamic adaptive pathways planning (DAPP) is increasingly being applied as a way of planning under deep uncertainty. Stress testing for robustness is an integral part of DAPP which provides decision-makers with confidence. We outline a seven-step approach—combining scoping workshops, systems mapping, DAPP, exploratory modelling, robust decision-making, real options analysis and validation workshops—to support decision-making for infrastructure in low-lying coastal areas. We apply the seven steps to two wastewater treatment plant (WWTP) case studies in New Zealand to quantify indicators, signals, triggers and adaptation thresholds within DAPP plans and to identify adaptation pathways that are robust against future uncertainty. Case study one focuses on the implementation of an existing DAPP at Helensville WWTP. Our modelling enabled the challenge of quantifying indicators for adaptation thresholds and triggers to be overcome. We show that an adaptation threshold occurs at 31 cm of RSLR, the trigger point is sufficient lead time to enable relocation, and the indicator is the rate of observed RSLR. Case study one demonstrates in a quantitative way how an existing DAPP can be functionally implemented by a water management agency. Modelling for case study two, the Seaview WWTP, showed that 26 cm and 56 cm of RSLR are key thresholds. Nuisance flooding may occur after 26 cm of RSLR, which could happen as early as 2040 under a high emissions scenario. Inundation of plant assets may occur after 56 cm of RSLR, which could occur as early as 2060. Modelling showed that implementing changes to plant layout would allow the plant to remain on site for its design life (until 2080). Five adaptation archetypes were developed—sequences of adaptive actions that achieve the performance objective of continuing levels of service and avoid inundation of WWTPs. The seven-step approach is a way to stress-test a DAPP, to quantify signals, triggers and adaptation thresholds and to simulate implementation of a DAPP under a range of scenarios. This can facilitate more robust decision-making for wastewater infrastructure assets under future uncertainty.

Impact factors and peaking simulation of carbon emissions in the building sector in Shandong Province

The building sector has the fastest growth rate of carbon emissions and the greatest potential for carbon reduction. The building sector in Shandong Province ranks first in China in terms of total carbon emissions and growth rate. The study of carbon emissions in the building sector in Shandong Province is an essential reference for Chinese building sector to achieve the “carbon peaking and carbon neutrality” target. This study uses historical data on carbon emissions in the building sector in Shandong Province, based on which the influencing factors and carbon reduction potential are analyzed. A dynamic and comprehensive carbon emission prediction model for buildings was developed to predict the trajectory of carbon emissions in the building sector in Shandong Province from 2020 to 2050 with the corresponding probability distribution. Results show that the main factors driving the increase in carbon emissions in the building sector in Shandong Province are the level of affluence of the residents and the level of building services. The building area demand index and building energy efficiency are the most important factors inhibiting the increase in carbon emissions in the building sector in Shandong Province during the same period. Under the baseline scenario, carbon emissions in the building sector in Shandong Province will peak at 243.573 (±46.022) million tonnes CO2 in 2035 (±3) years. The floor area demand index and level of affluence of the residents are the most critical factors affecting the uncertainty of future of carbon emissions in the building sector in Shandong Province.

Distribution network tariff design: Facilitate flexible resource under uncertain future energy scenarios

The uncertainty embedded in low-carbon transition pathways and future energy scenarios will reshape the way of distribution network planning and operation. Flexible solutions provided by flexible resources could help distribution network operators (DNOs) to reduce network costs and risks under uncertainties. However, the current tariff paradigm neglects such contributions, which cannot generate sufficient incentives to facilitate flexible resource development. To tackle this issue, this paper first introduces an improved price control method that caps DNOs' revenue while encouraging DNOs' sustainable investment under uncertainties. A bi-level planning model is then proposed to determine the optimal investment plan and evaluate the risk reduction effect by scheduling flexible loads. Finally, a novel pricing method is proposed to derive network tariffs for existing and potential flexible loads based on their contributions to risk reduction and demonstrated in a test system. The result shows a 24% network cost reduction given by 6% DNO risk compensation rate, and 0%–43% and 1%–22% network tariff reductions for existing and future flexible demands, respectively. This evidence proves the proposed method can incentivize DNOs to well manage risks, meanwhile promote the connection and engagement of flexible loads. Regulators and DNOs are recommended to review the existing pricing framework, enhance risk management in price regulation, and explore suitable tariff designs or business models for flexible loads to cope with future uncertainties through flexibility.

Technician routing and scheduling with employees’ learning through implicit cross-training strategy

With record high talent shortages and skill mismatches around the world, this paper investigates a variant of multi-period dynamic technician and routing problem that can be modeled as a Markov decision process. To deal with the double tradeoffs between the routing and service time costs, as well as the current and future costs, we propose an approximate dynamic programming (ADP)-based cost function approximation (CFA) algorithm — the implicit cross-training strategy (ICT). A two-phase routing and scheduling heuristic is developed to account for both employees’ learning and future information, and to facilitate an efficient implementation of CFA. Extensive computational results show that ICT can provide a better solution in the current decision with a global view in comparison with the myopic strategy. In depth analysis demonstrates that ICT trains the workforce with more balanced skillsets and workloads, which ensures the flexibility of the workforce and helps buffer against the future uncertainties with substantial routing cost savings. Additionally, ICT has much more advantages in large-scale problems with more diversified service requests and randomly distributed customers.

Future Projection and Uncertainty Analysis of Wind and Solar Energy in China Based on an Ensemble of CORDEX‐EA‐II Regional Climate Simulations

AbstractWind and solar energy are crucial for meeting the growing energy demand and mitigating the impact of climate change, and their sources show a climate‐dependence. Here, based on the outputs from two regional climate models (RCMs) driven by three global climate models within the Coordinated Regional Climate Downscaling Experiments‐East Asia (CORDEX‐EA‐II), the effects of future climate change on wind power density (WPD) and photovoltaic power potential (PVP) in China under the Representative Concentration Pathway (RCP) 2.6 scenario and RCP8.5 scenario are comprehensively investigated, and the sources of uncertainty are also quantified. Results show that all RCMs can reproduce the observed WPD and PVP in China by employing the Quantile Delta Mapping method for wind speed simulations. For the future projections, the annual averages of WPD and PVP in China tend to decrease by −11.67% to −1.7% and −4.6% to −1.12%, respectively, with more significant reduction under the RCP8.5 than under the RCP2.6. Note that uncertainties exist among the RCMs' simulations in terms of future projections and long‐term trends. Further analysis reveals that the uncertainty in both WPD and PVP projections are primarily driven by the internal variability in most sub‐regions except the Tibetan Plateau, where GCM uncertainty and emission scenarios play the dominant role in the uncertainty of WPD and PVP projection, respectively. This study highlights the potential benefits of controlling greenhouse gas emissions in enhancing and stabilizing renewable energy in China.

Online Learning and Teaching during the COVID-19 Pandemic in Higher Education in Qatar

The COVID-19 pandemic instigated a sudden upheaval in the realm of education, significantly impacting students and educators across the globe. The primary objective of this study was to delve into the experiences of both students (in the learning) and educators (in the teaching) amidst the emergency shift to online education within the higher education system during the COVID-19 pandemic, taking Qatar as a case study. This research adopted a quantitative approach, utilizing surveys as the primary data collection instrument. Data were collected separately from both students and educators, with a sample size of 660 students and 103 educators participating. In both surveys, a five-point Likert scale was employed to record responses. The principal findings of this research indicated that both students and educators exhibited a high level of agreement when it came to their perceptions of the digital tools employed during the educational transition and the accompanying support provided. Conversely, educators expressed a moderate level of agreement concerning their perspectives on learning materials and examination methods. The findings underscore the urgent need for higher education institutions (HEI) to strategically leverage digital tools. Recognizing the centrality of technology, this strategic utilization becomes imperative to foster a supportive and resilient learning environment for future uncertainty scenarios of lockdown and social distancing.

Plant invasion in Mediterranean Europe: current hotspots and future scenarios

The Mediterranean Basin has historically been subject to alien plant invasions that threaten its unique biodiversity. This seasonally dry and densely populated region is undergoing severe climatic and socioeconomic changes, and it is unclear whether these changes will worsen or mitigate plant invasions. Predictions are often biased, as species may not be in equilibrium in the invaded environment, depending on their invasion stage and ecological characteristics. To address future predictions uncertainty, we identified invasion hotspots across multiple biased modelling scenarios and ecological characteristics of successful invaders. We selected 92 alien plant species widespread in Mediterranean Europe and compiled data on their distribution in the Mediterranean and worldwide. We combined these data with environmental and propagule pressure variables to model global and regional species niches, and map their current and future habitat suitability. We identified invasion hotspots, examined their potential future shifts, and compared the results of different modelling strategies. Finally, we generalised our findings by using linear models to determine the traits and biogeographic features of invaders most likely to benefit from global change. Currently, invasion hotspots are found near ports and coastlines throughout Mediterranean Europe. However, many species occupy only a small portion of the environmental conditions to which they are preadapted, suggesting that their invasion is still an ongoing process. Future conditions will lead to declines in many currently widespread aliens, which will tend to move to higher elevations and latitudes. Our trait models indicate that future climates will generally favour species with conservative ecological strategies that can cope with reduced water availability, such as those with short stature and low specific leaf area. Taken together, our results suggest that in future environments, these conservative aliens will move farther from the introduction areas and upslope, threatening mountain ecosystems that have been spared from invasions so far.

Developing a conceptual model for understanding caregiving experience and their impacts on quality of life for Chinese breast cancer family caregivers: A qualitative study.

The purpose of this study was to understand the caregiving experiences of breast cancer family caregivers and explore the profound impacts of those experiences on their quality of life. A qualitative research method was used. We extended invitations to 23 family caregivers of outpatients and inpatients receiving breast surgery and oncology treatments in Taiyuan, China, to participate in semi-structured interviews. The interviews were audio-recorded and transcribed verbatim. Thematic analysis was employed to analyse the interview data. Four themes and associated categories were identified: (1) changes in family dynamics, (2) the socio-medical context, (3) interactions between family and society, (4) self-efficacy and nine subthemes and their related categories, where virtually all participants expressed future uncertainty, emotional contagion, and personal challenges, and self-efficacy had a moderating influence on the first three themes. This study did not involve direct participation of patients or the public. However, their experiences and perspectives on caregiving were indirectly reflected through the insights provided by the family caregivers who participated in the interviews. Their valuable input contributed to a deeper understanding of the caregiving experience and its impact on the quality of life for Chinese breast cancer family caregivers.

Empirical analysis of government interventions on Jordan's stock market during the COVID-19 pandemic: An ARDL approach

The objective of this study was to examine the impact of government actions on the Amman Stock Market's performance over the period from March 1, 2020, to June 30, 2021. The research uses the ARDL technique to examine the immediate and long-term connections between the variables. The findings indicate that government actions have an impact on the Amman stock market. From a larger standpoint, the research found that government actions and the stringency index had a detrimental impact on the Amman stock market in the long term. In contrast, the Containment Health Index had a favourable impact on the Amman Stock Market. Our analysis found a negative correlation between the number of new COVID-19 cases and the performance of the Amman Stock Market. These results highlight the complex relationship between government activities, market strictness, and public health initiatives in influencing the behaviour of financial markets. This finding is consistent with and strengthens the outcomes of prior research, enhancing our comprehension of how pandemic-related variables impact the performance of the stock market. The study's results are very relevant for politicians, decision-makers, and investors, offering crucial insights to inform their decision-making processes in times of future crises and uncertainty.

Navigating the green shipping: Stochastic hydrogen hub deployment in inland waterways

The maritime industry is shifting toward sustainability to help combat climate change, given the sector’s significant contribution to global greenhouse gas emissions. This study focuses on the strategic integration of hydrogen fuel—a zero-emission alternative—into inland waterway transport systems. We recognize the operational characteristics of inland shipping networks and the complexities of two-stage financial planning. Consequently, we propose a two-stage stochastic framework for the deployment of hydrogen refueling hubs, taking into account financial uncertainties in the future. This approach contrasts with a myopic strategy and a two-stage deterministic framework, addressing both immediate financial constraints and future uncertainties. We focus on the Yangtze River’s shipping network as a case study, and our analysis validates the superior performance of our proposed two-stage stochastic strategy. This suggests its potential to accommodate volatile future financial uncertainties while ensuring the economic and operational viability of hydrogen integration.

Agricultural commodities market reaction to COVID-19

This study examines the volatility transmission mechanism in agricultural futures returns during crisis periods, with a specific focus on the COVID-19 pandemic. The research employs the Markov Switching model to analyze the market dynamics of key agricultural commodities—cotton, sugar, rice, wheat, and corn. The study period is divided into three phases: Pre-COVID, COVID, and Post-COVID, allowing for a comprehensive analysis of market trends and shifts in response to the pandemic. Key findings reveal significant disruptions and heightened volatility in these markets during the COVID-19 period, with notable deviations from traditional market behaviors. The research reveals how various macroeconomic factors, such as the S&P 500, US 10-year Treasury bills, Brent crude oil prices, and the US Dollar Index, distinctly affect these commodity markets. Crucially, it sheds light on the dual nature of these markets, demonstrating both their resilience and susceptibility to worldwide disruptions, along with their recovery patterns following the pandemic. The policy implications of these findings are crucial for investors and policymakers. The study provides insights into managing and mitigating the impacts of global crises on essential commodities, emphasizing the need for robust strategies to navigate future market uncertainties. Additionally, the research contributes to the broader discourse on the interplay between macroeconomic conditions and agricultural market dynamics, offering valuable perspectives for informed decision-making in these sectors.

Impact of treating chronic hepatitis C with direct acting antivirals on health-related quality of life: a real-life Egyptian experience

BackgroundChronic hepatitis C virus (HCV) infection negatively impacts health-related quality of life (HRQL). We aimed to assess patient-reported outcomes (PROs) to evaluate the impact of treating chronic HCV with directly acting antivirals (DAAs) on HRQL.MethodsPROs were assessed prospectively using the PROQOL-HCV questionnaire before (week 0), at the end (week 12), and after DAA treatment at week 24. HRQL was measured in six different dimensions: physical health, emotional health, future uncertainty, intimate relationships, social health, and cognitive functions.ResultsA total of 500 HCV patients receiving DAAs were enrolled; of them, 399 were included in the analysis (median age 57 years, 59% females). HRQL increased significantly between baseline, end of treatment, and week 24 for all dimensions (P < 0.001), more often for physical health in females compared to males (OR = 1.69, 95% CI = 1.1–2.5), for future uncertainty among people with diabetes (1.75, 95% CI = 1.05–2.9), and for cognitive functions among obese patients (OR = 1.98; 95% CI = 1.1–3.3). Improvement in HRQL was less common for intimate relations among females (OR = 0.47; 95% CI = 0.3–0.7) and in patients with cirrhosis (OR = 0.35, 95% CI = 0.1–0.7). Improvement in HRQL was consistently higher in < 60 years compared to ≥ 60 years patients, with a significant difference in social health (P < 0.001) and future uncertainty (P < 0.049) HRQL domains.ConclusionHRQL improved with DAA therapy, a relation consistent across all HRQL dimensions up to 12 weeks after the end of treatment.

From Lab to Bouquet: The Biotechnological Frontier in Modern Floriculture for Sustainable and Resilient Flower Farming

Modern floriculture faces unprecedented challenges in meeting the rising global demand for flowers while addressing environmental concerns and the need for sustainability. This paper explores the transformative role of biotechnology in revolutionizing flower farming practices, offering solutions for sustainable and resilient cultivation. The integration of biotechnological tools such as genetic engineering, tissue culture, and molecular breeding techniques has opened new avenues for enhancing the efficiency and resilience of flower crops. Genetic modification allows the development of varieties with improved traits, such as disease resistance, longer vase life, and enhanced stress tolerance. Tissue culture facilitates the rapid propagation of elite plant material, ensuring consistent and high-quality flower production. Furthermore, molecular breeding enables the precise selection of desirable traits, accelerating the traditional breeding process. The use of molecular markers for disease resistance, flower colour, and fragrance can streamline the development of new flower varieties tailored to market preferences. Additionally, advanced omics technologies, including genomics, transcriptomic, and metabolomics, provide valuable insights into the molecular mechanisms underlying flower development and responses to environmental stress. Biotechnological interventions extend beyond genetic improvements, encompassing sustainable cultivation practices. Precision agriculture, enabled by sensor technologies and data analytics, optimizes resource utilization, minimizing environmental impact. Smart irrigation systems, nutrient management, and pest control strategies contribute to the efficient use of resources and reduced ecological footprint. The biotechnological frontier in modern floriculture not only addresses current challenges but also positions the industry to adapt to future uncertainties. Climate change, emerging pests, and shifting market demands necessitate resilient flower farming systems. Biotechnology offers the tools to develop adaptable flower varieties and sustainable production methods, promoting long-term ecological and economic viability.

Sensitivity calculation for Monte Carlo particle simulations of neutrals in the plasma edge

AbstractAlgorithmic differentiation (AD) is a promising tool to accurately and efficiently compute sensitivities of several code outputs w.r.t. the code input parameters for the complex Monte Carlo code EIRENE. We illustrate the procedure by assessing the sensitivity of the total atom content w.r.t. the atomic and molecular cross‐sections and rate coefficients. AD does not suffer from decorrelation between primal and perturbed particle trajectories, which easily happens for finite difference (FD) sensitivity calculations. Consequently, the resulting statistical errors on AD sensitivities are up to five orders of magnitude smaller than the statistical errors on FD sensitivities for a JET low‐recycling background plasma. Although this statistical error reduction is promising for the application of the AD sensitivities in a future optimization or uncertainty quantification framework, we show that the AD sensitivities can blow up for a minority of long‐lived particles, especially occurring in detachment. Finally, we conclude that the Monte Carlo simulation and estimator type possibly need to be adapted to improve the accuracy of the sensitivities.

Development of Resilience Framework and Respective Tool for Urban Stormwater Services

Resilience theory has gained significant traction in various urban fields, including natural disasters and risk management or climate change adaptation, and at different organizational levels, including academics, practitioners, and policymakers. It should be considered a complementary approach to sustainable development that enhances cities’ capacity to endure future uncertainties and promote rational urban development. However, the lack of a generally accepted definition of resilience hampers understanding and practical implementation in urban services like stormwater management. Conventionally, stormwater services aimed to minimize the impact of rainfall through fail-safe approaches. The resilience approach, on the other hand, embraces a holistic “safe-to-fail” perspective. The existing literature offers diverse approaches to measure flood and stormwater resilience. Still, there is room for the development and improvement of standardized but flexible frameworks for operationalizing resilience in urban drainage and flood management. To address this, a comprehensive resilience framework for urban stormwater services is proposed, entitled RESILISTORM. This framework incorporates a Strategic Dimension and a Performance Dimension, providing segmented and overall resilience ratings that enable utilities to address critical aspects undermining the service’s resilience. An open-source digital tool (RESILISTORM-tool) is also introduced to expedite answering, data integration, and visualization analysis of results.

An ensemble-based approach for pumping optimization in an island aquifer considering parameter, observation and climate uncertainty

Abstract. In coastal zones, a major objective of groundwater management is often to determine sustainable pumping rates which avoid well salinization. Understanding how model and climate uncertainties affect optimal management solutions is essential for providing groundwater managers with information about salinization risk and is facilitated by the use of optimization under uncertainty (OUU) methods. However, guidelines are missing for the widespread implementation of OUU in real-world coastal aquifers and for the incorporation of climate uncertainty into OUU approaches. An ensemble-based OUU approach was developed considering parameter, observation and climate uncertainty and was implemented in a real-world island aquifer in the Magdalen Islands (Quebec, Canada). A sharp-interface seawater intrusion model was developed using MODFLOW-SWI2 and a prior parameter ensemble was generated containing multiple equally plausible realizations. Ensemble-based history matching was conducted using an iterative ensemble smoother which yielded a posterior parameter ensemble conveying both parameter and observation uncertainty. Sea level and recharge ensembles were generated for the year 2050 and were then used to generate a predictive parameter ensemble conveying parameter, observation and climate uncertainty. Multi-objective OUU was then conducted, aiming to both maximize pumping rates and minimize the probability of well salinization. As a result, the optimal trade-off between pumping and the probability of salinization was quantified considering parameter, historical observation and future climate uncertainty simultaneously. The multi-objective, ensemble-based OUU led to optimal pumping rates that were very different from a previous deterministic OUU and close to the current and projected water demand for risk-averse stances. Incorporating climate uncertainty into the OUU was also critical since it reduced the maximum allowable pumping rates for users with a risk-averse stance. The workflow used tools adapted to very high-dimensional, nonlinear models and optimization problems to facilitate its implementation in a wide range of real-world settings.