Earths Future最新文献

Governance is increasingly understood to be a complex system shaped by diverse actors. However, understanding of how these actors interact with the institutions and infrastructure systems in which they are embedded to drive change is still limited. An interdisciplinary approach is needed to understand factors shaping actors' political behavior and how power dynamics influence their ability to create change. This study addresses this gap by combining analysis of actors' narratives about water issues and governance with dynamical systems modeling of the socio-hydrologic system in the context of California's San Joaquin Valley. Through interviews and focus groups with growers, advocacy groups, and rural residents, distinct narratives emerge around water issues and power dynamics. Modeling strategies that would maximize each actor's water access reveals that existing structures incentivize strategies that would mainly benefit larger growers, and thus conflict with the goals of environmental justice narratives. Comparing these modeled strategies with actors' actual strategies also reveals disparities in different actors' ability to pursue the “optimal” strategy. These findings highlight how system structures entrench certain interests as well as the potential of narratives for shaping strategies aimed at long-term transformation.

To cope with the future global extreme high temperatures, three adaptation strategies of green roofs, cool walls, and cool streets are constructed by applying rooftop vegetation planting and building materials of high reflectivity in cities. Hence, there is an urgent need to test these adaptation strategies. This study quantifies the performance of adaptation strategies for mitigating extreme heat in China under the future urbanization and climate conditions using a numerical model. Results are validated using observation data from 839 national meteorological stations in China. Simulation results show that implementing all three adaptation measures will decrease the heat exposure rate by 9.4% by enhancing ground reflection and lowering urban temperature. The contribution of enhancing the albedo of the building walls to reducing exposure is 1.5 times that of using roof planting. These adaptation strategies can reduce urban heat storage, but they may exacerbate urban water vapor accumulation. Most risks are concentrated on the southeast side of the Heihe-Tengchong Line. The urban geographical locations and population will influence the cooling effect of adaptation strategies. The cool walls have greater cooling effects in southern cities, whereas green roofs are more effective in northern regions. Applying cool walls strategy in densely populated areas of urban buildings can achieve the best cooling effects. When deploying adaptation strategies, not only the cooling capacity but also the climate and development conditions of different cities are necessary to be considered.

Understanding complex human-Earth system interactions often involves analyzing large scenario ensembles that encompass a wide range of plausible futures. These ensembles often require aggregation to summarize information based on specific criteria or conditions. However, previous research using global change scenario ensembles has largely overlooked how the choice of aggregation method influences the interpretation of results. To address this gap, we leverage a large ensemble data set designed to capture broad energy system dynamics generated using the Global Change Analysis Model. We first explore how energy-related uncertainties are propagated to both global and regional water-energy-food sectors. We then conduct a rank correlation analysis across seven ensemble aggregation measures and demonstrate the need to consider multiple measures in global change scenarios. Our results suggest that global water and food sector outcomes in the 21st century vary widely depending on different scenario assumptions. The global energy productivity is projected to improve by the end of the century across all scenarios. Moreover, regions facing water scarcity challenges in 2100 do not always overlap with those facing extreme energy and food sector outcomes. Although rank correlations across seven aggregation measures are relatively stable across sectors, we identify cases where relying on a single measure leads to losing critical information in the full ensemble. Reliance on a single aggregation measure can distort the interpretation of global change scenario outcomes. Instead, adopting multiple ensemble aggregation measures provides a more holistic understanding of global change scenario ensembles.

Several lines of evidence indicate that aviation-induced cirrus clouds contribute to global warming. These clouds produce both longwave and shortwave radiative forcing, yet their climate impacts are not well understood. To improve understanding of the climate effects of radiative forcing associated with aviation-induced cirrus clouds, we use the Community Earth System Model CESM1.2.2 to perform simulations with stylized longwave and shortwave forcing agents in different latitude bands. We find that for the same concentration, longwave absorbers in the sub-tropics have the largest magnitude of instantaneous radiative forcing but these absorbers in the polar regions show the largest impact on global temperature. In contrast, shortwave scatterers in the low latitudes have the largest magnitude of effective and instantaneous radiative forcing, but the global temperature response is not highly sensitive to the latitude of forcing. Our results suggest that contrail-induced warming could be reduced most effectively by avoiding aviation-induced cirrus clouds at night, and at high latitudes during their winters.

The current food system damages the environment, increases social inequalities, and is vulnerable to climate shocks and supply chain disruptions. This, and the global urbanization trends, pose the question of how cities will ensure food security for their inhabitants in a sustainable way. Within urban contexts, the most marginalized groups are also the most affected by premature deaths and non-communicable diseases, whose first preventable cause is nutrition. Here we analyze the potential of soil-based urban agriculture (UA) in providing fresh and nutritious food to urban populations. Using the megacity of São Paulo, Brazil, as a case study, we identify spaces for UA considering natural and context-related factors and select crops according to the gap between the current diet of the population and a reference balanced diet. To maximize water use efficiency, we test crops with an agro-hydrological model. Finally, we optimize crop-area allocation to find configurations capable of closing the diet gap for most people while minimizing irrigation water demand. The results show that the average hectare of urban garden in São Paulo could provide healthy food to more than 600 people, while theoretical city-scale implementations reach 13%–21% of the city's population. This demonstrates UA's high potential for significant fresh and nutritious food production but also the trade-offs that emerge when upscaling its implementation. While preliminary with respect to socio-economic considerations necessary for the implementation of such a strategy at the city scale, this coupled biophysical and nutritional framework can be repeated in other urban contexts.

Fertilizer use enhances crop yields but exacerbates nitrate leaching, threatening water quality in farming systems. This study optimizes nitrogen fertilization strategies by integrating numerical modeling and machine learning to balance corn yield and nitrate leaching in the US Midwest, 1979–2100. We evaluate the economic optimum nitrogen rate under climate-smart agricultural practices like no-tillage and cover crops. Findings show that the economic optimum nitrogen rate sustains yields but increases nitrate leaching, especially under future scenarios. In contrast, optimized strategies—such as a lower rate (−30%) than the economic optimum nitrogen rate combined with cover crops and no-tillage—could reduce yield-scaled nitrate leaching by over 60% from 2020 to 2100. The study underscores the synergistic benefits of integrated management in mitigating trade-offs between productivity and environmental impacts. Further predictions offer adaptive strategies for achieving sustainable, high yields while minimizing nitrate leaching under various climate scenarios.

Local zoning ordinances across the United States can restrict development of energy infrastructure, including utility-scale solar photovoltaics (PV). While ordinances may be developed for legitimate purposes to protect public health and safety, they could impede or increase costs of power sector decarbonization. We quantify the role of utility-scale solar zoning ordinances on power sector decarbonization across the Great Lakes region (Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin) by integrating 2,474 unique rural zoning ordinances (covering over 90% of county subdivisions in each state) into a power system planning model. Our analysis focuses on deploying utility-scale PV on agricultural lands, where most existing solar installations in our region are located. Relative to a hypothetical counterfactual without zoning ordinances, existing zoning ordinances cause an 18% decline in utility-scale PV investment (by 8 GW) and cost (by $4.8 billion), primarily due to “silent” ordinances that implicitly block solar development by failing to address its land-use. Investment shifts from PV to energy storage (900 MW), wind (300 MW), and natural gas plants (3 GW). Starker declines in PV investment occur at the state level, with Michigan and Wisconsin seeing a 42% reduction. Our results underscore the need for planning that aligns local zoning laws with state and regional goals.

To achieve sustainable development goals such as mitigating climate change and ensuring food security, China has undergone rapid land use/cover changes (LUCC), including afforestation, grassland restoration, and cropland redistribution, which have substantially transformed the terrestrial surface and affected hydrological conditions and water resources management. However, the hydrological impacts of these changes, particularly through atmospheric moisture recycling processes, remain insufficiently understood. This study quantified the hydrological impacts of LUCC in China from 2001 to 2020 using high-resolution data sets and an atmospheric moisture tracking model. Our findings revealed that LUCC had led to increased evapotranspiration (ET; 1.71 mm/yr) and precipitation (P; 1.24 mm/yr), while decreasing water availability (WA) (P − ET; −0.46 mm/yr). Specifically, forest expansion in the Eastern Monsoon Region and grassland restoration in the Tibetan Plateau and Northwestern Arid Region were the main factors contributing to higher ET. These changes in ET, through moisture recycling, had redistributed precipitation and subsequent WA across regions, increasing WA in the Tibetan Plateau (0.38 mm/yr) while decreasing WA in the Eastern Monsoon Region (−0.59 mm/yr) and Northwestern Arid Region (−1.14 mm/yr). The Northwestern Arid Region experienced the greatest decrease in WA primarily due to significant moisture outflow to the Tibetan Plateau. The study underscores the necessity of integrating moisture recycling into water resources management to address the mismatch between land and water resources. Our results provide valuable insights for sustainable land and water resources management in China.

The Agricultural water interventions can trigger human-water feedback, including unintended supply demand feedback—where increased water availability drives greater water use. In the Kamadhiya catchment, India, the introduction of check dams (CDs) led to a shift toward more water-intensive crops like cotton and wheat. This study formulates and tests hypotheses to understand these dynamics using an agent-based model (ABM) that integrates a spatially explicit hydrological model with a farmer behavior module. The ABM simulates 38,447 farmers using the RANAS behavioral framework, based on household surveys and observed data. Model results confirm the hypothesized feedback: increased water from CDs led to an 11.9% rise in cotton and 36.1% in wheat areas, boosting incomes and increasing adoption of drip and borewell irrigation, particularly near CDs. While drip irrigation systems improve water efficiency and post-monsoon groundwater levels, the saved water enables further wheat expansion—triggering a second supply demand feedback loop. These changes are spatially concentrated near CDs, exacerbating within-catchment disparities. Overall, about 54% of the additional recharge is used for irrigation expansion, lowering groundwater levels by 1.0 m and reducing the net benefit of recharge interventions. These findings underscore the need to critically understand human-water feedback and value of ABM as a tool to support more informed planning by offering strategies that mitigate negative externalities.

Hydrological extremes forecasting in data-scarce basins remains a longstanding challenge in hydrological science. Despite significant advancements in transferring hydrological knowledge from data-rich to data-sparse basins, such as regionalization techniques for hydrological prediction and novel deep learning (DL)-based Transfer learning (TL) methods, the application of models trained in data-rich basins introduces inevitable noise into predictions within data-sparse basins. This potential distortion could misinterpret rainfall-runoff patterns within specific basins. This study introduces a TL framework based on data augmentation (DA-TL) within the context of hydrological modeling. The framework employs augmented rainfall data as input for conceptual models to generate pretraining runoff samples, addressing the challenges of sample scarcity and imbalance in target basins. Subsequently, TL is applied to fine-tune predictions in the target basin, thereby mitigating inappropriate hydrological knowledge transfer associated with cross-basin learning. The DA-TL framework was validated across nine river basins in China, representing three distinct climate zones (semi-arid, semi-humid, and humid regions). Results indicate that the DA-TL approach outperforms current DL methods for regionalized hydrological modeling. Specifically, under varying data scarcity scenarios, DA-TL achieved average Nash–Sutcliffe Efficiency improvements of 3.8% and 1.0% compared to similar-basin modeling and all-basin modeling strategies, respectively. Model interpretability analyses reveal that the effectiveness of the DA-TL framework primarily stems from its adept learning of the runoff generation and routing processes in target basins. These findings underscore the potential of using synthetic data derived from process-based models for pretraining in TL, offering promising avenues for improving hydrological extremes forecasting accuracy in observation-limited regions.