Earths Future最新文献

Balancing hydropower development with river sediment connectivity is essential for meeting energy demands while mitigating impacts on river ecosystems and delta communities. Here, we introduce an integrated modeling framework to jointly explore water and sediment release strategies alongside dam planning, addressing the complex interplay between sediment transport, hydrology, and reservoir operations. Applied to the 3S river basin, a critical tributary of the Mekong River, the framework provides novel insights into optimizing sediment connectivity and hydropower production. By analyzing spatio-temporal sediment dynamics and employing multi-objective optimization, we assess how coordinated reservoir management can reconcile competing objectives. Our results show a fundamental shift in sediment transport dynamics, with hydropower infrastructure replacing hydro-climatic variability as the dominant control. Since 2018, sediment loads at the basin outlet have dropped from an average of 25 Mt/yr to less than 10 Mt/yr, primarily due to Lower Sesan 2 dam. Optimizing flushing and sluicing strategies, we reveal the importance of coordinated reservoir operations: 57% of the identified Pareto-optimal solutions outperform current dam management practices in all the objectives considered. This study highlights the importance of integrating sediment management with hydropower development and operations, providing a transferable framework for sustainable river basin management globally.

Analyses of observations point to a more detectable change signal in the frequency than magnitude of flood events across the conterminous United States (CONUS); however, little is known about how the frequency of these events is projected to change under different scenarios. Here we apply a statistical attribution-and-projection approach to thousands of streamgages across CONUS and assess how the frequency of flood events is expected to change under multiple scenarios. We find that more frequent flood events are projected for the eastern United States, whereas decreases are slightly more common than increases in the U.S. Southwest and Great Plains. Additionally, the seasonality of flood events is projected to shift, with distinct regional patterns based on geography, topography, and season. These changes are expected to manifest themselves by the middle of this century, highlighting the pressing needs for flood mitigation and adaptation efforts.

Rice production represents a major global food source and a significant contributor to greenhouse gas (GHG) emissions. While nitrogen fertilization and water-saving irrigation are recognized as effective GHG mitigation strategies in rice cultivation, their synergistic effects and underlying mechanisms remain inadequately characterized. Based on 2,689 observations from 203 peer-reviewed articles across 129 sites in major Asian rice cultivation areas, this meta-analysis quantifies the comprehensive impacts of coupled water-nitrogen management on rice yield and GHG emissions. Results revealed that nitrogen fertilization alone increased yield by 38.26% but elevated global warming potential (GWP) by 56.98%, whereas Alternate Wetting and Drying (AWD) irrigation reduced CH₄ emissions (50.71%) and GWP (43.73%) without significantly affecting yield. Combined implementation of both practices increased yield by 46.67% while reducing yield-scaled GWP (GHGI) by 36.17%. Under AWD conditions, nitrogen application rates of 180–270 kg ha⁻¹ maximized GHGI reduction potential (42.31%). Random forest analysis identified soil pH, fertilizer type and precipitation as key determinants of mitigation efficacy. Our spatially-explicit water-nitrogen optimization model demonstrated that 67% of regional rice fields are suitable for AWD implementation, and that current nitrogen inputs could be reduced by 31% while maintaining yields and achieving a 49% reduction in GHGI. This study reveals critical water-nitrogen synergistic mechanisms in rice agroecosystems and provides quantitative frameworks for optimizing management practices at regional scales, offering a sustainable pathway toward achieving future food security and climate change mitigation goals in Earth's most productive rice regions.

Flood protection authorities are not prepared for compound flood risk in estuaries—now and in the face of climate change. Climate projections are rarely downscaled appropriately to assess future changes in storm surge and concurrent river discharge extremes, and their interactions to exacerbate flooding. This is the first time that hourly and fine spatial resolution (7/2.2 km sea level/precipitation), physically consistent, climate projections are used to assess changes in storm surge and river discharge-driven compound events. The analysis, applied to the Dyfi estuary, western UK, uses 12 downscaled perturbed parameter ensembles for the high-emissions “RCP8.5” scenario from a global climate model (HadGEM3-GC3.0). Residual surge and river discharge projections are assessed independently to identify changes in magnitudes and return periods—then combined to identify changing patterns of dependence and timing of compound events. Under RCP8.5 scenario to 2080, river discharge is expected to increase by 28%–29% for 1/20 and 1/50-year events. Extreme (95th percentile) discharge events are more likely to occur concurrently with extreme surges, and compound events will occur more often, and with a shorter time lag between peak surge and peak discharge—potentially compounding flooding further. The analysis provided forcing conditions representative of future 1 in 20-year and 1 in 50-year events used to simulate a potential increased flood footprint in the estuary. The research raises the question of the wider pattern of future compound events throughout the UK, and worldwide, highlighting the critical need for downscaled, coastal and fluvial projections to futureproof flood management strategies.

Expanding public transportation and conserving biodiversity are two major components of the United Nations Sustainable Development Goals. However, these objectives often conflict, as habitat loss and fragmentation caused by transportation networks and subsequent development are regarded as significant threats to biodiversity. Quantifying the extent of these contradictions and identifying strategies for balancing these goals are essential. In this study, an index of natural habitat areas without roads (i.e., roadless areas) is employed to quantify the contradiction and analyze changes in China's roadless areas from 2000 to 2020, with projections for potential changes from 2020 to 2035. The findings indicate that China's roadless area decreased significantly from 4.94 million km² in 2000 to 4.31 million km² in 2020, reflecting a reduction rate of 12.7%. It is conservatively estimated that this loss will continue at a rate of 4.58% over the next 15 years. Currently, only 22.8% and 35.8% of the total roadless area in 2020 are situated within biodiversity conservation priority areas and protected areas, respectively, highlighting significant conservation gaps. To address the rapid decline and inadequate protection of roadless areas in China, this study proposes several recommendations, including optimizing transportation route planning to avoid unnecessary loss of roadless area, constructing wildlife crossing structures to enhance habitat connectivity, and integrating high ecological value roadless areas into the conservation framework. This study offers valuable insights into the effective protection of ecosystem integrity amidst the expansion of road networks and presents new solutions for ensuring timely compliance with international agreements, such as the Convention on Biological Diversity.

There is growing interest in how the climate would change under net zero carbon dioxide emissions pathways as many nations aim to reach net zero in coming decades. In today's rapidly warming world, many changes in the climate are detectable, even in the presence of internal variability, but whether climate changes under net zero are expected to be detectable is less well understood. Here, we use a set of 1000-year-long net zero carbon dioxide emissions simulations branching from different points in the 21st century to examine detectability of large-scale, regional and local climate changes as time passes under net zero emissions. We find that even after net zero, there are continued detectable changes to climate for centuries. While local changes and changes in extremes are more challenging to detect, Southern Hemisphere warming and Northern Hemisphere cooling become detectable at many locations within a few centuries under net zero emissions. We also study how detectable delays in achieving emissions cessation are across climate indices. We find that for global mean surface temperature and other large-scale indices, such as Antarctic and Arctic sea ice extent, the effects of an additional 5 years of high greenhouse gas emissions are detectable. Such delays in emissions cessation result in significantly different local temperatures for most of the planet, and most of the global population. The long simulations used here help with identifying local climate change signals. Multi-model frameworks will be useful to examine confidence in these changes and improve understanding of post-net zero climate changes.

Stratospheric aerosol injection (SAI) has been proposed as a complementary option to mitigate anthropogenic climate change risks. Using Community Earth System Model ensemble simulations, we assess the response of climate metrics relevant to a set of climate tipping elements in SAI scenarios targeting different temperature stabilization goals and for implementation at different latitudes. We analyze responses of tipping element metrics in simulations of a multi-objective SAI strategy that is designed to simultaneously stabilize global mean temperature (T0), interhemispheric temperature gradient (T1), and equator-to-pole temperature gradient (T2), as well as simulations of SAI strategies designed just to stabilize T0. We show that SAI strategies considered here would reduce the risks for many tipping elements, but may either increase or decrease the risk of Antarctic ice sheet collapse and Sahel greening, depending on the specifics of injection strategy. For the same 1.0°C temperature stabilization target, high-latitude injection would reduce the risk of northern cryosphere-related tipping elements more effectively, such as Greenland ice sheet, Barents winter sea ice, and boreal permafrost. Meanwhile, low-latitude injection would be more effective in stabilizing low-latitude biosphere-related tipping elements such as Amazon rainforest and coral reefs. The multi-objective SAI injection is more effective in reducing the risk of most high-latitude tipping elements than low-latitude injection, and is more effective in reducing the risk of most low-latitude tipping elements than high-latitude injection. Our study highlights the importance of careful consideration in the trade-offs between tipping element risk reduction and temperature pattern optimization in response to SAI strategies.

Addressing water scarcity requires significant attention to reducing water footprint (WF) related to food consumption. Since individuals' dietary behavior is largely influenced by their demographic and anthropometric attributes, it is crucial to identify individuals who have a high dietary WF and prioritize them as the focus of policies. Several studies analyzing the driving factors behind dietary WF exist but have multiple limitations. These include the statistical models with rather modest performances, lack of rigorous sensitivity analysis/feature importance (FI) analysis, and lack of generalization ability. Here, we developed a novel ML-based framework for analyzing the driving forces behind dietary WF. The framework incorporated three machine learning (ML) models (Extra-Trees (ET), Histogram-based Gradient Boosting (HGB), and eXtreme Gradient Boosting (XGB)) and an ML explanation approach Shapley Additive exPlanations (SHAP). This framework was applied to a case study on Chinese inhabitants. The derived results validated the proposed framework and demonstrated ML's superiority over conventional statistical methods. XGB was identified as the optimal model as it effectively captured the variability in the data and showed good generalization performance. The FI analysis for XGB revealed the most influential features on dietary WF, with income level, urbanization level, education level, and gender emerging as the top four features in descending order. Through the subsequent SHAP dependence analysis, the priority groups for dietary WF reduction interventions were identified as high-income residents, urban residents, highly educated residents, and male residents. In light of these findings and their underlying causes, the paper concluded with a set of policy recommendations.

The lack of seasonal observations of CO₂ in lakes at altitude gradients over the Qinghai-Tibet Plateau (QTP) limits the accurate assessment of CO₂ fluxes in lakes and the understanding of their variation mechanisms. We carried out seasonal observation of CO₂ fluxes in lakes at different elevation gradients over the QTP. We found that low-altitude (<3,000 m) lakes can release a large amount of CO₂ (1.03 Tg C a⁻¹), medium-altitude (3,000–4,500 m) lakes can reduce CO₂ release (0.04 Tg C a⁻¹), high-altitude (>4,500 m) lakes were capable of absorbing large amounts of CO₂ (−0.65 Tg C a⁻¹) from the atmosphere. The CO₂ fluxes of lakes on the QTP showed an inverse elevation effect. The multi-source data analysis showed that the high-altitude lakes of the QTP could also absorb a large amount of CO₂ (−0.25 Tg C a⁻¹) from the atmosphere from 2000s to 2020s, which was equivalent to 4.5% of the CO₂ absorbed by the terrestrial vegetation of the QTP.

Nitrogen cycles control the structure, function, and composition of ecosystems globally. Despite their importance, our understanding of long-term changes in global nitrogen cycles remains limited. The foliar nitrogen stable isotope ratio (δ¹⁵N) serves as a valuable metric for assessing changes in nitrogen cycling and potentially in plant nitrogen availability. However, existing observations of δ¹⁵N suffer from spatial bias and temporal discontinuity with contradictory findings across biomes, hindering our ability to detect and attribute drivers of change. Leveraging ground-based observations as our calibration source, we derived annual maps of foliar δ¹⁵N spanning from 1984 to 2022 globally from Landsat spectra. We found that the Landsat-derived δ¹⁵N effectively captured the observations, with an R² of 0.77 and a normalized root mean square error of 0.15. Globally, we found widespread temporal changes in δ¹⁵N with significant decreases for 44% and increases for 16% of vegetated ecosystems. Foliar δ¹⁵N mostly declined in forest ecosystems but increased in non-forest land cover types. Gross primary productivity and its trend consistently explained spatiotemporal variation of δ¹⁵N globally, indicating increasing plant demand could lead to decreasing δ¹⁵N. Our study presents an innovative approach to effectively monitor and track potential changes in global nitrogen cycles over the past four decades, setting the stage for more impactful management and conservation strategies.