Earths Future最新文献

Changes in climate and land-use/land-cover will impact surface water dynamics throughout the 21st century and influence global surface water availability. However, most projections of surface water dynamics focus on climate drivers using local-scale hydrological models, with few studies accounting for climate and human drivers such as land-use/land-cover change. We used a data-driven, machine learning model to project seasonal surface water areas (SWAs) in the southeastern U.S. from 2006 to 2099 that combined land-cover and climate projections under eight different development and emissions scenarios. The model was fitted with historic Landsat imagery, land-use/land-cover, and climate observation data (mean squared error 0.14). We assessed the change in SWA for each scenario, and we compared the surface water projections from our data-driven model and a process-based model. We found that the scenario with the largest forest-dominated land cover loss and most extreme climate change had watersheds with the greatest projected increases (in the South Atlantic Gulf) and decreases (in the Lower Mississippi) in SWA. When compared to the increase or decrease in surface water projected by the process-based model, most of the watersheds across scenarios agreed on the direction of change. Our findings highlight the importance of forest-dominated land cover in maintaining stable surface water availability throughout the 21st century, which can inform land-use management policies for adaptation and water-stress mitigation as well as strategies to prepare for future flood and drought events.

Understanding the climate and carbon cycle response to negative CO₂ emissions is important for developing climate mitigation strategies that aim to limit global warming to a specific threshold. In this study, using a coupled climate and carbon cycle model, a novel set of nine stylized simulations are conducted with cumulative emissions of 1,000 GtC, 2,000 GtC, and 5,000 GtC over 150, 250, and 500 years, followed by identical cumulative negative emissions so that the net cumulative emissions are zero. On millennial-timescales, the climate system returns close to the preindustrial state, independent of the emission and removal pathways. However, the thermal and biogeochemical inertia of the ocean play an important role in determining the climate and carbon cycle response during the emission and removal phases. When zero net emissions are reached, surface air temperature is larger by 0–1°C than the preindustrial state, and the atmospheric CO₂ concentration is less by 12–29 ppm. These changes increase with both the magnitude and duration of the emission and removal pulses. In contrast, hysteresis in the relationship between global mean surface temperature and cumulative carbon emissions increases with the magnitude but decreases with the duration of emission and removal pulses. Our study highlights the role of ocean inertia in the asymmetry in climate response to emissions and removals and indicates that an earlier emission reduction implying emission/removal pathways with smaller magnitudes and shorter durations for the positive and negative emission pulses would avoid larger climate and carbon cycle impacts on centennial-timescales.

Forestation is a key strategy for climate mitigation in China through its biogeochemical (BGC) effect of ecosystem carbon sequestration. Additionally, the BGC effect of forestation can be either reinforced or counteracted by concurrent biogeophysical processes (BGP effect) resulting in local land surface warming or cooling, which can be translated into CO₂e (i.e., BGC effect) using a local transient climate response. Previous evaluations of the climate mitigation potential of future forestation in China have, however, focused on the BGC effect only and neglected the BGP effect, potentially leading to suboptimal forestation areas. Here, we determined priority forestation areas in China by incorporating both effects to maximize its global climate mitigation effect. Our results suggest an additional 167.2 Mha potentially suitable for forestation in China, exceeding the largest forestation target (86.8 Mha) possibly assumed by the government in 2060. The forestation-induced BGP effect (18.7 ± 61.9 tCO₂e ha⁻¹) largely reinforces the BGC effect (458.2 ± 92.6 tCO₂e ha⁻¹) in China, yielding a total climate mitigation effect of 476.9 ± 114.2 tCO₂e ha⁻¹ over 40 years (2021–2060). Under the 2060 forestation target, considering both BGC and BGP effects will displace 17.7% (15.3 Mha) of the forestation area derived by considering the BGC effect alone. Integrating both BGC and BGP effects will lead to a CO₂ uptake of 28.8 GtCO₂e by 2060, 3.9 GtCO₂e higher than the value obtained when considering the BGC effect only. Our results highlight the importance of considering BGP effect when making forestation policies for climate mitigation.

The changing climate and intensifying human activities have made an impact on the hydrological processes in the upper Yangtze River (UYR), but quantifying their effects remains uncertain. This study used the Budyko framework to investigate the response of runoff (Q) to climate change and human activities during 1956–2017 and evaluate the impacts of human activities, including land use/cover change, water use, dam construction, and vegetation change, on watershed characteristic. Results show that climate change is the dominant driver of Q variations in the Wujiang River (WJR), Jialing River (JLR), and Jinsha River (JSR) watersheds, with contributions of 58.6%, 66.9%, and 67.6%, respectively. However, in Mingjiang River (MJR) and UYR watersheds, human activities contribute more to Q variations with 55.2% and 51.2%, respectively. Human activities play important roles in variation of watershed characteristics, and they can explain 22%, 26%, 36%, 25%, and 53% of the watershed character change in UYR, WJR, JLR, MJR, and JSR, respectively. This study conducts a comprehensive analysis of the causes of Q change in UYR, and provides a new perspective to explore the effects of specific human activities on watershed characteristics.

Extreme flooding events are becoming more frequent and costly, and impacts have been concentrated in cities where exposure and vulnerability are both heightened. To manage risks, governments, the private sector, and households now rely on flood hazard data from national-scale models that lack accuracy in urban areas due to unresolved drainage processes and infrastructure. Here we assess the uncertainties of First Street Foundation (FSF) flood hazard data, available across the U.S., using a new model (PRIMo-Drain) that resolves drainage infrastructure and fine resolution drainage dynamics. Using the case of Los Angeles, California, we find that FSF and PRIMo-Drain estimates of population and property value exposed to 1%- and 5%-annual-chance hazards diverge at finer scales of governance, for example, by 4- to 18-fold at the municipal scale. FSF and PRIMo-Drain data often predict opposite patterns of exposure inequality across social groups (e.g., Black, White, Disadvantaged). Further, at the county scale, we compute a Model Agreement Index of only 24%—a ∼1 in 4 chance of models agreeing upon which properties are at risk. Collectively, these differences point to limited capacity of FSF data to confidently assess which municipalities, social groups, and individual properties are at risk of flooding within urban areas. These results caution that national-scale model data at present may misinform urban flood risk strategies and lead to maladaptation, underscoring the importance of refined and validated urban models.

Following the recent rejection of a formal Anthropocene series/epoch by the Subcommission on Quaternary Stratigraphy (SQS) of the International Commission on Stratigraphy (ICS), and its subsequent confirmation by the International Union of Geological Sciences (IUGS), the opportunity arises to reset the definition of the Anthropocene. The case for informally recognizing the Anthropocene to be a major planetary event of Earth system transformation offers a promising way forward, but this has been criticized by proponents of an Anthropocene series/epoch. In order to move on from the assumption that it must be a time interval, and to foster a more transdisciplinary and inclusive approach, the main points of the critique must be directly addressed.

The compound drought-extreme precipitation event (CDEP) is one of the most impactful successive compound events that shift from drought to extreme precipitation in the same location within a short period. Due to its dual characteristics of drought and flood, CDEP tends to be more destructive than the impact of individual drought or flood. Yet few studies have analyzed the likelihood of CDEP at different time intervals and their potential variations under global warming. In this study, we assessed the coincidence rate between droughts and extreme precipitation events at 1-month (CDEP-1), 2-month (CDEP-2), and 3-month (CDEP-3) intervals, as well as their potential changes in a 1.5 and 2°C warming world (under both SSP2-4.5 and SSP5-8.5 scenarios). Our results suggest that global droughts and extreme precipitation events have coincided more frequently at 1-month interval than at 2- and 3-month intervals during the period 1985–2014. The global average coincidence rates of CDEP-1, CDEP-2, and CDEP-3 are 24%, 10%, and 7%, respectively. Notably, the coincidence rate of CDEP-1 exceeded 40% in Eastern Asia, north-eastern North America, and India, indicating that more than 40% of droughts have been followed by extreme precipitation events in the next month after drought termination. Under both SSP2-4.5 and SSP5-8.5 scenarios, climate warming will increase the coincidence rate of CDEP-1, CDEP-2, and CDEP-3, especially will lead to higher values in the coincidence rate of CDEP-1. This study contributes to a better understanding of the patterns of CDEP and helps to develop more targeted risk management strategies.

Manning's roughness coefficient, n, is used to describe channel roughness, and is a widely sought-after key parameter for estimating and predicting flood propagation. Due to its control of flow velocity and shear stress, n is critical for modeling timing of floods and pollutants, aquatic ecosystem health, infrastructural safety, and so on. While alternative formulations exist, open-channel n is typically regarded as temporally constant, determined from lookup tables or calibration, and its spatiotemporal variability was never examined holistically at large scales. Here, we developed and analyzed a continental-scale n dataset (along with alternative formulations) calculated from observed velocity, slope, and hydraulic radius in 200,000 surveys conducted over 5,000 U.S. sites. These large, diverse observations allowed training of a Random Forest (RF) model capable of predicting n (or alternative parameters) at high accuracy (Nash Sutcliffe model efficiency >0.7) in space and time. We show that predictable time variability explains a large fraction (∼35%) of n variance compared to spatial variability (50%). While exceptions abound, n is generally lower and more stable under higher streamflow conditions. Other factorial influences on n including land cover, sinuosity, and particle sizes largely agree with conventional intuition. Accounting for temporal variability in n could lead to substantially larger (45% at the median site) estimated flow velocities under high-flow conditions or lower (44%) velocities under low-flow conditions. Habitual exclusion of n temporal dynamics means flood peaks could arrive days before model-predicted flood waves, and peak magnitude estimation might also be erroneous. We therefore offer a model of great practical utility.

Woody Plant Encroachment (WPE) is a key driver of grassland collapse in the Southern Great Plain (SGP), resulting in a series of adverse ecological and socioeconomic consequences. Climate change will interact with ongoing WPE as it will likely shift the potential ranges of WPE species. In this study, we employed an ensemble approach integrating results from multiple Species Distribution Models to project future distribution ranges of four major WPE species (Ashe juniper, honey mesquite, post oak, and eastern redcedar) in the SGP across the 21st century. The findings highlighted a noteworthy trend: under future climate conditions, the distribution ranges for these WPE species were projected to shift northward and eastward. Of particular concern is honey mesquite with significant expansion in distribution range, potentially covering up to two-thirds of the SGP's non-agricultural area by the end of the 21st century. Conversely, the other three WPE species were expected to experience a contraction in their distribution ranges. Ashe juniper may experience a decline in its current habitats in central Texas but gain new habitats in northern Texas, Oklahoma, and Kansas. The suitable ranges of post oak and eastern redcedar were projected to shrink eastward, primarily being restricted to eastern portions of Oklahoma and Texas under the RCP4.5 and a smaller area in eastern Oklahoma under the RCP8.5. The projected shift in WPE ranges provides a scientific basis for governments to optimize the allocation of management resources and implement timely practices to control the spread of woody plants during the early encroachment stage. Our study methodology is applicable to other regions and continents with WPE issues, including Africa, South America, and Australia.

Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering new opportunities to improve our understanding of the complex Earth system. IML goes beyond conventional machine learning by not only making predictions but also seeking to elucidate the reasoning behind those predictions. The combination of predictive power and enhanced transparency makes IML a promising approach for uncovering relationships in data that may be overlooked by traditional analysis. Despite its potential, the broader implications for the field have yet to be fully appreciated. Meanwhile, the rapid proliferation of IML, still in its early stages, has been accompanied by instances of careless application. In response to these challenges, this paper focuses on how IML can effectively and appropriately aid geoscientists in advancing process understanding—areas that are often underexplored in more technical discussions of IML. Specifically, we identify pragmatic application scenarios for IML in typical geoscientific studies, such as quantifying relationships in specific contexts, generating hypotheses about potential mechanisms, and evaluating process-based models. Moreover, we present a general and practical workflow for using IML to address specific research questions. In particular, we identify several critical and common pitfalls in the use of IML that can lead to misleading conclusions, and propose corresponding good practices. Our goal is to facilitate a broader, yet more careful and thoughtful integration of IML into Earth science research, positioning it as a valuable data science tool capable of enhancing our current understanding of the Earth system.