Earths Future最新文献

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.

Photovoltaic (PV) installations are a leading technology for generating green electricity and reducing carbon emissions. Roofing highways with solar panels offers a new opportunity for PV development, but its potential of global deployment and associated socio-economic impacts have not been investigated. Here, we combine solar PV output modeling with the global highway distribution and levelized cost of electricity to estimate the potential and economic feasibility of deploying highway PV systems worldwide. We also quantify its co-benefits of reducing CO₂ equivalent emissions and traffic losses (road traffic deaths and socio-economic burdens). Our analysis reveals a potential for generating 17.58 PWh yr⁻¹ of electricity, of which nearly 56% can be realized at a cost below US$100 MWh⁻¹. Achieving the full highway PV potential could offset 28.78% (28.21%–29.1%) of the global total carbon emissions in 2018, prevent approximately 0.15 million road traffic deaths, and reduce US$0.43 ± 0.16 trillion socio-economic burdens per year. Highway PV projects could bring a net return of about US$14.42 ± 4.04 trillion over a 25-year lifetime. To exploit the full potential of highway PV, countries with various income levels must strengthen cooperation and balance the multiple socio-economic co-benefits.

Beijing is undergoing multiple challenges including urbanization, warming and aging. The Beijing megalopolis of 20 million people now suffers more cold-related than heat-related deaths. Stratospheric aerosol injection (SAI) geoengineering is designed to lower surface temperatures, so if SAI were ever done, it may reduce future heat-related mortality, while also increasing cold-related mortality. Here we use four Earth System Models (ESM) downscaled to 10 km resolution with the Weather Research and Forecasting (WRF) system to capture urban temperature, humidity and wind speeds. Temperature-related mortality risk were calculated using a distributed lag nonlinear model (DLNM) of the elderly (over 65s) under the dynamically downscaled moderate (RCP4.5) and extreme (RCP8.5) greenhouse gas, and the G4 SAI scenarios. We used population demographics for all five shared socioeconomic pathways (SSP) and various adaptation measures. Heat-related excess deaths under G4 are 630∼3,160 per year fewer than RCP4.5, while cold-related deaths are 370∼1,990 more than RCP4.5 during 2060–2069, with a marginally significant net reduction. G4 significantly reduces the excess deaths relative to RCP8.5. Both heat-related and cold-related mortality will increase by 240∼490% when the aging population is accounted for, and decrease by 11%, 23% and 44% under low, medium and high adaptation relative to a no adaptation scenario. Dynamical downscaling produces better quality climate simulations than commonly used statistical approaches, and in the case of Beijing, significantly fewer heat-related deaths. The marginal health benefits of modest future SAI in Beijing may be representative of the population impacts in the extra-tropics where deaths due to cold are more than those caused by heat.

In regions characterized by a high concentration of small reservoirs, there is often public debate about the effectiveness of these structures in locally adapting to and mitigating drought impacts, bearing in mind their potential to modify or induce drought events in downstream areas. In this study, we investigated the influence of a Dense Network of Small Reservoirs (DNR) on the emergence and intensification of drought impacts at catchment scale, as well as their local social benefits. This analysis was based on the Socio-Hydrological-Agricultural-Reservoir (SHARE) model, specially developed for this purpose, with a medium-sized catchment in the semi-arid region of Brazil as a case study. We identified that, while a DNR can prolong the effects of a hydrological drought on storage in a large strategic reservoir at the catchment outlet by obstructing surface-runoff connectivity, it plays a crucial role in mitigating drought impacts at a local level. Specifically, the presence of small reservoirs has the potential to boost local agricultural production by up to 5 times compared to scenarios without these structures. In addition, our simulation results suggest there is a notable reduction in the need for emergency water distribution by water trucks in the presence of a DNR. This study highlights the need for a balanced approach to implementing public policies, weighing the local benefits of small reservoirs against the possible downstream impacts on large reservoirs.

Heatwaves have significant effects on ecosystems and human health. Human habitability is impacted severely as human exposure to heatwaves is projected to increase, however, the contribution of soil moisture effects to the increased exposure is unknown. We use data from four climate models, in which two experiments are used to isolate soil moisture effects and in this way to examine projected changes of soil moisture contributions to projected increases in heatwave events. Contributions from soil moisture to future population exposure to heatwaves are also investigated. With soil moisture effects combined with global warming, the longest yearly heatwaves are found to increase by up to 20 days, intensify by up to 2°C in mean temperature, with an increasing of frequency by 15% (the percentage relative to the total number of days for a year) over most mid-latitude land regions by 2040–2070 under the SSP585 high emissions scenario. Furthermore, soil moisture changes are found to have a significant role in projected increases of multiple heatwave characteristics regionally compared with the global land area and contribute to more global population exposed to heatwaves.

Forests are a large carbon sink and could serve as natural climate solutions that help moderate future warming. Thus, establishing forest carbon baselines is essential for tracking climate-mitigation targets. Western US forests are natural climate solution hotspots but are profoundly threatened by drought and altered disturbance regimes. How these factors shape spatial patterns of carbon storage and carbon change over time is poorly resolved. Here, we estimate live and dead forest carbon density in 19 forested western US ecoregions with national inventory data (2005–2019) to determine: (a) current carbon distributions, (b) underpinning drivers, and (c) recent trends. Potential drivers of current carbon included harvest, wildfire, insect and disease, topography, and climate. Using random forests, we evaluated driver importance and relationships with current live and dead carbon within ecoregions. We assessed trends using linear models. Pacific Northwest (PNW) and Southwest (SW) ecoregions were most and least carbon dense, respectively. Climate was an important carbon driver in the SW and Lower Rockies. Fire reduced live and increased dead carbon, and was most important in the Upper Rockies and California. No ecoregion was unaffected by fire. Harvest and private ownership reduced carbon, particularly in the PNW. Since 2005, live carbon declined across much of the western US, likely from drought and fire. Carbon has increased in PNW ecoregions, likely recovering from past harvest, but recent record fire years may alter trajectories. Our results provide insight into western US forest carbon function and future vulnerabilities, which is vital for effective climate change mitigation strategies.

The concept of climate tipping points in socio-environmental systems is increasingly being used to describe nonlinear climate change impacts and encourage social transformations in response to climate change. However, the processes that lead to these tipping points and their impacts are highly complex and deeply uncertain. This is due to numerous interacting environmental and societal system components, constant system evolution, and uncertainty in the relationships between events and their consequences. In the face of this complexity and uncertainty, this research presents a conceptual framework that describes systemic processes that could lead to tipping points socio-environmental systems, with a focus on coastal communities facing sea level rise. Within this context, we propose an organizational framework for system description that consists of elements, state variables, links, internal processes, and exogenous influences. This framework is then used to describe three mechanisms by which socio-environmental tipping could occur: feedback processes, cascading linkages, and nonlinear relationships. We presented this conceptual framework to an expert panel of coastal practitioners and found that it has potential to characterize the effects of secondary climatic impacts that are rarely the focus of coastal risk analyses. Finally, we identify salient areas for further research that can build upon the proposed conceptual framework to inform practical efforts that support climate adaptation and resilience.

Heavy precipitation, which is changing significantly as Earth's climate warms, can result in flooding that seriously damages societies. However, little is known about how heavy precipitation of varying durations responds to the diverse gradients of urban development in China. Through statistical analyses spanning from 1990 to 2021, we have examined shorter-duration (≤3 days) and longer-duration (>3 days) heavy precipitation across a spectrum of urban development, encompassing long-term built-up (LTB), recently built-up (RTB), and rural background catchments within each urban agglomeration catchment (UAC) across China. We find that urbanization primarily influences shorter-duration heavy precipitation, with a more pronounced effect observed in the LTB catchments. Conversely, the influence of urbanization on longer-duration heavy precipitation appears to be more weakened in the RTB catchments. The intensification of shorter-duration heavy precipitation induced by urbanization is more pronounced in humid regions and within larger UACs, while the urban effect on longer-duration heavy precipitation is weaker in humid regions and within larger UACs. Notably, the attribution analysis results of the geographical detector model confirm our findings. Anthropogenic-related factors (population density, nighttime light data, impervious surface percent, land surface temperature) significantly influence shorter-duration heavy precipitation in more UACs than natural factors (distance from the coast, wind and elevation), while natural factors dominate longer-duration events in larger UACs across China. Our results highlight the necessity of considering the spatial difference between the UAC center and UAC periphery for accurate projections and effective prevention of heavy precipitation and potential flood risks in the future.