Ensuring food security is crucial in the context of climate change and increased extreme weather events. Crop production depends not only on yield but also on the harvestable fraction (HF), the ratio of harvested areas to planted areas. While the impacts of climate fluctuations on crop yields are well-document, the role of HF-a critical yet underexplored factor-remains poorly understood. This study introduces HF as a key metric for assessing how temperature, drought, and precipitation affect the production of major staple crops (winter wheat, spring wheat, and maize) in the United States at the county level. Our findings indicate that Killing Degree Days (KDD) and Drought Days (DD) are key drivers of production declines, reducing both yield and HF. From 1982 to 2020, changes in KDD and DD led to more significant reductions in crop production in the Midwest compared to other regions. Projections for 2021–2100 under different Shared Socioeconomic Pathways (SSPs) indicate even steeper declines in yield, HF and production, especially under the high-emission scenario (SSP5-8.5), with anticipated net production decreases of 6.08%, 8.2%, and 7.57% for the three crops. Although HF may increase in colder regions, this does not offset losses in warmer areas, leading to net HF decreases of 0.36%, 2.09%, and 5.82% for the three crops. This study finds that drought and extreme heat considerably reduce food production by simultaneously lowering yields and HF and underscores the need for adaptive strategies that address both yield and HF to enhance food security in a changing climate.
Forest disturbances pose an increasing threat to the ecosystem services provided by tropical forests in the Congo Basin, yet their distribution remains poorly understood. To address this, we create a high-confidence forest disturbance data set for the Congo Basin from 2000 to 2022 by harmonizing the Global Forest Change (GFC) and Tropical Moist Forest (TMF) data sets. Though the Democratic Republic of Congo (DRC) accounted for the most disturbance observed (61,174 km2), we identify Cameroon as an emerging deforestation frontier. Among all six Congo Basin countries, only Cameroon saw a significant increasing annual contribution to the total forest disturbance observed in the Congo Basin over the past 20 years (slope = 0.49% yr−1, p = 0.004) and the second-highest extent of forest disturbance (7,013 km2). Across the Congo Basin, disturbances mainly occurred near roads (26,737 km2) and outside formal land allocations (19,217 km2). In Cameroon, the extent of forest disturbance in community forests (5 ± 1%) was higher than in agro-industrial plantations (3 ± 2%) and logging concessions (3 ± 1%). We observe a basin-wide increase in the extent and frequency of forest disturbances, suggesting a shift toward commercial land-use practices associated with larger clearing. Our findings reveal changes in forest disturbance patterns in the Congo Basin over the past 20 years that warrant continued monitoring and improved understanding of their socioeconomic drivers to prevent large-scale deforestation as observed in the Amazon and Southeast Asia.
Recent advancements in observational data and experimental research have significantly enhanced our understanding of the mechanisms governing the magnitude, distribution, and dynamics of soil organic carbon (SOC) patterns. However, few studies have systematically explored the hierarchical drivers of SOC. This study addresses this gap by integrating multiple independent datasets—covering productivity, carbon allocation, carbon turnover, and carbon fractions—to construct a comprehensive framework for the hierarchical drivers shaping global SOC patterns. Using this framework, we examine the impact pathways and contributions of primary drivers. Our findings show that climate, as the fundamental primary driver, mainly influences SOC through carbon input pathways, while soil properties, as a secondary driver, predominantly affect SOC via carbon output pathways. Further analysis reveals that carbon input plays a key role in shaping topsoil SOC distribution, while carbon output is more influential in regulating subsoil SOC. In both cases, these drivers exert their effects primarily through stable organic carbon fractions, particularly mineral-associated organic carbon (MAOC). The influence of these drivers on particulate organic carbon (POC), however, is in the opposite direction. This study provides the first quantification of the impact pathways and relative strengths of the hierarchical drivers shaping global SOC. It also underscores the need to consider the hierarchical structure of these drivers when assessing the magnitude, distribution, and dynamics of SOC, particularly in relation to its fractions.
Land-based mitigation strategies, such as afforestation and avoided deforestation, are critical to achieving the Paris Agreement's goal of limiting global warming to 1.5°C or 2°C. However, the biophysical impacts of anthropogenic land use and land cover change (LULCC), particularly deforestation and afforestation, on extreme weather events in West Africa remain poorly understood at the regional scale. In this study, we present the first high-resolution LULCC experiments (at 3 km resolution, covering 2012–2022) using the advanced fully coupled atmosphere-hydrology WRF-Hydro model system to assess the potential impacts of idealized land use and land management scenarios on extreme events in the West African savannah region. By analyzing 18 extreme weather indices, we show that deforestation significantly affects temperature extremes (up to 0.45 ± 0.04°C), with effects on regional rainfall extremes being approximately twice as pronounced as those on mean rainfall conditions, along with a significant increase in the number of dry days. Conversely, afforestation generally leads to increases in both mean and extreme precipitation, along with fewer dry days and shorter drought durations. Notably, afforestation produces contrasting responses in temperature extremes depending on vegetation type: converting grassland to mixed or evergreen forest reduces extreme heat via increased transpiration, while conversion to savanna or woody savanna may intensify heat extremes due to albedo-induced warming effects.
The UK high-plus-plus (H++) scenario for high-end sea level rise is used in sensitivity testing for significant infrastructure (e.g., nuclear facilities) and forms part of the Environment Agency planning guidance in England. However, the existing H++ scenario, developed as part of the UK Climate Projections in 2009 (UKCP09), does not reflect the latest science knowledge on ice sheet instability processes and has limitations, as revealed in consultations with users of this information. Here, we outline a new, co-produced H++ framework to inform decision-making that involves: (a) screening decisions against an updated H++ storyline that reflects major scientific advances since UKCP09; (b) evaluating adaptation options and damage costs against a wider library of alternative, plausible storylines; and, (c) a decision-exploring initiative to facilitate long-term strategic thinking. Our H++ screening storyline is based on the Intergovernmental Panel on Climate Change Sixth Assessment Report low-likelihood high-impact sea level rise assessment. In response to user needs, all storylines within the H++ framework provide time-continuous, geographically-specific sea level rise projections for the UK to 2300 and information on sea level rise rates. For all UK capital city locations, our screening storyline projects high-end sea level rise greater than: 1 m by 2100; 4 m by 2150; 9 m by 2200; and, 15 m by 2300. At all locations, maximum rates reach over 100 mm/yr. Our H++ framework can be adapted for different climate impact drivers, sectors or regions, and respond to emerging evidence and user feedback, supporting robust adaptation planning and decision-making under deep uncertainty.
This study reviews results from literature to investigate top-down versus bottom-up emission estimates from oil and natural gas (O&NG) operations. Ten years ago, a landmark study by Brandt et al. (2014, https://doi.org/10.1126/science.1247045) found generally higher top-down emissions determinations than industry estimates. Here, we revisit this topic by examining 10 years of peer-reviewed literature that has been published since. A total of 73 results for top-down/bottom-up ratios from 57 articles were included. Most of the published literature focuses on methane emissions, with only 11 articles reporting other O&NG emissions. In the newer literature, 49 (86%) of the studies reported that inventory (bottom-up) emissions underestimated results from determinations based on ambient observations (top-down). This finding is similar to the Brandt et al. (2014) study, which found that 82% of literature reported higher top-down emissions than inventory-based estimates, suggesting little improvement in inventory data accuracy over the past decade despite this discrepancy having been well documented earlier. However, fewer extreme ratio values (top-down/bottom-up) >10 were reported after 2014. A meta-analysis building on carefully selected literature that has been published since 2014 resulted in a mean ratio value of 2.50 ± 0.62, implying that measured emissions were on average 250% of inventory values. For North American countries, the mean ratio values ranged from 2.59 to 3.75, exceeding the global average. The lowest ratio values were observed when United Nations Framework Convention on Climate Change (UNFCCC) reports were used as inventory comparisons to top-down measurements (mean = 1.45); all other inventory types resulted in mean ratios >2.
Changes in rainfall and temperature regimes increasingly threaten global crop productivity, particularly in water-limited regions. Climate-smart agriculture aims to improve yields while minimizing its climate impact, such as from soil greenhouse gas (GHG) emissions driven by microbial activity. From an irrigation perspective, this underscores the need to assess irrigation practices beyond the traditional objectives of maximizing yield and water use efficiency by also considering their climate impact from soil GHG emissions. To address this gap, we frame climate-smart irrigation as a multi-objective optimization problem and derive a dual-index framework for evaluating irrigation practices across productivity, water consumption, and climate impact dimensions. The Marginal Irrigation Water Productivity (MIWP) index quantifies additional yield per unit of irrigation water, while the Marginal Irrigation Climate Impact (MICI) index measures the associated changes in soil GHG emissions. We apply this dual-index framework to wheat and rice field irrigation studies with varying soil GHG compositions, showing its ability to assess irrigation across different crop systems. Crop model simulations further demonstrate how different irrigation practices are mapped within the MIWP-MICI space, where Pareto-optimal solutions highlight trade-offs between productivity and climate impact goals. Our approach provides a consistent, quantitative basis for comparing irrigation across multiple dimensions of climate-smart irrigation.
Urban green infrastructure (UGI) is critical for mitigating fine particulate matter (PM2.5) pollution, a major obstacle to sustainable urban development. However, the morphological spatial patterns of UGI and their impact on PM2.5 remain largely unexplored, as most related studies have focused solely on case studies. This study employed morphological spatial pattern analysis to document the national scale spatial distribution of seven UGI morphology space patterns (MSPs) across 288 Chinese cities. It verified the disparities of each MSP under varying geographic conditions and scalar categories. Using advanced interpretable machine learning methods that account for aggregated contribution of location features, the study confirmed the positive role of UGI proportion in mitigating PM2.5 levels. Significantly, the findings revealed that smaller non-core UGI areas, such as perforation and islet, exert a more pronounced positive impact on reducing PM2.5. Furthermore, the study explored the potential PM2.5 risks facing Chinese cities due to temporal changes of UGI. The study results not only fill the gap in UGI research, but also contributes a feasible urban planning method and provide a basis for reducing PM2.5 to promote sustainable urban development.
Climate change poses a serious risk for the freshwater ecosystem of Western Patagonia and threatens glacial and non-glacial water resources. Here, we model the historical glacio-hydrology of 2,236 catchments across the Western Patagonia, and project climate change impacts through the 21st century. To this end, we develop a novel modeling framework that combines Long Short-Term Memory (LSTM) neural networks with ice-dynamical glacier modeling using the Open Global Glacier Model (OGGM). We evaluate the ability of this hybrid framework to predict streamflow in ungauged basins (PUB) and regions (PUR) through 10-fold cross-validation and compare the results with those obtained with a LSTM model without a glacier component, and two process-based coupled glacio-hydrological models. The hybrid modeling approach outperforms all other approaches in 38% and 44% of the catchments considering PUB and PUR evaluations, respectively. Using our new hybrid approach, we estimate an average regional freshwater flux of 19,815 m3 s−1 for the period 2000–2019, with glacier melt contributing 29% during the summer season. Under a high-emission scenario (Shared socioeconomic pathways 5-8.5), the northern region (>46°S) is projected to experience the largest reductions in runoff, with dry season runoff decreasing by almost 50% by the end of the century. In contrast, runoff increases are projected for glacierized basins in the southern regions, with average relative changes of 10%–25% and a marked seasonality shift. The results highlight the potential of hybrid modeling in glacio-hydrology and provide important information for climate change adaptation in Western Patagonia.
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