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Large differences in projected future annual precipitation increases in North America exists across 27 CMIP6 models under four emission scenarios. These differences partly arise from weak representations of land-atmosphere interactions. Here we demonstrate an emergent constraint relationship between annual growth rates of future precipitation and growth rates of historical temperature. The original CMIP6 projections show 0.49% (SSP126), 0.98% (SSP245), 1.45% (SSP370) and 1.92% (SSP585) increases in precipitation per decade. Combining observed warming trends, the constrained results show that the best estimates of future precipitation increases are more likely to reach 0.40–0.48%, 0.83–0.93%, 1.29–1.45% and 1.70–1.87% respectively, implying an overestimated future precipitation increases across North America. The constrained results also are narrow the corresponding uncertainties (standard deviations) by 13.8–31.1%. The overestimated precipitation growth rates also reveal an overvalued annual growth rates in temperature (6.0–13.2% or 0.12–0.37°C) and in total evaporation (4.8–14.5%) by the original models' predictions. These findings highlight the important role of temperature for accurate climate predictions, which is important as temperature from current climate models' simulations often still have systematic errors. [ABSTRACT FROM AUTHOR]

Understanding and accurately predicting how the sensitivity of terrestrial vegetation productivity to rising atmospheric CO2 concentration (β) is crucial for assessing carbon sink dynamics. However, the temporal characteristics and driving mechanisms of β remain uncertain. Here, observational and CMIP6 modeling evidence suggest a decreasing trend in β at the Northern Middle and High Latitudes during the historical period of 1982–2015 (−0.082 ± 0.005% 100 ppm−1 year−1). This decreasing trend is projected to persist until the end of the 21st century (−0.082 ± 0.005% 100 ppm−1 year−1 under SSP370 and −0.166 ± 0.006% 100 ppm−1 year−1 under SSP585). The declining β indicates a weakening capacity of vegetation to mitigate warming climates, posing challenges for achieving the temperature goals of the Paris Agreement. The rise in vapor pressure deficit (VPD), that triggers stomata closure and weakens photosynthesis, is considered as the dominated factor contributing to the historical and future decline in β, accounting for 62.3%–75.2% of the effect. Nutrient availability and water availability contribute 15.7%–21.4% and 8.5%–16.3%, respectively. These findings underscore the significant role of VPD in shaping terrestrial carbon sink dynamics, an aspect that is currently insufficiently considered in many climate and ecological models. [ABSTRACT FROM AUTHOR]

The continuous advancement of remote sensing technology has been instrumental in improving models for estimating terrestrial gross primary productivity (GPP). However, challenges arise from inconsistent spatial distributions and interannual variations in GPP datasets, impeding our comprehensive understanding of the entire terrestrial carbon cycle. In contrast to previous models relying on remote sensing and environmental variables, we developed a an ensemble model based on random forest named GPPERF. This model utilized the GPP outputs from established remote sensing-based models (EC-LUE, GPP-kNDVI, GPP-NIRv, Revised-EC-LUE) as inputs for GPP estimations. GPPERF demonstrated significant effectiveness by explaining 83.7 % of the monthly variation in GPP across 171 sites. This performance surpassed the selected remote sensing models (72.4 %–77.1 %) and an independent random forest model using remote sensing and environmental variables (77.7 %). Over the period from 2001 to 2022, the global estimated GPP value using the ensemble model based on random forest was 131.2 PgC yr-1, exhibiting a trend of 0.45 PgC yr-2. Furthermore, evaluation results utilizing flux sites from ChinaFlux indicated that the dateset exhibited good generalization. In summary, the machine learning-based ensemble method helps to reduce the uncertainty in the estimation of a single remote sensing model and provides a more reliable estimation of global GPP. [ABSTRACT FROM AUTHOR]

Recently developed solar‐induced chlorophyll fluorescence‐related vegetation indices (e.g., near infrared reflectance of vegetation (NIRv) and kernel normalized difference vegetation index (kNDVI)) have been reported to be appropriate proxies for vegetation photosynthesis. These vegetation indices can be used to estimate gross primary productivity (GPP) without considering meteorological constraints. However, it is not clear whether such a statement holds true under various environmental conditions. In this study, we explored whether these vegetation indices require meteorological constraints to better characterize GPP under extreme drought conditions using three extreme drought cases in Europe in 2003, 2010, and 2018. According to the long‐term series of observations, vegetation indices (NIRv and kNDVI) alone explained 60% and 57%, respectively, of the weekly GPP variation across the 66 flux sites. The explained variation increased to 69% and 64%, respectively, for the models that take into account radiative effects (NIRv and kNDVI multiplied by radiation). However, without considering meteorological constraints, these vegetation index‐based estimations severely underestimated negative GPP anomalies under drought stress, especially in models that incorporate radiative effects. After incorporating vapor pressure deficit (VPD)‐based meteorological constraints, the GPP estimations exhibited more pronounced negative anomalies during drought periods while maintaining model accuracy (at 70% and 65%, respectively). In addition, the GPP models based on site observations were applied at the regional scale (Europe). Our results indicated that the models without meteorological constraints again underestimated the impact of drought on GPP. This study emphasizes the importance of meteorological constraints in the estimation of GPP, especially under extreme drought conditions. Plain Language Summary: The modeling of photosynthesis usually requires temperature and moisture constraints. However, the recently developed vegetation index has been reported to be a viable alternative for estimating gross primary productivity (GPP) without the need to account for these constraints; this concept warrants further investigation. Although the vegetation index can partially capture the response of vegetation to climate change, it is insufficient for estimating GPP. This is particularly the case under extreme drought conditions, and the variation in GPP is directly affected by meteorological constraints. In this study, we used drought events in Europe during 2003, 2010, and 2018 as case studies to investigate whether the incorporation of meteorological constraints is necessary for GPP estimation using these vegetation indices. Through site observation and regional estimation, we confirmed that vegetation index‐based models without meteorological constraints tend to underestimate the impact of drought on GPP. These findings provide a significant complement to the investigation of the relationship between remotely sensed vegetation indices and photosynthesis. Key Points: Vegetation index‐based models have high uncertainty in terms of model performance and response to extreme droughtModels considering only radiation and vegetation indices underestimate the impact of extreme drought on gross primary productivityIncorporating meteorological constraints improves estimates of gross primary productivity under extreme drought conditions [ABSTRACT FROM AUTHOR]

Fire weather compounded by extremely hot and dry conditions often severely impacts society and ecosystems. To mitigate and better adapt to these compound fire weather (CFW) events, a better understanding of recent and future CFW trends is needed. Here we show that in the period 1981–2020, the global average frequency and intensity of CFW events increased by 0.6 days/yr and 0.4%/yr, respectively. Increases in temperature and decreases in relative humidity were responsible for significant trends in the frequency of CFW events in 81.7% and 58.6% of locations, respectively. The same trends contributed to significant increases in CFW intensity in 72.1% and 57.9% of locations. We further demonstrate that anthropogenic climate change (due primarily to greenhouse gas emissions) has aggravated the frequency and intensity of CFW events, particularly in the Amazon region, with over 2‐fold and 1.3‐fold increases, respectively. Future projections reveal that other (individual) fire weather events are likely to shift toward CFW events accompanied by hot‐dry conditions, along with an expected rise in CFW intensity. Furthermore, the increased occurrence of CFW events is likely to substantially augment future population exposure to CFW conditions. Under the SSP5‐8.5 scenario, climate change is estimated to contribute 62.6% of the projected increase in population exposure to CFW events by the end of this century. Our findings underscore the urgent need for strong climate action to reduce population exposure to the growing threat of future fire weather events compounded with hot and dry conditions. Plain Language Summary: In recent years, the combined impact of wildfires, heatwaves, and droughts has inflicted significant global damage. However, existing wildfire risk assessments often neglect the specific hazards arising from the co‐occurrence of heatwaves and drought, which exacerbate the consequences of wildfires. To bridge this research gap, we systematically quantified the historical changes, meteorological drivers, and future projections of compound fire weather risk (CFW) resulting from the combination of heatwaves and drought. Our findings reveal accelerated increases in the occurrence of CFW events compared with other fire weather (OFW) events, with a rise of 0.6 days per year in frequency and 0.4% per year in intensity. The predominant climatic factors driving the trend of CFW events are changes in temperature and relative humidity. Furthermore, anthropogenic climate change has further intensified the frequency and intensity of CFW events. Projections indicate a substantial rise in fire weather risk associated with heatwaves and drought throughout the 21st century, while OFW risks (such as individual fire weather events) are expected to decline. This escalation in CFW events poses a grave threat of increased population exposure, particularly under high‐emission scenarios. Key Points: The risk of fire weather events combined with hot‐dry conditions increased faster than other fire weather (OFW) events from 1981 to 2020Temperature and relative humidity play a prominent role in the trends of compound fire weather (CFW) eventsFuture projections indicate a shift from OFW events to CFW events combined with hot‐dry conditions [ABSTRACT FROM AUTHOR]

Under intensifying human activities and climate change, runoff and sediment load have changed significantly in the Yangtze River Basin, China. Daily discharge data at six hydrological stations and daily precipitation at 175 weather stations from 1961–2020 were collected to analyze the changes in hydrological regimes. Two periods, namely the natural period (1961–1980) and the impacted period (1981–2020), were proposed to estimate the impacts of climate change and human activities by using regression analysis to reconstruct the natural runoff and the natural sediment load. The results show that there was a 0–3% difference in runoff reduction in the affected period, compared with the natural period at the five hydrological stations, except for the Datong station, while the sediment load in the whole river basin shows a downward trend varying from 17% to 40%. Human activities are the main reason for the reduced annual runoff at the other five stations during the impacted period, while climate change is the dominant factor for the increased runoff at Datong stations. This is because the precipitation-increased runoff in the basin of Lake Poyang exceeded the human-decreased runoff in the upper reaches. Human activities are the reason for the reduced sediment load in the mainstream during the impacted period, and their effects enhance over time, while climate change brings the opposite. [ABSTRACT FROM AUTHOR]

Understanding the mechanisms controlling downstream water‐level variations after the operation of the Three Gorges Dam is important for riverine flood and drought management. However, our quantitative understanding of the multiple controls of river morphology, vegetation, and floodplain resistance on water levels in the Middle Yangtze River (MYR) remains limited. Here, we analyze changes in river channels and floodplain resistance in the MYR using 450 cross‐sectional profiles as well as data on discharge, water levels, sediment, and satellite images from 2003 to 2015. Results show an overall decline in low‐flow water‐levels (at a given small discharge) due to severe incisions of low‐flow channels caused by a sharp reduction of ∼90% in sediment loads from 1950–2002 to 2003–2020. In contrast, high‐flow water‐levels (at a given large discharge) display minor changes. Our analysis shows that the notably increased floodplain resistance due to vegetation growth is likely the dominant factor elevating flood water‐levels, followed by riverbed coarsening and greater fluctuations in the river longitudinal profiles. Our findings further the understanding of downstream geomorphic response to dam operation and their impacts on water levels and have important implications for riverine flood management in dammed river systems. Key Points: Hungry‐water‐driven channel erosion causes the sharp reduction in low‐flow water‐levelsFloodplain resistance increase dominates the minor change in high‐flow water‐levels, followed by riverbed coarsening and greater fluctuations in the river longitudinal profileFloodplain resistance increase mainly caused by vegetation expansion in floodplain and channel bars due to the reduced floodplain submerged frequency [ABSTRACT FROM AUTHOR]

This study investigated the influence of the Three Gorges Dam (TGD) on the evolution of nearby channels in the Yangtze River (Changjiang River) system in order to better understand the environmental impact of large‐scale reservoir operations. From 2003 to 2017, the amounts of runoff and sediment transport in the Yangtze River were reduced by 3.3–14.5% and 67.8–92.7%, respectively, relative to 1955–2002 before the TGD was operational. Topographic measurements of the middle reaches (Yichang to Hukou) of the Yangtze River were analyzed from 1975 to 2017, during which time the cumulative erosion of the flood channel was 22.78 × 108 m3, and the dry channel accounted for 90.3% of the erosion. Following commissioning of the TGD, the scouring intensity of the sandy gravel section near the dam initially increased then decreased, whereas the scouring intensity of sandy sections away from the dam continued to increase. Beaches on convex banks of curved sections were scoured, and deep channels on concave banks became silted. In braided sections, the braids tended to shrink, and the diversion ratio of the main branch during dry seasons reduced, resulting in frequent branch alternation. Compared to changes in the downstream river channels of other large reservoirs worldwide, scouring from the TGD is extensive. The findings of this study are significant for river channel regulation and waterway planning in the Yangtze River and worldwide. [ABSTRACT FROM AUTHOR]

In estuaries, the morphology of inland and offshore areas usually evolves synergistically. This study examines the decadal link between longitudinal changes in morphology of branching channels and movement of the offshore depo‐center (where sediment deposition rate is maximum) of the Yangtze River estuary, under intense human interference. Integrated data analysis is provided on morphology, runoff discharge, and ebb partition ratio from 1950 to 2017. Channel‐volume reductions and change rates between isobaths in branching channels reflect the impact of estuarine engineering projects. Ebb partition ratio and duration of discharge ≥ 60 000 m3 s‐1 act as proxies for the water excavating force in branching channels and runoff intensity. It is found that deposition occurs in the lower/upper sub‐reaches (or further downstream/upstream channels) of the inland north/south branching channels, and the offshore depo‐center moves southward or southeastward, as runoff intensity grows; the reverse occurs as runoff intensity declines. This is because the horizontal circumfluence in the Yangtze estuary rotates clockwise as ebb partition ratios of the north/south branching channels increase/decrease for increasing runoff, and conversely rotates anticlockwise for decreasing runoff. Land reclamation activities, the Deepwater Channel Project, and the Qingcaosha Reservoir have impacted greatly on longitudinal changes of morphology in the North Branch and the South Passage and on ebb partition ratio variations in the North/South Channel and the North/South Passage. Dam‐induced runoff flattening has enhanced deposition in the upper/lower sub‐reaches of the north/south branching channels and caused northward movement of the offshore depo‐center, except in areas affected by estuarine engineering projects. Dam‐induced longitudinal evolution of branching channel morphology and offshore depo‐center movement will likely persist in the future, given the ongoing construction of large cascade dams in the upper Yangtze and the completion of major projects in the Yangtze estuary. © 2020 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Channel evolution and depo-center migrations in braided reaches are significantly influenced by variations in runoff. This study examines the effect of runoff variations on the erosion-deposition patterns and depocenter movements within branching channels of the near-estuary reach of the Yangtze River. We assume that variations in annual mean duration days of runoff discharges, ebb partition ratios in branching channels, and the erosional/depositional rates of entire channels and sub-reaches are representative of variations in runoff intensity, flow dynamics in branching channels, and morphological features in the channels. Our results show that the north region of Fujiangsha Waterway, the Liuhaisha branch of Rugaosha Waterway, the west branch of Tongzhousha Waterway, and the west branch of Langshansha Waterway experience deposition or reduced erosion under low runoff intensity, and erosion or reduced deposition under high runoff intensity, with the depocenters moving upstream and downstream, respectively. Other waterway branches undergo opposite trends in erosion-deposition patterns and depo-center movements as the runoff changes. These morphological changes may be associated with trends in ebb partition ratio as the runoff discharge rises and falls. By flattening the intra-annual distribution of runoff discharge, dam construction in the Yangtze Basin has altered the ebb partition ratios in waterway branches, affecting their erosion-deposition patterns and depo-center movements. Present trends are likely to continue into the future due to the succession of large cascade dams under construction along the upper Yangtze and ongoing climate change. [ABSTRACT FROM AUTHOR]

• Vegetation growth contributes 85.4–90.5% of the increased water consumption. • 94.3–98.8% of global vegetation land are sustainable due to rising precipitation. • Unsustainable regions will continuously expand due to the greening world. The global vegetation cover is increasing and is expected continue increasing till 2100, owing to CO 2 fertilisation and afforestation. This greening phenomenon may create conflicting demands for water between ecosystems, atmosphere, and humans, and has aroused a series of controversies. For exploring the effects of greening phenomenon on terrestrial water availability, we estimated the balance between water supply (precipitation and snowmelt runoff) and water consumption (evaporation, human water use, transpiration, and interception losses) in the global vegetation land based on the outputs of 30 models from the Coupled Model Intercomparison Project Phase 6. Water consumption showed an increasing trend in most regions of global vegetation land for the period 1982–2016 as well as 2040–2100, mainly driven by the increased transpiration and interception losses for vegetation growth (contributing 85.4–90.5%). Although the greening phenomenon widely exacerbates water shortages, 94.3–98.8% of the global vegetation land was still found to be sustainable during 1982–2100, due to the water supply exceeding the total water consumption. More attention should be paid to the unsustainable regions where the total water consumption exceeded the total water supply. Such areas were continuously found to increase from 2.1–5.3% (1982–2016) to 5.4–5.7% (2040–2100) of the global vegetation land. We predict that Oceania, Northern India, the southern coastal regions of North America will be transformed from sustainable regions in 1982–2016 to unsustainable regions in 2040–2100. [ABSTRACT FROM AUTHOR]

The machine learning algorithms application in atmospheric sciences along the Earth System Models has the potential of improving prediction, forecast, and reconstruction of missing data. In the current study, a combination of two machine learning techniques namely K-means, and decision tree (C4.5) algorithms, are used to separate observed precipitation into clusters and classified the associated large-scale circulation indices. Observed precipitation from the Chinese Meteorological Agency (CMA) during 1961–2016 for 83 stations in the Poyang Lake basin (PLB) is used. The results from K-Means clusters show two precipitation clusters splitting the PLB precipitation into a northern and southern cluster, with a silhouette coefficient ~0.5. The PLB precipitation leading cluster (C1) contains 48 stations accounting for 58% of the regional station density, while Cluster 2 (C2) covers 35, accounting for 42% of the stations. The interannual variability in precipitation exhibited significant differences for both clusters. The decision tree (C4.5) is employed to explore the large-scale atmospheric indices from National Climate Center (NCC) associated with each cluster during the preceding spring season as a predictor. The C1 precipitation was linked with the location and intensity of subtropical ridgeline position over Northern Africa, whereas the C2 precipitation was suggested to be associated with the Atlantic-European Polar Vortex Area Index. The precipitation anomalies further validated the results of both algorithms. The findings are in accordance with previous studies conducted globally and hence recommend the applications of machine learning techniques in atmospheric science on a sub-regional and sub-seasonal scale. Future studies should explore the dynamics of the K-Means, and C4.5 derived indicators for a better assessment on a regional scale. This research based on machine learning methods may bring a new solution to climate forecast. [ABSTRACT FROM AUTHOR]

The water level in the Yangtze River has significantly changed due to the effects of varied precipitation and dam operations, which have exerted significant effects on irrigation, navigation, and ecosystems. Based on the measured data and the proposed calculation method, we analyzed the adjustment mechanisms of the seasonal water level in the whole Yangtze River. The results were as follows. During the dry season, the rising precipitation and dam operation both increased the water level in the upper reaches and in the reaches from the Jianli to Datong stations during 1981–2014. Moreover, dam operations were the dominant factor (except at Datong station). In the reaches from the Yichang to Shashi stations, dam operations are the reason for the reduction of the measured water level in the dry season, while the rising precipitation had an opposite influence. During the flood season, dam operations helped to reduce the water level from the upper reaches to the estuary during 1981–2014, while climate variation stresses adversely affected the entire river basin. In the reaches between the Luoshan and Jianli stations, climate variation is the dominant factor for the increased water level during the flood season, while dam operation is the reason for the reduced water level at the other six hydrological stations. [ABSTRACT FROM AUTHOR]

Understanding and accurately predicting how the sensitivity of terrestrial vegetation productivity to rising atmospheric CO 2 concentration ( β ) is crucial for assessing carbon sink dynamics. However, the temporal characteristics and driving mechanisms of β remain uncertain. Here, observational and CMIP6 modeling evidence suggest a decreasing trend in β at the Northern Middle and High Latitudes during the historical period of 1982-2015 (-0.082 ± 0.005% 100 ppm -1 year -1 ). This decreasing trend is projected to persist until the end of the 21st century (-0.082 ± 0.005% 100 ppm -1 year -1 under SSP370 and -0.166 ± 0.006% 100 ppm -1 year -1 under SSP585). The declining β indicates a weakening capacity of vegetation to mitigate warming climates, posing challenges for achieving the temperature goals of the Paris Agreement. The rise in vapor pressure deficit (VPD), that triggers stomata closure and weakens photosynthesis, is considered as the dominated factor contributing to the historical and future decline in β , accounting for 62.3%-75.2% of the effect. Nutrient availability and water availability contribute 15.7%-21.4% and 8.5%-16.3%, respectively. These findings underscore the significant role of VPD in shaping terrestrial carbon sink dynamics, an aspect that is currently insufficiently considered in many climate and ecological models., Competing Interests: The authors declare no competing interests., (© 2024 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)