Abstract Due to global warming, the lengths of the four seasons, which are always taken as constant values, have experienced significant variations with rising temperature. Such changes play different roles on regional climate change, with the most significant effect on drylands. To guarantee local crop yields and preserve ecosystems, the identification of the changes of the four seasons in drylands is important. Our results show that, relative to humid lands, changing trends in lengths of spring, summer and autumn were particularly enhanced in drylands of the Northern Hemisphere mid-latitudes during 1951-2020. In this period, summer length has increased by 0.51 day per year, while spring and autumn lengths have contracted by 0.14 and 0.14 day per year, respectively. However, the enhanced changes in drylands did not appear in winter length. Such changes of spring, summer and autumn in drylands are dominated by internal variability over the entire study period, with a stronger external forcing effect on drylands than on humid lands. In drylands, the external forcing contributed to the changes in lengths of spring, summer and autumn by 30.1%, 42.2% and 29.4%, respectively. The external forcing has become an increasingly important component since 1990, with the ability to dominate all seasons in drylands after 2010. Nevertheless, only one out of the 16 Coupled Model Intercomparison Project Phase 6 (CMIP6) models used in this study can capture the enhanced changes in the lengths of spring, summer and autumn in drylands. Further investigation on the local effects of changes in seasons on agriculture and ecosystem would be needed, especially for the fragile regions.
In recent years, with the booming development of social networks and e-commerce, users have increasingly convenient access to information, while a large amount of data continues to emerge, leading to more and more serious information overload. To alleviate the problem of information overload, recommendation systems have emerged to assist users in sifting through massive amounts of information to find content that meets their needs. Sequential recommendation, as a form of recommendation system, mainly analyzes the interaction behavior between users and items, models user characteristics, and then uses various methods to capture users' long-term and short-term preferences to recommend items of interest to users. Based on the perspective of user preference change over time, this paper provides an in-depth analysis of the current research progress and methods of user behavior sequence recommendation. At the same time, this paper proposes corresponding solution strategies for the problems of cold start, sparse matrix and noise interference faced by traditional recommendation systems. Finally, we will discuss the challenges and future research directions of recommendation systems to provide the theoretical basis for further improvement of recommendation systems.
The top reach of the Yellow River Basin (TYRB) is located in the northwestern Tibetan Plateau, and the precipitation in this area greatly affects the water resources throughout the whole basin. By analyzing the changes in summer precipitation during 1982-2019, an 11.4% increase in precipitation occurred after 2002, accompanied by significant changes in the contribution of moisture sources. Using the dynamic precipitation recycling model (DRM) and moisture source attribution method, we found that 95.4% of water vapor for summer precipitable water in the TYRB comes from the local area (10.6%), the Tibetan Plateau area (35.8%), the central Eurasian area (22.5%), the North Africa-West Asia area (5.3%), the Southern Asia-North Indian Ocean area (14.6%), and the South China Sea-Western Pacific area (6.6%). Compared with 1990-2002, the contributions of the central Eurasian area, the North Africa-West Asia area and the South China Sea-Western Pacific area increased during 2003-2019. Such decadal changes can also be verified by evapotranspiration minus precipitation (E-P). Moisture in these places increased, contributing greatly to the increase in precipitation in the TYRB. The increased precipitation would cause an 11.7 mm runoff increase by the fitted equation, which can provide significant guidance for the problems of Yellow River cutoff and drought.
Objective: This study aims to evaluate the accuracy of different modeling methods and tree structural parameters extracted from airborne LiDAR for estimating carbon emissions reduction and assess their reliability as Certified Emission Reduction (CER) assessment techniques. Methods: LiDAR data was collected from an afforestation project in Beijing, China. Various modeling methods, including statistical regression and machine learning algorithms, were used to estimate biomass and carbon emissions reduction. The models were evaluated under two schemes: tree-species-specific modeling scheme (Scheme 1) and all-sample modeling scheme (Scheme 2) using cross-validation and compared with ground-based estimations and pre-estimated emission reductions. Results: Totally, the biomass estimation models in scheme 1 showed better accuracy than scheme 2. In scheme 1, The Random Forest (RF) and Cubist models achieved the highest prediction accuracy (R 2 = 0.89, RMSE = 22.87 kg, CV RMSE = 52.00 kg), followed by GDBT and Cubist, with SVR and GAM performing the weakest. In scheme 2, Cubist model had the highest accuracy (R 2 = 0.75, RMSE = 33.95 kg, CV RMSE = 36.05 kg), followed by RF and GBDT, with SVR and GAM performing the weakest. LiDAR-based estimates of carbon emissions reduction were closer to ground-based estimations and higher than pre-estimated values. Conclusion: This study demonstrates that LiDAR-based models using tree structural parameters can accurately assess carbon emissions reduction. The models outperformed traditional methods in terms of cost and accuracy. Considering tree species in the modeling process improved the accuracy of the models. LiDAR technology has the potential to be a reliable assessment technique for carbon emissions reduction in forestry projects. The pre-trained models can be used for multiple predictions, reducing the cost of carbon sink surveys. Overall, LiDAR-based models provide a promising approach for assessing carbon emissions reduction and can contribute to mitigating climate change.
The source area of the Yellow River Basin (SYRB) is located in the northeastern Tibetan Plateau, and the precipitation in the SYRB is of great importance to the water resources throughout the whole basin. By analyzing the summer precipitation in the SYRB, we found that an 11.4% increase in precipitation occurred during 2003–2019 compared with 1982–2002. Such interdecadal increase of summer precipitation was due to significant changes of moisture contribution from external moisture source. In the past 38 years, 95.4% of the water vapor for summer precipitable water in the SYRB came from local evapotranspiration (10.6%), the Tibetan Plateau area (35.8%), central Eurasian area (22.5%), South Asia-northern Indian Ocean area (14.6%), South China Sea-western Pacific area (6.6%), and North Africa-West Asia area (5.3%). Thus, external water vapor supplied about 84.8% of summer precipitable water in the SYRB. Compared with 1990–2002, the relative growth rates of moisture contribution during 2003–2019 from the central Eurasian area, North Africa-West Asia area and South China Sea-western Pacific area increased by 2.40%, 4.55% and 15.07%, respectively. Such interdecadal changes were verified by evapotranspiration minus precipitation for it can illustrate the supply capacity of the moisture source. Water vapor supplies in these areas increased during 2003–2019, which greatly contributed to the increase of summer precipitation in the SYRB.
The top reach of the Yellow River Basin (TYRB) is located in the northwestern Tibetan Plateau, and the precipitation in this area greatly affects the water resources throughout the whole basin. By analyzing the changes in summer precipitation during 1982-2019, an 11.4% increase in precipitation occurred after 2002, accompanied by significant changes in the contribution of moisture sources. Using the dynamic precipitation recycling model (DRM) and moisture source attribution method, we found that 95.4% of water vapor for summer precipitable water in the TYRB comes from the local area (10.6%), the Tibetan Plateau area (35.8%), the central Eurasian area (22.5%), the North Africa-West Asia area (5.3%), the Southern Asia-North Indian Ocean area (14.6%), and the South China Sea-Western Pacific area (6.6%). Compared with 1990-2002, the contributions of the central Eurasian area, the North Africa-West Asia area and the South China Sea-Western Pacific area increased during 2003-2019. Such decadal changes can also be verified by evapotranspiration minus precipitation (E-P). Moisture in these places increased, contributing greatly to the increase in precipitation in the TYRB. The increased precipitation would cause an 11.7 mm runoff increase by the fitted equation, which can provide significant guidance for the problems of Yellow River cutoff and drought.
The semi-arid regions of East Asia are located in the transition area between regions dominated by the monsoon system and by westerly winds; their interaction is the key to understand precipitation changes, especially in the summer. Our results show that the enhancement of both the monsoon and westerly winds occurs in wet years, leading to stronger convergence and more rainfall. Weakening of both the monsoon and westerly winds occurs in dry years and results in less rainfall. Such interaction between the monsoon and westerlies is not constant; the boundary of their effects is changing all the time. As the monsoon strengthens, it shifts to the west in wet years and covers most of the semi-arid regions, and the negative effect of the El Niño-Southern Oscillation (ENSO) system on precipitation in the semi-arid regions becomes obvious. However, westward expansion has not been evident over the past 70 years in historic data. In the future, the monsoon will obviously expand westward, and the precipitation over the Loess Plateau will gradually increase as the monsoon boundary expand westward until the end of the 21st century. This change indicates that more rainfall will occur in the semi-arid regions of East Asia, which could dramatically change the ecological environment, especially over the Loess Plateau.
Abstract Regional anthropogenic warming caused stronger and shorter cold events in the winter (December–February) of 2020–21, with the strongest cooling of −10 °C covering an area of 1.63 × 10 7 km 2 over East Asia. In contrast to previous cold events, the extreme cold events in 2020–21 were a result of meridional circulation change due to stronger regional anthropogenic warming. Our results show a multi-aspect anthropogenic effect in the process of cold events, and illustrate that anthropogenic effect played a role not only in the thermodynamic process but also in the dynamic process. The exchange of equilibrium from low to high index does not take fewer cold events anymore; new principles on equilibrium have appeared and will soon play an effect in more fields of climate change.
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