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This study uses the Weather Research and Forecasting (WRF) model with five different cumulus parameterization schemes (CPSs) at a resolution of 30 km to simulate the summer (June, July, and August) extreme precipitation event in the Yellow River Basin (YRB) during 2018. The goal of this study is to investigate the sensitivity of extreme precipitation simulation in the YRB during the summer of 2018 to CPSs in the WRF model. The results show that all five CPSs were capable of approximately simulating the direction of the rain bands in the YRB during the summer of 2018, but the simulation results of all CPSs tended to overestimate the value of precipitation amount. Upon further evaluation using seven different methods, it was found that the Betts–Miller–Janjic scheme provided the best simulation of this event. The complex orography of the YRB has a significant influence on moisture transport. The WRF model may have overestimated the moisture flux, which could have contributed to the overestimation of precipitation. The summer extreme precipitation event in the YRB during 2018 may have been influenced by an influx of excessive moisture from the western boundary.

【Objective】This study investigates the spatiotemporal dynamics of gross primary productivity (GPP) in the Henan Section of the Yellow River Basin, identify its extent and driving factors, and to provide a scientific basis for ecosystem conservation and management.【Method】We used Theil-Sen median trend analysis and the Mann-Kendall test to assess long-term GPP trends from 2001 to 2022. The GeoDetector model was used to evaluate the influence of climatic, topographic, and anthropogenic factors on spatial variation in GPP.【Result】From 2001 to 2022, the overall GPP in the region showed a fluctuating yet upward trend. Spatially, GPP distribution was highly heterogeneous, with clusters of high values primarily in the Southwest and lower values in central and Northern areas. Regions with increasing GPP accounted for 89.23% of the total area, concentrated in Sanmenxia, Luoyang, Jiyuan, and Xinxiang. Although the total area of cropland, grassland, and forestland declined during this period, their respective annual GPP levels increased. Slope aspect, elevation, and population size emerged as the primary factors influencing spatial variation in GPP. Interaction analysis indicated that the combined effects of slope gradient and elevation had the strongest influence on GPP. Furthermore, ecological factor analysis highlighted slope aspect, elevation, population density, and cropland area as significant contributors to GPP variation. Risk assessment revealed that areas with stable temperature, increased precipitation, and gentle terrain supported higher GPP growth.【Conclusion】The Henan Section of the Yellow River Basin has seen GPP improvement in the past two decades, despite reductions in vegetated land area. Topographic and climatic factors, especially elevation and slope-related variables, play the dominant role in shaping GPP patterns, while anthropogenic factors have a moderate influence. These findings underscore the need to consider terrain and climate stability in regional ecological management and suggest that targeted conservation in high-growth areas may further enhance ecosystem productivity.

In the context of promoting high-quality development in the Yellow River Basin (YRB) of China, urgent action is needed to achieve the “Dual Carbon” goal through energy savings, emission reductions, and industrial upgrading. This study measures carbon emissions from eight types of energy consumption across 43 industries from 2000 to 2019. Using the Kaya-LMDI model, factors affecting carbon emissions are analyzed, and the relationship between industrial structure and carbon emissions is explored through the coefficient of variation (CV). The findings reveal that coal consumption remains significantly higher than other energy sources, and the effect of energy structure adjustment on carbon emission reduction is limited compared to the impact of energy consumption increase on carbon emission growth. Moreover, the economic output effect is identified as the primary driving factor of carbon emissions, while energy utilization rate is crucial in achieving energy savings and emission reductions. Finally, the CV of carbon emissions across 43 industries is increasing. Based on these results, we suggest several policy recommendations, including prioritizing ecological concerns, developing comprehensive and scientifically sound plans, optimizing energy consumption structure, improving energy utilization efficiency, and adjusting industrial structure to promote sustainable development in the YRB.

The present study selects cities such as Zhengzhou, Kaifeng, Anyang, Hebi, Xinxiang, Jiaozuo, and Puyang along the Yellow River Basin in Henan Province. The data of six pollutants, such as PM2.5, PM10, SO2, CO, NO2, and O3, in various cities from 2019 to 2021, and the monthly primary pollutant data of seven cities in the past five years were collected through various channels. The air quality of the above seven cities was analyzed with the spatial-temporal distribution of pollutants as the research objective and geographic information system as the research tool. The results show that affected by the distribution of key pollution sources and meteorological conditions in the urban area, the PM2.5 concentration generally shows a zonal feature of decreasing from northwest to Southeast. The high-value area is located in the north and west of the integrated area of the seven cities, and the low-value area is located in the Southeast of the seven cities.

Scholars have turned their attention to the ecological protection and high-quality development of China’s Yellow River Basin in recent years. The basin is a major agricultural production area in China, hence investigating agricultural carbon emission reduction strategies in the basin is crucial. The research object in this article is the agricultural departmen.ts of China’s nine provinces in the Yellow River Basin from 2005 to 2018. Agricultural carbon emissions are measured using agricultural land usage, rice planting, crop planting, straw burning, and livestock breeding as agricultural carbon sources. In addition, the GTWR model is used to examine the spatiotemporal aspects of the impact of five factors on agricultural carbon emissions in this paper. The findings reveal that the five factors have varying degrees and directions of influence.

Understanding the impacts of drought and climate change on vegetation dynamics is of great significance in terms of formulating vegetation management strategies and predicting future vegetation growth. In this study, Pearson correlation analysis was used to investigate the correlations between drought, climatic factors and vegetation conditions, and linear regression analysis was adopted to investigate the time-lag and time-accumulation effects of climatic factors on vegetation coverage based on the standardized evapotranspiration deficit index (SEDI), normalized difference vegetation index (NDVI), and gridded meteorological dataset in the Yellow River Basin (YLRB) and Yangtze River Basin (YTRB), China. The results showed that (1) the SEDI in the YLRB showed no significant change over time and space during the growing season from 1982 to 2015, whereas it increased significantly in the YTRB (slope = 0.013/year, p < 0.01), and more than 40% of the area showed a significant trend of wetness. The NDVI of the two basins, YLRB and YTRB, increased significantly at rate of 0.011/decade and 0.016/decade, respectively (p < 0.01). (2) Drought had a significant impact on vegetation in 49% of the YLRB area, which was mainly located in the northern region. In the YTRB, the area significantly affected by drought accounted for 21% of the total area, which was mainly distributed in the Sichuan Basin. (3) In the YLRB, both temperature and precipitation generally had a one-month accumulated effect on vegetation conditions, while in the YTRB, temperature was the major factor leading to changes in vegetation. In most of the area of the YTRB, the effect of temperature on vegetation was also a one-month accumulated effect, but there was no time effect in the Sichuan Basin. Considering the time effects, the contribution of climatic factors to vegetation change in the YLRB and YTRB was 76.7% and 63.2%, respectively. The explanatory power of different vegetation types in the two basins both increased by 2% to 6%. The time-accumulation effect of climatic factors had a stronger explanatory power for vegetation growth than the time-lag effect.

The Yellow River Basin is an important water conservation and ecological barrier in China. Studying its water supply services is of great significance for the development of the Yellow River Basin and conservation of its ecosystems. This study is based on the InVEST model and scenario analysis method. We used data including land use cover, meteorology and soil type as inputs to analyze the spatial and temporal pattern of water yield in the Yellow River Basin from 1995 to 2018 and the impact of precipitation and land use change on water yield in the basin. The results show that from 1995 to 2018, the water yield in the Yellow River basin increased by 20,106 million m3, the high-value areas of water yield distributed in the southwest and southeast, the low-value areas distributed in the northwest region, and the spatial pattern of water yield changed insignificantly. From 1995 to 2005, the contribution rates of land use change and climate change to water production were 3.32% and 96.68%, respectively, and the contribution rates of land use change and climate change to water production from 1995 to 2018 were − 0.48% and 100.48%, respectively. In conclusion, the influence of precipitation on water production is more significant, and the effect of land use change on water production is smaller. This study reveals the temporal and spatial variation characteristics of water yield and its controlling factors in the Yellow River Basin, which is of great significance to the protection of ecological environment and the maintenance of ecological balance of the whole basin.

The Yellow River Basin (YRB) is an important ecological barrier and an important economic zone in China. Under the new requirements of realizing ecological protection and high-quality development of the YRB, the coordinated development of basin economic development and ecological environment is an urgent research topic. Taking 36 cities along the Yellow River Basin as samples, this paper constructs an evaluation index system of economic development and ecological environment. The coupling coordination model and geographical weighted regression were adopted to analyze the coupling coordination relationship between economic development and ecological environment from 2008 to 2017, and the influencing factors were analyzed. The results showed that (1) the coupling coordination of urban economic development and ecological environment along the YRB showed significant regional heterogeneity, forming a low-value sag of the Loess Plateau; (2) the regional hot spots in the downstream of the YRB continuously extended inland, while these in the midstream converged; (3) the coupling coordination degree between economic development and ecological environment is affected by factors such as population size, openness, and advanced industrial structure, and the intensity varies significantly among regions.

Drought is one of the most complex and least-understood environmental disasters that can trigger environmental, societal, and economic problems. To accurately assess the drought conditions in the Yellow River Basin, this study reconstructed the Land Surface Temperature (LST) using the Annual Temperature Cycle (ATC) model and the Normalized Difference Vegetation Index (NDVI). The Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature-Vegetation Drought Index (TVDI), which are four typical remote sensing drought indices, were calculated. Then, the air temperature, precipitation, and soil moisture data were used to evaluate the applicability of each drought index to different land types. Finally, this study characterized the spatial and temporal patterns of drought in the Yellow River Basin from 2003 to 2019. The results show that: (1) Using the LST reconstructed by the ATC model to calculate the drought index can effectively improve the accuracy of drought monitoring. In most areas, the reconstructed TCI, VHI, and TVDI are more reliable for monitoring drought conditions than the unreconstructed VCI. (2) The four drought indices (TCI, VCI, VH, TVDI) represent the same temporal and spatial patterns throughout the study area. However, in some small areas, the temporal and spatial patterns represented by different drought indices are different. (3) In the Yellow River Basin, the drought level is highest in the northwest and lowest in the southwest and southeast. The dry conditions in the Yellow River Basin were stable from 2003 to 2019. The results in this paper provide a basis for better understanding and evaluating the drought conditions in the Yellow River Basin and can guide water resources management, agricultural production, and ecological protection of this area.

Integrated River Basin Management (IRBM) and Adaptive Water Management (AWM) have been widely promoted as guiding principles, yet their practical realization at the basin scale remains difficult and underutilized. Sequential Interventions (SIs), stepwise actions applied in a predefined sequence at designated decision points, offer a practical and adaptive approach to implementing AWM. Such methods embody the iterative, learning‐by‐doing principles central to AWM, allowing strategies to evolve in response to monitoring results and changing conditions. This study develops a retrospective evaluation framework grounded in AWM, using an Integrated Hydrologic–Ecologic–Economic Model (IHEEM). The AWM‐grounded framework is employed to evaluate the effectiveness and relationships of SIs for the Yellow River Basin (YRB). The YRB is one of the world's most intensively managed basins, and SIs have been implemented in the basin since the 1980s, including major policies and infrastructure projects. The results show that SIs produce synergistic effects, progressively balancing upstream–downstream and human–nature water demands, enhancing environmental flows, and improving water‐use efficiency. In particular, the engineering intervention, that is, the Xiaolangdi Reservoir (XLD), plays a complementary role to policy interventions in maintaining downstream flows and sediment flushing. These findings illustrate how adaptive management, through iterative, evidence‐based interventions, can strengthen resilience in complex socio‐hydrological systems, and provide insights for advancing IRBM and AWM in large river basins globally with increasing hydrological and socio‐economic pressures.