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To accelerate the cultivation of new quality productive forces and provide reference for implementing the major national strategies of ecological protection and high-quality development in the Yellow River Basin, based on the connotation of new quality productive forces, this paper constructed an “technology-factor-industry” analytical framework to explore the inherent logic of how new quality productive forces empower ecological protection and high-quality development in the Yellow River Basin. Specifically, revolutionary technological breakthroughs provided new driving forces, innovative allocation of production factors strengthened data empowerment, and in-depth transformation and upgrading of industries provided carrier support. It pointed out that empowering ecological protection and high-quality development in the Yellow River Basin with new quality productive forces still faced challenges such as insufficient innovation drive, the need to improve the level of factor integration and allocation, and lagging industrial transformation and upgrading. Given the reality of the Yellow River Basin, this paper proposes an enhancement path for empowering ecological protection and high-quality development in the Yellow River Basin with new quality productive forces: improving the science and technology innovation system, achieving high-level self-reliance and self-strengthening in science and technology, deepening factor market allocation, promoting the flow of data factors, accelerating the transformation and upgrading of industrial structure, and enhancing the competitive advantage of the modern industrial system.
Empowering ecological civilization construction with data elements is the underlying logic for promoting ecological environment governance in the Yellow River Basin. Evaluating the coupling and coordination relationship between data element development and ecological civilization construction is particularly important for achieving high-quality development in the Yellow River Basin. To provide theoretical basis and reference decision-making for the coordinated development of data elements and ecological civilization construction policies in the Yellow River Basin, this study focused on nine provinces (regions) in the Yellow River Basin from 2013 to 2020. The entropy method, coupling coordination degree model, and kernel density function were used to investigate the coupling coordination level between data element development and ecological civilization construction. The results show that: 1) The overall development level of data elements and the level of ecological civilization construction are steadily improving, but there are still problems of uneven and insufficient development. 2) The coupling and coordination between the development of data elements and the construction of ecological civilization have gradually strengthened, and have gone through a process of mild imbalance to near imbalance. 3) The coupling and coordination between the development of data elements and the construction of ecological civilization exhibit non-equilibrium characteristics, with downstream areas>midstream areas>upstream areas, presenting a U-shaped layout from east to west and an increasing layout from north to south. Based on the empirical results, three types of coupling and coordination are identified: high data element development-high ecological civilization construction, low data element development-high ecological civilization construction, and low data element development-low ecological civilization construction, and corresponding improvement paths are proposed.
Accurately assessing the carbon storage of the ecosystem in the Henan section of the Yellow River Basin is of great significance for promoting low-carbon sustainable development in the region and achieving the "dual carbon" goals. Based on carbon density sampling data, a spatial density distribution dataset of carbon density in the Henan section of the Yellow River Basin was constructed. Combined with remote sensing data on land use, a systematic assessment was conducted of the carbon storage patterns and spatio-temporal evolution laws of the ecosystem in the Henan section of the Yellow River Basin for the years 1980, 1990, 2000, 2005, 2010, 2015, and 2020. The Geographic Detector was utilized to explore the influence of natural and socio-economic factors on carbon storage. The results indicate that the average carbon storage in the Henan section of the Yellow River Basin over the past 40 years was 431.16×106 t, with a spatial distribution pattern showing a slight increase from east to west and a decreasing trend from northeast to southwest, with high-value agglomeration areas mainly distributed in the downstream floodplain of the Yellow River. Before 2000, the ecosystem carbon storage in the Henan section of the Yellow River Basin decreased, followed by an increase. The carbon storage in the Yellow River Basin from 1980 to 2020 showed a downward trend. Between 1980 and 2020, 82.05% of the region in the Henan section of the Yellow River Basin maintained unchanged carbon storage, while 10.05% experienced a decrease and 7.90% an increase. Changes in land use type were the key drivers of dynamic changes in carbon storage in this region, especially the continuous expansion of construction land and encroachment on farmland and grassland, which were the main reasons for the significant decline in carbon storage. Altitude and temperature also had a certain impact on changes in carbon storage.
To investigate the spatiotemporal variation patterns of extreme precipitation in the Haihe River Basin, daily precipitation data from 37 meteorological stations during 1956-2022 were used to select 6 extreme precipitation indicators. Mann-Kendall non-parameter test, inverse distance weighted interpolation and Morlet wavelet analysis were employed to examine the spatiotemporal variation patterns of extreme precipitation in the Haihe River Basin. The results showed that in the past 67 years, the extreme precipitation indexes in Haihe River Basin showed a decreasing trend, but enter the 21st century, all indexes showed an increasing trend of different degrees, especially in 2021. The extreme precipitation frequency and extreme precipitation intensity index decreased to northwest and southeast in the north central and northeast plain areas, and the extreme precipitation indexes in mountainous and hilly areas are generally higher than that in plain areas. The number of heavy rain days, the maximum precipitation of 5 days and the extremely extreme precipitation showed more or less mutations in 1964, and the maximum precipitation of 1 day and the maximum continuous precipitation days showed more or less mutations in 1968 and 1978. The periodicity of different extreme precipitation index series is different. The first main cycle is within 41-51 years. Under the scale of the first main cycle, the average change period of the number of heavy rain days, extreme precipitation and maximum continuous precipitation days is 31 years, and the average change period of the 1-day maximum precipitation, the 5-day maximum precipitation and extremely extreme precipitation is 20-36 years.
Due to changes in climate and human factors, surface runoff changes. In this study, in order to measure the contribution of climate and human factors to the change of runoff in the middle and upper reaches of the Huaihe River from 1982 to 2019, this study first used Mann-Kendall trend analysis to conduct an abrupt change analysis of the runoff data in the basin. Then, determine the abrupt change year of runoff within the river basin, and divide the study time period into a baseline period and a mutation period. The contribution of climate and human factors to the runoff changes in the middle and upper reaches of the Huaihe River was quantitatively measured based on the extended Budyko model at three-time scales: month, season, and year. The results are as follows: a) On the month scale, the months in which climatic factors led to an increase in runoff are January, February, April, May, June, and December, and the months in which they led to a decrease in runoff are March, July, August, September, October, and November; Anthropogenic factors increase the runoff depth of the middle and upper Huaihe River in January, October, November, and December, and decrease it in the rest of the months. b) On a seasonal scale, human factors are dominant in reducing runoff in spring, summer, and autumn. Climate factors increase runoff in all four seasons, with the increase being smaller than the decrease caused by human factors. In winter, climate and human factors have a small-scale increasing effect on runoff changes. c) On an annual scale, climatic factors lead to an increase in runoff depth of 4.71 mm, and human factors lead to a decrease in runoff depth of 89.75 mm, and human factors have a greater effect on runoff change than climate factors.
This study investigated the effects of different rainfall intensities and slopes on runoff processes in the Hugou small watershed of the Yiluo River, providing a basis for the efficient utilization of water resources and ecological environment conservation in the Hugou small watershed. Based on long-term field monitoring data, this study investigated the variation characteristics of runoff volume and runoff rate on slopes under different combinations of rainfall intensity and slope gradient. A driving model of rainfall intensity and slope gradient for runoff volume was established. Results indicate that the initial runoff time is negatively correlated with rainfall intensity. At low rainfall intensities, steeper slopes result in shorter initial runoff times, while the slope effect diminishes as rainfall intensity increases and runoff duration exhibits a trend where steeper slopes correspond to longer durations. The runoff generation process exhibits a pattern characterized by an initial rapid rise followed by fluctuating attenuation after reaching its peak. Under low rainfall intensity, runoff fluctuations occur frequently; under moderate rainfall intensity, the process stabilizes relatively smoothly; and under high rainfall intensity, fluctuations become most pronounced. Rainfall intensity serves as the core factor affecting the dynamic stability of runoff generation, while slope influences the rhythm of runoff response, forming a coupled driving mechanism between rainfall intensity and slope.
A quantitative method was proposed to determine the composite roughness of under-ice flow, addressing the hydraulic differences between frozen rivers and open-channel conditions in cold regions. Based on the boundary conditions of a frozen river, an analytical formula for the composite roughness was derived and its solution procedure was presented. The method was validated against 41 sets of laboratory and field data and compared with the classical formulas of Sabaneev, Larsen and Pavlovskiy. Results show that: a) The proposed approach yields the closest agreement with observations, with a mean relative error of 3.94%; b) The error of the Larsen formula increases markedly when the width-to-depth ratio is small; c) When field parameters are scarce and rapid estimates are required, the Sabaneev formula can be preferentially adopted because of its comparatively small deviations. The research results can provide a new calculation method for hydraulic calculation and engineering design of ice-covered rivers.
In order to explore the evolution law of the hydrologic cycle in the water source conversion process under different working conditions, this paper took Jiuwutan water source in Zhengzhou as the research object, and constructed a simulation model of surface water-groundwater transfer process in the riverside source area based on geological, hydrological, meteorological and artificial water withdrawal data. Based on the daily surface water flow data from the Huayuankou hydrological station and groundwater level data from typical observation wells, the model parameters were calibrated and verified, and the distribution characteristics of water circulation and groundwater in Jiuwutan water source area were clarified. The results show that: 1) The Nash efficiency coefficients of MIKE 11 and MIKE SHE models are 0.94 and 0.71 respectively during calibration and verification, and the relative errors are controlled within ±15%, which indicates that the models are more accurate in simulating the daily flow of Huayuankou hydrology station and the groundwater level process of observation well; 2) There is still a significant groundwater drop funnel in the case of further water and sediment transfer and the recovery of groundwater level in the south, and the groundwater drawdown funnel will continuously diminish until it vanishes when the extraction volume is reduced to 1 million m3; 3) The groundwater in the study area basically keeps the trend of flow from north to south and from west to east, but under the current exploitation amount, the groundwater exploitation well area is fully replenished by the surrounding area.
Prediction and Analysis of Groundwater Level Around Water Conveyance River Based on Machine Learning
The water diversion project from the Yangtze River to the Huaihe River is a major water resources allocation project. Its Henan section supplies water from the Xifei River in Anhui Province to the upstream. The project uses the Qingshui River to transport water for 48.40 km, and the operation of the project will cause the change of groundwater level. Therefore, the influence of the change of groundwater level around the river on the safe and stable operation of the project is studied. In this study, BPNN, SVM and XGBoost algorithms were used to establish machine learning models to predict the groundwater level around the water conveyance channel. The training effects of different models were compared and the optimal prediction model was selected. The influence of different vertical distances from the channel on the prediction of groundwater level was analyzed. The results show that SVM has the best effect in predicting groundwater level, and the vertical distance from the channel has no effect on the prediction results of groundwater level.
To explore the coordinated relationship between ecological protection and high-quality development in energy-based cities in the Yellow River Basin, Ordos City was taken as an example to study the spatiotemporal evolution characteristics and influencing factors of ecological protection and high-quality development from 2001 to 2023. Core indicators were determined through correlation analysis and principal component analysis, and a comprehensive evaluation index system based on the PSR model was constructed. The combined weighting method of EWM-AHP-DEMATEL was used to determine the weights of indicators, the TOPSIS model was employed to calculate the ecological protection index and high-quality development index, and the coupling coordination degree was used to measure the coupling and coordination relationship between ecological protection and high-quality development. The results show that the ecological protection and high-quality development indices generally exhibit an upward trend, but the growth rate of the ecological protection index slows down due to the marginal diminishing effect of policies, while the high-quality development index fluctuates with economic transformation, showing a trend of first increasing, then decreasing, and subsequently increasing again. The coupling coordination degree spatial distribution is low in the west and high in the central and eastern regions. Granted patents per capita, carbon emission intensity, and the proportion of environmental governance investment are the main influencing factors.
