Spatiotemporal characteristics of ecological resilience and its influencing factors in the Yellow River Basin of China

Spatial-temporal differentiation and driving factors of ecological resilience in the Yellow River Basin, China

The integrated development of new-type urbanization and tourism is of great significance for social development. Based on panel data of nine provinces in the Yellow River Basin from 2010 to 2019, models such as entropy TOPSIS, the super-SBM model, the coupling coordination degree model, the GM (1,1) model, Tobit regression, and other methods were used to explore the spatiotemporal evolution characteristics and influencing factors of the coupling coordination degree (CCD) between new-type urbanization and tourism resource conversion efficiency (TRCE) in the Yellow River Basin. The results show that, during the research period: (1) The development index of new-type urbanization in the Yellow River Basin showed a steady upwards trend and TRCE was generally stable. (2) The CCD between new-type urbanization and TRCE in the Yellow River Basin fluctuated and rose with an average annual CCD of 0.716, indicating an intermediate coordination stage, and showed an overall spatial distribution pattern of “midstream region > downstream region > upstream region”. The center of gravity of the CCD shifted to the northwest and the type of CCD jumped rapidly to a better stage, especially in the midstream region. From the perspective of future trends, the coupling and coordination relationship between the two major systems will continue to be optimized from 2019 to 2025 and the midstream region will maintain a high growth level. (3) The industrial structure, cultural media investment, science and technology investment, and communication level are important factors that affect the CCD of new-type urbanization and TRCE in the Yellow River Basin. The CCD of new-type urbanization and TRCE in the Yellow River Basin tends to be better as a whole, but the regional differences are obvious. Therefore, in the process of policy implementation, special attention should be given to the understanding of regional differences to avoid policy failure. This is very important to the Yellow River Basin and equally important to other regions.

A growing consensus worldwide has indicated the need to protect the ecological environment and achieve sustainable development. Ensuring ecological protection and high-quality development of the Yellow River basin have become China's major national strategy. We reviewed extant literature, summarised government reports and guidance documents on the Yellow River basin, and proposed introducing a strong sustainable development theory into the study of total factor productivity (TFP). The spatial–temporal evolution and influencing factors of urban ecological TFP in the Yellow River basin are of great practical significance. We proposed a new ecological TFP indicator: the modified input-oriented Luenberger productivity indicator (MIL). Using panel data from 78 cities in the Yellow River basin during 2003–2019, we measured the urban ecological TFP. We adopted the geographic information system tool and kernel density estimation to analyse the temporal and spatial evolution of the indicator, as well as its spatial effects and influencing factors, using the global Moran's I index and dynamic spatial Durbin model (SDM). Our results show that, during the sample period, our indicator increased in cities in the region with an average annual growth rate of 0.627%, driven by technological progress. The average annual growth rate in urban areas showed a decreasing distribution of ‘downstream-midstream-upstream’. Fiscal decentralisation (FD), industrial structure (IND), financial development (FIN), urbanisation level (URB) and research and development (RD) investment improved growth rates in this and the adjacent regions through direct and indirect effects. However, environmental regulation (ER), opening level (OPEN) of cities and population density (POP) were obstacles to TFP growth.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于GEE平台的黄河流域森林植被净初级生产力时空变化特征 DOI: 10.5846/stxb202104271113 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(31960330);宁夏自然科学基金(2020AAC03112) Spatio-temporal variation characteristics of forest net primary productivity in the Yellow River Basin based on Google Earth Engine cloud platform Author: Affiliation: Fund Project: National Natural Science Foundation of China (31960330)；Ningxia Natural Science Foundation (2020AAC03112) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:黄河流域是我国重要的生态屏障,研究黄河流域森林植被净初级生产力(Net Primary Productivity,NPP)的时空变化特征及驱动机制,对解释黄河流域森林碳汇/源变化具有重要意义。基于Google Earth Engine (GEE)云平台,利用MOD17A3H V6 NPP数据、MCD12Q1 V6土地覆盖类型数据、ECMWF/ERA5气象数据和USGS/SRTMGL1_003高程数据,采用岭回归分析、Hurst指数和冗余分析(Redundancy Analysis,RDA)对黄河流域2001-2019年森林NPP的时空变化特征及影响因子进行分析。结果表明:(1)2001-2019年,黄河流域森林平均总面积为3.66万km2,其中阔叶林、针叶林、混交林平均面积分别为:2.64万km2、0.01万km2和1.01万km2,森林NPP年总量呈线性增加趋势,其均值为8.99Tg C,年均增速为0.36Tg C/a,19a增长率为173.60%;不同森林类型的NPP年总量均值分别为:4.79Tg C (阔叶林)、6.04×10-5Tg C (针叶林)和0.64Tg C (混交林),年均增速为:阔叶林(0.16Tg C/a)>混交林(0.04Tg C/a)>针叶林(6.98×10-6Tg C/a)。(2)2001-2019年,黄河流域森林年均NPP呈线性增加趋势,其均值为241.58g C m-2 a-1,年均增速为7.18g C m-2 a-1,19a增长率为108.63%;不同森林类型的年均NPP均值分别为:178.48g C m-2 a-1(阔叶林)、0.60g C m-2 a-1(针叶林)和62.49g C m-2 a-1(混交林),年均增速为:阔叶林(4.75g C m-2 a-1)>混交林(2.39g C m-2 a-1)>针叶林(0.04g C m-2 a-1)。(3)黄河流域森林NPP呈增加趋势的面积占94.50%,其中显著增加的面积占73.29%;呈减少趋势的面积占5.50%,其中显著减少的面积占1.57%。阔叶林NPP显著增加的面积最高(76.78%),其次为混交林(60.84%),针叶林最少(56.76%)。(4)黄河流域森林NPP的Hurst指数(H)介于0.38-1.00之间,平均值为0.87,其中H≥0.5的像元数约占99.34%,黄河流域森林NPP在未来一段时间内仍保持持续增加趋势。(5)归因分析表明环境因子对黄河流域森林NPP时空变化的总解释率为55.80%,显著影响的环境因子为经度(35.50%)、降水(8.00%)、气温(6.50%)和纬度(5.40%)。2001-2019年黄河流域森林NPP呈增加趋势,且呈现较强的可持续性;GEE云平台结合冗余分析可及时、高效获取黄河流域森林NPP的时空变化并对其进行归因分析。 Abstract:The Yellow River Basin is an important ecological barrier in China. To study the spatio-temporal variation characteristics and driving mechanisms of the Net Primary Productivity (NPP) of forest is of great significance to explain the change of forest carbon sink and source in the Yellow River Basin. Based on Google Earth Engine (GEE) cloud platform, MOD17A3H V6 NPP data, MCD12Q1 V6 land cover data, ECMWF/ERA5 weather data, USGS/SRTMGL1_003 elevation data, and ridge regression analysis, Hurst index, redundancy analysis (RDA) were used to analyze the spatio-temporal variation characteristics and influencing factors of the forest NPP in the Yellow River Basin from 2001 to 2019. The results showed that (1) from 2001 to 2019, the average total area of forest in the Yellow River Basin was 36600 km2, of which the average area of broadleaf forest, coniferous forest and mixed forest was 26400 km2, 100 km2 and 10100 km2. The annual total forest NPP showed a linear increasing trend, with an average value of 8.99 Tg C, an average annual growth rate of 0.36 Tg C/a, and a 19-year growth rate of 173.60%. The averagely annual total NPP of different forest types were:4.79 Tg C (broadleaf forest), 6.04×10-5 Tg C (coniferous forest) and 0.64 Tg C (mixed forest), and the averagely annual growth rate were:broadleaf forest (0.16 Tg C/a) > mixed forest (0.04 Tg C/a) > coniferous forest (6.98×10-6 Tg C/a). (2) From 2001 to 2019, the annual average NPP of the forest in the Yellow River Basin increased linearly, with an average value of 241.58 g C m-2 a-1, an average annual growth rate of 7.18 g C m-2 a-1, and a 19-year growth rate of 108.63%. The mean annual average NPP of different forest types were 178.48 g C m-2 a-1 (broadleaf forest), 0.60 g C m-2 a-1 (coniferous forest) and 62.49 g C m-2 a-1(mixed forest), and the averagely annual growth rate were:broadleaf forest (4.75 g C m-2 a-1) > mixed forest (2.39 g C m-2 a-1) > coniferous forest (0.04 g C m-2 a-1). (3) The area of forest NPP showed an increasing trend in the Yellow River Basin accounting for 94.50%, of which 73.29% was significantly increased; the area showed a decreasing trend accounting for 5.50%, of which the area with a significant decrease accounted for 1.57%. The area of the significantly increased of the broadleaf forest NPP was the highest (76.78%), followed by mixed forest (60.84%) and coniferous forest (56.76%). (4) The Hurst index (H) of the forest NPP in the Yellow River Basin was between 0.38-1.00, with an average value of 0.87. Among them, the amount of H ≥ 0.5 accounted for about 99.34%, and the forest NPP in the Yellow River Basin would continue to increase in the future. (5) Attribution analysis showed that the total interpretation rate of the environmental factors on the spatio-temporal variation of the forest NPP in the Yellow River Basin was 55.80%, and the environmental factors that had significant effects were longitude (35.50%), precipitation (8.00%), temperature (6.50%) and latitude (5.40%). From 2001 to 2019, the forest NPP of the Yellow River Basin increased and showed strong sustainability. The GEE cloud platform combined with redundant analysis can timely and efficiently obtain the spatio-temporal variation of the forest NPP of the Yellow River Basin and perform an attribution analysis. 参考文献 相似文献 引证文献

Based on Panel data collected from 2011 to 2020 targeted to 50 prefecture-level cities in the Yellow River Basin, this paper adopted standard deviation ellipse and spatial Dubin model to explore the nonlinear effects and spatial spillover effects of urbanization on air pollution and ecological resilience in the Yellow River Basin. The results show that the degree of air pollution in the southeast of the Yellow River Basin is higher than that in the northwest of the Yellow River Basin, the distribution range of air pollution is shrinking, the concentration of ecological resilience is enhanced, and the ecological environment is developing for the better. There is a significant U-shaped relationship between urbanization and air pollution in the Yellow River Basin, and an inverted U-shaped relationship between urbanization and ecological resilience. For every 1% increase in urbanization, air pollution decreases by 0.0873%, ecological resilience increases by 0.4046%. For every 1% increase in the square term of urbanization, air pollution increases by 0.2271%, ecological resilience decreases by 0.1789%. The urbanization of the Yellow River Basin has a spatial spillover effect on air pollution and ecological resilience, and urbanization has a significant negative impact on the ecological environment of neighboring cities. The robustness of the above conclusions is verified by introduce an inverse distance weight matrix replacing the spatial weight matrix.

In the 21st century, problems relating to energy, economy, and the environment have become increasingly severe across the world, and critical issues around environmental pollution, ecological imbalance, and an energy crisis have emerged. The Yellow River basin is an important ecological barrier, economic region, and energy base in Northern China. Environmental pollution in the Yellow River basin has become increasingly problematic, especially since the reform and opening up of China, along with the rapid development of the industrial economy and mining for energy resources. In this study, 64 of the 73 prefecture-level cities in the Yellow River basin were selected as the research object, including 18 cities in the downstream region, 26 cities in the midstream region, and 20 cities in the upstream region. The data used in this study were from 2004 to 2019. On the basis of temporal variation and spatial differentiation of the three factors of economy, energy, and environment, the impulse response function and the generalized method of moments (GMM) were adopted to evaluate the effects of energy utilization and economic growth on the ecological environment. Their roles in affecting the ecological environment were analyzed along with the underlying mechanisms. Overall, energy utilization, economic growth, and ecological environment are in good condition, showing a steady upward trend. Regional differences still exist, but the gap is gradually narrowing. There are some differences in the impulse response of the ecological environment to the economic growth and energy utilization in the upstream, midstream, and downstream regions of the Yellow River basin. The effect is leveled out or weakened in the middle and later phases of the impact. Compared with the downstream and upstream regions, economic growth and energy utilization in the midstream regions have less impact on the ecological environment. The two factors of energy utilization potential and economic potential have significant positive impacts on the ecological environment. The current situation of energy utilization has to some extent a positive impact on the ecological environment. Economic scale has a certain negative impact on the ecological environment.

In the Anthropocene era, human activities have become increasingly complex and diversified. The natural ecosystems need higher ecological resilience to ensure regional sustainable development due to rapid urbanization and industrialization as well as other intensified human activities, especially in arid and semi-arid areas. In the study, we chose the economic belt on the northern slope of the Tianshan Mountains (EBNSTM) in Xinjiang Uygur Autonomous Region of China as a case study. By collecting geographic data and statistical data from 2010 and 2020, we constructed an ecological resilience assessment model based on the ecosystem habitat quality (EHQ), ecosystem landscape stability (ELS), and ecosystem service value (ESV). Further, we analyzed the temporal and spatial variation characteristics of ecological resilience in the EBNSTM from 2010 to 2020 by spatial autocorrelation analysis, and explored its responses to climate change and human activities using the geographically weighted regression (GWR) model. The results showed that the ecological resilience of the EBNSTM was at a low level and increased from 0.2732 to 0.2773 during 2010–2020. The spatial autocorrelation analysis of ecological resilience exhibited a spatial heterogeneity characteristic of “high in the western region and low in the eastern region”, and the spatial clustering trend was enhanced during the study period. Desert, Gobi and rapidly urbanized areas showed low level of ecological resilience, and oasis and mountain areas exhibited high level of ecological resilience. Climate factors had an important impact on ecological resilience. Specifically, average annual temperature and annual precipitation were the key climate factors that improved ecological resilience, while average annual evapotranspiration was the main factor that blocked ecological resilience. Among the human activity factors, the distance from the main road showed a negative correlation with ecological resilience. Both night light index and PM2.5 concentration were negatively correlated with ecological resilience in the areas with better ecological conditions, whereas in the areas with poorer ecological conditions, the correlations were positive. The research findings could provide a scientific reference for protecting the ecological environment and promoting the harmony and stability of the human-land relationship in arid and semi-arid areas.

Realizing the decoupling development between the economic expansion and carbon dioxide emissions of the transport sector is of great importance if the Yellow River basin is to achieve green and low-carbon development. In this paper, we adopt the Tapio decoupling index to examine the decoupling relationship within the transport sector in the Yellow River basin, and then introduce the standard deviational ellipse to dynamically analyze the spatial heterogeneity of carbon emissions and economic growth at the provincial level. Furthermore, based on the decoupling method, we expand the traditional logarithmic mean Divisia index decomposition (LMDI) model to decompose the decoupling index into eight sub-indices, and we identify the impact of each factor on the decoupling relationship. The results indicate that the carbon emissions of the transport sector in the Yellow River basin show the non-equilibrium characteristics of “upstream region < midstream region < downstream region”. The decoupling state of the transport sector shows obvious spatial differences. The less-developed regions are more likely to present non-ideal decoupling states. The growth rate of carbon emissions in Sichuan, Qinghai, and Shandong provinces is relatively fast, and the azimuth of the transport sector’s carbon emissions shows a clockwise trend. Moreover, the inhibitory effects of urbanization on decoupling in the Yellow River basin are much greater than the non-urbanization factors. In addition to the effect of urbanization, the transport structure has a major negative effect on decoupling development in the upstream and midstream regions, while energy intensity and energy structure are key to realizing a decoupled status in the downstream region. Finally, we propose some differentiated policy recommendations.

The energy-driven expansion of artificial surfaces has resulted in severe ecological problems. Scientific evaluation of regional ecological resilience under different scenarios is crucial for promoting ecological restoration. This study chose the Shanxi–Shaanxi–Inner Mongolia Energy Zone (SEZ) and modeled an ecological resilience evaluation based on resistance, adaptability, and recovery. Land-use change and ecological resilience from 1980 to 2020 were then analyzed. Moreover, the SEZ land-use patterns and ecological resilience in 2030 were simulated under business as usual (BAU), energy and mineral development (EMD), and ecological conservation and restoration (ECR) scenarios. The results showed that (1) the SEZ was dominated by cultivated land, grassland, and unused land. (2) Ecological resilience showed a changing trend of decreasing and then increasing, with high ecological resilience areas mainly located in the Yellow River Basin, whereas low ecological resilience areas spread outward from the central urban areas. (3) The ecological resilience level was the lowest under the EMD scenario and the highest under the ECR scenario. This study not only expands the analysis framework of ecological resilience research but also provides scientific support for ecological conservation in ecologically fragile areas with intensive human activity worldwide.

Water and land resources are related to the security and stability of agricultural production, and the degree of matching in time and space directly affects regional agricultural production capacity and sustainable agricultural development. This paper intends to use the panel data of nine provinces in the Yellow River Basin from 2000 to 2019 and incorporate the static and dynamic spatial Durbin models with spatial effects under the geographical adjacency matrix and the comprehensive weight matrix of economic geography, so as to explore the direct effects and indirect effects, short-term effects and long-term effects of the matching coefficient of agricultural water and land resources on the agricultural economic growth in the Yellow River Basin. The results show the following: (1) The matching situation of agricultural water and land resources in different provinces along the Yellow River Basin are different; some are relatively short of water resources, some are relatively balanced in water and land resources, and some are relatively short of land resources. (2) The static spatial Durbin model shows that the direct effect of the matching coefficient of agricultural water and land resources on the agricultural economic growth of the province is not significant; the indirect effect and the total effect of the spatial spillover is significantly positive. (3) The dynamic spatial Durbin model under the two matrix forms shows that the short-term total effect of the matching coefficient of agricultural water and land resources on agricultural economic growth is significantly positive, while the long-term total effect is significantly negative, and the direction and degree of the short-term and long-term effects are inconsistent. This study provides a comprehensive analysis framework from the perspective of local and neighborhood effect, and short-term and long-term effect, which can provide a reference to reasonably adjust the matching of agricultural water and land resources to promote agricultural sustainable economic growth, especially for developing countries.

With economic progression in China, Yellow River Basin serves as a critical economic belt, which has also been recognized as a cradle of Chinese culture. A watershed is a complex structure of social, economic, and natural factors, and the diversity of its components determines its complexity. Studies on the spatiotemporal distribution characteristics and factors influencing the tourism eco-efficiency at the watershed scale are crucial for the sustainable regional socio-economic development, maintaining a virtuous cycle of various ecosystems, and comprehensively considering the utilization and coordinated development of various elements. Based on tourism eco-efficiency, the coordination degree of regional human-land system and the sustainable development levels can be accurately measured. With the tourism eco-efficiency in the Yellow River Basin from 2009 to 2019, the present study considers 63 cities in the Yellow River Basin as the research area by adopting the super-efficiency slacks-based measure (Super-SBM) model. Methods such as trend surface analysis, spatial autocorrelation analysis, elliptic standard deviation analysis, and hot spot analysis were used to explore their spatiotemporal distribution and evolution characteristics. The geographical and temporal weighted regression (GTWR) model was used to determine the factors influencing the tourism eco-efficiency value. The findings are as follows: ①The level of tourism eco-efficiency in the Yellow River Basin is not high, exhibiting a fluctuating upward trend. ②The tourism eco-efficiency in the Yellow River Basin shows significant spatial interdependence and agglomeration. Furthermore, the track of the center of gravity moves from northeast to southwest. ③ The tourism eco-efficiency in the Yellow River Basin is affected by various factors, with the economic development level having the greatest influence.

Land-use change is a crucial element influencing the patterns of carbon sinks/sources in the Yellow River Basin (YRB). Therefore, studying land-use carbon emissions (LUCE) in the YRB and the decoupling from economic development can help formulate emission reduction strategies. In order to explore the spatiotemporal characteristics of LUCE in the YRB, we estimated the LUCE in 69 cities in the YRB using the downscale energy balance table estimation method and land-use remote sensing data for seven phases from 1990 to 2020. The spatial and temporal features of LUCE were researched from three different spatial scales: the whole spatial scale of the YRB, the sub-basin level, and the city level. Furthermore, the Tapio decoupling model was utilized to research the decoupling state between LUCE and economic development using a multi-scale approach. The Logarithmic Mean Divisia Index (LMDI) model was employed to explore the influencing factors of LUCE in the YRB. These results showed the following: (1) The LUCE in the YRB went through two stages: “stable growth” (1990–2000) and “rapid growth” (2000–2020). The LUCE increased from 165 million tons in 1990 to 1.414 billion tons in 2020, and the average annual growth rate was 25.12%. The spatial pattern of LUCE in the YRB exhibited significant variations, with the LUCE showing a geographic differentiation of midstream > downstream > upstream. (2) Except for the expansive coupling state during 2000–2005 (e: 0.952) and the expansive negative decoupling state during 2015–2020 (e: 2.151), the YRB was in the weak decoupling state for the majority of the time periods. (3) Economic development was the major positive driving factor for the rise of LUCE in this basin, while energy consumption intensity was the primary inhibiting factor. Through a discussion of the features and influencing factors of LUCE, these results can be utilized to provide carbon emission reduction recommendations tailored to the characteristics of cities’ resources and economic development, which will be helpful for achieving low-carbon and sustainable development in the YRB.

To reveal the relationship between agricultural water resource consumption and grain production in the Yellow River Basin, the irrigation water productivity (WPI), crop water productivity (WPC), total inflow water productivity (WPT), and eleven influencing factors were selected. The spatial and temporal distribution characteristics and driving factors of water productivity of the main crops in the Yellow River Basin were analyzed with the spatial autocorrelation analysis, grey correlation analysis, sensitivity analysis, and relative contribution rate. The results showed that the minimum mean values of WPI, WPC, and WPT were 0.22, 0.35, and 0.18 kg/m3 in Qinghai, respectively, the maximum mean value of WPI was 2.11 kg/m3 in Henan, and the maximum mean values of WPC and WPT were 0.71 and 0.61 kg/m3 in Shandong, respectively. The changing trends in WPI and WPT in Qinghai and in WPC in Shandong were insignificant, whereas the WPI, WPC, and WPT in other provinces showed a significant increasing trend. Water productivity displayed a certain spatial clustering feature in the Yellow River Basin in different years, such as a high-high (H-H) aggregation in Henan in 2005, and an H-H aggregation in Shanxi in 2015 for WPI. The water productivity had a significant positive correlation with the consumption of chemical fertilizer with a 100% effective component (CFCEC), effective irrigated area (EIA), plastic film used for agriculture (PFUA), and total power of agricultural machinery (AMTP), while it had a significant negative correlation with the persons engaged in rural areas (PERA). There was a large grey correlation degree between the water productivity and the average annual precipitation (AAP), CFCEC, PFUA, consumption of chemical pesticides (CFC), and AMTP in the Yellow River Basin, but their sensitivity was relatively small. The main driving factors were EIA (8.98%), agricultural water (AW, 15.55%), AMTP (12.64%), CFCEC (12.06%), and CPC (9.77%) for WPI; AMTP (16.46%), CFCEC (13.25%), average annual evaporation (AAE, 12.94%), EIA (10.49%), and PERA (10.19%) for WPC; and EIA (14.26%), AMTP (13.38%), AAP (12.30%), CFCEC (10.49%), and PFUA (9.69%) for WPT in the Yellow River Basin. The results can provide support for improving the utilization efficiency of agricultural water resources, optimizing the allocation of water resources, and implementing high-quality agricultural developments in the Yellow River Basin.

Addressing the need to harmonize environment conservation and sustainable economic development within the Yellow River Basin (YRB) requires a profound comprehension of the spatiotemporal dynamics of urban ecosystem resilience. This study developed an index system utilizing the resistance-adaptability-recovery framework to measure these dynamics. By applying the advanced multi-attribute boundary area comparison method and a spatial autocorrelation model, we investigated the spatiotemporal variations and spatial correlation patterns of urban ecological resilience across the YRB. The results of this study indicated that: (1) from 2011 to 2020, the value of urban ecological resilience index (UERI) in the YRB consistently ranged between 0.43 and 0.83, and the resilience degree of the urban ecosystem in the YRB progressively improved, with notably higher resilience in the southeast compared to the northwest; (2) the resilience degree of the urban ecosystem in the YRB was non-equilibrium in space. Spatial analysis indicates significant disparities in resilience levels across different areas within the YRB, marked by considerable fluctuations in the global Moran’s I index and significant changes in local autocorrelation clustering patterns; and (3) key factors such as wastewater discharge volume, sewage treatment rate, and the rate of non-hazardous treatment of domestic waste were identified as critical determinants of the overall ecological resilience. This research not only deepens our understanding of the factors driving urban ecological resilience but also aids in the formulation of strategic regional policy for sustainable development across the YRB.

To have a more comprehensive understanding of the water ecological security status of the Yellow River Basin, this paper constructs a water ecological security evaluation index system founded on the Pressure-State-Response (PSR) model. The indicators are selected by considering factors such as meteorological conditions, population, economy, water resources, water environment, water ecology, land ecology, ecological service functions, pollution control, and capital investment. Then, the “single index quantification-multiple indices syntheses-poly-criteria integration (SMI-P) method was used to determine the water ecological security index (WESI) of 62 cities in the Yellow River Basin, to classify the safety levels, and combined with the spatial autocorrelation analysis to study the regional characteristics. The results prove that: (a) The overall water ecological security of the Yellow River Basin is relatively poor. Half of the 62 cities have reached the second-level warning level, and most of them are concentrated in the upper and middle reaches of the basin. (b) Wetland area is a long-term key factor in the construction of water ecological safety, and the greening rate of built-up areas has an increasing impact on water ecological safety. (c) The overall water ecological security index shows a slow upward trend, with the annual average growth rate was 0.59%. (d) The water ecological security of 62 cities in the Yellow River Basin shows significant spatial autocorrelation. The findings can offer a practical basis for the water ecological management to promote the high-quality development of the Yellow River Basin.