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Exploring the dynamic response of land use and ecological vulnerability (EV) to future climate change and human ecological restoration policies is crucial for optimizing regional ecosystem services and formulating sustainable socioeconomic development strategies. This study comprehensively assesses future land use changes and EV in the Yellow River Basin (YRB), a climate-sensitive and ecologically fragile area, by integrating climate change, land management, and ecological protection policies under various scenarios. To achieve this, we developed an EV assessment framework combining a scenario weight matrix, Markov chain, Patch-generating Land Use Simulation model, and exposure–sensitivity–adaptation. We further explored the spatiotemporal variations of EV and their potential socioeconomic impacts at the watershed scale. Our results show significant geospatial variations in future EV under the three scenarios, with the northern region of the upstream area being the most severely affected. Under the ecological conservation management scenario and historical trend scenario, the ecological environment of the basin improves, with a decrease in very high vulnerability areas by 4.45% and 3.08%, respectively, due to the protection and restoration of ecological land. Conversely, under the urban development and construction scenario, intensified climate change and increased land use artificialization exacerbate EV, with medium and high vulnerability areas increasing by 1.86% and 7.78%, respectively. The population in high and very high vulnerability areas is projected to constitute 32.75–33.68% and 34.59–39.21% of the YRB's total population in 2040 and 2060, respectively, and may continue to grow. Overall, our scenario analysis effectively demonstrates the positive impact of ecological protection on reducing EV and the negative impact of urban expansion and economic development on increasing EV. Our work offers new insights into land resource allocation and the development of ecological restoration policies. [ABSTRACT FROM AUTHOR]

Investigating the spatiotemporal dynamics of vegetation net primary productivity (NPP) and its influencing factors are crucial for green and low-carbon development and facilitate human well-being in the Yellow River Basin (YRB). Although the research on NPP has advanced rapidly, in view of the regional particularity of the YRB, the persistence of its NPP change trend needs to be further discussed and more comprehensive impact factors need to be included in the analysis. Meanwhile, the spatial non-stationarity and scale effects of the impact on NPP when multiple factors are involved remain uncertain. Here, we selected a total of twelve natural and anthropogenic factors and used multi-scale geographically weighted regression (MGWR) to disentangle the spatial non-stationary relationship between vegetation NPP and related factors and identify the impact scale difference in the YRB. Additionally, we analyze the spatiotemporal variation trend and persistence of NPP during 2000–2020. The results revealed the following: (1) The annual NPP showed a fluctuating increasing trend, and the vegetation NPP in most regions will exhibit a future trend of increasing to decreasing. (2) The effects of different factors show significant spatial non-stationarity. Among them, the intensity of the impact of most natural factors shows a clear strip-shaped distribution in the east-west direction. It is closely related to the spatial distribution characteristics of natural factors in the YRB. In contrast, the regularity of anthropogenic influences is less obvious. (3) The impact scales of different factors on vegetation NPP were significantly different, and this scale changed with time. The factors with small impact scales could better explain the change in vegetation NPP. Interestingly, the impact size and scale of relative humidity on NPP in the YRB are both larger. This may be due to the arid and semi-arid characteristics of the YRB. Our findings could provide policy makers with specific and quantitative insights for protecting the ecological environment in the YRB. [ABSTRACT FROM AUTHOR]

Detecting and attributing vegetation variations in the Yellow River Basin (YRB) is vital for adjusting ecological restoration strategies to address the possible threats posed by changing environments. On the basis of the kernel normalized difference vegetation index (kNDVI) and key climate drivers (precipitation (PRE), temperature (TEM), solar radiation (SR), and potential evapotranspiration (PET)) in the basin during the period from 1982 to 2022, we utilized the multivariate statistical approach to analyze the spatiotemporal patterns of vegetation dynamics, identified the key climate variables, and discerned the respective impacts of climate change (CC) and human activities (HA) on these variations. Our analysis revealed a widespread greening trend across 93.1% of the YRB, with 83.2% exhibiting significant increases in kNDVI (p < 0.05). Conversely, 6.9% of vegetated areas displayed a browning trend, particularly concentrated in the alpine and urban areas. With the Hurst index of kNDVI exceeding 0.5 in 97.5% of vegetated areas, the YRB tends to be extensively greened in the future. Climate variability emerges as a pivotal determinant shaping diverse spatial and temporal vegetation patterns, with PRE exerting dominance in 41.9% of vegetated areas, followed by TEM (35.4%), SR (13%), and PET (9.7%). Spatially, increased PRE significantly enhanced vegetation growth in arid zones, while TEM and SR controlled vegetation variations in alpine areas and non-water-limited areas such as irrigation zones. Vegetation dynamics in the YRB were driven by a combination of CC and HA, with relative contributions of 55.8% and 44.2%, respectively, suggesting that long-term CC is the dominant force. Specifically, climate change contributed to the vegetation greening seen in the alpine region and southeastern part of the basin, and human-induced factors benefited vegetation growth on the Loess Plateau (LP) while inhibiting growth in urban and alpine pastoral areas. These findings provide critical insights that inform the formulation and adaptation of ecological conservation strategies in the basin, thereby enhancing resilience to changing environmental conditions. [ABSTRACT FROM AUTHOR]

Vegetation greening is time-dependent and region-specific. The uncertainty of vegetation greening under global warming has been highlighted. Thus, it is crucial to investigate vegetation greening and its response to climate change at the regional scale. The Yellow River Basin (YRB) is a vital ecological barrier in China with high ecological vulnerability and climatic sensitivity. The relationship between vegetation greening and climate change in the YRB and the relative contribution of climate change remain to be explored. Using the Enhanced Vegetation Index (EVI) and meteorological observation data, the spatiotemporal patterns of vegetation greening across the YRB in response to climate change at the basin and vegetation sub-regional scales from 2000 to 2020 were analyzed. The impact of human activities on regional greening was further quantified. Results showed that approximately 92% of the basin had experienced greening, at average annual and growing season rates of 0.0024 and 0.0034 year–1, respectively. Greening was particularly prominent in the central and eastern YRB. Browning was more prevalent in urban areas with a high intensity of human activities, occupying less than 6.3% of the total basin, but this proportion increased significantly at seasonal scales, especially in spring. Regional greening was positively correlated with the overall warmer and wetter climate, and the partial correlation coefficients between EVI and precipitation were higher than those between EVI and temperature. However, this response varied among different seasonal scales and vegetation sub-regions. The combined effects of climate change and human activities were conducive to vegetation greening in 84.5% of the YRB during the growing season, while human activities had a stronger impact than climate change. The relative contributions of human activities to greening and browning were 65.15% and 70.30%, respectively, mainly due to the promotion of ecological rehabilitation programs and the inhibition of urbanization and construction projects. [ABSTRACT FROM AUTHOR]

Exploring the dynamic response of land use and ecological vulnerability (EV) to future climate change and human ecological restoration policies is crucial for optimizing regional ecosystem services and formulating sustainable socioeconomic development strategies. This study comprehensively assesses future land use changes and EV in the Yellow River Basin (YRB), a climate-sensitive and ecologically fragile area, by integrating climate change, land management, and ecological protection policies under various scenarios. To achieve this, we developed an EV assessment framework combining a scenario weight matrix, Markov chain, Patch-generating Land Use Simulation model, and exposure–sensitivity–adaptation. We further explored the spatiotemporal variations of EV and their potential socioeconomic impacts at the watershed scale. Our results show significant geospatial variations in future EV under the three scenarios, with the northern region of the upstream area being the most severely affected. Under the ecological conservation management scenario and historical trend scenario, the ecological environment of the basin improves, with a decrease in very high vulnerability areas by 4.45% and 3.08%, respectively, due to the protection and restoration of ecological land. Conversely, under the urban development and construction scenario, intensified climate change and increased land use artificialization exacerbate EV, with medium and high vulnerability areas increasing by 1.86% and 7.78%, respectively. The population in high and very high vulnerability areas is projected to constitute 32.75–33.68% and 34.59–39.21% of the YRB’s total population in 2040 and 2060, respectively, and may continue to grow. Overall, our scenario analysis effectively demonstrates the positive impact of ecological protection on reducing EV and the negative impact of urban expansion and economic development on increasing EV. Our work offers new insights into land resource allocation and the development of ecological restoration policies.

Investigating the spatiotemporal dynamics of vegetation net primary productivity (NPP) and its influencing factors are crucial for green and low-carbon development and facilitate human well-being in the Yellow River Basin (YRB). Although the research on NPP has advanced rapidly, in view of the regional particularity of the YRB, the persistence of its NPP change trend needs to be further discussed and more comprehensive impact factors need to be included in the analysis. Meanwhile, the spatial non-stationarity and scale effects of the impact on NPP when multiple factors are involved remain uncertain. Here, we selected a total of twelve natural and anthropogenic factors and used multi-scale geographically weighted regression (MGWR) to disentangle the spatial non-stationary relationship between vegetation NPP and related factors and identify the impact scale difference in the YRB. Additionally, we analyze the spatiotemporal variation trend and persistence of NPP during 2000–2020. The results revealed the following: (1) The annual NPP showed a fluctuating increasing trend, and the vegetation NPP in most regions will exhibit a future trend of increasing to decreasing. (2) The effects of different factors show significant spatial non-stationarity. Among them, the intensity of the impact of most natural factors shows a clear strip-shaped distribution in the east-west direction. It is closely related to the spatial distribution characteristics of natural factors in the YRB. In contrast, the regularity of anthropogenic influences is less obvious. (3) The impact scales of different factors on vegetation NPP were significantly different, and this scale changed with time. The factors with small impact scales could better explain the change in vegetation NPP. Interestingly, the impact size and scale of relative humidity on NPP in the YRB are both larger. This may be due to the arid and semi-arid characteristics of the YRB. Our findings could provide policy makers with specific and quantitative insights for protecting the ecological environment in the YRB.

Vegetation net primary productivity (NPP) serves as a crucial and intuitive indicator for assessing ecosystem health. However, the nonlinear dynamics and influencing factors operating at various time scales are not yet fully understood. Here, the ensemble empirical mode decomposition (EEMD) method was used to analyze the spatiotemporal patterns of NPP and its association with hydrothermal factors and anthropogenic activities across different temporal scales for the Yellow River Basin (YRB) from 2000 to 2020. The results indicate that: (1) the annual average NPP was 236.37 g C/m2 in the YRB and increased at rates of 4.64 g C/m2/a1 (R2 = 0.86, p < 0.01) during 2000 to 2020. Spatially, nonlinear analysis indicates that 72.77% of the study area exhibits a predominantly increasing trend in NPP, while 25.17% exhibits a reversing trend. (2) On a 3-year time scale, warming has resulted in an increase in NPP in the majority of areas of the study area (69.49%). As the time scale widens, the response of vegetation to climate change becomes more prominent; especially under the long-term trend, the percentage areas of the correlation between vegetation and precipitation and temperature increased with significance, reaching 48.21% and 11.57%, respectively. (3) Through comprehensive time analysis and multivariate regression analysis, it was confirmed that both human activities and climate factors had comparable impacts on vegetation growth. Among different vegetation types, climate was still the main factor affecting grassland NPP, and only 15.74% of grassland was affected by human activities. For shrubland, forest, and farmland, human activity was a dominating factor for vegetation NPP change. There are still few studies on vegetation change using nonlinear methods in the Yellow River Basin, and most studies have not considered the effect of time scale on vegetation evolution. The findings highlight the significance of multi-time scale analysis in understanding the vegetation dynamics and providing scientific guidance for future vegetation restoration and conservation efforts. [ABSTRACT FROM AUTHOR]

The accurate estimation of gross primary productivity (GPP) plays an important role in accurately projecting the terrestrial carbon cycle and climate change. Satellite-driven near-infrared reflectance (NIRv) can be used to estimate GPP based on their nearly linear relationship. Notably, previous studies have reported that the relationship between NIRv and GPP seems to be biome-specific (or land cover) at the ecosystem scale due to both biotic and abiotic effects. Hence, the NIRv-based estimation of GPP may be influenced by land cover changes (LCC) and the discrepancies in multisource products (DMP). However, these issues have not been well understood until now. Therefore, this study took the Yellow River basin (YRB) as the study area. This area has experienced remarkable land cover changes in recent decades. We used Moderate-Resolution Imaging Spectroradiometer (MODIS) and European Space Agency (ESA) Climate Change Initiative (CCI) land cover products (termed MCD12C1 and ESACCI, respectively) during 2001–2018 to explore the impact of land cover on NIRv-estimated GPP. Paired comparisons between the static and dynamic schemes of land cover using the two products were carried out to investigate the influences of LCC and DMP on GPP estimation by NIRv. Our results showed that the dominant land cover types in the YRB were grassland, followed by cropland and forest. Meanwhile, the main transfer was characterized by the conversion from other land cover types (e.g., barren) to grassland in the northwest of the YRB and from grassland and shrubland to cropland in the southeast of the YRB during the study period. Moreover, the temporal and spatial pattern of GPP was highly consistent with that of NIRv, and the average increase in GPP was 2.14 gCm−2yr−1 across the YRB. Nevertheless, it is shown that both LCC and DMP had significant influences on the estimation of GPP by NIRv. That is, the areas with obvious differences in NIRv-based GPP closely correspond to the areas where land cover types dramatically changed. The achievements of this study indicate that considering the land cover change and discrepancies in multisource products would help to improve the accuracy of NIRv-based estimated GPP. [ABSTRACT FROM AUTHOR]

As a vital ecological barrier in China, Yellow River Basin (YRB) is strategically significant for China's national development and modernization. However, YRB has fragile ecosystems, and is sensitive to climatic change. Extreme climate events (e.g., heavy precipitation, heatwaves, and extreme hot and cold) occur frequently in this basin, but the implications (positive and negative effects) of these events on vegetation dynamics remains insufficiently understood. Combing with net primary productivity (NPP), the normalized difference vegetation index (NDVI) and extreme climate indexes, we explored the spatio–temporal characteristics of plants' growth and extreme climate, together with the reaction of plants' growth to extreme climate in the Yellow River Basin. This study demonstrated that annual NPP and NDVI of cropland, forest, and grassland in the study region all revealed a climbing tendency. The multi-year monthly averaged NPP and NDVI were characterized by a typical unimodal distribution, with the maximum values of NPP (66.18 gC·m−2) and NDVI (0.54) occurring in July and August, respectively. Spatially, multi–year averaged of vegetation indicators decreased from southeast to northwest. During the study period, carbon flux (NPP) and vegetation index (NDVI) both exhibited improvement in most of the YRB. The extreme precipitation indexes and extreme high temperature indexes indicated an increasing tendency; however, the extreme low temperature indexes reduced over time. NPP and NDVI were negatively associated with extreme low temperature indexes and positively correlated with extreme high temperature indexes, and extreme precipitation indicators other than consecutive dry days. Time lag cross–correlation analysis displayed that the influences of extreme temperature indexes on vegetation indexes (NPP and NDVI) were delayed by approximately six months, while the effects of extreme precipitation indexes were immediate. The study outcomes contribute to our comprehension of plants' growth, and also their reaction to extreme climates, and offer essential support for evidence–based ecological management practices in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Detecting and attributing vegetation variations in the Yellow River Basin (YRB) is vital for adjusting ecological restoration strategies to address the possible threats posed by changing environments. On the basis of the kernel normalized difference vegetation index (kNDVI) and key climate drivers (precipitation (PRE), temperature (TEM), solar radiation (SR), and potential evapotranspiration (PET)) in the basin during the period from 1982 to 2022, we utilized the multivariate statistical approach to analyze the spatiotemporal patterns of vegetation dynamics, identified the key climate variables, and discerned the respective impacts of climate change (CC) and human activities (HA) on these variations. Our analysis revealed a widespread greening trend across 93.1% of the YRB, with 83.2% exhibiting significant increases in kNDVI (p < 0.05). Conversely, 6.9% of vegetated areas displayed a browning trend, particularly concentrated in the alpine and urban areas. With the Hurst index of kNDVI exceeding 0.5 in 97.5% of vegetated areas, the YRB tends to be extensively greened in the future. Climate variability emerges as a pivotal determinant shaping diverse spatial and temporal vegetation patterns, with PRE exerting dominance in 41.9% of vegetated areas, followed by TEM (35.4%), SR (13%), and PET (9.7%). Spatially, increased PRE significantly enhanced vegetation growth in arid zones, while TEM and SR controlled vegetation variations in alpine areas and non-water-limited areas such as irrigation zones. Vegetation dynamics in the YRB were driven by a combination of CC and HA, with relative contributions of 55.8% and 44.2%, respectively, suggesting that long-term CC is the dominant force. Specifically, climate change contributed to the vegetation greening seen in the alpine region and southeastern part of the basin, and human-induced factors benefited vegetation growth on the Loess Plateau (LP) while inhibiting growth in urban and alpine pastoral areas. These findings provide critical insights that inform the formulation and adaptation of ecological conservation strategies in the basin, thereby enhancing resilience to changing environmental conditions.

Net primary productivity (NPP) is a main contributor to ecosystem carbon pools. It is crucial to monitor the spatial and temporal dynamics of NPP, as well as to assess the impacts of climate change and human activities to cope with global change. The dynamic of the NPP in China's Yellow River Basin (YRB) from 2000 to 2019 and its influencing factors were analyzed by using trend and persistence tests and the GeoDetector method. The results show that the NPP had strong spatial heterogeneity, with a low NPP in the west and north, and a high NPP in the east and south. From 2000 to 2019, the NPP showed a statistically significant increase (at a mean of 5.5 g C m−2 yr−1, for a cumulative increase of 94.5 Tg C). A Hurst analysis showed that for the NPP in 76.3% of the YRB, the time series was anti-persistent. The spatial heterogeneity of the NPP in the YRB was mainly explained by precipitation and relative humidity (q value ranged from 0.24 to 0.44). However, the strength of the precipitation explained the decreased variation over time (q value decreased from 0.40 in 2000 to 0.26 in 2019). Interactions between the climate factors and human activities affected the NPP more strongly than individual factors. The results emphasize the importance of strengthening future research on the interaction between climate change and human activities. The results reveal the risk and optimal ranges of the driving factors and provide a quantification of the impacts of those factors regarding NPP. These findings can provide a scientific basis for vegetation restoration in the YRB. [ABSTRACT FROM AUTHOR]

Accurate land cover (LC) datasets are the basis for global environmental and climate change studies. Recently, numerous open-source annual LC datasets have been created due to advances in remote sensing technology. However, the agreements and sources of error that affect the accuracy of current annual LC datasets are not well understood, which limits the widespread use of these datasets. We compared four annual LC datasets, namely the CLCD, MCD12Q1, CCI-LC, and GLASS-LC, in the Yellow River Basin (YRB) to identify their spatial and temporal agreement for nine LC classes and to analyze their sources of error. The Mann–Kendall test, Sen's slope analysis, Taylor diagram, and error decomposition analysis were used in this study. Our results showed that the main LC classes in the four datasets were grassland and cropland (total area percentage > 80%), but their trends in area of change were different. For the main LC classes, the temporal agreement was the highest between the CCI-LC and CLCD (0.85), followed by the MCD12Q1 (0.21), while the lowest was between the GLASS-LC and CLCD (−0.11). The spatial distribution of area for the main LC classes was largely similar between the four datasets, but the spatial agreement in their trends in area of change varied considerably. The spatial variation in the trends in area of change for the cropland, forest, grassland, barren, and impervious LC classes were mainly located in the upstream area region (UA) and the midstream area region (MA) of the YRB, where the percentage of systematic error was high (>68.55%). This indicated that the spatial variation between the four datasets was mainly caused by systematic errors. Between the four datasets, the total error increased along with landscape heterogeneity. These results not only improve our understanding of the spatial and temporal agreement and sources of error between the various current annual LC datasets, but also provide support for land policy making in the YRB. [ABSTRACT FROM AUTHOR]

Large-scale irrigation can alter the regional water cycle process, which changes the structure and spatiotemporal distribution of local and downwind precipitation, impacting soil erosion in both the irrigated areas and the surrounding regions. However, the effects of large-scale irrigation on soil erosion in downwind vulnerable areas have not been investigated. The study used the high-resolution regional climate model (RegCM4) and the revised universal soil loss equation (RUSLE) to examine the effects of irrigation-induced precipitation in Northwest China on the frequency, distribution, and intensity of precipitation in the Yellow River Basin (YRB) under different Representative Concentration Pathways (RCPs). The response characteristics of soil erosion to the irrigation-induced precipitation effects and its relationship with slope, elevation, and land use type were analyzed as well. The results indicate that soil erosion in most regions of the YRB is below moderate, covering 84.57% of the basin. Irrigation leads to a 10% increase in summer precipitation indices (e.g., total wet-day precipitation, consecutive wet days, number of wet days with precipitation ≥ 1 mm, and number of heavy precipitation days with precipitation ≥ 12 mm) in the northwest of the basin. Irrigation also leads to a change in local circulation, resulting in reduced precipitation in the southeast of the basin, particularly under the RCP8.5 scenario. The transformation of erosion intensity between low-grade and high-grade erosion is relatively stable and small under the influence of precipitation. However, soil erosion changes display strong spatial heterogeneity, inter-annual and intra-annual fluctuations, and uncertainties. The findings of this study can be helpful for policymakers and water resource managers to better understand the impacts of large-scale irrigation on the environment and to develop sustainable water management strategies. [ABSTRACT FROM AUTHOR]

Rapid urbanization brings a series of dilemmas to the development of human society. To address urban sustainability, Sustainable Development Goal 11 (SDG 11) is formulated by the United Nations (UN). Quantifying progress and interactions toward SDG 11 indicators is essential to achieving Sustainable Development Goals (SDGs). However, it is limited by a lack of data in many countries, particularly at small scales. To address the gap, this study used systematic methods to calculate the integrated index of SDG 11 at prefecture-level cities with different economic groups in the Yellow River Basin based on Big Earth Data and statistical data, analyzed its spatial aggregation characteristics using spatial statistical analysis methods, and quantified synergies and trade-offs among indicators under SDG 11. We found the following results: (1) except for SDG 11.1.1, the performance of the integrated index and seven indicators improved from 2015 to 2020. (2) In GDP and disposable income groups, the top 10 cities had higher values, whereas the bottom 10 cities experienced greater growth rates in the integrated index. However, the indicators' values and growth rates varied between the two groups. (3) There were four pairs of indicators with trade-offs that were required to overcome and eight pairs with synergies that were crucial to be reinforced and cross-leveraged in the future within SDG 11 at a 0.05 significance level. Our study identified indicators that urgently paid attention to the urban development of the Yellow River Basin and laid the foundation for local decision-makers to more effectively implement the 2030 Agenda for Sustainable Development (the 2030 Agenda). [ABSTRACT FROM AUTHOR]

Vegetation greening is time-dependent and region-specific. The uncertainty of vegetation greening under global warming has been highlighted. Thus, it is crucial to investigate vegetation greening and its response to climate change at the regional scale. The Yellow River Basin (YRB) is a vital ecological barrier in China with high ecological vulnerability and climatic sensitivity. The relationship between vegetation greening and climate change in the YRB and the relative contribution of climate change remain to be explored. Using the Enhanced Vegetation Index (EVI) and meteorological observation data, the spatiotemporal patterns of vegetation greening across the YRB in response to climate change at the basin and vegetation sub-regional scales from 2000 to 2020 were analyzed. The impact of human activities on regional greening was further quantified. Results showed that approximately 92% of the basin had experienced greening, at average annual and growing season rates of 0.0024 and 0.0034 year–1, respectively. Greening was particularly prominent in the central and eastern YRB. Browning was more prevalent in urban areas with a high intensity of human activities, occupying less than 6.3% of the total basin, but this proportion increased significantly at seasonal scales, especially in spring. Regional greening was positively correlated with the overall warmer and wetter climate, and the partial correlation coefficients between EVI and precipitation were higher than those between EVI and temperature. However, this response varied among different seasonal scales and vegetation sub-regions. The combined effects of climate change and human activities were conducive to vegetation greening in 84.5% of the YRB during the growing season, while human activities had a stronger impact than climate change. The relative contributions of human activities to greening and browning were 65.15% and 70.30%, respectively, mainly due to the promotion of ecological rehabilitation programs and the inhibition of urbanization and construction projects.

Yellow River Basin urban agglomeration (YRBU) is the main carrier of regional socio-economic development in the Yellow River Basin, and its eco-environmental quality, urbanization, and coupling coordination degree are facing higher demands. It is of great significance for the development of YRBU to understand the interactive coupling relationship between the eco-environment and urbanization development from the multi-scale perspective. This research intended to understand the spatio-temporal characteristics of eco-environmental quality, urbanization, and coupling coordination degree in the study area from 2013 to 2021. We proposed an Adjusted Remote Sensing Ecological Index (A-RSEI), integrated Sentinel-2A, Landsat 8, and other remote sensing data to evaluate the eco-environmental quality of the study area, from 2013 to 2021. Coupled coordination degree (CCD) model was used to obtain the CCD between eco-environmental quality and urbanization. In addition, spatio-temporal and multi-scale analysis was carried out from the perspectives of urban agglomeration, municipal, county, and pixel scales. Combined with spatial autocorrelation analysis and Tapio decoupling model, the CCD was further explored. The results show that the proposed A-RSEI model is more suitable for monitoring the eco-environmental quality of the Yellow River Basin. The coupling coordination degree of eco-environment and urbanization in most regions of the study area are rising in a relatively green development trend. The multi-scale analysis among eco-environmental quality, urbanization, and CCD can not only indicate the impact of the central city on its surrounding areas but also help to describe the details of CCD combined with the terrain. The comprehensive discrimination of urban agglomeration and county scale is helpful to express the relationship between urbanization and eco-environmental quality centered on a certain city. The results can provide scientific support for eco-environment protection and high-quality development of the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Net ecosystem productivity (NEP) is an important indicator for estimating regional carbon sources/sinks. The study focuses on a comprehensive computational simulation and spatiotemporal variation study of the NEP in the Yellow River basin from 2000 to 2020 using NPP data products from MODIS combined with a quantitative NEP estimation model followed by a comprehensive analysis of the spatiotemporal variation characteristics and dynamic procession persistence analysis based on meteorological data and land use data. The results show that: (1) The total NEP in the Yellow River basin had an overall increasing trend from 2000 to 2020, with a Theil–Sen trend from −23.37 to 43.66 gCm−2a−1 and a mean increase of 4.64 gCm−2a−1 (p < 0.01, 2-tailed). (2) Most areas of the Yellow River basin are carbon sink areas, and the annual average NEP per unit area was 208.56 gCm−2a−1 from 2000 to 2020. There were, however, substantial spatial and temporal variations in the NEP. Most of the carbon source area was located in the Kubuqi Desert and its surroundings. (3) Changes in land use patterns were the main cause of changes in regional NEP. During the 2000–2020 period, 1154.24 t of NEP were added, mainly due to changes in land use, e.g., the conversion of farmland to forests and grasslands. (4) The future development in 83.43% of the area is uncertain according to the Hurst index dynamic persistence analysis. In conclusion, although the carbon−sink capacity of the terrestrial ecosystem in the Yellow River basin is increasing and the regional carbon sink potential is increasing in the future, the future development of new energy resources has regional uncertainties, and the stability of the basin ecosystem needs to be enhanced. [ABSTRACT FROM AUTHOR]

Impervious surfaces (IS) are one of the most important components of the earth's surface, and understanding how IS have expanded is vital. However, few studies on IS or urbanization have focused on the cradle of the Chinese nation—the Yellow River Basin (YRB). In this study, the Random Forest and Temporal Consistency Check methods were employed to generate long-term maps of IS in the YRB based on Landsat imagery. To explore the dynamics and differences in IS, we developed a spatiotemporal analysis and put forward regional comparisons between different research units of the YRB. We documented the remote sensing-based ecological index (RSEI) in multiple circular zones to discuss the ecological effects of the expansion of IS. The IS extraction strategy achieved excellent performance, with an average overall accuracy of 90.93% and kappa coefficient of 0.79. The statistical results demonstrated that the spatial extent of IS areas in the YRB increased to 18,287.36 km2 in 2020 which was seven times more than that in 1986, at rates of 166 km2/a during 1986–2001, 365 km2/a during 2001–2010, and 1044 km2/a during 2011–2020. Our results indicated that the expansion and densification of IS was slow in core urban areas with high initial IS fraction (ISF), significant in the suburban or rural areas with low initial ISF, and obvious but not significant in the exurb rural or depopulated areas with an initial ISF close to 0. The multiyear RSEI indicated that environmental quality of the YRB had improved with fluctuations. The ecological effects of the impervious expansion slightly differed in urban core areas versus outside these areas. When controlling the urban boundary, more attention should be paid to the rational distribution of ecologically important land. These results provide comprehensive information about IS expansion and can provide references for delineating urban growth boundaries. [ABSTRACT FROM AUTHOR]

Vegetation net primary productivity (NPP) serves as a crucial and intuitive indicator for assessing ecosystem health. However, the nonlinear dynamics and influencing factors operating at various time scales are not yet fully understood. Here, the ensemble empirical mode decomposition (EEMD) method was used to analyze the spatiotemporal patterns of NPP and its association with hydrothermal factors and anthropogenic activities across different temporal scales for the Yellow River Basin (YRB) from 2000 to 2020. The results indicate that: (1) the annual average NPP was 236.37 g C/m2 in the YRB and increased at rates of 4.64 g C/m2/a1 (R2 = 0.86, p < 0.01) during 2000 to 2020. Spatially, nonlinear analysis indicates that 72.77% of the study area exhibits a predominantly increasing trend in NPP, while 25.17% exhibits a reversing trend. (2) On a 3-year time scale, warming has resulted in an increase in NPP in the majority of areas of the study area (69.49%). As the time scale widens, the response of vegetation to climate change becomes more prominent; especially under the long-term trend, the percentage areas of the correlation between vegetation and precipitation and temperature increased with significance, reaching 48.21% and 11.57%, respectively. (3) Through comprehensive time analysis and multivariate regression analysis, it was confirmed that both human activities and climate factors had comparable impacts on vegetation growth. Among different vegetation types, climate was still the main factor affecting grassland NPP, and only 15.74% of grassland was affected by human activities. For shrubland, forest, and farmland, human activity was a dominating factor for vegetation NPP change. There are still few studies on vegetation change using nonlinear methods in the Yellow River Basin, and most studies have not considered the effect of time scale on vegetation evolution. The findings highlight the significance of multi-time scale analysis in understanding the vegetation dynamics and providing scientific guidance for future vegetation restoration and conservation efforts.

As a vital ecological barrier in China, Yellow River Basin (YRB) is strategically significant for China’s national development and modernization. However, YRB has fragile ecosystems, and is sensitive to climatic change. Extreme climate events (e.g., heavy precipitation, heatwaves, and extreme hot and cold) occur frequently in this basin, but the implications (positive and negative effects) of these events on vegetation dynamics remains insufficiently understood. Combing with net primary productivity (NPP), the normalized difference vegetation index (NDVI) and extreme climate indexes, we explored the spatio–temporal characteristics of plants’ growth and extreme climate, together with the reaction of plants’ growth to extreme climate in the Yellow River Basin. This study demonstrated that annual NPP and NDVI of cropland, forest, and grassland in the study region all revealed a climbing tendency. The multi-year monthly averaged NPP and NDVI were characterized by a typical unimodal distribution, with the maximum values of NPP (66.18 gC·m−2) and NDVI (0.54) occurring in July and August, respectively. Spatially, multi–year averaged of vegetation indicators decreased from southeast to northwest. During the study period, carbon flux (NPP) and vegetation index (NDVI) both exhibited improvement in most of the YRB. The extreme precipitation indexes and extreme high temperature indexes indicated an increasing tendency; however, the extreme low temperature indexes reduced over time. NPP and NDVI were negatively associated with extreme low temperature indexes and positively correlated with extreme high temperature indexes, and extreme precipitation indicators other than consecutive dry days. Time lag cross–correlation analysis displayed that the influences of extreme temperature indexes on vegetation indexes (NPP and NDVI) were delayed by approximately six months, while the effects of extreme precipitation indexes were immediate. The study outcomes contribute to our comprehension of plants’ growth, and also their reaction to extreme climates, and offer essential support for evidence–based ecological management practices in the Yellow River Basin.

The accurate estimation of gross primary productivity (GPP) plays an important role in accurately projecting the terrestrial carbon cycle and climate change. Satellite-driven near-infrared reflectance (NIRv) can be used to estimate GPP based on their nearly linear relationship. Notably, previous studies have reported that the relationship between NIRv and GPP seems to be biome-specific (or land cover) at the ecosystem scale due to both biotic and abiotic effects. Hence, the NIRv-based estimation of GPP may be influenced by land cover changes (LCC) and the discrepancies in multisource products (DMP). However, these issues have not been well understood until now. Therefore, this study took the Yellow River basin (YRB) as the study area. This area has experienced remarkable land cover changes in recent decades. We used Moderate-Resolution Imaging Spectroradiometer (MODIS) and European Space Agency (ESA) Climate Change Initiative (CCI) land cover products (termed MCD12C1 and ESACCI, respectively) during 2001–2018 to explore the impact of land cover on NIRv-estimated GPP. Paired comparisons between the static and dynamic schemes of land cover using the two products were carried out to investigate the influences of LCC and DMP on GPP estimation by NIRv. Our results showed that the dominant land cover types in the YRB were grassland, followed by cropland and forest. Meanwhile, the main transfer was characterized by the conversion from other land cover types (e.g., barren) to grassland in the northwest of the YRB and from grassland and shrubland to cropland in the southeast of the YRB during the study period. Moreover, the temporal and spatial pattern of GPP was highly consistent with that of NIRv, and the average increase in GPP was 2.14 gCm−2yr−1 across the YRB. Nevertheless, it is shown that both LCC and DMP had significant influences on the estimation of GPP by NIRv. That is, the areas with obvious differences in NIRv-based GPP closely correspond to the areas where land cover types dramatically changed. The achievements of this study indicate that considering the land cover change and discrepancies in multisource products would help to improve the accuracy of NIRv-based estimated GPP.

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.

Reference evapotranspiration (ET0) is an integral part of the regional hydrological cycle and energy balance and is extremely sensitive to climate change. Based on temperature data from 24 global climate models (GCMs) in the Coupled Model Intercomparison Project Phase 6 (CMIP6), this study developed a multi-model ensemble based on delta statistical downscaling with multiple interpolation methods and evaluation indicators to predict the spatial and temporal evolution trends of ET0 in the Yellow River Basin (YRB) under four emission scenarios (SSP126, SSP245, SSP370, and SSP585) for the near- (2022–2040), mid- (2041–2060), and long- (2081–2100) term future. Results demonstrate that regional data generated based on delta statistical downscaling had good simulation performance for the monthly mean, maximum, and minimum temperatures in the YRB, and the developed multi-model ensemble had better simulation capability than any single model. Compared to the historical period (1901–2014), the annual ET0 showed a highly significant increase for different future emission scenarios, and the increase is faster with increasing radiative forcing. The first main cycle of ET0 change was 52, 53, 60, and 48 years for the SSP126, SSP245, SSP370, and SSP585, respectively. ET0 in the YRB had positive values for EOF1 under all four emission scenarios, responding to a spatially consistent trend across the region. Compared to the historical period, the spatial distribution of ET0 under different future emission scenarios was characterized by being larger in the west and smaller in the east. As the radiative forcing scenario increased and time extended, ET0 significantly increased, with a maximum variation of 112.91% occurring in the western part of the YRB in the long-term future under the SSP585 scenario. This study can provide insight into the water cycle patterns of watersheds and scientific decision support for relevant departments to address the challenges of climate change. [ABSTRACT FROM AUTHOR]

In arid and semi-arid climate zones, understanding the spatial patterns and biogeographical mechanisms of net primary production (NPP) and precipitation use efficiency (PUE) is crucial for assessing the function and stability of ecosystem services, as well as directing ecological restoration. Although the vegetation coverage has changed dramatically after the construction of several ecological restoration projects, due to limited observation data, fewer studies have provided a thorough understanding of NPP and PUE's recent spatial patterns and the controlling factors of different vegetation types in the Yellow River Basin (YRB). To narrow this gap, we integrated remote-sensing land-cover maps with long-term MODIS NPP and meteorological datasets to comprehend NPP and PUE spatial patterns in YRB. Furthermore, we applied structural equation models (SEM) to estimate the effect intensity of NPP and PUE controlling factors. The results showed that along geographical coordinates NPP and PUE decreased from southeast to northwest and trends were roughly consistent along latitude, longitude, and elevation gradients with segmented patterns of increasing and decreasing trends. As for climate gradients, NPP showed significant linear positive and negative trends across the mean annual precipitation (MAP) and the arid index (AI), while segmented changes for PUE. However, the mean annual average temperature (MAT) showed a positive slope for below zero temperature and no change above zero temperature for both NPP and PUE. SEM results suggested that AI determined the spatial pattern of NPP, whereas PUE was controlled by MAP and NPP. As the AI becomes higher in the further, vegetation tends to have decreased NPP with higher sensitivity to water availability. While artificial vegetation had a substantially lower NPP than original vegetation but increased water competition between the ecosystem and human society. Hence further optimization of artificial vegetation is needed to satisfy both ecological and economic needs. This study advanced our understanding of spatial patterns and biogeographic mechanisms of NPP and PUE at YRB, therefore giving theoretical guidance for ecological restoration and ecosystem function evaluation in the face of further climate change. [ABSTRACT FROM AUTHOR]

Vegetation dynamics are often affected by climate variability, but the past state of vegetation has a non-negligible impact on current vegetation growth. However, seasonal differences in the effects of these drivers on vegetation growth remain unclear, particularly in ecologically fragile areas. We used the normalized difference vegetation index (NDVI), gross primary productivity (GPP), and leaf area index (LAI) to describe the vegetation dynamic in the upper and middle reaches of the Yellow River basin (YRB). Three active vegetation growing seasons (early, peak, and late) were defined based on phenological metrics. In light of three vegetation indicators and the climatic data, we identified the correlation between the inter-annual variation of vegetation growth in the three sub-seasons. Then, we quantified the contributions of climate variability and the vegetation growth carryover (VGC) effect on seasonal vegetation greening between 2000–2019. Results showed that both the vegetation coverage and productivity in the study area increased over a 20-year period. The VGC effect dominated vegetation growth during the three active growing seasons, and the effect increased from early to late growing season. Vegetation in drought regions was found to generally have a stronger vegetation carryover ability, implying that negative disturbances might have severer effects on vegetation in these areas. The concurrent seasonal precipitation was another positive driving factor of vegetation greening. However, sunshine duration, including its immediate and lagged impacts, had a negative effect on vegetation growth. In addition, the VGC effect can sustain into the second year. The VGC effect showed that initial ecological restoration and sustainable conservation would promote vegetation growth and increase vegetation productivity. This study provides a comprehensive perspective on understanding the climate–vegetation interactions on a seasonal scale, which helps to accurately predict future vegetation dynamics over time in ecologically fragile areas. [ABSTRACT FROM AUTHOR]

In 1999, the Yellow River Basin (YRB) launched the Grain for Green Program (GGP), which has had a huge impact on the Yellow River Basin vegetation. Research regarding the causes of vegetation changes can provide beneficial information for the management and construction of the ecological environment in the Yellow River Basin. In this study, after reconstructing the relationship between vegetation and climate change under natural conditions, topographic factors were introduced to understand vegetation change in the Yellow River Basin before and after the initiation of the Grain for Green Program, and the contribution rates of the driving factors of change were analyzed. Results show that human activities have had a great impact on the vegetation cover in the Yellow River Basin. We found that after the start of the Grain for Green Program, the vegetation recovery rate was more than six times (slope = 0.0067) that before its start (slope = 0.0011); high NDVI levels moved to lower altitudes, while low NDVI levels moved to high altitudes; and most vegetation types turned to gentle slopes. Human activities and climate change are the dominant factors influencing vegetation coverage, and the contribution rate of human activities had reached 59.3% after 2000, with a tendency to gradually dominate. [ABSTRACT FROM AUTHOR]

The Yellow River Basin is essential to China's economic and social development and ecological security. In order to assess the temporal and spatial distribution of cloud water in the Yellow River Basin, we analyzed the distribution characteristics of water vapor content and cloud water content using ERA5 monthly averaged data from 1980 to 2019. The results showed that the high-value area of the annual average atmospheric water vapor content distribution was concentrated above the North China Plain in the eastern part of the basin, and the value was mostly between 21 and 24 mm. The low-value areas were mainly centered above the high mountain areas in the western part of the basin, and the value mostly fell between 3 and 6 mm. The seasonal distribution characteristics of the annual average water vapor content were relatively consistent with the annual average distribution characteristics. The high-value cloud ice water content area was in the northeastern part of the Qinghai-Tibet Plateau (4.5 g·m−2), while the low-value area appeared on the Loess Plateau (2 g·m−2). The high-value area of cloud liquid water content was on the north side of the West Qinling Mountains (12 g·m−2). The low-value area appeared on the Loess Plateau and the northern part of the Qinghai-Tibet Plateau (3 g·m−2). The cloud water content was higher in the eastern region than in the western region in the overall spatial distribution, and the content of cloud liquid water was higher than that of cloud ice water. The average annual atmospheric water vapor content was increasing, and the annual average content of cloud ice water and cloud liquid water was declining. The change in the total amount and spatial distribution of cloud water was not obvious in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

The Yellow River basin (YRB) has played an important role in the forming of Chinese civilization. Located in the upper reaches of the YRB and the southeastern edge of the Qinghai–Tibet Plateau (QTP), the Gannan Plateau (GP), which consists of mainly alpine and mountain ecosystems, is one of the most important water conservation areas for the Yellow River and recharges 6.59 billion cubic meters of water to the Yellow River each year, accounting for 11.4% of the total runoff of the Yellow River. In the past 30 years, due to climate change and intense human activities, the GP is facing increasing challenges in maintaining its ecosystem integrity and security. Vegetation is a central component of the terrestrial ecosystem and is also key to maintaining ecosystem functioning and services. To form sound ecological restoration projects for the GP and the upper reaches of the YRB in general, this study assesses the trend in FVC (Fractional Vegetation Cover) and its drivers across the GP by integrating high-resolution satellite remote sensing images and meteorological data from 2000 to 2020. Results showed that the mean value of FVC for the entire GP between 2000 and 2020 was 89.26%. Aridity was found to be the main factor that determined the spatial distribution of FVC, while ecosystem type exhibited the secondary effect with forests having the highest FVC within each aridity class. From 2000 to 2020, the FVC in 84.11% of the study area did not exhibit significant change, though 10.32% of the study area still experienced a significant increase in FVC. A multi-factor analysis revealed that precipitation surpassed temperature as the main driver for the FVC trend in semi-arid and semi-humid areas, while this pattern was reversed in humid areas. A further residual analysis indicated that human activities only played a minor role in determining the FVC trend in most naturally vegetated areas of the study area, except for semi-arid crops where a significant positive role of human influences on the FVC trend was observed. The findings highlight the fact that aridity and vegetation types interact to explain the relative sensitivity of alpine and mountain ecosystems to climate trends and human influences. Results from this study provide an observational basis for better understanding and pattern prediction of ecosystem functioning and services in the GP under future climate change, which is key to the success of the national strategy that aims to preserve ecosystem integrity and promote high-quality development over the entire YRB. [ABSTRACT FROM AUTHOR]

Net primary productivity (NPP) is a main contributor to ecosystem carbon pools. It is crucial to monitor the spatial and temporal dynamics of NPP, as well as to assess the impacts of climate change and human activities to cope with global change. The dynamic of the NPP in China’s Yellow River Basin (YRB) from 2000 to 2019 and its influencing factors were analyzed by using trend and persistence tests and the GeoDetector method. The results show that the NPP had strong spatial heterogeneity, with a low NPP in the west and north, and a high NPP in the east and south. From 2000 to 2019, the NPP showed a statistically significant increase (at a mean of 5.5 g C m−2 yr−1, for a cumulative increase of 94.5 Tg C). A Hurst analysis showed that for the NPP in 76.3% of the YRB, the time series was anti-persistent. The spatial heterogeneity of the NPP in the YRB was mainly explained by precipitation and relative humidity (q value ranged from 0.24 to 0.44). However, the strength of the precipitation explained the decreased variation over time (q value decreased from 0.40 in 2000 to 0.26 in 2019). Interactions between the climate factors and human activities affected the NPP more strongly than individual factors. The results emphasize the importance of strengthening future research on the interaction between climate change and human activities. The results reveal the risk and optimal ranges of the driving factors and provide a quantification of the impacts of those factors regarding NPP. These findings can provide a scientific basis for vegetation restoration in the YRB.

Accurate land cover (LC) datasets are the basis for global environmental and climate change studies. Recently, numerous open-source annual LC datasets have been created due to advances in remote sensing technology. However, the agreements and sources of error that affect the accuracy of current annual LC datasets are not well understood, which limits the widespread use of these datasets. We compared four annual LC datasets, namely the CLCD, MCD12Q1, CCI-LC, and GLASS-LC, in the Yellow River Basin (YRB) to identify their spatial and temporal agreement for nine LC classes and to analyze their sources of error. The Mann–Kendall test, Sen’s slope analysis, Taylor diagram, and error decomposition analysis were used in this study. Our results showed that the main LC classes in the four datasets were grassland and cropland (total area percentage > 80%), but their trends in area of change were different. For the main LC classes, the temporal agreement was the highest between the CCI-LC and CLCD (0.85), followed by the MCD12Q1 (0.21), while the lowest was between the GLASS-LC and CLCD (−0.11). The spatial distribution of area for the main LC classes was largely similar between the four datasets, but the spatial agreement in their trends in area of change varied considerably. The spatial variation in the trends in area of change for the cropland, forest, grassland, barren, and impervious LC classes were mainly located in the upstream area region (UA) and the midstream area region (MA) of the YRB, where the percentage of systematic error was high (>68.55%). This indicated that the spatial variation between the four datasets was mainly caused by systematic errors. Between the four datasets, the total error increased along with landscape heterogeneity. These results not only improve our understanding of the spatial and temporal agreement and sources of error between the various current annual LC datasets, but also provide support for land policy making in the YRB.

Large-scale irrigation can alter the regional water cycle process, which changes the structure and spatiotemporal distribution of local and downwind precipitation, impacting soil erosion in both the irrigated areas and the surrounding regions. However, the effects of large-scale irrigation on soil erosion in downwind vulnerable areas have not been investigated. The study used the high-resolution regional climate model (RegCM4) and the revised universal soil loss equation (RUSLE) to examine the effects of irrigation-induced precipitation in Northwest China on the frequency, distribution, and intensity of precipitation in the Yellow River Basin (YRB) under different Representative Concentration Pathways (RCPs). The response characteristics of soil erosion to the irrigation-induced precipitation effects and its relationship with slope, elevation, and land use type were analyzed as well. The results indicate that soil erosion in most regions of the YRB is below moderate, covering 84.57% of the basin. Irrigation leads to a 10% increase in summer precipitation indices (e.g., total wet-day precipitation, consecutive wet days, number of wet days with precipitation ≥ 1 mm, and number of heavy precipitation days with precipitation ≥ 12 mm) in the northwest of the basin. Irrigation also leads to a change in local circulation, resulting in reduced precipitation in the southeast of the basin, particularly under the RCP8.5 scenario. The transformation of erosion intensity between low-grade and high-grade erosion is relatively stable and small under the influence of precipitation. However, soil erosion changes display strong spatial heterogeneity, inter-annual and intra-annual fluctuations, and uncertainties. The findings of this study can be helpful for policymakers and water resource managers to better understand the impacts of large-scale irrigation on the environment and to develop sustainable water management strategies.

Rapid urbanization brings a series of dilemmas to the development of human society. To address urban sustainability, Sustainable Development Goal 11 (SDG 11) is formulated by the United Nations (UN). Quantifying progress and interactions toward SDG 11 indicators is essential to achieving Sustainable Development Goals (SDGs). However, it is limited by a lack of data in many countries, particularly at small scales. To address the gap, this study used systematic methods to calculate the integrated index of SDG 11 at prefecture-level cities with different economic groups in the Yellow River Basin based on Big Earth Data and statistical data, analyzed its spatial aggregation characteristics using spatial statistical analysis methods, and quantified synergies and trade-offs among indicators under SDG 11. We found the following results: (1) except for SDG 11.1.1, the performance of the integrated index and seven indicators improved from 2015 to 2020. (2) In GDP and disposable income groups, the top 10 cities had higher values, whereas the bottom 10 cities experienced greater growth rates in the integrated index. However, the indicators’ values and growth rates varied between the two groups. (3) There were four pairs of indicators with trade-offs that were required to overcome and eight pairs with synergies that were crucial to be reinforced and cross-leveraged in the future within SDG 11 at a 0.05 significance level. Our study identified indicators that urgently paid attention to the urban development of the Yellow River Basin and laid the foundation for local decision-makers to more effectively implement the 2030 Agenda for Sustainable Development (the 2030 Agenda).

Yellow River Basin urban agglomeration (YRBU) is the main carrier of regional socio-economic development in the Yellow River Basin, and its eco-environmental quality, urbanization, and coupling coordination degree are facing higher demands. It is of great significance for the development of YRBU to understand the interactive coupling relationship between the eco-environment and urbanization development from the multi-scale perspective. This research intended to understand the spatio-temporal characteristics of eco-environmental quality, urbanization, and coupling coordination degree in the study area from 2013 to 2021. We proposed an Adjusted Remote Sensing Ecological Index (A-RSEI), integrated Sentinel-2A, Landsat 8, and other remote sensing data to evaluate the eco-environmental quality of the study area, from 2013 to 2021. Coupled coordination degree (CCD) model was used to obtain the CCD between eco-environmental quality and urbanization. In addition, spatio-temporal and multi-scale analysis was carried out from the perspectives of urban agglomeration, municipal, county, and pixel scales. Combined with spatial autocorrelation analysis and Tapio decoupling model, the CCD was further explored. The results show that the proposed A-RSEI model is more suitable for monitoring the eco-environmental quality of the Yellow River Basin. The coupling coordination degree of eco-environment and urbanization in most regions of the study area are rising in a relatively green development trend. The multi-scale analysis among eco-environmental quality, urbanization, and CCD can not only indicate the impact of the central city on its surrounding areas but also help to describe the details of CCD combined with the terrain. The comprehensive discrimination of urban agglomeration and county scale is helpful to express the relationship between urbanization and eco-environmental quality centered on a certain city. The results can provide scientific support for eco-environment protection and high-quality development of the Yellow River Basin.

Net ecosystem productivity (NEP) is an important indicator for estimating regional carbon sources/sinks. The study focuses on a comprehensive computational simulation and spatiotemporal variation study of the NEP in the Yellow River basin from 2000 to 2020 using NPP data products from MODIS combined with a quantitative NEP estimation model followed by a comprehensive analysis of the spatiotemporal variation characteristics and dynamic procession persistence analysis based on meteorological data and land use data. The results show that: (1) The total NEP in the Yellow River basin had an overall increasing trend from 2000 to 2020, with a Theil–Sen trend from −23.37 to 43.66 gCm−2a−1 and a mean increase of 4.64 gCm−2a−1 (p < 0.01, 2-tailed). (2) Most areas of the Yellow River basin are carbon sink areas, and the annual average NEP per unit area was 208.56 gCm−2a−1 from 2000 to 2020. There were, however, substantial spatial and temporal variations in the NEP. Most of the carbon source area was located in the Kubuqi Desert and its surroundings. (3) Changes in land use patterns were the main cause of changes in regional NEP. During the 2000–2020 period, 1154.24 t of NEP were added, mainly due to changes in land use, e.g., the conversion of farmland to forests and grasslands. (4) The future development in 83.43% of the area is uncertain according to the Hurst index dynamic persistence analysis. In conclusion, although the carbon−sink capacity of the terrestrial ecosystem in the Yellow River basin is increasing and the regional carbon sink potential is increasing in the future, the future development of new energy resources has regional uncertainties, and the stability of the basin ecosystem needs to be enhanced.

Impervious surfaces (IS) are one of the most important components of the earth’s surface, and understanding how IS have expanded is vital. However, few studies on IS or urbanization have focused on the cradle of the Chinese nation—the Yellow River Basin (YRB). In this study, the Random Forest and Temporal Consistency Check methods were employed to generate long-term maps of IS in the YRB based on Landsat imagery. To explore the dynamics and differences in IS, we developed a spatiotemporal analysis and put forward regional comparisons between different research units of the YRB. We documented the remote sensing-based ecological index (RSEI) in multiple circular zones to discuss the ecological effects of the expansion of IS. The IS extraction strategy achieved excellent performance, with an average overall accuracy of 90.93% and kappa coefficient of 0.79. The statistical results demonstrated that the spatial extent of IS areas in the YRB increased to 18,287.36 km2 in 2020 which was seven times more than that in 1986, at rates of 166 km2/a during 1986–2001, 365 km2/a during 2001–2010, and 1044 km2/a during 2011–2020. Our results indicated that the expansion and densification of IS was slow in core urban areas with high initial IS fraction (ISF), significant in the suburban or rural areas with low initial ISF, and obvious but not significant in the exurb rural or depopulated areas with an initial ISF close to 0. The multiyear RSEI indicated that environmental quality of the YRB had improved with fluctuations. The ecological effects of the impervious expansion slightly differed in urban core areas versus outside these areas. When controlling the urban boundary, more attention should be paid to the rational distribution of ecologically important land. These results provide comprehensive information about IS expansion and can provide references for delineating urban growth boundaries.

Rapid urbanization in the lower Yellow River basin has greatly contributed to the socio-economic development of Northern China, but it has also exacerbated land use/land cover change, with significant impacts on ecology. Ecological quality is a comprehensive spatial and temporal measure of an ecosystem’s elements, structure and function, reflecting the ecological state under external pressures. However, how land use/land cover change affects the ecological quality during urbanization has rarely been explored. In this study, Jinan, a megacity in the lower Yellow River basin, was taken as a typical region, and the response of ecological quality to the land use/land cover change in 2000, 2010 and 2020 was retrieved using the remote sensing ecological index. For the mixed land use/land cover change types, a type-decomposition and spatial heterogeneity quantification method based on the abundance index was proposed, and the impact mechanisms of the land use/land cover change on the ecological quality were revealed by coupling with GeoDetector. The results show that: (1) Farmland and built-up areas, as the dominant land use/land cover types, were the primary factors controlling the spatial pattern of ecological quality. (2) Urban expansion and farmland protection policies resulted in the transfer of farmland and woodland to built-up areas as well as the transfer of woodland and grassland to farmland, which intensified the degradation of ecological quality. (3) Ecological protection policies prompted the transfer of farmland and grassland to woodland and the transfer of farmland to grassland as the main cause for the improvement of ecological quality. (4) Although ecological protection and urban development were implemented in parallel, uneven land use/land cover changes resulted in a 1.4 times expanded area of poorer ecological quality with increasingly serious spatial agglomeration effects. This study can provide scientific references for the ecological conservation and high-quality, sustainable development of cities in the lower Yellow River basin.

Reference evapotranspiration (ET0) is an integral part of the regional hydrological cycle and energy balance and is extremely sensitive to climate change. Based on temperature data from 24 global climate models (GCMs) in the Coupled Model Intercomparison Project Phase 6 (CMIP6), this study developed a multi-model ensemble based on delta statistical downscaling with multiple interpolation methods and evaluation indicators to predict the spatial and temporal evolution trends of ET0 in the Yellow River Basin (YRB) under four emission scenarios (SSP126, SSP245, SSP370, and SSP585) for the near- (2022–2040), mid- (2041–2060), and long- (2081–2100) term future. Results demonstrate that regional data generated based on delta statistical downscaling had good simulation performance for the monthly mean, maximum, and minimum temperatures in the YRB, and the developed multi-model ensemble had better simulation capability than any single model. Compared to the historical period (1901–2014), the annual ET0 showed a highly significant increase for different future emission scenarios, and the increase is faster with increasing radiative forcing. The first main cycle of ET0 change was 52, 53, 60, and 48 years for the SSP126, SSP245, SSP370, and SSP585, respectively. ET0 in the YRB had positive values for EOF1 under all four emission scenarios, responding to a spatially consistent trend across the region. Compared to the historical period, the spatial distribution of ET0 under different future emission scenarios was characterized by being larger in the west and smaller in the east. As the radiative forcing scenario increased and time extended, ET0 significantly increased, with a maximum variation of 112.91% occurring in the western part of the YRB in the long-term future under the SSP585 scenario. This study can provide insight into the water cycle patterns of watersheds and scientific decision support for relevant departments to address the challenges of climate change.

In arid and semi-arid climate zones, understanding the spatial patterns and biogeographical mechanisms of net primary production (NPP) and precipitation use efficiency (PUE) is crucial for assessing the function and stability of ecosystem services, as well as directing ecological restoration. Although the vegetation coverage has changed dramatically after the construction of several ecological restoration projects, due to limited observation data, fewer studies have provided a thorough understanding of NPP and PUE’s recent spatial patterns and the controlling factors of different vegetation types in the Yellow River Basin (YRB). To narrow this gap, we integrated remote-sensing land-cover maps with long-term MODIS NPP and meteorological datasets to comprehend NPP and PUE spatial patterns in YRB. Furthermore, we applied structural equation models (SEM) to estimate the effect intensity of NPP and PUE controlling factors. The results showed that along geographical coordinates NPP and PUE decreased from southeast to northwest and trends were roughly consistent along latitude, longitude, and elevation gradients with segmented patterns of increasing and decreasing trends. As for climate gradients, NPP showed significant linear positive and negative trends across the mean annual precipitation (MAP) and the arid index (AI), while segmented changes for PUE. However, the mean annual average temperature (MAT) showed a positive slope for below zero temperature and no change above zero temperature for both NPP and PUE. SEM results suggested that AI determined the spatial pattern of NPP, whereas PUE was controlled by MAP and NPP. As the AI becomes higher in the further, vegetation tends to have decreased NPP with higher sensitivity to water availability. While artificial vegetation had a substantially lower NPP than original vegetation but increased water competition between the ecosystem and human society. Hence further optimization of artificial vegetation is needed to satisfy both ecological and economic needs. This study advanced our understanding of spatial patterns and biogeographic mechanisms of NPP and PUE at YRB, therefore giving theoretical guidance for ecological restoration and ecosystem function evaluation in the face of further climate change.

Vegetation dynamics are often affected by climate variability, but the past state of vegetation has a non-negligible impact on current vegetation growth. However, seasonal differences in the effects of these drivers on vegetation growth remain unclear, particularly in ecologically fragile areas. We used the normalized difference vegetation index (NDVI), gross primary productivity (GPP), and leaf area index (LAI) to describe the vegetation dynamic in the upper and middle reaches of the Yellow River basin (YRB). Three active vegetation growing seasons (early, peak, and late) were defined based on phenological metrics. In light of three vegetation indicators and the climatic data, we identified the correlation between the inter-annual variation of vegetation growth in the three sub-seasons. Then, we quantified the contributions of climate variability and the vegetation growth carryover (VGC) effect on seasonal vegetation greening between 2000–2019. Results showed that both the vegetation coverage and productivity in the study area increased over a 20-year period. The VGC effect dominated vegetation growth during the three active growing seasons, and the effect increased from early to late growing season. Vegetation in drought regions was found to generally have a stronger vegetation carryover ability, implying that negative disturbances might have severer effects on vegetation in these areas. The concurrent seasonal precipitation was another positive driving factor of vegetation greening. However, sunshine duration, including its immediate and lagged impacts, had a negative effect on vegetation growth. In addition, the VGC effect can sustain into the second year. The VGC effect showed that initial ecological restoration and sustainable conservation would promote vegetation growth and increase vegetation productivity. This study provides a comprehensive perspective on understanding the climate–vegetation interactions on a seasonal scale, which helps to accurately predict future vegetation dynamics over time in ecologically fragile areas.

In 1999, the Yellow River Basin (YRB) launched the Grain for Green Program (GGP), which has had a huge impact on the Yellow River Basin vegetation. Research regarding the causes of vegetation changes can provide beneficial information for the management and construction of the ecological environment in the Yellow River Basin. In this study, after reconstructing the relationship between vegetation and climate change under natural conditions, topographic factors were introduced to understand vegetation change in the Yellow River Basin before and after the initiation of the Grain for Green Program, and the contribution rates of the driving factors of change were analyzed. Results show that human activities have had a great impact on the vegetation cover in the Yellow River Basin. We found that after the start of the Grain for Green Program, the vegetation recovery rate was more than six times (slope = 0.0067) that before its start (slope = 0.0011); high NDVI levels moved to lower altitudes, while low NDVI levels moved to high altitudes; and most vegetation types turned to gentle slopes. Human activities and climate change are the dominant factors influencing vegetation coverage, and the contribution rate of human activities had reached 59.3% after 2000, with a tendency to gradually dominate.

The Yellow River basin (YRB) has played an important role in the forming of Chinese civilization. Located in the upper reaches of the YRB and the southeastern edge of the Qinghai–Tibet Plateau (QTP), the Gannan Plateau (GP), which consists of mainly alpine and mountain ecosystems, is one of the most important water conservation areas for the Yellow River and recharges 6.59 billion cubic meters of water to the Yellow River each year, accounting for 11.4% of the total runoff of the Yellow River. In the past 30 years, due to climate change and intense human activities, the GP is facing increasing challenges in maintaining its ecosystem integrity and security. Vegetation is a central component of the terrestrial ecosystem and is also key to maintaining ecosystem functioning and services. To form sound ecological restoration projects for the GP and the upper reaches of the YRB in general, this study assesses the trend in FVC (Fractional Vegetation Cover) and its drivers across the GP by integrating high-resolution satellite remote sensing images and meteorological data from 2000 to 2020. Results showed that the mean value of FVC for the entire GP between 2000 and 2020 was 89.26%. Aridity was found to be the main factor that determined the spatial distribution of FVC, while ecosystem type exhibited the secondary effect with forests having the highest FVC within each aridity class. From 2000 to 2020, the FVC in 84.11% of the study area did not exhibit significant change, though 10.32% of the study area still experienced a significant increase in FVC. A multi-factor analysis revealed that precipitation surpassed temperature as the main driver for the FVC trend in semi-arid and semi-humid areas, while this pattern was reversed in humid areas. A further residual analysis indicated that human activities only played a minor role in determining the FVC trend in most naturally vegetated areas of the study area, except for semi-arid crops where a significant positive role of human influences on the FVC trend was observed. The findings highlight the fact that aridity and vegetation types interact to explain the relative sensitivity of alpine and mountain ecosystems to climate trends and human influences. Results from this study provide an observational basis for better understanding and pattern prediction of ecosystem functioning and services in the GP under future climate change, which is key to the success of the national strategy that aims to preserve ecosystem integrity and promote high-quality development over the entire YRB.

The Yellow River Basin is essential to China’s economic and social development and ecological security. In order to assess the temporal and spatial distribution of cloud water in the Yellow River Basin, we analyzed the distribution characteristics of water vapor content and cloud water content using ERA5 monthly averaged data from 1980 to 2019. The results showed that the high-value area of the annual average atmospheric water vapor content distribution was concentrated above the North China Plain in the eastern part of the basin, and the value was mostly between 21 and 24 mm. The low-value areas were mainly centered above the high mountain areas in the western part of the basin, and the value mostly fell between 3 and 6 mm. The seasonal distribution characteristics of the annual average water vapor content were relatively consistent with the annual average distribution characteristics. The high-value cloud ice water content area was in the northeastern part of the Qinghai-Tibet Plateau (4.5 g·m−2), while the low-value area appeared on the Loess Plateau (2 g·m−2). The high-value area of cloud liquid water content was on the north side of the West Qinling Mountains (12 g·m−2). The low-value area appeared on the Loess Plateau and the northern part of the Qinghai-Tibet Plateau (3 g·m−2). The cloud water content was higher in the eastern region than in the western region in the overall spatial distribution, and the content of cloud liquid water was higher than that of cloud ice water. The average annual atmospheric water vapor content was increasing, and the annual average content of cloud ice water and cloud liquid water was declining. The change in the total amount and spatial distribution of cloud water was not obvious in the Yellow River Basin.

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 dependence of water use efficiency (WUE) on changes in land cover types is crucial for understanding of long-term water availability and assessment of water-saving strategies. Investigating the impact of land cover types on ecosystem WUE has important implications when revealing water dynamics and land management. However, the determination of WUE and its dominant factors have always been subject to high data dependency and large calculation consumption within large basins. This paper proposes a framework for processing actual evapotranspiration (AET) and WUE calculation by coupling the Maximum Entropy Production (MEP) method with the Google Earth Engine (GEE). By employing the proposed framework and three data sources available in the GEE platform, results for actual ET and WUE from 2001 to 2020 were obtained in the Yellow River Basin (YRB). The results show that the proposed framework provides an acceptable estimation of actual ET via validation with Eddy Covariance flux sites in the YRB. The calculated WUE values varied greatly in different sub-basins within the YRB, indicating a cumulative growth rate of about 56% during the past 20 years. The dominant factor that led to these changes was the transition from Grasslands into other land-use types. Our results suggest that the use of the GEE platform coupled with the MEP method offers new possibilities for advancing understanding of water exchange and water resource management.

A changing climate has been posing significant impacts on vegetation growth, especially in the Yellow River Basin (YRB) where agriculture and ecosystems are extremely vulnerable. In this study, the data for normalized difference vegetation index (NDVI) obtained from moderate-resolution imaging spectroradiometer (MODIS) sensors and climate data (precipitation and temperature) derived from the national meteorological stations were employed to examine the spatiotemporal differences in vegetation growth and its reaction to climate changes in the YRB from 2000–2019, using several sophisticated statistical methods. The results showed that both NDVI and climatic variables exhibited overall increasing trends during this period, and positive correlations at different significant levels were found between temperature/precipitation and NDVI. Furthermore, NDVI in spring had the strongest response to temperature/precipitation, and the correlation coefficient of NDVI with temperature and precipitation was 0.485 and 0.726, respectively. However, an opposite situation was detected in autumn (September to November) since NDVIs exhibited the weakest responses to temperatures/precipitation, and the NDVI’s correlation with both temperature and precipitation was 0.13. This indicated that, compared to other seasons, increasing the temperature and precipitation has the most significant effect on NDVI in spring (March to May). Except for a few places in the northern, southern, and southwestern regions of the YRB, NDVI was positively correlated with precipitation in most areas. There was an inverse relationship between NDVI and temperature in most parts of the central YRB, especially in summer (June to August) and growing season (May to September); however, there was a positive correlation in most areas of the YRB in spring. Finally, continuous attention must be given to the influence of other factors in the YRB.

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.

Terrestrial water storage (TWS) variations are a result of the interconnected impact of various variables including climate, hydrology, ecology, and anthropogenic activities. Previous studies have indicated that climate factors (e.g., precipitation and potential evapotranspiration), vegetation restoration, and water withdrawals (irrigational and industrial water use) are the major determinants of TWS depletion across the Yellow River Basin (YRB). However, few studies have provided explicit information about the main forcing variables that determine spatiotemporal variations in TWS and the synergies among these factors. This study explored the explicit understanding of hydro-climatic and socio-ecological determinants and the key interacting processes that affected the TWS variations across the Yellow River Basin in northern China. The multivariate adaptive regression splines model was employed to establish the relationship function of the long-term trends for the dependent (TWS) and independent (explanatory) variables consisting of normalized difference vegetation index (NDVI), hydro-climate, and human water withdrawal. The long-term trends estimated from the MARS model reproduced the ones calculated by Gravity Recovery and Climate Experiment gravity satellites, with a determination coefficient (R2) of 0.83 and a mean absolute error (MAE) of 1.2 mm. The results showed that precipitation, minimum temperature, runoff, base flow, water withdrawal for electricity, and NDVI were the main drivers of the spatiotemporal variations in the TWS, of which minimum temperature and runoff played a considerable role in TWS variations through the interplay with other variables. The critical values of the trend for interactive variables, which could alter the acting direction of the synergy on the TWS, were also estimated. In view of the connotation of interactive variables, we suggested that spatiotemporal variations in TWS resulted from the coupling of the hydrological energy system, hydrological ecosystem, and hydrological system in the YRB, of which the hydrological system plays the most significant role, followed by the hydrological ecosystem.

Changes in the land use/cover alter the Earth system processes and affect the provision of ecosystem services, posing a challenge to achieve sustainable development. In the past few decades, the Yellow River (YR) basin faced enormous social and environmental sustainability challenges associated with environmental degradation, soil erosion, vegetation restoration, and economic development, which makes it important to understand the long-term land use/cover dynamics of this region. Here, using three decades of Landsat imagery (17,080 images) and incorporating physiography data, we developed an effective annual land use/cover mapping framework and provided a set of 90 m resolution continuous annual land use/cover maps of the YR basin from 1986 to 2018 based on the Google Earth Engine and the Classification and Regression Trees algorithm. The independent random sampling validations based on the field surveys (640 points) and Google Earth (3456 points) indicated that the overall accuracy of these maps is 78.3% and 80.0%, respectively. The analysis of the land system of the YR basin showed that this region presents complex temporal and spatial changes, and the main change patterns include no change or little change, cropland loss and urban expansion, grassland restoration, increase in orchard and terrace, and increase in forest during the entire study period. The major land use/cover change has occurred in the transitions from forests, grasslands, and croplands to the class of orchard and terrace (19.8% of all change area), which not only increase the greenness but also raised the income, suggesting that YR progress towards sustainable development goals for livelihood security, economic growth, and ecological protection. Based on these data and analysis, we can further understand the role of the land system in the mutual feedback between society and the environment, and provide support for ecological conservation, high-quality development, and the formulation of sustainable management policies in this basin, highlighting the importance of continuous land use/cover information for understanding the interactions between the human and natural systems.

Accurate evapotranspiration (ET) estimation is important in understanding the hydrological cycle and improving water resource management. The operational simplified surface energy balance (SSEBop) model can be set up quickly for the routine monitoring of ET. Several studies have suggested that the SSEBop model, which can simulate ET, has performed inconsistently across the United States. There are few detailed studies on the evaluation of ET simulated by SSEBop in other regions. To explore the potential and application scope of the SSEBop model, more evaluation of the ET simulated by SSEBop is clearly needed. We calculated the SSEBop-model-based ET (ETSSEBopYRB) with land surface temperature product of MOD11A2 and climate variables as inputs for the Yellow River Basin (YRB), China. We also compared the ETSSEBopYRB with eight coarse resolution ET products, including China ETMTE, produced using the upscaling energy flux method; China ETCR, which is generated using the non-linear complementary relationship model; three global products based on the Penman–Monteith logic (ETPMLv2, ETMODIS, and ETBESS), two global ET products based on the surface energy balance (ETSEBS, ETSSEBopGlo), and integrated ET products based on the Bayesian model averaging method (ETGLASS), using the annual ET data derived from the water balance method (WB-ET) for fourteen catchments. We found that ETSSEBopYRB and the other eight ET products were able to explain 23 to 52% of the variability in the water balance ET for fourteen small catchments in the YRB. ETSSEBopYRB had a better agreement with WB-ET than ETSEBS, ETMODIS, ETCR, and ETGLASS, with lower RMSE (88.3 mm yr−1 vs. 121.7 mm yr−1), higher R2 (0.49 vs. 0.43), and lower absolute RPE (−3.3% vs. –19.9%) values for the years 2003–2015. We also found that the uncertainties of the spatial patterns of the average annual ET values and the ET trends were still large for different ET products. Third, we found that the free global ET product derived from the SSEBop model (ETSSEBopGlo) highly underestimated the annual total ET trend for the YRB. The poor performance of the land surface temperature product of MOD11A2 in 2015 caused the large ETSSEBopYRB uncertainty at eight-day and monthly scales. Further evaluation of ET based on the SSEBop model for site measurements is needed.