Search Results (79)

As a crucial manifestation of ecosystem water regulation and supply functions, water conservation plays a vital role in regional ecosystem development and sustainable water resource management. This study investigates nine major Chinese river basins (Songliao, Haihe, Huaihe, Yellow, Yangtze, Pearl, Southeast Rivers, Southwest Rivers, and Inland Rivers) through integrated application of the InVEST model and geographical detector model. We systematically examine the spatiotemporal heterogeneity of water conservation capacity and its driving mechanisms from 1990 to 2020. The results reveal a distinct northwest–southeast spatial gradient in water conservation across China, with lower values predominating in northwestern regions. Minimum conservation values were recorded in the Inland River Basin (15.88 mm), Haihe River Basin (42.07 mm), and Yellow River Basin (43.55 mm), while maximum capacities occurred in the Pearl River Basin (483.68 mm) and Southeast Rivers Basin (517.21 mm). Temporal analysis showed interannual fluctuations, peaking in 2020 at 130.98 mm and reaching its lowest point in 2015 at 113.04 mm. Precipitation emerged as the dominant factor governing spatial patterns, with higher rainfall correlating strongly with enhanced conservation capacity. Land cover analysis revealed superior water retention in vegetated areas (forests, grasslands, and cultivated land) compared to urbanized and bare land surfaces. Our findings demonstrate that water conservation dynamics result from synergistic interactions among multiple factors rather than single-variable influences. Accordingly, we propose that future water resource policies adopt an integrated management approach addressing climate patterns, land use optimization, and socioeconomic factors to develop targeted conservation strategies. Full article

Understanding the propagation dynamics from meteorological to hydrological droughts, particularly in regions heavily influenced by human activities, is essential for the effective monitoring and prevention of hydrological drought risks. This study focuses on the Haihe River Basin, investigating the evolution of meteorological and hydrological droughts using the Standardized Precipitation and Evapotranspiration Index and the Standardized Runoff Index, supplemented by run theory analysis. Using correlation analysis, we examine the propagation lag times between meteorological and hydrological droughts. Our results indicate a worsening drought trend in the Haihe River Basin over the past six decades. Notably, a turning point occurred in 1991, where meteorological droughts began to abate, while hydrological droughts intensified, highlighting a divergence in trends between meteorological and hydrological droughts. We identify four distinct pathways for the transition from meteorological to hydrological droughts in the region. This study identifies a hydrological drought lag time of 3 months. The occurrence of droughts in the Haihe River Basin is becoming increasingly frequent. Furthermore, our findings reveal that the severity of hydrological droughts is increasingly exceeding that of meteorological droughts, and the influence of meteorological conditions on hydrological droughts is diminishing, while human activities may become a more significant contributing factor. The findings from this research enhance our comprehension of how drought propagation trends and characteristics are shaped by significant human influences, thereby offering pivotal insights for managing water resources at the basin level. Full article

The net primary productivity (NPP) of vegetation—a critical component of ecosystem carbon cycling and a key indicator of the quality and functionality of ecosystems—is jointly influenced by natural and anthropogenic factors. As NPP is a vital agricultural and ecological region in China, understanding the spatiotemporal dynamics and driving mechanisms of vegetation NPP in the North China Plain (NCP) has significant implications for regional sustainable development. Utilizing MODIS NPP, temperature, precipitation, and human activity data from 2003 to 2023, this study employs univariate linear regression, ArcGIS spatial analysis, and the Hurst index to investigate the spatiotemporal characteristics, driving factors, and future trends in vegetation NPP. The results indicate that vegetation NPP exhibited a fluctuating upward trend over the 21-year period, with an annual increase of 2.60 g C/m². Spatially, NPP displayed a “high in the south, low in the north” pattern. There is significant spatial heterogeneity between temperature, precipitation, and vegetation NPP in the study area, with natural factors generally exerting a greater influence than human activities; however, the coupling of human activities with other factors significantly amplify their impact. The Hurst index (mean: 0.43) revealed an anti-persistent future trend in vegetation NPP, suggesting substantial uncertainties regarding its long-term dynamics. These findings enhance our understanding of the responses of vegetation to global change and provide a scientific basis for balancing food security and ecological conservation in the NCP. Full article

Water quality prediction serves as an important foundation for risk control and the proactive management of the aquatic environment, and the Long Short-Term Memory (LSTM) network has gained recognition as an effective approach for achieving high-precision water quality predictions. However, despite its potential, there is a significant gap in the literature regarding the confidence analysis of its prediction accuracy and the underlying causes of variability across different water quality indicators and basins. To address this gap, the present study introduces a novel confidence evaluation method to systematically assess the performance of LSTM in predicting key water quality parameters, including ammonia nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), hydrogen ion concentration (pH), and total phosphorus (TP). This evaluation was conducted across three basins with distinct geographical, climatic, and water quality conditions: the Huangshui River Basin (HSB), the Haihe River Basin (HRB), and the Yangtze River Basin (YRB). The results of the confidence evaluation revealed that LSTM exhibited higher credibility in the Haihe River Basin compared to the Yangtze River Basin. Additionally, LSTM demonstrated greater accuracy and stability in predicting total phosphorus (TP) compared to other water quality indicators in both basins, with median NSE values of 0.71 in the HRB and 0.73 in the YRB. Additionally, the research demonstrated a linear relationship between the ability of LSTM models to predict the water quality and temporal autocorrelation as well as the cross-correlation coefficients of the water quality parameters. The coefficients of determination (R²) ranged from 0.59 to 0.85, with values of 0.59 and 0.79 for the YRB and 0.85 and 0.80 for the HRB, respectively. This finding underscores the importance of considering these correlation metrics when evaluating the reliability of LSTM-based predictions. Full article

The Haihe River system, located in the East Asian monsoon climate zone, experiences uneven precipitation and significant variability, leading to frequent droughts and floods that disrupted economic and social development. While many studies have assessed the risks of droughts and floods in the Haihe River Basin, most focus on the basin as a whole, leaving a notable gap in research on the dynamics of the northern region. This study analyzed historical drought and flood data, incorporating instrument precipitation records from 1960 to 2009 to reconstruct conditions in the northern Haihe River Basin from 1470 to 2009. Using methods like the Mann–Kendall test, sliding averages, continuous wavelet technology, and spatial analysis, this study examined the trends, change points, periodicity, and spatial patterns of drought and flood variability. The findings showed that from 1470 to 2009, drought and flood variabilities occurred 73.15% of the time in the northern Haihe system, with peak disaster periods in the 17th, 19th, and 20th centuries. The region has alternated between wet and dry cycles, with a notable dry trend emerging in the 21st century. A prominent 35~50-year cycle in drought and flood occurrences was identified, along with high-frequency oscillations. Flood periods were most frequent in the eastern plains, while drought periods were more prevalent in the western areas, gradually shifting eastward since 1950. The research also revealed correlations between drought and flood variability and solar activity, with peak years coinciding with higher frequencies of these events. El Niño events were associated with drought periods, while La Niña events tended to cause flood periods. Factors such as solar activity, El Niño–Southern Oscillation, monsoon climate patterns, topography, and human influences shaped the dynamics of drought and flood variability in the northern Haihe River Basin. A comparison with other regions showed consistent wet and dry cycles over the past 500 years, particularly between the northern and southern parts of the basin. However, since the 21st century, the southern region has remained humid, while the northern region has become increasingly drier. Despite similar temperature trends, humidity changes have diverged in the modern warming period. Although the underlying factors driving drought and flood variability were not fully understood and required a further exploration of the global climate system’s interactions, these findings emphasized the need for targeted strategies to address the ongoing challenges of drought and flood management in the northern Haihe River Basin. Full article

Drought is a critical hydrological challenge with ecological and socio-economic impacts, but its long-term variability and drivers remain insufficiently understood. This study proposes a deep learning-based framework to explore drought dynamics and their underlying drivers across China’s major basins over the past four decades. The Long Short-Term Memory network was employed to reconstruct gaps in satellite-derived soil moisture (SM) datasets, achieving high accuracy (R² = 0.928 and RMSE = 0.020 m³m⁻³). An advanced explainable artificial intelligence (XAI) approach was applied to unravel the mechanistic relationships between SM and critical hydrometeorological variables. Our results revealed a slight increasing trend in SM value across China’s major basins over the past four decades, with a more pronounced downward trend in cropland that was more sensitive to water resource management. XAI results demonstrated distinct regional disparities: the northern arid regions displayed pronounced seasonality in drought dynamics, whereas the southern humid regions were less influenced by seasonal fluctuations. Surface solar radiation and air temperature were identified as the primary drivers of droughts in the Haihe, Yellow, Southwest, and Pearl River Basins, whereas precipitation is the dominant factor in the Middle and Lower Yangtze River Basins. Collectively, our study offers valuable insights for sustainable water resource management and land-use planning. Full article

Total nitrogen (TN) and total phosphorus (TP) are essential indicators for assessing water quality. This study systematically analyzes the spatial and temporal distribution of TN and TP in China’s surface waters and examines the influence of natural factors and human activities on their concentrations. Utilizing data from 1387 monitoring sites (2020–2021) and employing K-means clustering and geographically weighted regression (GWR), we found that the national average concentrations were 3.89 mg/L for TN and 0.096 mg/L for TP. Spatially, higher TN and TP levels were observed in northern regions, coastal areas, and plains compared with southern, inland, and mountainous areas. Notably, TN concentrations reached up to 29.49 mg/L in the Haihe River basin and related plains, while TP peaked at 0.497 mg/L in the southeastern Shandong and northern Jiangsu coastal zones. Temporally, TN levels were approximately 50% higher in winter than summer, whereas TP levels were about 40% higher in summer. Key influencing factors included rainfall, elevation, fertilizer use, and population density, with spatial heterogeneity observed. Rainfall was the primary factor for TN change and the secondary factor for TP change. Soil type positively correlates with TN and TP changes, affecting non-point source pollution. Human activities such as land use, fertilizer application and population density had a significant effect on the nitrogen and phosphorus concentrations, while woodland had a significant impact on the improvement of water quality. The geographically weighted regression analysis showed spatial heterogeneity in the effects of each factor on TN and TP concentrations, and the best fit was at the watershed scale. The findings highlight the need for enhanced control of agricultural runoff, improved sewage treatment, and region-specific management strategies to inform effective water environment policies in China. Full article

Soil moisture is one of the main factors influencing evapotranspiration (ET) under soil water stress conditions. The TSEB_SM model used soil moisture to constrain soil evaporation. However, the transpiration schemes constrained by soil moisture require greater physical realism and the soil evaporation schemes parameters usually need calibration. In this study, the TSEB_SM model was enhanced by incorporating Jarvis’s canopy resistance which considered the influence of soil moisture on transpiration schemes. We assessed the new model (TSEB_SM+) in the Heihe and Haihe basins of China. The TSEB_SM+ model displayed a consistency to the TSEB in the ET estimation at the A’rou site, but approximately 30% and 35% reductions in RMSEs at the Huazhaizi and Huailai sites. It produced approximately 20% and 10% of the reductions in the ET RMSEs at the Huailai and A’rou sites compared to the TSEB_SM model, but had a similar performance at the Huazhaizi site. Moreover, the TSEB_SM+ model estimated ET in the Heihe River Basin with an RMSE of 0.58 mm·day⁻¹, and it was sensitive to the soil moisture, particularly when the soil moisture was below 30%. In conjunction to soil moisture, the TSEB_SM+ model could potentially be a more effective tool for monitoring the ET. Full article

The severe water quality pollution of the Bohai Sea impacts both the ecosystem and the economy of the region. This study assesses the water quality of the Bohai Sea using a two-year (2020–2021) dataset to investigate the spatial distribution and sources of contamination. Multivariate statistical analysis methods, including principal component analysis (PCA), cluster analysis (CA), and discriminant analysis, are employed. Thirteen chemical indicators are analyzed through PCA, resulting in the extraction of three principal components that reflect different pollution sources related to domestic, industrial, and agricultural activities. Additionally, the corresponding water quality index (WQI) is calculated to categorize the water quality into three levels using CA. The PCA-based WQI method is feasible and shows similarities to the traditional WQI method. Higher pollution levels are observed in Panjin and Tianjin, while Huludao, Yantai, and Dalian exhibit relatively good water quality. The results indicate complex, multifactorial pollution causes in the Bohai Sea, including eutrophication, heavy metal contamination, and ammonia pollution. These findings can guide region-specific water quality management: Panjin should control heavy metal discharges from industry and transportation, while Tianjin requires improvements in runoff management of ammonia-based fertilizers. Together, these strategies support the ecological and sustainable development of the Bohai Sea. Full article

The medium development characteristics and controlling factors of the karst peak forest plain water system constitute the core of analyzing the complex and variable hydrogeological environment, especially in revealing the controlling factors between the hydrological system and karst development characteristics, which is crucial for a deeper understanding of karst hydrogeological environments. This study takes Zengpiyan in Guilin as an example and conducts a dynamic clustering analysis on the advantageous occurrence of fracture development in three sampling areas. A total of 3472 karst channels and fractures were identified and measured. Our research reveals the following: (1) The high degree of development of fissures on surface rock outcrops is mainly formed by the expansion of shear joints through dissolution and erosion. The dip angles of fissures are mainly characterized by low angles, with fissures with dip angles between 18° and 80° accounting for 65.44% of the total observed fissures. The linear density of fissures is 3.64 per meter. (2) There are significant differences in the line density of cracks and fissures in different areas of the research area. For example, the line density in Sampling Area 1 is 0.99 lines per meter, while the line density in Sampling Area 3 reaches 5.02 lines per meter. In addition, the extension length of cracks is generally long, with joints with extension lengths exceeding 1.5 m accounting for 77.46% of the total observed joints and through cracks with extension lengths exceeding 5 m accounting for 23.33%. (3) The development characteristics of underground karst reveal that underground karst caves are mainly distributed at elevations of 120 to 160 m, with a drilling encounter rate of about 43.3%. It is also noted that geological structures control the horizontal distribution of karst, and geological lithology, hydrodynamic conditions, and water carbon dioxide concentrations are key factors affecting the vertical zoning of karst. This study provides an important scientific basis for understanding the development characteristics and controlling factors of karst water system media in peak forest plains and has important guiding significance for water resource management in karst areas and disaster prevention during tunnel excavation. Full article

With the intensification of global warming, droughts and heatwaves occur frequently and widely, which have a serious impact on the healthy growth of vegetation. The challenge is to accurately characterize vegetation vulnerability under compound heat and drought stress using correlation-based methods. This article uses the Haihe River Basin, an ecologically sensitive area known for experiencing droughts nine out of ten years, as an example. Firstly, using daily precipitation and maximum temperature data from 38 meteorological stations in the basin from 1965 to 2019, methods such as univariate linear regression and the Mann–Kendall mutation test were employed to identify the temporal variation patterns of meteorological elements in the basin. Secondly, the Pearson correlation coefficient and other methods were applied to determine the most likely months for compound dry and hot events, and the joint distribution pattern and recurrence period of concurrent high temperature and intense drought events were explored. Finally, a vegetation vulnerability assessment model based on Vine Copula in compound dry and hot climates was constructed to quantify the relationship of the response of watershed vegetation to different extreme events (high temperature, drought, and compound dry and hot climates). The results indicated that the basin’s precipitation keeps decreasing, evaporation rises, and the supply–demand conflict grows more severe. The correlation between the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) is strongest at the 3-month scale from June to August. Meanwhile, in most areas of the basin, the Standardized Normalized Difference Vegetation Index (sNDVI) is positively correlated with the SPI and negatively correlated with the STI. Compared to a single drought or high-temperature event, compound dry and hot climates further exacerbate the vegetation vulnerability of the Haihe River Basin. In compound dry and hot climates, the probability of vegetation loss in June, July, and August is as high as 0.45, 0.32, and 0.38, respectively. Moreover, vegetation vulnerability in the southern and northwestern mountainous areas of the basin is higher, and the ecological risk is severe. The research results contribute to an understanding of the vegetation’s response to extreme climate events, aiming to address terrestrial ecosystem risk management in response to climate change. Full article

Understanding the water and carbon cycles within terrestrial ecosystems is crucial for effective monitoring and management of regional water resources and the ecological environment. However, physical models like the SEB- and LUE-based ones can be complex and demand extensive input data. In our study, we leveraged multiple variables (vegetation growth, surface moisture, radiative energy, and other relative variables) as inputs for various regression algorithms, including Multiple Linear Regression (MLR), Random Forest Regression (RFR), and Backpropagation Neural Network (BPNN), to estimate water (ET) and carbon fluxes (NEE) in the Haihe River Basin, and compared the estimated results with the observations from six eddy covariance flux towers. We aimed to (1) assess the impacts of different input variables on the accuracy of ET and NEE estimations, (2) compare the accuracy of the three regression methods, including three machine learning algorithms and Multiple Linear Regression, and (3) evaluate the performance of ET and NEE estimation models across various regions. The key findings include: (1) Increasing the number of input variables typically improved the accuracy of ET and NEE estimations. (2) RFR proved to be the most accurate for both ET and NEE estimations among the three regression algorithms. Of these, the four types of variables used together with RFR resulted in the best accuracy for ET (R² of 0.81 and an RMSE of 1.13 mm) and NEE (R² of 0.83 and an RMSE of 2.83 gC/m²) estimations. (3) Vegetation growth variables (i.e., VIs) are the most important inputs for ET and NEE estimation. (4) The proposed ET and NEE estimation models exhibited some variation in accuracy across different validation sites. Despite these variations, the accuracy levels across all six validation sites remained relatively high. Overall, this study lays the groundwork for an efficient approach to agricultural water resources and ecosystem monitoring and management. Full article

Urbanization in the Haihe River Basin in northern China, particularly the upstream mountainous basin of Baiyangdian, has significantly altered land use and runoff processes. The runoff is a key water source for downstream areas like Baiyangdian and the Xiong’an New Area, making it essential to understand these changes’ implications for water security. However, the exact implications of these processes remain unclear. To address this gap, a simulation framework combining SWAT+ and CLUE-S was used to analyze runoff responses under different land use scenarios: natural development (ND), farmland protection (FP), and ecological protection (EP). The model simulation results were good, with NSE above 0.7 for SWAT+. The Kappa coefficient for CLUE-S model validation was 0.83. The further study found that from 2005 to 2015, urban construction land increased by 11.50 km² per year, leading to a 0.5–1.3 mm rise in annual runoff. Although urban expansion continued, the other scenarios, which emphasized farmland and forest preservation, slowed this growth. Monthly runoff changes were most significant during the rainy season, with annual runoff in ND, FP, and EP varying by 8.9%, 10.9%, and 7.7%, respectively. While the differences in annual runoff between scenarios were not dramatic, these findings provide a theoretical foundation for future water resource planning and management in the upstream mountainous area of Baiyangdian and offer valuable insights for the sustainable development of Xiong’an New Area. Additionally, these results contribute to the broader field of hydrology by highlighting the importance of considering multiple land use scenarios in runoff change analysis. Full article

With the global energy crisis and the decline of fossil fuel resources, biofuels are gaining attention as alternative energy sources. China, as a major developing country, has long depended on coal and is now looking to biofuels to diversify its energy structure and ensure sustainable development. However, due to its large population and limited arable land, it cannot widely use corn or sugarcane as raw materials for bioenergy. Instead, the Chinese government encourages the planting of non-food crops on marginal lands to safeguard food security and support the biofuel sector. The Southern China Karst Region, with its typical karst landscape and fragile ecological environment, offers a wealth of potential marginal land resources that are suitable for planting non-food energy crops. This area is also one of the most impoverished rural regions in China, confronting a variety of challenges, such as harsh natural conditions, scarcity of land, and ecological deterioration. Idesia polycarpa, as a fast-growing tree species that is drought-tolerant and can thrive in poor soil, is well adapted to the karst region and has important value for ecological restoration and biodiesel production. By integrating 19 bioclimatic variables and karst landform data, our analysis reveals that the Maximum Entropy (MaxEnt) model surpasses the Random Forest (RF) model in predictive accuracy for Idesia polycarpa’s distribution. The karst areas of Sichuan, Chongqing, Hubei, Hunan, and Guizhou provinces are identified as highly suitable for the species, aligning with regions of ecological vulnerability and poverty. This research provides critical insights into the strategic cultivation of Idesia polycarpa, contributing to ecological restoration, local economic development, and the advancement of China’s biofuel industry. Full article

When shield tunneling is carried out in poor geological areas with high water head and low strength, there is a great construction risk, and the external water pressure is one of the key factors affecting the stability and safety of the tunnel during shield tunneling. Taking the Yinguruxin tunnel of Xin’an County as the engineering background, the geological conditions and water-bearing characteristics of the water-rich area in the tunnel excavation path are analyzed by means of drilling and high-density electrical method, and the pumping test is used to evaluate the groundwater linkage in the water-rich area. The 2022 version of Midas GTS NX is used to study the distribution characteristics of external water pressure during tunnel excavation in fault zones, and the influence of different drainage rates on the external water pressure of the tunnel is analyzed. The results show that the rock mass in the unfavorable geological section of the tunnel excavation is broken and rich in water, but the complexity of the stratum leads to uneven water richness in the axis direction of the tunnel. The drainage rate is the key to affecting the external water pressure of the lining. The drainage rate is the key to affecting the external water pressure of the tunnel. The correct drainage rate is an effective measure to reduce the external water pressure of the tunnel. The internal and external water pressure of the tunnel decreases with the increase of the drainage rate. When the drainage rate reaches 66.67% of the water inflow, the external water pressure of the tunnel can be reduced to 0.3 MPa to ensure the safety of the earth pressure balance shield machine in the tunneling process. The conclusion provides a useful reference for the high water pressure control of the tunnel shield tunneling process. Full article