Search Results (86)

The introduction of the ”dual carbon” target has increased the need for products that can accurately measure carbon dioxide levels, reflecting the rising demand. Due to challenges in achieving the required spatiotemporal resolution, accuracy, and spatial continuity with current carbon dioxide concentration products, it is essential to explore methods for obtaining carbon dioxide concentration products with completeness in space and time. Based on the 2018 OCO-2 carbon dioxide products and environmental variables such as vegetation coverage (FVC, LAI), net primary productivity (NPP), relative humidity (RH), evapotranspiration (ET), temperature (T) and wind (U, V), this study constructed a multiple regression model to obtain the spatial continuous carbon dioxide concentration products in the provinces of Huai River Basin. Using indicators such as correlation coefficient, root mean square error (RMSE), local variance, and percentage of valid pixels, the performance of model was validated. The validation results are shown as follows: (1) Among the selected environmental variables, the primary factors affecting the spatiotemporal distribution of carbon dioxide concentration are ET, LAI, FVC, NPP, T, U, and RH. (2) Compared with the OCO-2 carbon dioxide products, the percentage of valid pixels of the reconstructed carbon dioxide concentration data increased from less than 1% to over 90%. (3) The local variance in reconstructed data was significantly larger than that of original OCO-2 ${\mathrm{C}\mathrm{O}}_2$ products. (4) The average monthly RMSE is 2.69. Therefore, according to the model developed in this study, we can obtain a carbon dioxide concentration dataset that is spatially complete, meets precision requirements, and is rich in local detail information, which can better reflect the spatial pattern of carbon dioxide concentration and can be used to examine the carbon cycle between the terrestrial environment, biosphere, and atmosphere. Full article

Catastrophic floods in monsoon-driven river systems pose significant challenges to flood resilience. In July 2020, China’s Huai River Basin (HRB) encountered an unprecedented basin-wide flood event characterized by anomalous southward displacement of the rain belt. This event established a new historical record with the three typical hydrological stations (Wangjiaba, Runheji, and Lutaizi sections) along the mainstem of the Huai River exceeded their guaranteed water levels within 11 h and synchronously reached peak flood levels within a 9-h window, whereas the inter-station lag times during the 2003 and 2007 floods ranged from 24 to 48 h, causing a critical emergency in the flood defense. By integrating operational hydrological data, meteorological reports, and empirical rainfall-runoff model schemes for the Meiyu periods of 2003, 2007, and 2020, this research systematically dissects the 2020 flood’s spatial composition patterns. Comparative analyses across spatiotemporal rainfall distribution, intensity metrics, and flood peak response dynamics reveal distinct characteristics of southward-shifted torrential rain and flood variability. The findings provide critical technical guidance for defending against extreme weather events and unprecedented hydrological disasters, directly supporting revisions to flood control planning in the Huai River Ecological and Economic Zone. Full article

Currently, regional flood research often lacks a synergistic assessment of both flood occurrence risk and flood duration, limiting the comprehensive understanding needed for sustainable disaster risk reduction. To address this gap, this study applies advanced machine learning approaches to assess flood hazards in the Yangtze River Delta, one of China’s most economically and environmentally significant regions. Specifically, XGBoost is employed to evaluate flood occurrence risk, while LSTM is used to predict flood duration. A novel flood risk index (FRI) is proposed to quantify the integrated risk by combining these two dimensions, supporting more sustainable and effective flood risk management strategies. Furthermore, SHAP analysis is conducted to identify the most critical factors contributing to flooding. The results demonstrate that XGBoost delivers strong predictive performance, with average precision, recall, F1-score, accuracy, and AUC values of 0.823398, 0.831667, 0.827090, 0.826435, and 0.871062, respectively. Areas with high flood risk, long duration, and elevated FRI values are mainly concentrated in major river basins and coastal zones. The range of flood risk spans from 0.000073 to 0.998483 (mean: 0.237031), flood duration from 0.223598 to 2.077040 (mean: 0.940050), and FRI from 0 to 0.934256 (mean: 0.091711). Cities with over 40% of their areas falling in medium to high FRI zones include Suzhou (48.99%), Jiaxing (48.07%), Yangzhou (46.87%), Suqian (44.19%), Changzhou (43.43%), Wuxi (43.20%), Lianyungang (42.21%), Yancheng (40.88%), Huai’an (40.73%), and Bengbu (40.06%). SHAP analysis reveals that elevation and rainfall are the most critical factors influencing flood occurrence, underscoring the importance of integrating environmental variables into sustainable flood risk governance. Full article

Potential evapotranspiration (ETp) is an important component of the water and energy cycle. This study investigated the changing patterns of both summer ETp and its drivers in the Huai River Basin for the first time using the newly proposed anomaly contribution analysis method, as summer is usually the peak period of ETp but little has been done to study it specifically. The anomaly contribution analysis method is able to calculate the contribution rates of climate factors to summer ETp for every year, which helps to reveal the dynamic changes in the contribution of climate factors to summer ETp. The results show that the evaporation paradox is not accurate for the basin since summer ETp declines significantly while the trend of summer Tm is insignificant. Influenced by the abrupt changes in summer Sh and Ws, summer ETp underwent a mutation around the 1970s and 1980s. Sensitivity analysis and contribution analysis show that the most sensitive meteorological factors may not contribute the most to summer ETp. Contribution analysis at a multi–year scale and the results of the anomaly contribution analysis method demonstrate that dominant factors of ETp may be different at multi–year and seasonal scales in the same region. Moreover, the dominant meteorological factors of summer ETp are also different at station and basin scales due to scale effects. Further, dynamic changes in contribution rates show that contributions of summer climate factors have clear positive–negative alterations. Additionally, there are also differences in the spatial distribution of contribution rates between the north–south and east–west directions. These findings will not only provide valuable information for regional water resources management but also provide new insights into the evolution of ETp under climate change. Full article

This study aims to develop an integrated framework that combines an attention-enhanced adaptive neuro-fuzzy inference system (ANFIS) with multi-objective optimization to address the challenges of subjective indicator weight allocation and insufficient nonlinear relationship modeling in environmental performance evaluation of water infrastructure. Drawing on the tri-dimensional theory of performance evaluation—success, results, and actions—the framework organizes environmental performance indicators into five primary and nine secondary dimensions. Through empirical analysis across China’s five major river basins (Yangtze, Yellow, Pearl, Huai, and Hai Rivers), our model demonstrates comprehensive superiority with faster convergence (46 iterations) and superior accuracy (R² = 0.915), significantly outperforming basic attention (62 iterations, R² = 0.862) and traditional ANFIS (85 iterations, R² = 0.828) models across all metrics. There is a significant gradient difference in environmental performance scores across the five major river basins: the Yangtze River Basin performs the best (0.99), followed by the Yellow River Basin (0.98), with the Hai River (0.92) and Huai River (0.86) in the middle, and the Pearl River Basin scoring the lowest (0.77). This disparity reflects the differences in basin characteristics and governance. Full article

As climate change intensifies, extreme drought events have become more frequent, and investigating the mechanisms of watershed drought has become highly significant for basin water resource management. This study utilizes the WRF-Hydro model in conjunction with standardized drought indices, including the standardized precipitation index (SPI), standardized soil moisture index (SSMI), and Standardized Streamflow Index (SSFI), to comprehensively investigate the spatiotemporal characteristics of drought in the Huai River Basin, China, from 2012 to 2018. The simulation performance of the WRF-Hydro model was evaluated by comparing model outputs with reanalysis data at the regional scale and site observational data at the site scale, respectively. Our results demonstrate that the model showed a correlation coefficient of 0.74, a bias of −0.29, and a root mean square error of 2.66% when compared with reanalysis data in the 0–10 cm soil layer. Against the six observational sites, the model achieved a maximum correlation coefficient of 0.81, a minimum bias of −0.54, and a minimum root mean square error of 3.12%. The simulation results at both regional and site scales demonstrate that the model achieves high accuracy in simulating soil moisture in this basin. The analysis of SPI, SSMI, and SSFI from 2012 to 2018 shows that the summer months rarely experience drought, and droughts predominantly occurred in December, January, and February in the Huai River Basin. Moreover, we found that the drought characteristics in this basin have significant seasonal and interannual variability and spatial heterogeneity. On the one hand, the middle and southern parts of the basin experience more frequent and severe agricultural droughts compared to the northern regions. On the other hand, we identified a time–lag relationship among meteorological, agricultural, and hydrological droughts, uncovering interactions and propagation mechanisms across different drought types in this basin. Finally, we concluded that the WRF-Hydro model can provide highly accurate soil moisture simulation results and can be used to assess the spatiotemporal variations in regional drought events and the propagation mechanisms between different types of droughts. Full article

Rice is a major global staple crop, but rising temperatures and freshwater shortages have made drought one of the most severe abiotic stresses affecting agriculture. Additionally, rice blast disease and brown planthopper infestations significantly impact yields. Therefore, developing water-saving, drought-resistant, high-yielding, and disease-resistant rice varieties is critical for sustainable rice production. The new water-saving and drought-resistant (WDR) rice ‘Huhan 1516’, bred using marker-assisted selection (MAS) and marker-assisted backcrossing (MABC) techniques, addresses these challenges. This variety is highly adaptable to drought-prone and water-scarce regions such as the Yangtze and Huai River basins. With its high yield, drought tolerance, and broad-spectrum resistance to rice blast (conferred by the Pi2 gene) and brown planthopper (BPH), ‘Huhan 1516’ is suitable for various farming systems and environments. Field trials show that this variety outperforms control varieties by 2.2% in yield and exhibits moderate resistance to both rice blast and brown planthopper. ‘Huhan 1516’ has been recognized as a new water-saving and drought-resistant rice variety by the state, and as a released cultivar, it has great potential for market promotion and application. Full article

The problem of cultivated land soil quality in the Huaihe River Basin has become increasingly prominent. How to accurately and quantitatively evaluate the soil quality of regional cultivated land and realize its efficient use has become an urgent problem. In order to explore the spatial autocorrelation and variation in soil nutrients in cultivated land in the plain of Fengtai County in the Huaihe River Basin, a total of 306 soil samples and mature wheat samples were collected in the study area to analyze soil pH, total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), available potassium (AK) and slow-release potassium (SK) content and wheat biomass, and combined with geostatistical methods and GIS technology. The Kriging interpolation method and Moram‘s I index method were systematically analyzed. Principal component analysis (PCA) and Pearson correlation analysis were used to establish the minimum data set (MDS) of soil quality, which was used to calculate the soil quality index (SQI) and determine the key factors affecting soil quality. The results showed that the soil pH was in weak variation, and the other nutrient indexes were in medium variation. The spatial variability of soil-available potassium nutrients was affected by random factors such as human activities and structural factors such as soil parent materials. The spatial autocorrelation of organic matter, total nitrogen, alkali-hydrolyzable nitrogen, available phosphorus, available potassium and mitigation potassium was weak, which was mainly affected by random factors such as human activities. An unequivocal positive spatial nexus was discerned across all nutrients. Cumulatively, the nutrient dispersion across the investigated territory was somewhat diffuse, manifesting in a mosaic pattern with pronounced zonal nutrient allocation disparities in the meridional, median, and septentrional segments. An explicit latitudinal dichotomy delineating zones of nutrient opulence and paucity was also observed. These insights can pave the way for tailored fertilization strategies and judicious pedological stewardship in Fengtai County. Full article

It is rare to conduct a comparative analysis of precipitation characteristics across regions based on long-term homogeneous active satellite observations. By collocating the Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR) observations with European Centre for Medium-Range Weather Forecasts 5th Reanalysis (ERA5) data, this study comparatively examines the microphysics of monsoon precipitation in the rainy season over the Yangtze-and-Huai River Basin (YHRB) and South China (SC) from 2014 to 2023. The comparative analysis is made in terms of precipitation types and intensities, precipitation efficiency index (PEI), and ice phase layer (IPL) width. The results show that the mean near-surface precipitation rate and PEI are generally higher over SC (2.87 mm/h, 3.43 h⁻¹) than over YHRB (2.27 mm/h, 3.22 h⁻¹) due to the more frequent occurrence of convective precipitation. The DSD characteristics of heavy precipitation in the wet season for both regions are similar to those of deep ocean convection, which is associated with a greater amount of water vapor. However, over SC, there are larger but fewer raindrops in the near-surface precipitation. Moreover, moderate PEI precipitation is the main contributor to heavy precipitation (>8 mm/h). Stratiform precipitation over YHRB is frequent enough to contribute more than convective precipitation to heavy precipitation (8–20 mm/h). The combined effect of stronger convective available potential energy and low-level vertical wind favors intense convection over SC, resulting in a larger storm top height (STH) than that over YHRB. Consequently, it is conducive to enhancing the microphysical processes of the ice and melt phases within the precipitation. The vertical wind can also influence the liquid phase processes below the melting layer. Collectively, these dynamic microphysical processes are important in shaping the efficiency and intensity of precipitation. Full article

Black locust (Robinia pseudoacacia L.), one of the major afforestation species adopted in vegetation restoration, is notable for its rapid root growth and drought resistance. It plays a vital role in improving the natural environment and soil fertility, contributing significantly to soil and water conservation and biodiversity protection. However, compared with natural forests, due to the low diversity, simple structure and poor stability, planted forests including Robinia pseudoacacia L. are more sensitive to the changing climate, especially in the aspects of growth trend and adaptive range. Studying the ecological characteristics and geographical boundaries of Robinia pseudoacacia L. is therefore important to explore the adaptation of suitable niches to climate change. Here, based on 162 effective distribution records in China and 22 environmental variables, the potential distribution of suitable niches for Robinia pseudoacacia L. plantations in past, present and future climates was simulated by using a Maximum Entropy (MaxEnt) model. The results showed that the accuracy of the MaxEnt model was excellent and the area under the curve (AUC) value reached 0.937. Key environmental factors constraining the distribution and suitable intervals were identified, and the geographical distribution and area changes of Robinia pseudoacacia L. plantations in future climate scenarios were also predicted. The results showed that the current suitable niches for Robinia pseudoacacia L. plantations covered 9.2 × 10⁵ km², mainly distributed in the Loess Plateau, Huai River Basin, Sichuan Basin, eastern part of the Yunnan–Guizhou Plateau, Shandong Peninsula, and Liaodong Peninsula. The main environmental variables constraining the distribution included the mean temperature of the driest quarter, precipitation of driest the quarter, temperature seasonality and altitude. Among them, the temperature of the driest quarter was the most important factor. Over the past 90 years, the suitable niches in the Sichuan Basin and Yunnan–Guizhou Plateau have not changed significantly, while the suitable niches north of the Qinling Mountains have expanded northward by 2° and the eastern area of Liaoning Province has expanded northward by 1.2°. In future climate scenarios, the potential suitable niches for Robinia pseudoacacia L. are expected to expand significantly in both the periods 2041–2060 and 2061–2080, with a notable increase in highly suitable niches, widely distributed in southern China. A warning was issued for the native vegetation in the above-mentioned areas. This work will be beneficial for developing reasonable afforestation strategies and understanding the adaptability of planted forests to climate change. Full article

Vegetation phenology is a biological factor that directly or indirectly affects the dynamic equilibrium between water and carbon fluxes in ecosystems. Quantitative evaluations of the regulatory mechanisms of vegetation phenology on water–carbon coupling are of great significance for carbon neutrality and sustainable development. In this study, the interannual variation and partial correlation between vegetation phenology (the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS)) and ET (evapotranspiration), GPP (gross primary productivity), WUE (water use efficiency; water–carbon coupling index) in the Huang-Huai-Hai and Yangtze River Basins in China from 2001 to 2019 were systematically quantified. The response patterns of spring (autumn) and growing season WUE to SOS, EOS, and LOS, as well as the interpretation rate of interannual changes, were evaluated. Further analysis was conducted on the differences in vegetation phenology in response to WUE across different river basins. The results showed that during the vegetation growth season, ET and GPP were greatly influenced by phenology. Due to the different increases in ET and GPP caused by extending LOS, WUE showed differences in different basins. For example, an extended LOS in the Huang-Huai-Hai basins reduced WUE, while in the Yangtze River Basin, it increased WUE. After extending the growing season for 1 day, ET and GPP increased by 3.01–4.79 mm and 4.22–6.07 gC/m², respectively, while WUE decreased by 0.002–0.008 gC/kgH₂O. Further analysis of WUE response patterns indicates that compared to ET, early SOS (longer LOS) in the Yellow River and Hai River basins led to a greater increase in vegetation GPP, therefore weakening WUE. This suggests that phenological changes may increase ineffective water use in arid, semi-arid, and semi-humid areas and may further exacerbate drought. For the humid areas dominated by the Yangtze River Basin, changes in phenology improved local water use efficiency. Full article

Over the past three decades, China has seen aerosol levels substantially surpass the global average, significantly impacting regional climate. This study investigates the long-term and seasonal variations of aerosols in the Huai River Basin (HRB) using MODIS, CALIOP observations from 2007 to 2021, and ground-based measurements. A notable finding is a significant decline in the annual mean Aerosol Optical Depth (AOD) across the HRB, with MODIS showing a decrease of approximately 0.023 to 0.027 per year, while CALIOP, which misses thin aerosol layers, recorded a decrease of about 0.016 per year. This downward trend is corroborated by improvements in air quality, as evidenced by PM2.5 measurements and visibility-based aerosol extinction coefficients. Aerosol decreases occurred at all heights, but for aerosols below 800 m, with an annual AOD decrease of 0.011. The study also quantifies the long-term trends of five major aerosol types, identifying Polluted Dust (PD) as the predominant frequency type (46%), which has significantly decreased, contributing to about 68% of the total AOD reduction observed by CALIOP (0.011 per year). Despite this, Dust and Polluted Continental (PC) aerosols persist, with PC showing no clear trend of decrease. Seasonal analysis reveals aerosol peaks in summer, contrary to surface measurements, attributed to variations in the Boundary Layer (BL) depth, affecting aerosol distribution and extinction. Furthermore, the study explores the influence of seasonal wind patterns on aerosol type variation, noting that shifts in wind direction contribute to the observed changes in aerosol types, particularly affecting Dust and PD occurrences. The integration of satellite and ground measurements provides a comprehensive view of regional aerosol properties, highlighting the effectiveness of China’s environmental policies in aerosol reduction. Nonetheless, the persistence of high PD and PC levels underscores the need for continued efforts to reduce both primary and secondary aerosol production to further enhance regional air quality. Full article

To grasp the spatial distribution of soybean planting areas in time is the prerequisite for the work of growth monitoring, crop damage assessment and yield estimation. The research on remote sensing identification of soybean conducted in China mainly focuses on the major producing areas in Northeast China, while paying little attention to the Huang-Huai-Hai region and the Yangtze River Basin, where the complex planting structures and fragmented farmland landscape bring great challenges to soybean mapping in these areas. This study used Chinese GF-6 WFV imagery acquired during the pod-setting stage of soybean in the 2019 growing season, and two counties i.e., Guoyang situated in the northern plain of Anhui Province and Mingguang located in the Jianghuai hilly regionwere selected as the study areas. Three machine learning algorithms were employed to establish soybean identification models, and the distribution of soybean planting areas in the two study areas was separately extracted. This study adopted a stepwise hierarchical extraction strategy. First, a set of filtering rules was established to eliminate non-cropland objects, so the targets of subsequent work could thereby focus on field vegetation. The focal task of this study involved the selection of well-behaved features and classifier. In addition to the 8 spectral bands, a variety of texture features, color space components, and vegetation indices were employed, and the ReliefF algorithm was applied to evaluate the importance of each candidate feature. Then, a SFS (Sequential Forward Selection) method was applied to conduct feature selection, which was performed coupled with three candidate classifiers, i.e., SVM, RF and BPNN to screen out the features conductive to soybean mapping. The accuracy evaluation results showed that, the soybean identification model generated from SVM algorithm and corresponding feature subset outperformed RF and BPNN in both two study areas. The Kappa coefficients of the ground samples in Guoyang ranged from 0.69 to 0.80, while those in Mingguang fell within the range of 0.71 to 0.76. The near-infrared band (B4) and red edge bands (B5 and B6), the ‘Mean’ texture feature and the vegetation indices, i.e., EVI, SAVI and CI_green, demonstrated advantages in soybean identification. The feature selection operation achieved a balance between extraction accuracy and data volume, and the accuracy level could also meet practical requirements, showing a good application prospect. This method and findings of this study may serve as a reference for research on soybean identification in areas with similar planting structures, and the detailed soybean map can provide an objective and reliable basis for local agricultural departments to carry out agricultural production management and policy formulation. Full article

Optimizing the land use structure is one of the most effective means of improving the surface water aquatic environment. The relationship between land use patterns and water quality is complex due to the influence of dams and sluices. To further investigate the impact of land use patterns on water quality in different basins, we use the Shaying River as an example, which is a typical tributary of the Huai River Basin. Utilizing 2020 land use data and surface water quality monitoring data from two periods, this study employs GIS spatial analysis, the Random Forest Model, redundancy analysis, and Partial Least-Squares Regression to quantitatively explore how different-scale buffer zone land use patterns impact surface water quality. The key findings include: (1) notable seasonal differences in water quality indicators within the basin. The Water Quality Index (WQI) is significantly better in the non-flood season compared to the flood season, with water quality deteriorating towards the lower reaches. Key indicators affecting water quality include dissolved oxygen (DO), ammonia nitrogen (NH₃-N), total phosphorus (TP), and turbidity (Tur) in the flood season and NH₃-N, permanganate index (COD_Mn), and electrical conductivity (EC) in the non-flood season. (2) Cultivated land and construction land are the main land uses in the basin. The sub-basin buffer zone was identified as the most effective scale for land use impact on water quality indicators in the Shaying River. (3) Partial Least-Squares Regression (PLSR) analysis revealed that cultivated land, construction land, and grass are the primary land use types influencing surface water quality changes, and the PLSR model is better during the non-flood season. Cultivated and construction lands show a positive correlation with most water quality indicators, while forest land, water bodies, and grasslands correlate positively with DO and negatively with other indicators. The study underscores that rational land use planning in the sub-basin is crucial for enhancing the quality of the surface water environment. Full article

Ponds constitute a pivotal component of aquatic ecosystems. The aquatic ecosystem of the Huai River Basin (HRB) in China was once damaged by severe pollution, and numerous ponds in the basin have not been secured. In this paper, Shenqiu County, a typical county in HRB with many ponds, is selected. Based on high-resolution images with ALOS in 2010, GF-2 in 2016, and GF-1 in 2022, we employed discriminant analysis (DA), classification and regression tree, support vector machine, and random forest to extract the ponds based on object-oriented and further analyzed the spatial-temporal variations of the ponds in this county. The results of the DA in these three years exhibited a higher kappa coefficient (>0.7), and overall accuracy (>75%), signifying superior performance when compared to the other three methods. There were 4625, 5315, and 4748 ponds in 2010, 2016, and 2022, with a total area of 12.87, 11.99, and 9.37 km², respectively. The number of ponds had a trend of rising in the initial period (2010–2016) and falling later (2016–2022), while the total area revealed a continuous decline. Meanwhile, these ponds showed a clustering phenomenon with three main clustering areas, and the scope of the clustering areas also changed to a certain extent from 2010 to 2022. Our study offers valuable methodological support for the ecological monitoring and management of water environments in regions characterized by a dense concentration of ponds. The crucial data related to ponds in this study will help inform both environmental and social development initiatives within the area. Full article