Qiuhua He

Lake water quality is heavily influenced by agricultural activities, hydrological variations, and regional climate extremes, yet holistic assessments of their relative contributions and non-linear thresholds remain limited. We evaluated the water quality variation characteristics of Lake Dongting from 2014 to 2023 and found that total phosphorus (TP), total nitrogen (TN), and dissolved oxygen (DO) decreased significantly, while ammonia nitrogen (NH₃-N) showed no significant temporal trend. CatBoost models identified water level and sediment load as important predictors of TP and DO, whereas TXx and SU25 were important predictors of TN and NH₃-N. The testing R² values were 0.62, 0.51, 0.39, and 0.58 for TP, TN, NH₃-N, and DO, respectively, with testing RMSE values of 0.031, 0.480, 0.200, and 0.790, respectively. GAMs identified indicator-specific nonlinear responses: TP showed a model-estimated response point at WL = 28.36 m (95% CI: 27.74–28.89 m) and a hump-shaped response to Rx1 day, with a model-estimated point at 44.81 mm (95% CI: 40.32–49.45 mm); TN showed a response point at SU25 = 18.38 days (95% CI: 15.21–21.74 days), whereas NH₃-N decreased monotonically with increasing SU25 and no accumulation threshold was inferred. Annual-scale variance partitioning and correlation analyses indicated that decadal nutrient variations co-varied with changes in agricultural activities. Our results indicate that lake water quality is associated with agricultural inputs and hydro-climatic conditions, underscoring the value of integrating agricultural non-point source control with climate-adaptive hydrological regulation for future water environment management.

Compound extreme climate events have become increasingly frequent under climate change and may alter terrestrial carbon cycling through different atmospheric and soil pathways. Focusing on the Dongting Lake Eco-Economic Zone, this study identified three types of compound extreme events during 2003–2024: atmospheric compound hot–dry events (ACHDs), soil compound hot–dry events (SCHDs), and drought-to-rewetting events (DRWs). We then examined their associations with monthly anomalies of net primary production (NPP), heterotrophic respiration (RH), and net ecosystem exchange (NEE) under different land cover backgrounds. The results showed that ACHDs and SCHDs both increased significantly, whereas DRWs exhibited a slight decreasing trend and a more scattered spatial distribution. During the same period, regional NPP increased significantly, RH decreased slightly, and NEE became more negative, indicating an overall strengthening of net carbon uptake. Different event types were associated with contrasting carbon flux response pathways: ACHDs were mainly associated with reduced NPP and slightly increased RH, thereby shifting NEE toward more positive values and weakening regional net carbon uptake, whereas SCHDs and DRWs were more strongly associated with reduced RH and more negative NEE. In addition, the event–carbon relationships differed among land cover types, with cropland, built-up land, and sparsely vegetated surfaces showing higher sensitivity to ACHDs, whereas the responses to SCHDs and DRWs varied markedly among forest, grassland, wetland, and open water classes. These results highlight that compound atmospheric and soil extremes influence regional carbon cycling through distinct component-specific pathways, and that land use background is an important factor associated with differences in carbon flux sensitivity in humid lake–floodplain systems.

Ecological restoration in mining cities is essential for regional sustainable development, yet limited scientific understanding of its patterns and mechanisms has widened the gap between intended goals and actual outcomes. Taking Huangshi, a typical mining city, as a case study, this paper constructs an “elements-disturbances-status” (EDS) framework. By integrating interpretable machine learning, the study reveals the staged dynamics of city-wide ecological quality and its drivers, offering new perspectives for tailored restoration strategies. The results showed that: (1) ecological restoration followed a positive trajectory, advancing in three stages: negatively driven stage (2000–2010), rapid improvement stage (2010–2015), and stabilization and adjustment stage (2015–2023); (2) the excellent-grade areas followed a V-shaped trend (35.57% → 17.30% → 28.27% → 30.77%), while good-grade areas steadily expanded, forming a broad, high-quality ecological restoration landscape. Poor-grade areas shrank from 23.67% to 5.41%, indicating effective remediation of severely degraded zones. Although typical mining sites exhibited positive successional trends, ecological quality in core extraction zones warranted continued attention; (3) anthropogenic disturbances were the dominant drivers of spatial heterogeneity in ecological restoration. The ecological footprint of mining disturbances contracted over time, with the distance threshold for negative impact decreasing from 5 km to 3 km. However, annual precipitation exceeding 1419–1543 mm began to suppress ecological quality improvement. The persistent suppressive effect of high urbanization highlights the conflict between urban expansion and ecological protection, especially in areas with high precipitation. This study offers practical insights for enhancing ecological resilience and adaptive management in mining cities.

Information on crop planting structure serves as a key reference for crop growth monitoring and agricultural structural adjustment. Mapping the spatial distribution of crops through feature-based classification serves as a fundamental component of sustainable agricultural development. However, current crop classification methods often face challenges such as the discontinuity of optical data due to cloud cover and the limited discriminative capability of traditional SAR backscatter intensity for spectrally similar crops. In this case study, we assess multi-temporal Sentinel-1 and Sentinel-2 Satellite images for crop classification in a paddy rice growing region in Helonghu Town, located in the central region of Xiangyin County, Yueyang City, Hunan Province, China (28.5° N–29.0° N, 112.8° E–113.2° E). We systematically investigate three key aspects: (1) the classification performance using optical time-series Sentinel-2 imagery; (2) the time-series classification performance utilizing polarimetric SAR decomposition features from Sentinel-1 dual-polarimetric SAR images; and (3) the classification performance based on a combination of Sentinel-1 and Sentinel-2 images. Optimal classification results, with the highest overall accuracy and Kappa coefficient, are achieved through the combination of Sentinel-1 (SAR) and Sentinel-2 (optical) data. This case study evaluates the time-series classification performance of Sentinel-1 and Sentinel-2 data to determine the optimal approach for crop classification in Helonghu Town.

The sustainable development of resource-based cities faces challenges due to the imbalance between ecosystem service supply and demand. This study examines Xingtai, a typical resource-based city located in northern China, using ecosystem service bundle theory to analyze the supply–demand relationships of six ecosystem services—water yield, soil retention, habitat quality, urban cooling, PM_2.5 removal, and carbon sequestration—from 2000 to 2020. Based on the ratio of supply–demand, we identify ecosystem service bundles and explore their driving factors using redundancy analysis (RDA) and the geographically and temporally weighted regression (GTWR) model. Results show a clear “mountain–plain” supply gradient, with high supply in the western Taihang Mountains and low supply in urbanized eastern plains. Demand follows a “center-high, periphery-low” pattern, with urban centers showing higher demand for urban cooling and PM_2.5 removal. A severe supply–demand imbalance exists: soil retention, PM_2.5 removal, habitat quality, and carbon sequestration are undersupplied in urbanized areas, while water yield and urban cooling are oversupplied in the western mountains. Natural factors (precipitation and temperature) shape western mountain supply, while human activities (GDP and nighttime light) drive demand polarization in the east. GTWR results reveal that urban GDP growth and land expansion intensify demand, while stable supply in mountain areas relies on precipitation and forest cover. This study provides scientific support for the sustainable development of resource-based cities.

It is very well-known that the reliable coupling relationship between water area and water level is very important in analyzing the risks of floods and droughts for big lakes, such as Dongting Lake, especially when remote sensing images are absent and in situ measurements cannot be carried out. To obtain this relationship, two types of mathematical models—polynomial regression (PR) based on the least square algorithm and machine learning regression (MLR) based on the BP (Backpropagation) neural network algorithm—are constructed using the water area data extracted from multiple temporal remote sensing images and water levels recorded at several representative hydrological stations for nearly 30 years. In this study, Dongting Lake is divided into three parts: East Dongting Lake (EDL), South Dongting Lake (SDL), and West Dongting Lake (WDL). This is because water slope exists on its surface, which is formed by several inflow rivers and the high and low terrain. To calculate the total water area of this lake, two ways are put forward by choosing the water levels: from EDL, SDL, and WDL in their turn; or from all three simultaneously. In other words, three univariate and one multivariate regression. For PR, there are perfect coefficients of determination (most nearly 0.95, the smallest being 0.76), which is in line with regression test relative errors (between 0.27% and 6.7%). For MLR, which was initially applied to this problem, the best node number (10 for the first way, 8 for the second way) in the hidden layer of the neural network is adaptively chosen, with coefficients of determination (similar to PR), together with training and testing error performances (between 1% and 10%). These results confirm the validity and reliability of them. The regression and prediction results on the two models are better than the documented way (only focus on the water level of EDL). These results can provide some references for researchers and decision makers in studying similar big Lakes.