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The Qilian Mountains (QLMs) encompass a diverse alpine grassland ecosystem undergoing notable changes due to global climate change. Understanding the driving forces behind these changes is crucial for effective regional vegetation restoration and management, particularly given the contrasting climatic conditions between the eastern and western regions of the QLMs. This study investigated the spatiotemporal evolution of grassland NDVI and the influence of 13 variables on NDVI changes in six distinct QLM sub-basins from 1990 to 2019 using Landsat data and the Geodetector model, respectively. Results revealed a consistent greening trend in all sub-basins, albeit with varying slopes. Notably, the Qinghai Lake basin exhibited a recent slowdown in NDVI, highlighting the need for enhanced vegetation restoration efforts, especially in permafrost-covered areas. Environmental factors affecting NDVI differed among basins. In the western QLMs, sunshine duration and relative humidity emerged as key factors influencing NDVI distribution, while in the eastern basins, altitude and air temperature played significant roles. This study underscored the importance of hydrothermal conditions, both horizontally and vertically, in shaping grassland NDVI. Other factors, such as vegetation types, soil characteristics, and permafrost degradation, also influenced NDVI, while slope and slope aspects had a limited impact. Furthermore, the research identified threshold-dependent responses of NDVI to the interaction of these factors. These findings emphasized the need to customize vegetation restoration strategies based on the specific environmental conditions of each basin within the QLMs. This nuanced understanding of the distinct driving characteristics between the eastern and western QLMs enables more effective and targeted approaches to restoring and managing grassland ecosystems in this region.

Recently, the Chinese government has implemented stringent water requirements based on the concept of ‘Basing four aspects on water resources’. However, existing research has inadequately addressed the constraints of water resources on population, city boundaries, land, and production, failing to adequately analyze the interplay between water resource limitations and urban development. Recognizing the interconnectedness between urban water use and economic development, a multi-objective model becomes crucial for optimizing urban water resources. This study establishes a nonlinear multi-objective water resources joint optimization model, aligning with the “Basing four aspects on water resources” requirement to maximize urban GDP and minimize total water use. A genetic algorithm (NSGA-II Algorithm) is applied to solve this complex nonlinear multi-objective model and obtain the Pareto solution set, addressing information loss inherent in the traditional water quota method. The model was tested in Wujiang District, an area located in China’s Jiangsu Province that has been rapidly urbanizing over the past few decades, and yielded 50 non-inferior water resource optimization schemes. The results reveal that the Pareto solution set visually illustrates the competition among objectives and comprehensively displays the interplay between water and urban development. The model takes a holistic approach to consider the relationships between water resources and urban population, land use, and industries, clearly presenting their intricate interdependencies. This study serves as a valuable reference for the rational optimization of water resources in urban development.

Urban wetlands play an important role in sustainable urban development. A wetland city recognized by the Ramsar Convention is a city with a remarkable ecological performance of urban wetlands and can provide an example for urban wetland protection. Therefore identifying the detailed characteristics of wetlands is of great value to the conservation of urban wetlands. However, there is no detailed wetland type extraction for wetland cities using multisource remote sensing data. This paper proposes a framework for fine- urban wetland extraction with multisource and multiperiod remote sensing data, which mainly includes Sentinel 1/2 and Landsat 8 data, and obtains the detailed wetland type data containing 14 wetland categories at a 10-m resolution. The classification methods of random forests were used to extract land cover types, and knowledge rules were used to classify detailed wetland types. The wetland cities, Yinchuan, and Haikou, located in southern and northern China, respectively, were chosen as case studies. The results indicate that the overall accuracy and wetland accuracy of the two cities in 2015 and 2020 were greater than 88% and 87%, respectively. The detailed wetland mapping results were more accurate and had a high resolution, and wetland categories were more diverse compared with other wetland-related products. According to the classification results, the wetland area in Haikou was 326.86 km2 (13.43%) in 2015 and 308.58 km2 (12.66%) in 2020. The wetland area in Yinchuan was 80.39 km2 (4.46%) in 2015 and 74.15 km2 (4.12%) in 2020. The fine urban wetland classification framework we propose and the detailed wetland results could serve for the creation of wetland cities and support the protection and rational use of urban wetlands.

Study region: Mu Us Sandy Land, China. Study focus: The groundwater table in unconfined aquifers is often a subdued replica of the topography or land surface. In hilly terrain, spatial interpolation methods are prone to errors such as higher interpolation water level than the ground surface due to sparse observation wells. Meanwhile, in ridge areas, groundwater depth can easily be overestimated with limited groundwater observations. To address these issues, this paper proposes a framework to learn groundwater depth using related groundwater variables based on the extreme gradient boosting (XGB) machine learning method. Moreover, the groundwater level map is drawn based on the predicted groundwater depths and elevation data.New Hydrological Insights for the RegionThe results obtained by the XGB algorithm are compared with those of common interpolation methods (including inverse distance weighting, ordinary kriging, and collaborative ordinary kriging) and machine learning methods (including multiple linear regression, geographically weighted regression, support vector regression, and random forest). The results showed that the XGB using the Tweedie loss function performs best in learning groundwater depth compared with interpolation methods and other machine learning methods, and almost completely overcomes the effects of imbalanced data. The topographic factors such as elevation, topographic position index, and topographic roughness index are key factors for groundwater depth. This study provides a reference for improving regional GL mapping, especially in areas of complex terrain.

Accurately quantifying the spatiotemporal nutrient loads from nonpoint sources (NPSs) is still difficult due to the high spatial heterogeneity and temporal variability in rainfall-runoff processes. The export coefficient model (ECM) is a widely used tool to estimate the NPS nutrient load on an annual time scale, but it hardly considers the spatiotemporal distribution of the export coefficient. To address this issue, the ECM is improved by incorporating a grid-based rainfall-runoff module together with land-use data and analysing the spatiotemporal characteristics of the NPS nutrient loads in Yancheng city located in eastern China. Moreover, the effect of NPS nutrient loads on river water quality is assessed using a conceptual model. The results indicate that the improved ECM can simulate reasonable spatiotemporal variation in NPS nutrient loads, and the relative error in the total phosphorus (TP) load is less than 17.14 %. Precipitation has a considerable influence on both the total nitrogen (TN) and TP loads, which varied greatly from 1990 to 2018, with average annual values of 30.57 × 103 tons and 2.44 × 103 tons, respectively. The spatial distributions of the TN and TP loads were greatly affected by land-use patterns, and bodies of water and rural land had relatively high load intensities. Both TN and TP concentrations in the river exceeded the national standard, but TP was significantly reduced in the past 10 years, while TN was still at high level. This study provides a reference for reducing the river pollution in Yancheng city, especially by identifying pollution sources requiring priority control.

Wetland cities were proposed by the International Wetland Convention Organization for the protection of urban wetlands. Few studies have performed land cover classifications for internationally recognized wetland cities or explored what contribution the classification results can make to the establishment of additional wetland cities to date. Based on Sentinel-1 and 2 data, this study used a 10-fold random forest method to classify the land cover of the first six wetland cities recognized in China. A land cover dataset, which had a resolution of 10 m and included four wetland types, was obtained and the wetland area and protected wetland areas of the six cities were calculated. The results showed that (1) the classification accuracy of six cities was good, the overall accuracy was above 90%, and the Kappa coefficient was above 0.88. (2) Cropland or forested areas were the most common non-wetland land coverage type in wetland cities and accounted for more than 20% or 40% of the land coverages, while water was the most common wetland type and accounted for more than 2% of the land coverages. From 2015 to 2020, the built area in most cities increased, while cropland and forest decreased significantly. (3) The wetland rate was 6.68–37.56% and the wetland protection rate was 49.48–73.74% in the six wetland cities. From 2015 to 2020, the wetland rate of the six cities were relatively stable, and the wetland protection rate of inland cities (Yinchuan, Changde, Harbin and Changshu) increased significantly, while those of coastal cities (Haikou and Dongying) decreased, which might be related to the change in coastline. Therefore, we found that the wetlands in these cities were well protected. Land cover classification for wetland cities can provide a reference for using remote sensing techniques used to monitor internationally wetland cities while also supporting the creation of additional wetland cities.

Hebei Province of China is facing a severe water resource shortage, making it urgent to formulate economical and effective water conservation policies. However, few studies have focused on analyzing the resource and economic impacts of a water policy. This study developed an improved computable general equilibrium (CGE) model with an extended water resources module as a policy analysis tool. The extended water resources module includes different water resources as commodities and water sectors, and introduces a substitution mechanism among the water resources. Policy scenarios containing different policy types and policy objects were established, including water price, technology (tech) improvement, structure adjustment, and water reuse policies in primary, secondary, and tertiary sectors. The impact on the water resource and economy of the scenarios was analyzed using the CGE model. The recommended policies include: an agricultural technology improvement policy that decreases groundwater usage by 240 hm3; an industrial technology improvement policy under which water usage per 10,000 CNY of industrial added value decreases by 13%; an industrial water reuse policy that increases unconventional water usage by 20%; and a structure adjustment policy to increase the proportion of the tertiary sector. The study provides an analysis tool for simulating and evaluating a water resource policy.

Water pollution poses threats to urban environments and subsequently impacts the ecological health and sustainable development of urban areas. Identifying the spatiotemporal variation in non-point sources (NPS) pollution is a prerequisite for improving water quality. This paper aimed to assess the NPS pollution load and then recognized the spatiotemporal characteristics of the pollution sources in a typical urbanized area. A combination model based on land use type was used to simulate the NPS pollution load. The results showed the following: (1) ponds and farmlands had higher pollution production intensities than other land use types, but the intensity and magnitude of pollution emissions were generally greater in urban areas; (2) monthly and annual total nitrogen (TN) and total phosphorus (TP) emissions had the same pattern as rainfall, and TN and TP emissions accounted for 56.2% and 58.0%, respectively, of the total in summer; (3) TN pollution was more serious than TP pollution in the study area, especially in farmlands; (4) urban runoff (UR) and livestock and poultry breeding (LPB) were the main sources of NPS, TN and TP emissions in the study area. If these NPS pollutants cannot be removed from this area, a large amount of freshwater is needed to dilute the current rivers to meet the requirement of the fourth category of China national environmental quality standards for surface water. This problem is serious in the control of polluted rivers in many cities throughout China.

Due to the widespread presence of noise, such as clouds and cloud shadows, continuous, high spatiotemporal-resolution dynamic monitoring of lake water extents is still limited using remote sensing data. This study aims to take an approach to mapping continuous time series of highly-accurate lake water extents. Four lakes from diverse regions of China were selected as cases. In order to reduce the impact of noise and ensure high spatial and temporal resolution of the final results, two sets of MODIS products (including MOD09A1 and MOD13Q1) are used to extract water bodies. This approach mainly comprises preliminary classification, post processing and data fusion. The preliminary classification used the Random Forest (RF) classifier to efficiently and automatically obtain the initial classification results. Post-processing is implemented to repair the classification results affected by noise as much as possible. The processed results of the two sets of products are fused by using the Homologous Data-Based Spatial and Temporal Adaptive Fusion Method (HDSTAFM), which reduces the effect of noise and also improve the temporal and spatial resolution for the final water results. We determined the accuracy using Landsat-based water results, and the values of overall accuracy (OA), user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficients (KC) are mostly greater than 0.9. Good correlation was achieved for a time series of water area and altimetry data, obtained by multiple satellites, and also for water-level data selected from hydrological stations.

Lakes have an important role in human life and the ecological environment, but they are easily affected by human activity and climate change, especially around urban areas. Hence, it is critical to extract water with a high precision method and monitor long-term sequence dynamic changes in lakes. As the greatest natural lake of the Beijing-Tianjin-Hebei region, Baiyangdian Lake has a significant function in human life, socio-economic development, and regional ecological balance. This lake area has shown large changes due to human activity and climate change. The change monitoring process of the water surface is of great significance in providing support for the management and protection of the lake. The Spectrum Matching based on Discrete Particle Swarm Optimization (SMDPSO) method is a new, robust, and low-cost method for water extraction, that has obvious advantages in extracting complex water surfaces. In this paper, the SMDPSO method was used to extract the water surface of Baiyangdian Lake by Landsat images from 1984 to 2018. This method has a good effect on complex water surface extraction with vegetation, shadows, and so forth, and the Landsat images have higher resolution and longer time series. The main contents and results of this paper are as follows: (1) We verified the applicability of the SMDPSO method in the Baiyangdian Lake using visual interpretation and correlation analysis. The relative errors between observed and extracted results were all less than 5% in spring, summer, and fall, and the correlation coefficient between the water area and water level was 0.96. (2) According to seasonal verification and comparison of the extraction results, the SMDPSO method was used to extract the water surface area of Baiyangdian Lake during spring of the years 1984–2018. Water area changes of Baiyangdian Lake can be divided into four periods: Dry period (1984–1988), degraded period (1989–2000), stable period (2000–2008), and recovery period (2008–2018). The water area reached a maximum of 280 km2 in 1989 and a minimum of 44 km2 in 2002. (3) The possible causes of the changes in the water area of Baiyangdian Lake were also analyzed. The changes were caused by climate and human activities during the first and second periods, but mainly human activities during the third and fourth periods. In fact, effective policies combined with water conservancy projects were directly conducive to improving or even recovering the water and ecological environment of Baiyangdian Lake. Considering its importance for the benign development of the Beijing-Tianjin-Hebei Region and the construction of the Xiong’an New Area, a policy is necessary to ensure that the lake’s ecological environment will not be destroyed under the premise of economic development.

The use of remote sensing to monitor surface water bodies has gradually matured. Long-term serial water change analysis and floods monitoring are currently research hotspots of remote sensing hydrology. However, these studies are also faced with some problems, such as coarse temporal or spatial resolution of some remote sensing data. In general, flood monitoring requires high temporal resolution, and small-scale surface water extraction requires high spatial resolution. The machine learning method has been proven to be effective against long-term serial surface water extraction, such as random forests (RFs). MODIS data are well suited for large-scale surface water dynamic analysis and flood monitoring because of its short return cycle and medium spatial resolution. In this paper, the Yangtze River Basin (YRB) in China was selected as the study area, and two MODIS products (MOD09A1 and MOD13Q1) and RF method were used to extract the surface water from 2000 to 2016. Considering the disadvantages of temporal or spatial resolution of these two MODIS products, this study also presents a data fusion method to combine them and get higher spatiotemporal resolution water results. Finally, 762 surface water maps from 2000 to 2016 are obtained, whose temporal and spatial resolution is every eight days and 250 m, respectively. In addition, water extent variation is analyzed and compared to observed precipitation data. The main conclusions are as follows: (1) this constructed approach for long-term serial surface water extraction based on the RF classifier is feasible, and a good fusion method is used to obtain the surface water body with higher spatiotemporal resolution; (2) the maximum area of the surface water extent is 48.53 × 103 km2, and seasonal and permanent water areas are 20.51 × 103 km2 and 28.01 × 103 km2, respectively; (3) surface water area is increasing in the YRB, such that seasonal water area decreased by 3450 km2, and the permanent water area increased by 3565 km2 in 2001–2015; (4) precipitation is the main factor causing variation in the surface water bodies, and they both show an increasing trend in 2000–2016. As such, the approach is worth referring to other remote sensing applications, and these products are very both valuable for water resource management and flood monitoring in the study area.