Understanding the land use/cover change (LUCC) in watersheds is essential for sustainable development. The CA-Markov model has been proven to be an effective method for land use modeling because of its simplicity and potential for evolution. However, it is difficult to apply this method to meet the requirement of land use planning and management since it fails to consider the driving forces of LUCC. To evaluate the factors that influence LUCC comprehensively, we developed and implemented a machine learning-based CA-Markov model to understand the dynamics of LUCC in a coastal watershed in Southeast China, the Minjiang River Watershed (MRW). The proposed method performed well for each land use category, with average AUC values of 0.999 and 0.916 for the training and testing periods, respectively, for suitable images. The overall accuracy for LUCC was 0.971. The urbanization process in the MRW was speeding up recently. Urban area increased by 2.22% of the total area during 2015–2020, and most of that was from conversion of woodland and agricultural land. Additionally, the proposed method provided a much deeper understating of the forces driving the LUCC on a regional scale. Population and gross domestic product (GDP) were the major factors influencing the distribution of urbanized land in the MRW. In contrast, woodland distribution was highly related to topographic factors in the MRW. Scenario analysis was also employed to identify patterns of LUCC under different scenarios. The results showed that the process of urbanization may become more complex with increasing population and GDP and that land use evolution may be more sustainable with scientific spatial plans which consider facilities for people and ecological protection. The proposed method quantifies the LUCC in changing environmental settings and can serve as a helpful tool for sustainable watershed management.

The Huashan Creek watershed is the largest water source and the main production area of honeydew in Pinghe County, whose extensive cultivation of honeydew has exacerbated soil and water pollution. However, the spatial application of remote sensing ecological index (RSEI) in this watershed and key driving factors are not clear considering the applicability of data quality and the diversity of methodological scales. To explore the RSEI and driving factors at distinct scales in Huashan Creek watershed, this study constructed the RSEI based on the environmental balance matrix at seven scales in 2020, revealed its spatial response characteristics at different scales, and analyzed the key drivers. The results show that the 240 m grid as well as rural and watershed scale convergence analyses satisfy the assessment of RSEI, whose Moran indexes are 0.558, 0.595, and 0.146, respectively. The RSEIs at different scales have significant spatial aggregation characteristics, but the overall status is moderate. The central town–riparian area with poor RSEI contrasts with the western mountainous area, which has comparatively better quality. Population has a major influence on RSEI at multiple scales (0.8), with elevation and patch index acting significantly at the village and grid scales, respectively. These findings help to identify the spatial distribution of quality and control mechanisms of RSEI in the Huashan Creek watershed and provide new insights into key scales and drivers of ecological restoration practices in the watershed.

A coupled model is an effective tool to understand the nutrient fate associated with hydrodynamic and ecosystem processes and thereby developing a water resource management strategy. This paper presents a coupled modeling approach that consists of a watershed model and a hydrodynamic model to evaluate the nutrient fate in a river–reservoir system. The results obtained from the model showed a good agreement with field observations. The results revealed that the Shuikou reservoir (Fuzhou, China)exhibited complicated hydrodynamic characteristics, which may induce the pattern of nutrient export. Reservoirs can greatly lower water quality as a result of decreasing water movement. Three scenarios were analyzed for water management. The NH₃-N (Ammonia Nitrogen) decreased sharply in the outlet of Shuikou reservoir after NH₃-N level in its tributary was reduced. After removing the farming cages, the water quality of the outlet of Shuikou reservoir was improved significantly. The DO (Dissolved Oxygen) had increased by 3%–10%, NH₃-N had reduced by 5%–17%, and TP (Total Phosphorus) had reduced by 6%–21%. This study demonstrates that the proposed coupled modeling approach can effectively characterize waterway risks for water management in such a river–reservoir system.

Riverine dissolved inorganic nitrogen (DIN), when elevated by human activities (e.g., land-use change), can accelerate the nitrogen cycle and downstream dispersal. However, estimating DIN export coefficients for individual land-use types can be complex due to mosaic land-use patterns and interactions between fertilizers and hydrological processes. We propose a framework that integrates an empirical model, a moving-window method, and an elasticity method to quantify seasonal DIN export coefficients for each land use in the Shixi Creek catchment, southeast China. Our model showed good agreement with field observations according to root mean square error and a normalized objective function. The export coefficients of farmland and forest were the highest (9.16 mg L⁻¹) and lowest (2.91 mg L⁻¹) ones, resulting in annual DIN exports, respectively, for farmland and forests of 1951 kg km⁻² yr⁻¹ and 619 kg km⁻² yr⁻¹, respectively. Urbanization was a dominant factor influencing DIN export; the export coefficient of built-up areas showed the highest elasticity and highest uncertainty, with abrupt fluctuations from dry to wet years. Our framework revealed the complex role of built-up areas in nitrogen export. Our results can shed light on how to improve riverine N management in a catchment by considering the interactive effects of climate and land use.