Permafrost impacts the subsurface hydrology and determines the transport of buried biochemical substances. Current evaluations of permafrost mostly focus on the overlying active layer. However, the basic but missing information of permafrost thickness constrains the quantification of trends and effects of permafrost degradation on subsurface hydrological processes. Our study quantified the long-term variations in permafrost thickness on the Tibetan Plateau (TP) between 1851 and 2100 based on layered soil temperatures calculated from eight earth system models (ESMs) of Coupled Model Intercomparison Project (the sixth phase) and validated by field observations and previous permafrost pattern from remote sensing. The calculated permafrost distribution based on ESMs was validated by the pattern derived from the MODIS datasets and field survey. Our results show that permafrost thicker than 10 m covers approximately 0.97 million km${}^2$ of the total area of the TP, which represents an areal extent of over 36.49% of the whole TP. The mean permafrost thickness of the TP was 43.20 m between 1851 and 2014, and it would decrease at an average rate of 9.42, 14.99, 18.78, and 20.75 cm per year under scenarios SSP126, SSP245, SSP370, and SSP585 from 2015 to 2100, respectively. The permafrost thickness will decrease by over 50 cm per year in Qiangtang Basin under SSP585. Our study provides new insights for spatiotemporal changes in permafrost thickness and a basic dataset combined results of remote sensing, field measurements for further exploring relevant hydrological, geomorphic processes and biogeochemical cycles in the plateau cryospheric environment.

The tradeoff between engineering costs and water treatment of the artificial lake system has a significant effect on engineering decision-making. However, decision-makers have little access to scientific tools to balance engineering costs against corresponding water treatment. In this study, a framework integrating numerical modeling, surrogate models and multi-objective optimization is proposed. This framework was applied to a practical case in Chengdu, China. A water quality model (MIKE21) was developed, providing training datasets for surrogate modeling. The Artificial Neural Network (ANN) and Support Vector Machine (SVM) were utilized for training surrogate models. Both surrogate models were validated with the coefficient of determinations (R²) greater than 0.98. SVM performed more stably with limited training data sizes while ANN demonstrated higher accuracies with more training samples. The multi-objective optimization model was developed using the genetic algorithm, with targets of reducing both engineering costs and target aquatic pollutant concentrations. An optimal target concentration after treatment was identified, characterized by the ammonia concentration (1.3 mg/L) in the artificial lake. Furthermore, scenarios with varying water quality in the upstream river were evaluated. Given the assumption of deteriorated upstream water quality in the future, the optimal proportion of pre-treatment in the total costs is increasing.

The Minjiang River is an important first-level tributary of the Yangtze River. Understanding the driving factors of water quality variations in the Minjiang River is crucial for future policy planning of watershed ecology protection of the Yangtze River. The water quality of the Minjiang River is impacted by both meteorological factors and anthropogenic factors. By using wavelet analysis, machine learning, and Shapley analysis approaches, the impacts of meteorological factors and anthropogenic factors on the permanganate index (COD_Mn) and ammonia nitrogen (NH₃-N) concentrations at the outlet of the Minjiang River Basin were quantified. The observed COD_Mn and NH₃-N concentration data in the Minjiang River from 2016 to 2020 were decomposed into long-term trend signals and periodic signals. The long-term trends in water qualities showed that anthropogenic factors were the major driving factors, accounting for 98.38% of the impact on COD_Mn concentrations and 98.18% of the impact on NH₃-N concentrations. The periodic fluctuations in water qualities in the Minjiang River Basin were mainly controlled by meteorological factors, with an impact of 68.89% on COD_Mn concentrations and 63.94% on NH₃-N concentrations. Compared to anthropogenic factors, meteorological factors have a greater impact on water quality in the Minjiang River Basin during both the high-temperature and rainy seasons from July to September and during the winter from December to February. The separate quantification of impacts of driving factors on the varying water quality signals contributed to the originality in this work, providing more intuitive insights for the assessment of the influences of policies and the climate change on the water quality.