Over recent decades, coastal wetlands in Jiangsu Province have faced multiple challenges, including overfishing, reclamation for aquaculture, wetland shrinkage, and biodiversity loss. Implementing wetland ecological restoration proves crucial for mitigating the degradation of coastal wetland ecosystems. Quantifying ecosystem service values and establishing rational ecological compensation standards provide essential references for ecological compensation research and alleviating human–land conflicts. The Jianchuan Ecological Restoration Project, located in Dafeng District of Yancheng City, Jiangsu Province, employs integrated wetland, woodland, and farmland construction to rebuild biodiversity, enhance water conservation capacity, and improve water purification functions, thereby significantly boosting regional ecological services. Results have demonstrated that the total ecosystem service value of this project reaches CNY 76.2896 million, with climate regulation representing the highest value (CNY 68.1496 million, 89.33% of total). Subsequent values include biodiversity maintenance (3.40%), water purification (3.31%), and food production (2.95%), while carbon sequestration/oxygen release (0.96%) and soil retention (0.05%) show relatively lower contributions. Notably, this project innovatively integrates carbon finance mechanisms through “carbon sink loans”, achieving efficient transformation of ecological value from “paper accounts” to market realization. This study establishes a scientific foundation for ecological restoration projects through ecosystem service-based value assessment and pathway exploration, offering both theoretical framework and practical references.

We evaluated the diagnostic and predictive performance of the coupled General Ocean Turbulence Model–Framework for Aquatic Biogeochemical Models–Water Ecosystems Tool (GOTM–FABM–WET) in shallow Kun Cheng Lake, China. Monthly near-surface observations supported model calibration during 2021–2023 and out-of-sample ecological validation during 2024–2025. Model skill was assessed through one-at-a-time parameter screening, moment-independent Delta sensitivity analysis with sample-size convergence diagnostics, sequential multi-parameter calibration, and comparisons against statistical and nutrient mass-balance benchmarks. Water temperature calibration was strong (Nash–Sutcliffe efficiency, NSE = 0.933), although temperature observations were available only for 2021–2023. Dissolved oxygen (DO) exhibited modest validation skill (NSE = 0.290). Nitrogen parameter refinement improved correlation direction and simulated variability, yet the simulated seasonal TN peak remained five months earlier than observed. Total nitrogen (TN), total phosphorus (TP), and chlorophyll a (Chl-a) all retained negative validation NSE values. In particular, Chl-a calibration remained inadequate (calibration NSE = −0.405; validation NSE = −0.481), and its dedicated calibration stage retained the incoming seed parameters. Only 0.50–0.78% of evaluated candidates satisfied the cross-variable calibration guardrails. Temporally constant nutrient boundaries, coarse TP reporting resolution, and frequent numerical state repairs highlight key priorities for model improvement. Overall, the current evidence supports retrospective near-surface thermal diagnosis and conditional interpretation of broad seasonal DO patterns, whereas reliable nutrient and algal prediction will require time-varying inflow and nutrient chemistry, process-level field observations, and independent verification of seasonal timing and spatial representativeness.