Pan Yang

Process‐based watershed models require robust, spatially distributed data for accurate total nitrogen (TN) simulation. However, traditional calibrations are often constrained by sparse in situ monitoring networks, resulting in substantial estimation errors and biases, particularly in ungauged sub‐basins. While newly available spatial inversion data offer high‐resolution observations of TN, their complex, heterogeneous, and non‐stationary error structures pose challenges for rigorous integration into process‐based models. This study presents a Hybrid Inversion‐Observation Bayesian Data Fusion (HIOBF) framework to address this issue. The framework incorporates a geography‐driven spatial extrapolation model to characterize inversion data errors in unmonitored areas, systematically merging high‐resolution spatial inversion data with conventional point‐scale measurements. We implemented and tested this framework using the Soil and Water Assessment Tool model in the Dongjiang River Basin, China. Results from independent validation demonstrated that by synergizing the spatial coverage of inversion data with the temporal precision of gauge measurements, the HIOBF framework significantly reduced parametric uncertainty and improved the Nash‐Sutcliffe Efficiency by an average margin of 55% compared to the traditional gauge measurement‐only baseline. This work provides a transferable methodology, offering a robust approach to enhance simulation accuracy and provide defensible uncertainty estimates for water resource management in data‐scarce contexts.

Urban waterlogging has become a frequent and threatening issue in recent years due to rapid urbanization and extreme weather conditions, resulting in economic losses and health hazards. In this context, green roofs (GRs) emerge as a sustainable and innovative solution to mitigate these issues by absorbing rainfall, reducing runoff, and enhancing urban biodiversity. Despite the apparent benefits, the adoption of GRs remains limited, largely due to a lack of quantitative understanding of the factors that influence urban residents' GR adoption willingness.This study aims to fill this knowledge gap via a survey approach, and distribute and collect survey responses from 999 residents in Shenzhen, a rapidly developing coastal city in China. The survey is designed to capture a range of variables that may influence residents' decision-making regarding GR adoption, including demographic information, housing characteristics, waterlogging experiences, roof utilization preference, knowledge of and preference for GR, and willingness to adopt GR. The GR adoption willingness is collected assuming two policy scenarios, one with government subsidy and the other without. By leveraging a machine learning model for data analysis, the study identifies five key predictors that commonly influence GR adoption willingness with and without subsidy: recognition of the advantages of GRs (GR_advantage), whether a resident lives on the top floor (Top_floor), the degree of concern about GRs (GR_concern), the duration of waterlogging experienced in and around the community (WL_time), and the individual's level of education (Education). Interestingly, the study also reveals that GR adoption willingness is affected differently under scenarios with and without policy incentives. In the absence of subsidies, the property fee (Pro_fee) is a significant factor; conversely, when policy incentives are present, age and house ownership (House_own) emerge as influential factors.The complexity of these influencing factors is further evaluated using the SHAP (SHapley Additive explanation) model, which provides a nuanced interpretation of how these factors interact and exert nonlinear impacts on residents' willingness to adopt GRs. The insights derived from this analysis are critical for policymakers and urban planners who are looking to promote GRs as part of an integrated urban water management strategy. Specifically, a combination of long-term subsidies and one-time subsidies can be combined to motivate residential adoption. Recognizing the general unfamiliarity with GRs and related policies among residents, relevant outreach and education programs are essential. In addition, targeted subsidy levels could be helpful in stimulating more GR adoptions. An important consideration in this targeting process is the frequency of waterlogging events, which has been shown to significantly influence residents' willingness to pay for GRs.

The application of remote sensing technology for water quality monitoring has attracted much attention recently. Remote sensing inversion in coastal waters with complex hydrodynamics for non-optically active parameters such as total nitrogen (TN) and total phosphorus (TP) remains a challenge. Existing studies build the relationships between remote sensing spectral data and TN/TP directly or indirectly via the mediation of optically active parameters (e.g., total suspended solids). Such models are often prone to overfitting, performing well with the training set but underperforming with the testing set, even though both datasets are from the same region. Using the Hong Kong coastal region as a case study, we address this issue by incorporating spatial covariates such as hydrometeorological and locational variables as additional input features for machine learning-based inversion models. The proposed model effectively alleviates overfitting while maintaining a decent level of accuracy (R² exceeding 0.7) during the training, validation and testing steps. The gap between model R² values in training and testing sets is controlled within 7%. A bootstrap uncertainty analysis shows significantly improved model performance as compared to the model with only remote sensing inputs. We further employ the Shapely Additive Explanations (SHAP) analysis to explore each input’s contribution to the model prediction, verifying the important role of hydrometeorological and locational variables. Our results provide a new perspective for the development of remote sensing inversion models for TN and TP in similar coastal waters.

Water quality data represent a critical resource for evaluation of the well-being of aquatic ecosystems and assurance of clean water sources for human populations. While the availability of water quality datasets is growing, the absence of a publicly accessible national water quality dataset for both inland and the ocean in China has been notable. To address this issue, we utilized R and Python programming languages to collect, tidy, reorganize, curate, and compile three publicly available datasets, thereby creating an extensive spatiotemporal repository of surface water quality data for China. Distinguished as the most expansive, clean, and easily accessible water quality dataset in China to date, this repository comprised over 330 000 observations encompassing daily (3588), weekly (217 751), and monthly (114 954) records of surface water quality covering the period from 1980 to 2022. It spanned 18 distinct indicators, meticulously gathered at 2384 monitoring sites, which were further categorized as daily (244 sites), weekly (149 sites), and monthly (1991 sites), ranging from inland locations to coastal and oceanic areas. This dataset will support studies relevant to the assessment, modeling, and projection of water quality, ocean biomass, and biodiversity in China, and therefore make substantial contributions to both national and global water resources management. This water quality dataset and supplementary metadata are available for download from the figshare repository at https://doi.org/10.6084/m9.figshare.22584742 (Lin et al., 2023b).

Water data is a crucial asset for sustainable water resource management. However, the availability of China’s water datasets lags far behind modern expectations for open geoscientific data. This dataset is a part of the China Water Data Archive (CWDA), an upcoming national collection of water-related data covering all aspects of water data for boosting data sharing in China. The CWDA aims at providing free, clean, non-sensitive, coherent, and reliable water data within China for global researchers to support the national and global water resources management and the United Nations-Water Integrated Monitoring Initiative for Sustainable Development Goals 6 and 14. In this paper, we used Python and R language to collect, tidy, reorganize, and curate the publicly available inland and coastal/ocean surface water quality data in China, following a series of data quality dimensions (integrity, completeness, consistency, and accuracy). As the most comprehensive, publicly available, handy, and clean water quality dataset in China so far, it included water quality data for daily, weekly, and monthly in the period of 1980–2022, with 17 indicators for over 330,000 observations at 2384 sites from inland to coastal/ocean areas. This dataset will greatly support works relevant to the assessment, modelling, and projection of water quality, ocean biomass, and biodiversity in China.