River ecological flow early warning forecasting using baseflow separation and machine learning in the Jiaojiang River Basin, Southeast China

Assessment of urban flood susceptibility based on a novel integrated machine learning method

Flood susceptibility assessment is the premise and foundation to prevent flood disaster events effectively. To accurately assess urban flood susceptibility (UFS), this study first analyzes the advantages and disadvantages of multi-layer perceptron (MLP), and light gradient boosting machine (LightGBM), and designs a new integrated machine learning method by combining logistic regression (LR) method, i.e., LG-MLP-LR. Then, we verify the performance of LG-MLP-LR by taking the flood disaster events in Fuzhou from 2013 to 2016 as example and analyze the contribution of flood conditioning factors by calculating the SHapley Additive exPlanations values. Finally, the assessment results are compared with MLP, LightGBM, XG-MLP-LR, and CB-MLP-LR. The results show that (1) the selected flood conditioning factors can accurately depict the UFS of the study area; (2) compared with MLP, LightGBM, XG-MLP-LR, and CB-MLP-LR, the assessment results by LG-MLP-LR have higher average accuracy (94.950%) and higher average AUC (98.813%); (3) the factors affecting the occurrence and damage degree of flood disaster events in Fuzhou from 2013 to 2016 were elevation, topographic wetness index, maximum one-day rainfall, and stream power index, respectively. This study provides a new idea and method for the effective prevention and control of flood disasters in cities.

Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning

Baseflow is a crucial water source in the inland river basins of high-cold mountainous region, playing a significant role in maintaining runoff stability. It is challenging to select the most suitable baseflow separation method in data-scarce high-cold mountainous region and to evaluate effects of climate factors and underlying surface changes on baseflow variability and seasonal distribution characteristics. Here we attempt to address how meteorological factors and underlying surface changes affect baseflow using the Grey Wolf Optimizer Digital Filter Method (GWO-DFM) for rapid baseflow separation and the Long Short-Term Memory (LSTM) neural network model for baseflow prediction, clarifying interpretability of the LSTM model in baseflow forecasting. The proposed method was successfully implemented using a 63-year time series (1958-2020) of flow data from the Tai Lan River (TLR) basin in the high-cold mountainous region, along with 21 years of ERA5-land meteorological data and MODIS data (2000-2020). The results indicate that: (1) GWO-DFM can rapidly identify the optimal filtering parameters. It employs the arithmetic average of three methods, namely Chapman, Chapman-Maxwell and Eckhardt filter, as the best baseflow separation approach for the TLR basin. Additionally, the baseflow significantly increases after the second mutation of the baseflow rate. (2) Baseflow sources are mainly influenced by precipitation infiltration, glacier frozen soil layers, and seasonal ponding. (3) Solar radiation, temperature, precipitation, and NDVI are the primary factors influencing baseflow changes, with Nash-Sutcliffe efficiency coefficients exceeding 0.78 in both the LSTM model training and prediction periods. (4) Changes in baseflow are most influenced by solar radiation, temperature, and NDVI. This study systematically analyzes the changes in baseflow and response mechanisms in high-cold mountainous region, contributing to the management of water resources in mountainous basins under changing environmental conditions.

Occurrence and partitioning of p-phenylenediamine antioxidants and their quinone derivatives in water and sediment

p-Phenylenediamine antioxidants (PPDs) and PPDs-derived quinones (PPDQs) may pose a threat to the river ecosystem. However, the knowledge on the occurrence and environmental behaviors of PPDs and PPDQs in the natural river environment remains unknown. In this study, we collected paired water (n = 30) and sediment samples (n = 30) from Jiaojiang River, China and analyzed them for nine PPDs and seven PPDQs. Our results showed that target PPDs and PPDQs are frequently detected in water samples, with the dominance of N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD; mean 12 ng/L, range 4.0-72 ng/L) and 6PPD-derived quinone (6PPDQ; 7.0 ng/L, Collapse

Key Words

• PPDQs
• PPDs
• Partitioning
• Sediment
• Water

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Affiliation(s)

Jianqiang Zhu Department of Environmental Engineering, Taizhou University, Taizhou, Zhejiang 318000, PR China
Ruyue Guo Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, PR China
Fangfang Ren Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, PR China
Shengtao Jiang Department of Environmental Engineering, Taizhou University, Taizhou, Zhejiang 318000, PR China
Hangbiao Jin Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, PR China; Innovation Research Center of Advanced Environmental Technology, Eco-Industrial Innovation Institute ZJUT, Quzhou, Zhejiang 324400, PR China.

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