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• A novel data-driven regionalization modeling framework for PUB is proposed. • The DTW-KMeans technique is suitable for catchment classification. • Matching model complexity and number of training catchments can improve performance. • Catchment similarity plays a crucial role on regionalization modeling. Streamflow prediction in ungauged catchments is a challenging task in hydrological studies. Recently, data-driven models have demonstrated their superiority over traditional hydrological models in predicting streamflow in ungauged catchments. However, previous studies have overlooked the similarities between the training and the target catchments. Therefore, this study explores the role of catchment similarity in regionalization modeling using the publicly available CAMELS dataset. We employed the dynamic time warping-based KMeans (DTW-KMeans) time-series clustering technique to cluster the streamflow data from gauged catchments. We utilized the long short-term memory (LSTM) neural network to construct regional models for different classes of gauged catchment. Additionally, the mapping relationship between gauged catchment classes and static attributes was established using the random forest (RF). By combining the trained RF model with the static attributes of an ungauged catchment, we determined its class and used the corresponding regional LSTM to predict streamflow. To evaluate the effectiveness of the framework, we applied the classification-based regionalization modeling (CRM) and non-classification-based regionalization modeling (NRM) approach for comparison. The results indicate that: (1) The DTW-KMeans-based catchment classification method is generally accurate and reasonable; (2) the complexity of the LSTM model and the number of training catchments should be appropriately matched to improve streamflow prediction; and (3) catchment similarity plays a crucial role in regionalization modeling, the proportion of training catchments with high similarity to ungauged catchments significantly affects prediction results. [ABSTRACT FROM AUTHOR]

• The most suitable ecological streamflow was calculated via kernel density estimation method. • Ecological drought was identified using the variable threshold method based on ecological streamflow. • Impacts of climate variability and human activities on ecological drought were quantified. • Human activities are the dominant factor aggravating the ecological drought in the Weihe River Basin. Although various studies have investigated the impacts of climate variability and human activities on drought, researches specifically analysing the impact on ecological drought are still limited. A deep understanding of the climatic and anthropogenic effects on ecological drought processes is crucial for ecological regulation and management in the changing environments. In the present study, an integrated approach for comprehensive understanding and quantification of ecological drought in rivers was proposed which first applied the nonparametric kernel density estimation (KDE) method to calculate the most suitable ecological streamflow (MSES) for a river ecosystem. Then, the variable threshold level method based on the MSES for each month and the run theory method were applied to identify the ecological drought duration and deficit volumes. Finally, a quantification approach based on hydrological model simulation was proposed to attribute the impacts of climate variability and human activities on ecological drought. The proposed approach was applied on two catchments, Xianyang (XY) and Huaxian (HX) within the Weihe River Basin (WRB) in northern China. Comparison results obtained using the two empirical methods revealed that the MSES calculated using the KDE method was reasonable and can be used for ecological drought identification. The identification results showed that both the median and upper quartile values of the drought duration and deficit volumes during the disturbed period (1991–2017) were greater than those during the undisturbed period (1961–1990). Quantification results showed that human activities were the dominant factor aggravating ecological drought in the WRB after 1990. The contribution rates of climate variability and human activities toward ecological drought variations were 25.6% and 74.4%, respectively, for the XY station and 42.7% and 57.3%, respectively, for the HX station. Although the WRB was selected as a case study, the proposed approach can also be applied to other regions to provide scientific guidance for regional ecological management. [ABSTRACT FROM AUTHOR]