Although machine learning (ML) techniques are increasingly popular in water resource studies, they are not extensively utilized in modeling snowmelt. In this study, we developed a model based on a deep learning long short-term memory (LSTM) for snowmelt-driven discharge modeling in a Himalayan basin. For comparison, we developed the nonlinear autoregressive exogenous model (NARX), Gaussian process regression (GPR), and support vector regression (SVR) models. The snow area derived from moderate resolution imaging spectroradiometer (MODIS) snow images along with remotely sensed meteorological products were utilized as inputs to the models. The Gamma test was conducted to determine the appropriate input combination for the models. The shallow LSTM model with a hidden layer achieved superior results than the deeper LSTM models with multiple hidden layers. Out of seven optimizers tested, Adamax proved to be the aptest optimizer for this study. The evaluation of the ML models was done by the coefficient of determination (R2), mean absolute error (MAE), modified Kling–Gupta efficiency (KGE’), Nash–Sutcliffe efficiency (NSE), and root-mean-squared error (RMSE). The LSTM model (KGE’ = 0.99) enriched with snow cover input achieved the best results followed by NARX (KGE’ = 0.974), GPR (KGE’ = 0.95), and SVR (KGE’ = 0.949), respectively. The outcome of this study proves the applicability of the ML models, especially the LSTM model, in predicting snowmelt driven discharge in the data-scant mountainous watersheds.

Compared with marine organisms, research on microplastics (MPs) in freshwater organisms is still less although MPs have been widely found in the freshwater ecosystem. Hypoxia is a ubiquitous issue in freshwater aquaculture, and under such scenarios, the toxic effects of MPs on typical aquaculture fish need to be clarified. In this study, we studied the effects of MPs (polystyrene) on specific growth rate (SGR), hypoxia-inducible factor-1α (HIF-1α), tumor necrosis factor-α (TNF-α), interleukin-8 (IL-8), and interferon (IFN) in the yellow catfish (Pelteobagrus fulvidraco) under hypoxic conditions. After 15 days of exposure, the SGR was not affected by MPs or hypoxia. MPs significantly increased the expressions of HIF-1α and TNF-α but inhibited the expression of IFN at high concentration MPs under normoxia. However, hypoxia significantly inhibited the expression of IL-8 and TNF-α under high MP concentration and low MP concentration, respectively. In addition, MPs had significant concentration-dependent inhibitory effects on IFN under hypoxia. Surprisingly, a positive correction between HIF-1α and TNF-α was found in fish. Although hypoxia might alleviate the effects of MPs with low concentrations, the interaction of hypoxia and MPs aggravated the negative effects of MPs on immune factors at high concentration MPs. This study provided new insight into the complex effects of hypoxia and MPs on aquatic organisms, and future studies should focus on the cellular pathways of immune cells in fish. Given that MPs could induce the immune response in fish, considerations should be paid to the impacts of MPs on freshwater aquaculture, and hypoxia should be taken into consideration when evaluating the effects of MPs.