Distribution and bioaccumulation of trace elements in two Cyprinidae fish species in the Indus river, Pakistan, including the impact of hydraulic structure on macroinvertebrates' biodiversity.

The concentration of trace elements (chromium, lead, zinc, copper, manganese, and iron) was determined in water, sediment and tissues of two Cyprinidae fish species - Labeo rohita and Tor putitora - collected from the eight sampling stations of Indus River in 2022 for four successive seasons (autumn, winter, spring, summer), and also study the present condition of macroinvertebrates after the construction of hydraulic structure. The obtained results of trace element concentrations in the Indus River were higher than the acceptable drinking water standards by WHO. The nitrate concentration ranges from 5.2 to 59.6 mg l-1, turbidity ranges from 3.00 to 63.9 NTU, total suspended solids and ammonium ions are below the detection limit (<0.05). In the liver, highest dry wt trace elements (µg/g) such as Cr (4.32), Pb (7.07), Zn (58.26), Cu (8.38), Mn (50.27), and Fe (83.9) for the Labeo rohita; and Tor Putitora has significantly greater accumulated concentration (Cr, Pb, Zn, Cu, Mn, Fe) in muscle and liver than did Labeo rohita species. Additionally, lower number of macroinvertebrates were recorded during the monsoonal season than pre-monsoon and post-monsoon. Local communities surrounded by polluted environments are more probably to consume more fish and expose them to higher concentrations of toxic trace elements (lead and copper). The findings also provide a basis for broader ecological management of the Indus River, which significantly influenced human beings and socioeconomic disasters, particularly in the local community.

From lake to fisheries: Interactive effect of climate and landuse changes hit on lake fish catch?

Global warming and unpredictable nature possess a negative impact on fisheries and the daily activities of other habitats. GIS and remote sensing approach is an effective tool to determine the morphological characteristics of the lake. The present study addresses the interactive effect of climate and landuse changes hit on fish catch in lake fisheries. We used a combination of the landscape disturbance index, vulnerability index, and loss index to construct a complete ecological risk assessment framework based on the landscape structure of regional ecosystems. The results indicate an increase from around 45%-76% in the percentage of land susceptible to moderate to ecological severe risk in the landscape from 2004 to 2023. Since 1950, temperature changes have increased by 0.4%, precipitation has decreased by 6%, and water levels have decreased by 4.2%, based on the results. The results indicate that landuse, water temperature, precipitation, and water depth significantly impact the aquaculture system. The findings strongly suggest integrating possible consequences of environmental change on fish yield for governance modeling techniques to minimize their effects.

Research on runoff process vectorization and integration of deep learning algorithms for flood forecasting.

Accurate multi-step ahead flood forecasting is crucial for flood prevention and mitigation efforts as well as optimizing water resource management. In this study, we propose a Runoff Process Vectorization (RPV) method and integrate it with three Deep Learning (DL) models, namely Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Transformer, to develop a series of RPV-DL flood forecasting models, namely RPV-LSTM, RPV-TCN, and RPV-Transformer models. The models are evaluated using observed flood runoff data from nine typical basins in the middle Yellow River region. The key findings are as follows: Under the same lead time conditions, the RPV-DL models outperform the DL models in terms of Nash-Sutcliffe efficiency coefficient, root mean square error, and relative error for peak flows in the nine typical basins of the middle Yellow River region. Based on the comprehensive evaluation results of the train and test periods, the RPV-DL model outperforms the DL model by an average of 2.82%-22.21% in terms of NSE across nine basins, with RMSE and RE reductions of 10.86-28.81% and 36.14%-51.35%, respectively. The vectorization method significantly improves the accuracy of DL flood forecasting, and the RPV-DL models exhibit better predictive performance, particularly when the lead time is 4h-6h. When the lead time is 4-6h, the percentage improvement in NSE is 9.77%, 15.07%, and 17.94%. The RPV-TCN model shows superior performance in overcoming forecast errors among the nine basins. The research findings provide scientific evidence for flood prevention and mitigation efforts in river basins.

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.