Modeling and Investigating the Mechanisms of Groundwater Level Variation in the Jhuoshui River Basin of Central Taiwan

Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models

The development of deep learning-based groundwater level forecast models can tackle the challenge of high dimensional groundwater dynamics, predict groundwater variation trends accurately, and manage groundwater resources effectively, thereby contributing to sustainable water resources management. This study proposed a novel ConvAE-LSTM model, which fused a Convolutional-based Autoencoder model (ConvAE) and a Long Short-Term Memory Neural Network model (LSTM), to provide accurate spatiotemporal groundwater level forecasts over the next three months. The HBV-light and LSTM models are chosen as benchmarks. An ensemble of point data and the corresponding derived images concerning the past (observations) and the future (forecasts from a conceptual model) of groundwater levels at 33 groundwater wells in Jhuoshuei River basin of Taiwan between 2000 and 2019 constituted the case study. The findings showcase the effectiveness of the ConvAE-LSTM model in extracting crucial features from both point and imagery datasets. This model successfully establishes spatiotemporal dependencies between regional images and groundwater level data over diverse time frames, leading to accurate multi-step-ahead forecasts of groundwater levels. Notably, the ConvAE-LSTM model exhibits a substantial improvement, with the R-squared values showing an increase of more than 18%, 22%, and 49% for the R1, R2, and R3 regions, respectively, compared to the HBV-light model. Additionally, it outperforms the LSTM model in this regard. This study represents a noteworthy milestone in environmental modeling, offering key insights for designing sustainable groundwater management strategies to ensure the long-term availability of this vital resource.

Evaluation of the Groundwater and Irrigation Quality in the Zhuoshui River Alluvial Fan between Wet and Dry Seasons

The Zhuoshui River alluvial fan is one of the most important groundwater and agricultural areas in Taiwan. Abundant groundwater resources are the main source of domestic water supply and irrigation water. However, groundwater recharge and groundwater quality have been greatly affected under extreme climate and hydrological conditions. Hence, the quality of groundwater has been a topic of concern to the public. In this study, groundwater level and groundwater quality data of the Zhuoshui River alluvial fan from 2008 to 2020 were used to divide the wet and dry season groups according to the sampling dates. An independent samples t-test was used to evaluate the differences in the mean groundwater level and the mean concentration between the wet and dry seasons. The test results show that there was no statistically significant difference in the mean groundwater level between the wet and dry seasons. This may result from the time lag effects of groundwater recharge. Except for groundwater temperature, bicarbonate, and total organic carbon (TOC), there were no significant differences among the mean concentrations of other groundwater quality parameters in Aquifer 1 and Aquifer 2 between the wet and dry seasons. In terms of the alluvial fan location, although the soil texture, land utilization, cropping systems, and hydrogeology of the proximal, mid-, and distal fan may affect groundwater quality variations, it seems that only Aquifer 1 is affected by surface water infiltration, resulting in significant differences in mean groundwater temperature, mean concentrations of major ions, and nitrate between the wet and dry seasons, whereas Aquifer 2 is less affected. At the same time, owing to the geological conditions and intensive cultivation in the Zhuoshui River alluvial fan, nitrate and arsenic could represent a high risk to the public’s health if groundwater is used as a source for domestic water supply or irrigation water in the distal fan area, whether in the wet season or dry season. Meanwhile, due to global climate change and uneven droughts and floods, the hydrological conditions of the so-called “wet season” and “dry season” are obviously different from those in the past. Compared with precipitation, groundwater level may be a better indicator for understanding variations in groundwater quality.

Investigation of the Influence of Excess Pumping on Groundwater Salinity in the Gaza Coastal Aquifer (Palestine) Using Three Predicted Future Scenarios

The Gaza coastal aquifer (GCA) is the only source of water for about two million citizens living in Gaza Strip, Palestine. The groundwater quality in GCA has deteriorated rapidly due to many factors. The most crucial factor is the excess pumping due to the high population density. The objective of this article was to evaluate the influence of excess pumping on GCA’s salinity using 10-year predicted future scenarios based on artificial neural networks (ANNs). The ANN-based model was generated to predict the GCA’s salinity for three future scenarios that were designed based on different pumping rates. The results showed that when the pumping rate remains at the present conditions, salinity will increase rapidly in most GCA areas, and the availability of fresh water will decrease in disquieting rates by 2030. Only about 8% of the overall GCA’s area is expected to stay within 500 mg/L of the chloride concentration. Results also indicate that salinity would be improved slightly if the pumping rate is kept at 50% of the current pumping rates while the improvement rate is much faster if the pumping is stopped completely, which is an unfeasible scenario. The results are considered as an urgent call for developing an integrated water management strategy aiming at improving GCA quality by providing other drinking water resources to secure the increasing water demand.