Conservation of groundwater from over-exploitation-Scientific analyses for groundwater resources management

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.

Predicting Heavy Metal Concentrations in Shallow Aquifer Systems Based on Low-Cost Physiochemical Parameters Using Machine Learning Techniques

Monitoring ex-situ water parameters, namely heavy metals, needs time and laboratory work for water sampling and analytical processes, which can retard the response to ongoing pollution events. Previous studies have successfully applied fast modeling techniques such as artificial intelligence algorithms to predict heavy metals. However, neither low-cost feature predictability nor explainability assessments have been considered in the modeling process. This study proposes a reliable and explainable framework to find an effective model and feature set to predict heavy metals in groundwater. The integrated assessment framework has four steps: model selection uncertainty, feature selection uncertainty, predictive uncertainty, and model interpretability. The results show that Random Forest is the most suitable model, and quick-measure parameters can be used as predictors for arsenic (As), iron (Fe), and manganese (Mn). Although the model performance is auspicious, it likely produces significant uncertainties. The findings also demonstrate that arsenic is related to nutrients and spatial distribution, while Fe and Mn are affected by spatial distribution and salinity. Some limitations and suggestions are also discussed to improve the prediction accuracy and interpretability.

Mapping Interflow Potential and the Validation of Index-Overlay Weightings by Using Coupled Surface Water and Groundwater Flow Model

Interflow is an important water source contributing to river flow. It directly influences the near-surface water cycles for water resource management. This study focuses on assessing the interflow potential and quantifying the interflow in the downstream area along the Kaoping River in southern Taiwan. The interflow potential is first determined based on the modified index-overlay model, which employs the analytical hierarchy process (AHP) to calculate the ratings and weightings of the selected factors. The groundwater and surface water flow (GSFLOW) numerical model is then used to link the index-overlay model to quantify the interflow potential for practical applications. This study uses the Monte Carlo simulations to assess the influence of rainfall-induced variations on the interflow uncertainty in the study area. Results show that the high potential interflow zones are located in the high to middle elevation regions along the Kaoping River. Numerical simulations of the GSFLOW model show an interflow variation pattern that is similar to the interflow potential results obtained from the index-overlay model. The average interflow rates are approximately 3.5 × 104 (m3/d) in the high elevation zones and 2.0 × 104 (m3/d) near the coastal zones. The rainfall uncertainty strongly influences interflow rates in the wet seasons, especially the peaks of the storms or heavy rainfall events. Interflow rates are relatively stable in the dry seasons, indicating that interflow is a reliable water resource in the study area.

Mapping Groundwater Potential for Irrigation, by Geographical Information System and Remote Sensing Techniques: A Case Study of District Lower Dir, Pakistan

The changing climate and global warming have rendered existing surface water insufficient, which is projected to adversely influence the irrigated farming systems globally. Consequently, groundwater demand has increased significantly owing to increasing population and demand for plant-based foods especially in South Asia and Pakistan. This study aimed to determine the potential areas for groundwater use for agriculture sector development in the study area Lower Dir District. ArcGIS 10.4 was utilized for geospatial analysis, which is referred to as Multi Influencing Factor (MIF) methodology. Seven parameters including land cover, geology, soil, rainfall, underground faults (liniment) density, drainage density, and slope, were utilized for delineation purpose. Considering relative significance and influence of each parameter in the groundwater recharge rating and weightage was given and potential groundwater areas were classified into very high, high, good, and poor. The result of classification disclosed that the areas of 113.10, 659.38, 674.68, and 124.17 km2 had very high, high, good, and poor potential for groundwater agricultural uses, respectively. Field surveys for water table indicated groundwater potentiality, which was high for Kotkay and Lalqila union councils having shallow water table. However, groundwater potentiality was poor in Zimdara, Khal, and Talash, characterized with a very deep water table. Moreover, the study effectively revealed that remote sensing and GIS could be developed as potent tools for mapping potential sites for groundwater utilization. Furthermore, MIF technique could be a suitable approach for delineation of groundwater potential zone, which can be applied for further research in different areas.

Farmers' decision to use drought early warning system in developing countries

Drought is a persistent, sluggish natural disaster in developing countries that has generated a financial burden and an unstable climate. Farmers should adopt early warning systems (EWS) in their strategies for monitoring drought to reduce its serious consequences. However, farmers in developing countries are reluctant to use EWS as their management strategies. Hence, the aim of this study was to investigate the decision of farmers to use climate knowledge through the model of farming activity in Kermanshah Township, Iran. A surveyor questionnaire was used to gather data from 370 wheat farmers using random sampling methods in multi-stage clusters. Results revealed that the decision to use climate information is affected by personal factors, attitude towards climate information, objectives of using climate information, and external/physical farming factors. The result of this study has implications for drought management practitioners. To be specific, the results can aid policymakers to design early alert programs to minimize the risk of drought and thus move from conventional to climate smart agriculture.

Groundwater hydrogeochemistry and probabilistic health risk assessment through exposure to arsenic-contaminated groundwater of Meghna floodplain, central-east Bangladesh

A clear understanding of various hydrogeochemical processes is essential for the protection of groundwater quality, which is a prime concern in Bangladesh. The present study deals with the geochemistry of groundwater at various depths to investigate the hydrogeochemical processes controlling the water quality of Meghna floodplain, the sources and mechanisms of arsenic (As) liberation, and the estimation of carcinogenic and non-carcinogenic health risks (using probabilistic and deterministic approaches) to the adults and children of the Comilla district, central-east Bangladesh. The groundwaters were generally of Ca-Mg-HCO₃ type, and water-sediment interaction was the dominant factor in evolving the chemical signatures. The dissolution of carbonates, weathering of silicates, and cation exchange processes governed the major ion chemistry. Dissolved As concentration ranged from 0.002 to 0.36 mg/L and Monte Carlo simulation-based probabilistic estimation of cancer risk suggested that; (1) ~ 83% of the waters exceeded the higher end of the acceptable limit of 1 × 10^-4; (2) the probability of additional cases of cancer in every 10,000 adults and children were on average ~9 and ~5, respectively; (3) adults were more susceptible than children; and (4) ingestion was the main pathway of As poisoning and the contribution of dermal contact was negligible (<1%). According to sensitivity analysis, the duration of exposure to As and its concentration in groundwater posed the greatest impact on cancer risk assessment. However, hydrogeochemical investigations on the sources and mobilization mechanisms of As suggested that the reductive dissolution of Fe and Mn oxyhydroxides was the principal process of As release in groundwater. The oxidation of pyrite and competitive exchange of fertilizer-derived phosphate for the sorbed As were not postulated as the plausible explanation for As liberation.

Human health risk assessment of some bottled waters from Romania

The paper presents the quality status of 14 brands of bottled water, with sources of groundwaters from different mountain areas alongside the Carpathian Mountains from Romania. A number of 12 physico-chemical parameters (ammonium, bicarbonate, electrical conductivity, carbonate, chemical oxygen demand, chloride, nitrate, nitrite, pH, sulphate, total hardness, turbidity), 9 metals and metalloids (Li, B, Na, Mg, Al, K, Ca, Sr, Ba) and 17 heavy metals (V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Mo, Ag, Cd, In, Tl, Pb, Bi) were determined and studied. The quality status, the potential contamination and the health risk assessment of bottled waters were assessed, by using the drinking water quality index, the heavy metal pollution index, the heavy metal evaluation index, the degree of contamination and the human health risk indices. Hierarchical cluster analysis was applied, indicating similarities among the studied bottled waters based on their metal content. The Piper diagram reveals that the majority of bottled water samples fall into the Ca, Mg, Na, K, Cl^-, SO₄^2-, CO₃^2-, HCO₃^- categories. The quality of bottled waters based on the indices results indicated marginal, poor and very-poor quality status of the studied water samples, while the health risk assessment indices presented potential risks at aluminium, chloride and nitrate for the inhabitants who used those water samples with the purpose of drinking. The pollution indices with respect to metals generally reflected a low pollution status. This study represents the first attempt in assessing the overall quality of some bottled water collected from the mountain area, Romania, likewise assessing the comprehensive human health risk due to several chemical elements determined in water in amounts around and exceeding the maximum allowable concentrations. This research can be useful for development of potential strategies for risk control and management in the field of drinking water.

An Analysis of the Cost of Water Supply Linked to the Tourism Industry. An Application to the Case of the Island of Ibiza in Spain

Tourist activity has a number of impacts on the destinations in which it takes place, among which are the environmental ones. A particular problem is the increase in water demand and wastewater production, which can compromise the balance of ecosystems. As many authors point out, there is a research gap in the comparative analysis between available water resources and the demand associated with tourism. In this respect, the main objective of this work is, on the one hand, to assess the water needs linked to the tourism industry and the capacity of natural resources to meet such a demand and, on the other hand, to estimate the economic cost of the water supply associated with the growing tourist demand in a territory, such as the island of Ibiza in Spain. It has been determined that the resources available are not sufficient to meet the water demand of the resident population at this destination, which is why it is necessary to resort to producing desalinated water. Therefore, the additional requirements associated with tourism must be met fully with desalinated water, which results in an increased cost of water management for the region. This paper also points at water losses in distribution networks and tourism seasonality as two phenomena that aggravate this issue.

Building an Intelligent Hydroinformatics Integration Platform for Regional Flood Inundation Warning Systems

Flood disasters have had a great impact on city development. Early flood warning systems (EFWS) are promising countermeasures against flood hazards and losses. Machine learning (ML) is the kernel for building a satisfactory EFWS. This paper first summarizes the ML methods proposed in this special issue for flood forecasts and their significant advantages. Then, it develops an intelligent hydroinformatics integration platform (IHIP) to derive a user-friendly web interface system through the state-of-the-art machine learning, visualization and system developing techniques for improving online forecast capability and flood risk management. The holistic framework of the IHIP includes five layers (data access, data integration, servicer, functional subsystem, and end-user application) and one database for effectively dealing with flood disasters. The IHIP provides real-time flood-related data, such as rainfall and multi-step-ahead regional flood inundation maps. The interface of Google Maps fused into the IHIP significantly removes the obstacles for users to access this system, helps communities in making better-informed decisions about the occurrence of floods, and alerts communities in advance. The IHIP has been implemented in the Tainan City of Taiwan as the study case. The modular design and adaptive structure of the IHIP could be applied with similar efforts to other cities of interest for assisting the authorities in flood risk management.

Building ANN-Based Regional Multi-Step-Ahead Flood Inundation Forecast Models

A regional inundation early warning system is crucial to alleviating flood risks and reducing loss of life and property. This study aims to provide real-time multi-step-ahead forecasting of flood inundation maps during storm events for flood early warnings in inundation-prone regions. For decades, the Kemaman River Basin, located on the east coast of the West Malaysia Peninsular, has suffered from monsoon floods that have caused serious damage. The downstream region with an area of approximately 100 km2 located on the east side of this basin is selected as the study area. We explore and implement a hybrid ANN-based regional flood inundation forecast system in the study area. The system combines two popular artificial neural networks—the self-organizing map (SOM) and the recurrent nonlinear autoregressive with exogenous inputs (RNARX)—to sequentially produce regional flood inundation maps during storm events. The results show that: (1) the 4 × 4 SOM network can effectively cluster regional inundation depths; (2) RNARX networks can accurately forecast the long-term (3–12 h) regional average inundation depths; and (3) the hybrid models can produce adequate real-time regional flood inundation maps. The proposed ANN-based model was shown to very quickly carry out multi-step-ahead forecasting of area-wide inundation depths with sufficient lead time (up to 12 h) and can visualize the forecasted results on Google Earth using user devices to help decision makers and residents take precautionary measures against flooding.

Using self-organizing map for coastal water quality classification: Towards a better understanding of patterns and processes

Self-organizing map (SOM) was used to explore the spatial characteristics of water quality in the middle and southern Fujian coastal area. Nineteen water quality variables (temperature, salinity, pH, dissolved oxygen, alkalinity, chemical oxygen demand, nutrients NH₄-N, H₂SiO₃, PO₄^-, NO₂^-, and NO₃^-, heavy metals/metalloid Cu, Zn, As, Cd, Pb, Hg, and Cr⁶⁺, and oil) were measured in the surface, middle, and bottom water layers at 94 different sampling sites. Patterns of water quality variables were visualized by the SOM planes, and similar patterns were observed for those variables that correlated with each other, indicating a common source. pH, COD, As, Hg, Pb, and Cr⁶⁺ likely originated from industries, while nutrients NH₄-N, NO₂^-, NO₃^-, and PO₄^3- were mainly attributed to agriculture and aquaculture. The k-means clustering in the SOM grouped the water quality data into nine clusters, which revealed three representative water types, ranging from low salinity to high salinity with different levels of heavy metal/metalloid pollution and nutrient pollution. Spatial changes in water quality reflected the impacts of natural factors (riverine outflows, tides, and alongshore currents), as well as anthropogenic activities (mariculture, industrial and urban discharges, and agricultural effluents). Principal component analysis (PCA) confirmed the clustering results obtained by SOM, while the latter provides a more detailed classification and additional information about the dominant variables governing the classification processes. The results of this study suggest that SOM is an effective tool for a better understanding of patterns and processes driving water quality.

Groundwater Governance in the Azores Archipelago (Portugal): Valuing and Protecting a Strategic Resource in Small Islands

Studies on groundwater governance status at EU national and river basin district levels are rare, hindering lessons learned at each administrative scale to be shared. Groundwater is a common-pool resource of strategic significance in the Azores archipelago (Portugal), thus calling for sustainable development. Groundwater governance emerged in the last decades as a path to sustainable resources management, and the present paper characterizes the current status of governance in the Azores, where management is pursued according to a vertically-integrated system. A survey made among 43 specialists showed that despite the instrumental role of groundwater for water supply there is a need to increase awareness on groundwater valuing and protection. The application of benchmark criteria to evaluate the groundwater governance state-of-art shows that technical capacities are diminishing governance effectiveness due to the lack of quantitative data, and further enforcing of the groundwater legal framework to the specificities of the Azores is needed. The empowerment of the government agency being responsible for the groundwater management is also envisaged. The failure to account for the economic dimension of the groundwater governance, the insufficient development of cross-sectorial approaches, and the unsuccessful public participation are other weaknesses on the groundwater governance in the Azores.