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Patel, D.; Pamidimukkala, P.; Chakraborty, D. |
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Title |
Groundwater quality evaluation of Narmada district, Gujarat using principal component analysis |
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Journal Article |
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Year |
2024 |
Publication |
Groundwater for Sustainable Development |
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24 |
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Pages |
101050 |
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Keywords |
Fluoride, Groundwater quality index, Principal component analysis, Uranium |
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Abstract |
In the present study, the ground water quality parameters were monitored during pre- and post-monsoon seasons across Narmada district, Gujarat, India. Monitoring was done in 89 drinking water samples collected by grid sampling method from the study area. Uranium and fluoride were analyzed along with associated parameters such as pH, dissolved oxygen, Cl−, NO3−, F−, SO42−, total alkalinity, total dissolved solids and hardness. In 4% samples the fluoride content was found to be above WHO permissible limits of 1.5 mg/L (2.36 mg/L in Undaimandava, 1.55 mg/L in Shira, 3.04 mg/L in Fatehpur and 1.83 mg/L in Dholivav) during pre-monsoon season (PRM) and 4.74 mg/L, 2.41 mg/L, 2.34 mg/L and 3.99 mg/L respectively in Bantawadi, Shira, Undai Mandava and Fatepur villages during post-monsoon (POM). The uranium level was within WHO limits in both POM and PRM seasons. The quality of the water was evaluated by Principal Component and Pearson Correlation statistical analysis techniques. The PRM and POM correlation study indicated a strong correlation of TDS with EC, Chloride, total alkalinity and bicarbonate and U while moderately strong correlation of TDS with fluoride were observed indicating that chloride, total alkalinity, bicarbonate, U and fluoride contributed to TDS and EC. Principal component analysis was applied for 14 variables, from which 3 factors were extracted during PRM and POM seasons. The extracted components, contributed 84.391% and 83.315%, to variation during PRM and POM seasons respectively. The study indicated that the analyzed water samples in Narmada district were safe for drinking purpose. However, Tilakwada tehsil groundwater was observed to be unsustainable for drinking, without further water treatment, but was appropriate for agricultural purposes. The study will help the residents of the district to understand the present water quality status and will also help in future management to protect the ground water of Narmada district. |
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2352-801x |
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THL @ christoph.kuells @ patel_groundwater_2024 |
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148 |
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Pham, H.C.; Alila, Y. |
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Title |
Science of forests and floods: The quantum leap forward needed, literally and metaphorically |
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Journal Article |
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Year |
2024 |
Publication |
Science of The Total Environment |
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912 |
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169646 |
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Hydrological causality, Extreme value analyses, Land use impact, Peakflows, Extreme events epistemology, Experimental design |
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A century of research has generated considerable disagreement on the effect of forests on floods. Here we call for a causal inference framework to advance the science and management of the effect of any forest or its removal on flood severity and frequency. The causes of floods are multiple and chancy and, hence, can only be investigated via a probabilistic approach. We use the stochastic hydrology literature to infer a blueprint framework which could guide future research on the understanding and prediction of the effects of forests on floods in environments where rain is the dominant form of precipitation. Drawing parallels from other disciplines, we show that the introduction of probability in forest hydrology could stimulate a gestalt switch in the science of forests and floods. In light of increasing flood risk caused by climate change, this probabilistic framework can help policymakers develop robust forest and water management plans based on a defensible and clear understanding of floods. |
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0048-9697 |
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THL @ christoph.kuells @ Pham2024169646 |
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244 |
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Pisa, P.F.; Nehren, U.; Sebesvari, Z.; Rai, S.; Wong, I. |
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Chapter 17 – Nature-based solutions to reduce risks and build resilience in mountain regions |
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2024 |
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Safeguarding Mountain Social-Ecological Systems |
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115-126 |
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Nature-based solutions, mountains, climate change adaptation, disaster risk reduction, ecosystem services, SDGs |
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Nature-based solutions (NbS) are increasingly recognized as effective environmental-management measures to address societal challenges such as climate change, water and food security, and disaster risk reduction, thus contributing to human well-being and protecting biodiversity. In addition to being particularly susceptible to these challenges, mountain areas are prone to multihazard conditions, due to their steep topography and particular climatic conditions. NbS can contribute greatly to the sustainable development of mountain ecosystems. This chapter presents examples of NbS in mountain areas around the globe that demonstrate how this approach contributes to achieving sustainable development. |
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Elsevier |
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Schneiderbauer, S.; Pisa, P.F.; Shroder, J.F.; Szarzynski, J. |
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978-0-12-822095-5 |
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THL @ christoph.kuells @ Fontanellapisa2024115 |
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263 |
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Seidl, C.; Wheeler, S.A.; Page, D. |
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Title |
Understanding the global success criteria for managed aquifer recharge schemes |
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Journal Article |
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Year |
2024 |
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Journal of Hydrology |
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628 |
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130469 |
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Managed Aquifer Recharge (MAR), Fuzzy-set Qualitative Comparative Analysis, Water banking, Groundwater, Water management, Water storage |
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Water availability and quality issues will only gain importance in the future, with climate change impacts putting increasing pressure on global water resources. Dealing with these challenges requires drawing on all available water management tools, including Managed Aquifer Recharge (MAR). Although MAR has seen increasing global implementation during the last half a century, it is still often overlooked as a management tool. While technical, bio-physical, and hydrogeological aspects of MAR are well researched, this cannot be said for socio-economic and other governance factors. Where information is available, this study seeks to understand the conditions necessary for MAR success. We apply fuzzy-set Qualitative Comparative Analysis on 313 world MAR applications, and also model separately for high- and low-middle-income countries. Results show that sophisticated hydrogeological site understanding and scheme operation is paramount for MAR success, as is utilizing natural water sources for high value end uses. Successful high-income country MAR schemes tend to be large and utilize natural water sources and sophisticated water injection and treatment methods to augment potable water supply; while successful low-middle-income country schemes are not large, older than 20 years, and use gravity infiltration methods and (limited) no water treatment. These findings will help inform the future suitability of MAR application design and its likely success within various contexts. |
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0022-1694 |
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THL @ christoph.kuells @ Seidl2024130469 |
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273 |
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Author |
Singh, A.; Patel, S.; Bhadani, V.; Kumar, V.; Gaurav, K. |
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Title |
AutoML-GWL: Automated machine learning model for the prediction of groundwater level |
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Journal Article |
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Year |
2024 |
Publication |
Engineering Applications of Artificial Intelligence |
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127 |
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107405 |
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AutoML, Bayesian optimisation, Groundwater, Machine learning |
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Predicting groundwater levels is pivotal in curbing overexploitation and ensuring effective water resource governance. However, groundwater level prediction is intricate, driven by dynamic nonlinear factors. To comprehend the dynamic interaction among these drivers, leveraging machine learning models can provide valuable insights. The drastic increase in computational capabilities has catalysed a substantial surge in the utilisation of machine learning-based solutions for effective groundwater management. The performance of these models highly depends on the selection of hyperparameters. The optimisation of hyperparameters is a complex process that often requires application-specific expertise for a skillful prediction. To mitigate the challenge posed by hyperparameter tuning’s problem-specific nature, we present an innovative approach by introducing the automated machine learning (AutoML-GWL) framework. This framework is specifically designed for precise groundwater level mapping. It seamlessly integrates the selection of best machine learning model and adeptly fine-tunes its hyperparameters by using Bayesian optimisation. We used long time series (1997-2018) data of precipitation, temperature, evaporation, soil type, relative humidity, and lag of groundwater level as input features to train the AutoML-GWL model while considering the influence of Land Use Land Cover (LULC) as a contextual factor. Among these input features, the lag of groundwater level emerged as the most relevant input feature. Once the model is trained, it performs well over the unseen data with a strong correlation of coefficient (R = 0.90), low root mean square error (RMSE = 1.22), and minimal bias = 0.23. Further, we compared the performance of the proposed AutoML-GWL with sixteen benchmark algorithms comprising baseline and novel algorithms. The AutoML-GWL outperforms all the benchmark algorithms. Furthermore, the proposed algorithm ranked first in Friedman’s statistical test, confirming its reliability. Moreover, we conducted a spatial distribution and uncertainty analysis for the proposed algorithm. The outcomes of this analysis affirmed that the AutoML-GWL can effectively manage data with spatial variations and demonstrates remarkable stability when faced with small uncertainties in the input parameters. This study holds significant promise in revolutionising groundwater management practices by establishing an automated framework for simulating groundwater levels for sustainable water resource management. |
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0952-1976 |
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THL @ christoph.kuells @ singh_automl-gwl_2024 |
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168 |
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