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Leeuwen, Z.R. van; Klaar, M.J.; Smith, M.W.; Brown, L.E. |
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Title |
Quantifying the natural flood management potential of leaky dams in upland catchments, Part II: Leaky dam impacts on flood peak magnitude |
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Journal Article |
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Year |
2024 |
Publication |
Journal of Hydrology |
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628 |
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130449 |
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Keywords |
Nature based solutions, Large wood, Empirical, Hydrograph analysis, Ecosystem services, Transfer function noise model |
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Abstract  |
Leaky dams are an increasingly popular natural flood management measure, yet their impacts on flood peak magnitude have not yet been empirically quantified for a range of event types and magnitudes, even at the stream scale. In this study, the novel application of a transfer function noise modelling approach to empirical Before-After-Control-Impact stage data from an upland catchment allowed leaky dam effectiveness in reducing flood peak magnitude to be quantified. Flood peak stage and discharge magnitude changes were assessed from empirical data for 50 single and multi-peaked high flow events with return periods ranging from less than one year to six years. Overall, event peak magnitude was significantly reduced following the installation of eight leaky dams on the impact stream. Effectiveness was highly variable, but on average, flood peak magnitude was reduced by 10% for events with a return period up to one year. Some of the variability was explained by the size of the event and whether it was a single or multi-peaked event. This finding emphasises the need to manage expectations by considering both a range of event magnitudes and types when designing or assessing leaky dam natural flood management schemes. |
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0022-1694 |
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THL @ christoph.kuells @ Vanleeuwen2024130449 |
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228 |
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Pisa, P.F.; Nehren, U.; Sebesvari, Z.; Rai, S.; Wong, I. |
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Title |
Chapter 17 – Nature-based solutions to reduce risks and build resilience in mountain regions |
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Book Chapter |
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2024 |
Publication |
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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Singh, A.; Patel, S.; Bhadani, V.; Kumar, V.; Gaurav, K. |
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AutoML-GWL: Automated machine learning model for the prediction of groundwater level |
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Journal Article |
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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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Ola, I.; Drebenstedt, C.; Burgess, R.M.; Mensah, M.; Hoth, N.; Okoroafor, P.; Külls, C. |
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Assessing petroleum contamination in parts of the Niger Delta based on a sub-catchment delineated field assessment |
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Journal Article |
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2024 |
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Environmental Monitoring and Assessment |
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196 |
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6 |
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585 |
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The Niger Delta in Nigeria is a complex and heavily contaminated area with over 150,000 interconnected contaminated sites. This intricate issue is compounded by the region’s strong hydrological processes and high-energy environment, necessitating a science-based approach for effective contamination assessment and management. This study introduces the concept of sub-catchment contamination assessment and management, providing an overarching perspective rather than addressing each site individually. A description of the sub-catchment delineation process using the digital elevation model data from an impacted area within the Delta is provided. Additionally, the contamination status from the delineated sub-catchment is reported. Sediment, surface water and groundwater samples from the sub-catchment were analyzed for total petroleum hydrocarbons (TPH) and polycyclic aromatic hydrocarbons (PAHs), respectively. Surface sediment TPH concentrations ranged from 129 to 20,600 mg/kg, with subsurface (2-m depth) concentrations from 15.5 to 729 mg/kg. PAHs in surface and subsurface sediment reached 9.55 mg/kg and 0.46 mg/kg, respectively. Surface water exhibited TPH concentrations from 10 to 620 mg/L, while PAHs ranged from below detection limits to 1 mg/L. Groundwater TPH concentrations spanned 3 to 473 mg/L, with total PAHs varying from below detection limits to 0.28 mg/L. These elevated TPH and PAH levels indicate extensive petroleum contamination in the investigated sediment and water environment. Along with severe impacts on large areas of mangroves and wetlands, comparison of TPH and PAH concentrations with sediment and water quality criteria found 54 to 100% of stations demonstrated exceedances, suggesting adverse biological effects on aquatic and sediment biota are likely occurring. |
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1573-2959 |
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THL @ christoph.kuells @ Ola2024 |
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290 |
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Marteleto, T. de P.; Abreu, A.E.S. de; Barbosa, M.B.; Yoshinaga-Pereira, S.; Bertolo, R.A.; Enzweiler, J. |
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Groundwater apparent ages and isotopic composition in Crystalline, Diabase and Tubarão aquifers contact area in Campinas, Southeastern Brazil |
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Journal Article |
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2024 |
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Journal of South American Earth Sciences |
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135 |
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104783 |
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Fractured aquifer, Groundwater mixing, Isotopes, Water management |
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This study refines the hydrogeological conceptual model of an area with three interconnected aquifers, namely the Crystalline Aquifer System (CAS – igneous and metamorphic rocks), which is in contact with the Tubarão Aquifer System (TAS – sedimentary rocks) and the Diabase Aquifer System (DAS – diabase rocks). The detailed investigation involved geophysical logging and hydraulic and hydrodynamic characterization with straddle packers in a local tubular well, in which groundwater presents high uranium concentrations. Hydrogeochemical and isotope (δ2H, δ18O, 3H, δ13C, 14C) analysis in this well and in other three neighboring wells, with lower U concentrations, showed that ancient and modern waters (3H from <0.8 to 1.12 TU, 14C from 69.43 to 78.72 pMC) mix within the aquifer. During groundwater pumping, vertical fractures in the diabase aquifer possibly induce water mixing and recharge of the deeper levels of the aquifers from shallow layers. The high [U] are related to ancient waters from a confined aquifer hosted in CAS that reaches the wells through hydraulically active fractures located deeper than 159 m depth. Groundwater apparent ages do not increase systematically with depth, revealing a complex circulation model for CAS. The results obtained from the other wells, which are all located on drainage lineaments, reveal that one extracts modern water from DAS and TAS, another one extracts modern and ancient water from DAS and CAS, and the third extracts only ancient water from CAS, confirming the complexity of the local hydrogeology. Regarding regional groundwater management, the study revealed the need to characterize the sources of groundwater in each well, in order to protect modern waters from anthropogenic contamination and to protect ancient groundwater from overexploitation, as CAS hosts groundwaters recharged thousands of years ago or more. |
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0895-9811 |
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THL @ christoph.kuells @ Depaulamarteleto2024104783 |
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221 |
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