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Managing Impacts on Baseflows in Streams and the Associated Impacts...
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Managing Impacts on Baseflows in Streams and the Associated Impacts on Ecosystems and Water Quality

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrogeology".

Deadline for manuscript submissions: closed (20 February 2025) | Viewed by 7917

Special Issue Editors

Dr. Glen R. Walker

E-Mail Website1 Website2
Guest Editor
CSIRO, Grounded in Water, 2/490 Portrush Rd, St Georges, Adelaide 5064, Australia
Interests: groundwater recharge; dryland salinity; climate change; groundwater-surface water interaction; groundwater management
Special Issues, Collections and Topics in MDPI journals
Dr. Sebastien Lamontagne

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Guest Editor
CSIRO Land and Water, Highett, VIC, Australia
Interests: environmental tracers; groundwater resources; groundwater-surface water interactions, submarine groundwater discharge
Prof. Dr. Fran Sheldon

E-Mail Website
Guest Editor
School of Environment and Science, Australian Rivers Institute, Griffith University, Queensland 4111, Australia
Interests: freshwater ecology; stream and river health; riparian restoration
Special Issues, Collections and Topics in MDPI journals
Dr. Francis Chiew

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Guest Editor
CSIRO Land and Water, Black Mountain, Canberra, ACT 2601, Australia
Interests: water resources assessment; forecasting and prediction; climate change adaptation; integrated basin management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Baseflows are important for ecosystem health, water quality and conveyancing urban water during dry periods. Because they form a small fraction of the mean annual flow in streams, adverse impacts on this flow component can be easily dismissed. Baseflows are modified by management of surface and groundwater, changes in land use and climate variability and change. They are not easily measured nor modelled at relevant spatial and temporal scales; connectivity of flow within streams and between streams and groundwater can be complex; and the interaction between the ecosystems and this component of the flow regime needs to be better understood.

The aim of this special issue is to provide a focus on the need for a cross-disciplinary understanding of baseflows in order to better manage them for river health.

This edition encourages papers, including case studies, across a range of disciplines (hydrogeology, hydrology, ecology, hydrochemistry) that improve characterisation of hydrological and ecological processes associated with low stream flows; assessment and modelling of low flows and riparian processes; assessment and management of risks associated with low flows; and determination of water management requirements of baseflow component of the flow regime for multiple users of streams. Papers may be of a single discipline, but needs to relate to other disciplines and be relevant to management of baseflows.

Dr. Glen R. Walker
Dr. Sebastien Lamontagne
Prof. Dr. Fran Sheldon
Dr. Francis Chiew
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • baseflow
  • groundwater-surface water interaction
  • baseflow-dependent ecosystems
  • river losses
  • environmental water management
  • water quality

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Published Papers (4 papers)

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Research

15 pages, 5152 KiB  
Article
Quick Groundwater Flow to Tropical Savanna Springs (Mataranka, Northern Territory, Australia)
by Sébastien Lamontagne, Clément Duvert and Axel Suckow
Water 2024, 16(23), 3395; https://doi.org/10.3390/w16233395 - 25 Nov 2024
Viewed by 839
Abstract
The Mataranka Springs Complex is a regional groundwater discharge for the Cambrian Limestone Aquifer (CLA) of the Northern Territory (Australia) and forms the headwaters for the environmentally sensitive Roper River. Whilst a regional groundwater contribution to the spring flow is well established, the [...] Read more.
The Mataranka Springs Complex is a regional groundwater discharge for the Cambrian Limestone Aquifer (CLA) of the Northern Territory (Australia) and forms the headwaters for the environmentally sensitive Roper River. Whilst a regional groundwater contribution to the spring flow is well established, the absence of cover over the CLA in the vicinity of the springs and the prevalence of karst suggest that a component of quick flow during the wet season is possible. A quick flow contribution to the springs was evaluated using a biweekly monitoring programme for several environmental tracers (major ions, stable isotopes of water, and 222Rn) at two large springs (Rainbow Spring and Bitter Spring) and at one minor spring (Fig Tree Spring) over a two-year period that included a relatively dry (2019–2020) and a relatively wet (2020–2021) rainy season. There were limited variations in all tracers at Rainbow and Bitter springs throughout the monitoring programme, indicating an absence or a minimal contribution from quick flow. In contrast, all tracers responded to large rainfall events at a scale of days to weeks in Fig Tree Spring, consistent with a component of quick flow. However, the tracer response at Fig Tree was complex and possibly involved a combination of quick flow, unsaturated zone processes, and changes in the geochemical environment in the aquifer. Quick flow may be favoured in the parts of the Mataranka Springs Complex, where flow paths flow through the karstic tufa layer overlying the CLA. Full article
(This article belongs to the Special Issue Managing Impacts on Baseflows in Streams and the Associated Impacts on Ecosystems and Water Quality)
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22 pages, 14306 KiB  
Article
Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods
by Yue Zhang, Zimo Zhou, Ying Deng, Daiwei Pan, Jesse Van Griensven Thé, Simon X. Yang and Bahram Gharabaghi
Water 2024, 16(9), 1284; https://doi.org/10.3390/w16091284 - 30 Apr 2024
Cited by 6 | Viewed by 2741
Abstract
Considering the increased risk of urban flooding and drought due to global climate change and rapid urbanization, the imperative for more accurate methods for streamflow forecasting has intensified. This study introduces a pioneering approach leveraging the available network of real-time monitoring stations and [...] Read more.
Considering the increased risk of urban flooding and drought due to global climate change and rapid urbanization, the imperative for more accurate methods for streamflow forecasting has intensified. This study introduces a pioneering approach leveraging the available network of real-time monitoring stations and advanced machine learning algorithms that can accurately simulate spatial–temporal problems. The Spatio-Temporal Attention Gated Recurrent Unit (STA-GRU) model is renowned for its computational efficacy in forecasting streamflow events with a forecast horizon of 7 days. The novel integration of the groundwater level, precipitation, and river discharge as predictive variables offers a holistic view of the hydrological cycle, enhancing the model’s accuracy. Our findings reveal that for a 7-day forecasting period, the STA-GRU model demonstrates superior performance, with a notable improvement in mean absolute percentage error (MAPE) values and R-square (R2) alongside reductions in the root mean squared error (RMSE) and mean absolute error (MAE) metrics, underscoring the model’s generalizability and reliability. Comparative analysis with seven conventional deep learning models, including the Long Short-Term Memory (LSTM), the Convolutional Neural Network LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the Spatio-Temporal Attention LSTM (STA-LSTM), the Gated Recurrent Unit (GRU), the Convolutional Neural Network GRU (CNNGRU), and the STA-GRU, confirms the superior predictive power of the STA-LSTM and STA-GRU models when faced with long-term prediction. This research marks a significant shift towards an integrated network of real-time monitoring stations with advanced deep-learning algorithms for streamflow forecasting, emphasizing the importance of spatially and temporally encompassing streamflow variability within an urban watershed’s stream network. Full article
(This article belongs to the Special Issue Managing Impacts on Baseflows in Streams and the Associated Impacts on Ecosystems and Water Quality)
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25 pages, 2874 KiB  
Article
Identification of Trends in Dam Monitoring Data Series Based on Machine Learning and Individual Conditional Expectation Curves
by Miguel Á. Fernández-Centeno, Patricia Alocén and Miguel Á. Toledo
Water 2024, 16(9), 1239; https://doi.org/10.3390/w16091239 - 26 Apr 2024
Viewed by 1675
Abstract
Dams are complex systems that involve both the structure itself and its foundation. Rheological phenomena, expansive reactions, or alterations in the geotechnical parameters of the foundation, among others, result in non-reversible and cumulative modifications in the dam response, leading to trends in the [...] Read more.
Dams are complex systems that involve both the structure itself and its foundation. Rheological phenomena, expansive reactions, or alterations in the geotechnical parameters of the foundation, among others, result in non-reversible and cumulative modifications in the dam response, leading to trends in the monitoring data series. The accurate identification and definition of these trends to study their evolution are key aspects of dam safety. This manuscript proposes a methodology to identify trends in dam behavioural data series by identifying the influence of the time variable on the predictions provided by the ML models. Initially, ICE curves and SHAP values are employed to extract temporal dependence, and the ICE curves are found to be more precise and efficient in terms of computational cost. The temporal dependencies found are adjusted using a GWO algorithm to different function characteristics of irreversible processes in dams. The function that provides the best fit is selected as the most plausible. The results obtained allow us to conclude that the proposed methodology is capable of obtaining estimates of the most common trends that affect movements in concrete dams with greater precision than the statistical models most commonly used to predict the behaviour of these types of variables. These results are promising for its general application to other types of dam monitoring data series, given the versatility demonstrated for the unsupervised identification of temporal dependencies. Full article
(This article belongs to the Special Issue Managing Impacts on Baseflows in Streams and the Associated Impacts on Ecosystems and Water Quality)
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15 pages, 2283 KiB  
Article
Functional Traits Drive the Changes in Diversity and Composition of Benthic Invertebrate Communities in Response to Hydrological Regulation
by Anna Marino, Francesca Bona, Stefano Fenoglio and Tiziano Bo
Water 2024, 16(7), 989; https://doi.org/10.3390/w16070989 - 29 Mar 2024
Cited by 1 | Viewed by 1720
Abstract
Of all the environmental elements that influence the biological communities of rivers, water flow characteristics are undoubtedly the most important. Unfortunately, natural hydrological characteristics are increasingly threatened by human activities, especially in alpine or high mountain areas where there are numerous hydropower plants. [...] Read more.
Of all the environmental elements that influence the biological communities of rivers, water flow characteristics are undoubtedly the most important. Unfortunately, natural hydrological characteristics are increasingly threatened by human activities, especially in alpine or high mountain areas where there are numerous hydropower plants. In this study, we analysed the impact of hydrological alterations on the macroinvertebrate community of a lowland river in NW Italy. Specifically, we analysed the macroinvertebrate communities of an unaffected site by comparing them with those of a site subject to hydrological alteration. We adopted an approach that is not only taxonomic but also functional, allowing us to study a component of biodiversity that is generally less known. Our results show that the flow-altered site hosted a benthic community with lower species and functional diversity than the control site. Interestingly, we also detected a number of significant differences between the summer and autumn samples. In particular, examination of community-weighted mean (CWM) trait values reveals significant variation in body size, voltinism, substrate, locomotion, feeding habits and other traits between sites and seasons. The integration of taxonomic and functional approaches provides a comprehensive understanding of how human-induced hydrological variations can affect aquatic biodiversity and ecological functions. Full article
(This article belongs to the Special Issue Managing Impacts on Baseflows in Streams and the Associated Impacts on Ecosystems and Water Quality)
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