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Volume 12, January
 
 

Hydrology, Volume 12, Issue 2 (February 2025) – 13 articles

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16 pages, 3161 KiB  
Article
Eutrophication Conditions in Two High Mountain Lakes: The Influence of Climate Conditions and Environmental Pollution
by Fátima Goretti García-Miranda, Claudia Muro, Yolanda Alvarado, José Luis Expósito-Castillo and Héctor Víctor Cabadas-Báez
Hydrology 2025, 12(2), 32; https://doi.org/10.3390/hydrology12020032 - 13 Feb 2025
Abstract
The lakes known as El Sol and La Luna are high mountain water deposits located in Mexico within an inactive volcanic system. These lakes are of ecological importance because they are unique in Mexico. However, currently, the lakes have experienced changes in their [...] Read more.
The lakes known as El Sol and La Luna are high mountain water deposits located in Mexico within an inactive volcanic system. These lakes are of ecological importance because they are unique in Mexico. However, currently, the lakes have experienced changes in their shape and an increase in algae blooms, coupled with the degradation of the basin, which has alerted government entities to the need to address the lakes’ problems. To address the environmental status of El Sol and La Luna, a trophic study was conducted during the period of 2021–2023, including an analysis of the influence of climatic variables, lake water quality, and eutrophication conditions. The trophic state was established based on the eutrophication index. The Pearson correlations defined the eutrophication interrelation between the distinct factors influencing the lakes’ status. El Sol registered higher eutrophication conditions than La Luna. El Sol was identified as seasonal eutrophic and La Luna as transitioning from oligotrophic to mesotrophic, showing high levels of chlorophyll, total phosphorus, and total nitrogen and low water transparency. The principal factors altering the eutrophic conditions were water pollution and climatic variables (precipitation and ambient temperature). Eutrophication was the prime factor impacting perimeter loss at El Sol, whereas at La Luna, it was due to a decline in precipitation. Full article
(This article belongs to the Topic Advances in Hydrological Remote Sensing)
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24 pages, 6615 KiB  
Article
Creation and Comparison of High-Resolution Daily Precipitation Gridded Datasets for Greece Using a Variety of Interpolation Techniques
by Giorgos Ntagkounakis, Panagiotis Nastos, John Kapsomenakis and Kostas Douvis
Hydrology 2025, 12(2), 31; https://doi.org/10.3390/hydrology12020031 - 10 Feb 2025
Abstract
This study investigates a range of precipitation interpolation techniques with the objective of generating high-resolution gridded daily precipitation datasets for the Greek region. The study utilizes a comprehensive station dataset, incorporating geographical variables derived from satellite-based elevation data and integrating precipitation data from [...] Read more.
This study investigates a range of precipitation interpolation techniques with the objective of generating high-resolution gridded daily precipitation datasets for the Greek region. The study utilizes a comprehensive station dataset, incorporating geographical variables derived from satellite-based elevation data and integrating precipitation data from the ERA5 reanalysis. A total of three different modeling approaches are developed. Firstly, we utilize a General Additive Model in conjunction with an Indicator Kriging model using only station data and limited geographical variables. In the second iteration of the model, we blend ERA5 reanalysis data in the interpolation methodology and incorporate more geographical variables. Finally, we developed a novel modeling framework that integrates ERA5 data, a variety of geographical data, and a multi-model interpolation process which utilizes different models to predict precipitation at distinct thresholds. Our results show that using the ERA5 data can increase the accuracy of the interpolated precipitation when the station dataset used is sparse. Additionally, the implementation of multi-model interpolation techniques which use distinct models for different precipitation thresholds can improve the accuracy of precipitation and extreme precipitation modeling, addressing important limitations of previous modeling approaches. Full article
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10 pages, 2722 KiB  
Article
Stable Isotope Investigations of Icicle Formation and Evolution
by Thomas Brubaker and R. V. Krishnamurthy
Hydrology 2025, 12(2), 30; https://doi.org/10.3390/hydrology12020030 - 9 Feb 2025
Abstract
Icicles are elongated structures formed from water flowing over hangings and crystallizing in sub-freezing conditions. These features are ubiquitous in several parts of the world that experience severe to moderate winter seasons. It has been suggested that they could be a source of [...] Read more.
Icicles are elongated structures formed from water flowing over hangings and crystallizing in sub-freezing conditions. These features are ubiquitous in several parts of the world that experience severe to moderate winter seasons. It has been suggested that they could be a source of recharge to groundwater. Icicles are presumed to affect groundwater quality via incorporation of atmospheric and roof top contaminants. Relatively little attention has been paid to these wintry features, insofar as only a few theoretical models have attempted to describe their formation. Stable isotope measurements (δ18O and δ2H) of icicles that were melted stepwise into fractions are presented as support for the models that invoke the rapid formation of icicles. Icicles exhibit minimal fraction to fraction isotope variation, suggesting a lack of isotope equilibrium and that kinetic effects dominate the freezing process. Deviations from the Global Meteoric Water Line (GMWL), which is similar to the Local Meteoric Water Line (LMWL), indicate that post-depositional processes, namely sublimation, may occur throughout the freezing process. Isotopic evidence lends support to a “growth-cessation-growth” variation of the already proposed methods of rapid icicle formation, where a cessation period occurs between pulses of rapid freezing during icicle growth. Full article
(This article belongs to the Special Issue Isotope Hydrology in the U.S.)
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19 pages, 6000 KiB  
Article
Policy Measures to Lead Sustainable Development of Agriculture Catchment: Socio-Hydrology Modeling Insights
by Mahendran Roobavannan, Jaya Kandasamy and Saravanamuthu Vigneswaran
Hydrology 2025, 12(2), 29; https://doi.org/10.3390/hydrology12020029 - 9 Feb 2025
Abstract
Achieving sustainable development in agricultural catchments requires well-designed policy measures. This study examines the intricate interactions between social dynamics and hydrological processes within agricultural systems to propose targeted policy interventions. By employing socio-hydrology models that integrate socio-economic and hydrological data, the research provides [...] Read more.
Achieving sustainable development in agricultural catchments requires well-designed policy measures. This study examines the intricate interactions between social dynamics and hydrological processes within agricultural systems to propose targeted policy interventions. By employing socio-hydrology models that integrate socio-economic and hydrological data, the research provides valuable insights into the feedback loops and interdependencies that influence catchment sustainability. In this study, we find that policies on population management should aim to balance natural growth rates with the carrying capacity of the basin. Strategies such as education, healthcare access, and family planning can help manage demographic pressures. Migration policies should consider the economic and environmental impacts of population influx and support balanced regional development to distribute the demographic pressures more evenly. Wage growth should be aligned with economic productivity to prevent unemployment and inequality. Policies that promote equitable wage structures and enhance labor mobility between sectors can mitigate disparities. The findings emphasize the necessity of adaptive policies that address both environmental and societal factors, advocating for interdisciplinary approaches in water resource management and agricultural policy development. This study also highlights the pivotal role of technological innovations and the societal values and norms that shape sustainability and resilience in agricultural catchments. Full article
(This article belongs to the Special Issue Hydrological Processes in Agricultural Watersheds)
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21 pages, 4960 KiB  
Article
Evaluating Expert Opinion-Based Reservoir Operation in Cfa/Csa Climatic Conditions
by Mahdi Sedighkia and Bithin Datta
Hydrology 2025, 12(2), 28; https://doi.org/10.3390/hydrology12020028 - 6 Feb 2025
Abstract
This study evaluates the application of an expert opinion-based fuzzy method for reservoir operation in humid subtropical climate/hot-summer Mediterranean climatic classes (Cfa/Csa in the Köppen–Geiger climate classification system), which are characterized by humid subtropical to Mediterranean conditions with ample rainfall and seasonal water [...] Read more.
This study evaluates the application of an expert opinion-based fuzzy method for reservoir operation in humid subtropical climate/hot-summer Mediterranean climatic classes (Cfa/Csa in the Köppen–Geiger climate classification system), which are characterized by humid subtropical to Mediterranean conditions with ample rainfall and seasonal water availability challenges. Effective reservoir management in these regions is critical for balancing water storage and downstream release and maintaining ecosystem health under variable hydrological conditions. The performance of the fuzzy method was compared to two meta-heuristic algorithms: gravitational search algorithm (GSA) and shuffled frog leaping algorithm (SFLA). System performance was assessed using key indices such as the reliability index as a measure of meeting water demands. The fuzzy method achieved the highest reliability index of 0.690, outperforming GSA (0.677) and SFLA (0.688), demonstrating its superior ability to ensure consistent water supply downstream. The fuzzy method, leveraging expert knowledge, not only enhanced downstream water supply reliability but also reduced computational time compared to the meta-heuristic approaches. The incorporation of expert opinions provides a practical, robust, and efficient framework for reservoir management in challenging climate conditions such as Cfa/Csa classes. Additionally, the fuzzy solution demonstrated superior adaptability to diverse hydrological conditions, balancing ecological and water supply needs effectively. These findings highlight the potential of using expert opinions to support sustainable reservoir operations by achieving optimal trade-offs between competing objectives and addressing challenges in water resource management under varying climatic conditions. Full article
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20 pages, 5362 KiB  
Article
Investigating the Water, Ecosystem, and Agriculture Nexus in Three Inland River Basins of the Arid Hexi Corridor, China, Using Integrated Hydrological Modeling
by Yuan Chen and Yong Tian
Hydrology 2025, 12(2), 27; https://doi.org/10.3390/hydrology12020027 - 6 Feb 2025
Abstract
The Water–Ecosystem–Agriculture (WEA) relationship is pivotal to the sustainable development of arid and semi-arid areas. The WEA nexus in these areas is essential for making policies towards sustainable development. This study aims to explore the WEA nexus in three large inland river basins [...] Read more.
The Water–Ecosystem–Agriculture (WEA) relationship is pivotal to the sustainable development of arid and semi-arid areas. The WEA nexus in these areas is essential for making policies towards sustainable development. This study aims to explore the WEA nexus in three large inland river basins (Heihe River Basin, Shiyang River Basin, and Shule River Basin) in the Hexi Corridor, Northwest China, using an integrated hydrological modeling approach. The integrated model was calibrated and validated against observed streamflow data, achieving Nash–Sutcliffe Efficiencies ranging from 0.83 to 0.94 in the validation period. The major findings are as follows. First, altering the amount of irrigation water significantly affects hydrological and ecological processes in both midstream and downstream areas, influencing the WEA nexus. For example, a 20% reduction in irrigation demand led to a 0.46 billion m3/year recovery in midstream groundwater storage and a 4.3% increase in downstream ecosystem health, but resulted in a 5.4% decrease in midstream agricultural productivity. Second, intense trade-offs among agricultural productivity, ecosystem health, and groundwater sustainability were identified. These trade-offs are highly sensitive to water management strategies, particularly those affecting groundwater sustainability. Third, implementing stricter groundwater-level drawdown constraints significantly improved groundwater sustainability and ecosystem health. Fourth, this study highlighted unique WEA nexus characteristics in each of the three basins. This study provides insights into the understanding the complex WEA nexus, and the quantitative results underscore the trade-offs and synergies within the WEA nexus, providing a foundation for informed decision-making in water resource management. Full article
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16 pages, 4882 KiB  
Article
Effects of Inconsistency in Drought Event Definitions on Drought Characteristics
by Frank Joseph Wambura
Hydrology 2025, 12(2), 26; https://doi.org/10.3390/hydrology12020026 - 5 Feb 2025
Abstract
Drought, as one of the hazards exacerbated by climate change, has attracted the attention of many scientists. Many drought studies have used different drought event definitions (DEDs). However, little is known about the effects of these definitions on drought characteristics. This study investigated [...] Read more.
Drought, as one of the hazards exacerbated by climate change, has attracted the attention of many scientists. Many drought studies have used different drought event definitions (DEDs). However, little is known about the effects of these definitions on drought characteristics. This study investigated the effects of DEDs on drought characteristics using the standardized precipitation evapotranspiration index (SPEI) in the Upper Pangani Basin in northeast Tanzania. First, rainfall and air temperature data from the Climatic Research Unit database were used to compute the SPEI. Then, four different types of DEDs were used to identify drought events in the SPEI time series. The identified drought events were examined for agreements and correlations using Kappa and Phi coefficients, respectively, and finally characterized. The findings show that different DEDs produced different types and frequencies of drought events. The patterns of drought events for these DEDs had agreements ranging from 52 to 78% and correlations ranging from 79% to 95%. Different DEDs also led to different drought intensities, ranging from mild to extreme, although the overall drought intensities were either mild or moderate. From this study, we can infer that using suitable DEDs is essential for identifying drought events, as they enable accurate comparisons of droughts across regions and periods, consequently reducing errors and biases in evaluating drought hazards. Full article
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41 pages, 24123 KiB  
Article
Coupling HEC-RAS and AI for River Morphodynamics Assessment Under Changing Flow Regimes: Enhancing Disaster Preparedness for the Ottawa River
by Mohammad Uzair Anwar Qureshi, Afshin Amiri, Isa Ebtehaj, Silvio José Guimere, Juraj Cunderlik and Hossein Bonakdari
Hydrology 2025, 12(2), 25; https://doi.org/10.3390/hydrology12020025 - 4 Feb 2025
Abstract
Despite significant advancements in flood forecasting using machine learning (ML) algorithms, recent events have revealed hydrological behaviors deviating from historical model development trends. The record-breaking 2019 flood in the Ottawa River basin, which exceeded the 100-year flood threshold, underscores the escalating impact of [...] Read more.
Despite significant advancements in flood forecasting using machine learning (ML) algorithms, recent events have revealed hydrological behaviors deviating from historical model development trends. The record-breaking 2019 flood in the Ottawa River basin, which exceeded the 100-year flood threshold, underscores the escalating impact of climate change on hydrological extremes. These unprecedented events highlight the limitations of traditional ML models, which rely heavily on historical data and often struggle to predict extreme floods that lack representation in past records. This calls for integrating more comprehensive datasets and innovative approaches to enhance model robustness and adaptability to changing climatic conditions. This study introduces the Next-Gen Group Method of Data Handling (Next-Gen GMDH), an innovative ML model leveraging second- and third-order polynomials to address the limitations of traditional ML models in predicting extreme flood events. Using HEC-RAS simulations, a synthetic dataset of river flow discharges was created, covering a wide range of potential future floods with return periods of up to 10,000 years, to enhance the accuracy and generalization of flood predictions under evolving climatic conditions. The Next-Gen GMDH addresses the complexity and limitations of standard GMDH by incorporating non-adjacent connections and optimizing intermediate layers, significantly reducing computational overhead while enhancing performance. The Gen GMDH demonstrated improved stability and tighter clustering of predictions, particularly for extreme flood scenarios. Testing results revealed exceptional predictive accuracy, with Mean Absolute Percentage Error (MAPE) values of 4.72% for channel width, 1.80% for channel depth, and 0.06% for water surface elevation. These results vastly outperformed the standard GMDH, which yielded MAPE values of 25.00%, 8.30%, and 0.11%, respectively. Additionally, computational complexity was reduced by approximately 40%, with a 33.88% decrease in the Akaike Information Criterion (AIC) for channel width and an impressive 581.82% improvement for channel depth. This methodology integrates hydrodynamic modeling with advanced ML, providing a robust framework for accurate flood prediction and adaptive floodplain management in a changing climate. Full article
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19 pages, 4538 KiB  
Article
The Use of Fluorescent Organic Matter as a Natural Transit Time Tracer in the Unsaturated Zone of the Fontaine De Vaucluse Karst System
by Leïla Serène, Naomi Mazzilli, Christelle Batiot-Guilhe, Christophe Emblanch, Milanka Babic, Julien Dupont, Roland Simler and Matthieu Blanc
Hydrology 2025, 12(2), 24; https://doi.org/10.3390/hydrology12020024 - 1 Feb 2025
Abstract
The fluorescence index called the Transit Time index (TTi) is based on the fluorescence of natural organic matter in order to qualitatively assess the transit time of karst groundwater, using springs affected by human activities. This study aims to further evaluate the potential [...] Read more.
The fluorescence index called the Transit Time index (TTi) is based on the fluorescence of natural organic matter in order to qualitatively assess the transit time of karst groundwater, using springs affected by human activities. This study aims to further evaluate the potential of fluorescent compounds as a natural tracer of transit time when applied to unsaturated zone flows with natural catchments, in contrast to the first study. For this purpose, a bi-monthly sampling of one year of monitoring for organic matter fluorescence, TOC, major elements and water-stable isotopes was performed. A conceptual model of the sources and fates of fluorescent compounds is built, emphasizing the allochthonous origin of humic-like C compounds, and the autochthonous production of humic-like M and protein-like compounds within the unsaturated zone. Fluorescent compound intensity interpretation according to this model reveals consistent relative transit times with flow behavior and also provides complementary information. The results also show the TTi’s ability to summarize fluorescent compounds, its consistency with relative transit time, and its higher sensitivity as compared to other natural tracers. However, prior to its use, a thorough assessment of soil organic matter, microbial activity, and potential anthropogenic contamination is required, encouraging interdisciplinary collaboration between hydrogeologists, microbiologists and soil scientists. Full article
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18 pages, 1162 KiB  
Article
Modelling Hydrological Droughts in Canadian Rivers Based on Markov Chains Using the Standardized Hydrological Index as a Platform
by Tribeni C. Sharma and Umed S. Panu
Hydrology 2025, 12(2), 23; https://doi.org/10.3390/hydrology12020023 - 31 Jan 2025
Abstract
The standardized hydrological index (SHI) is the standardized but not normalized (normal probability variate) value of the streamflow used to characterize a hydrological drought, akin to the standardized precipitation index (SPI, which is both standardized and normalized) in the [...] Read more.
The standardized hydrological index (SHI) is the standardized but not normalized (normal probability variate) value of the streamflow used to characterize a hydrological drought, akin to the standardized precipitation index (SPI, which is both standardized and normalized) in the realm of the meteorological drought. The time series of the SHI can be used as a platform for deriving the longest duration, LT, and the largest magnitude, MT (in standardized form), of a hydrological drought over a desired return period of T time units (year, month, or week). These parameters are predicted based on the SHI series derived from the annual, monthly, and weekly flow sequences of Canadian rivers. An important point to be reckoned with is that the monthly and weekly sequences are non-stationary compared to the annual sequences, which fulfil the conditions of stochastic stationarity. The parameters, such as the mean, standard deviation (or coefficient of variation), lag 1 autocorrelation, and conditional probabilities from SHI sequences, when used in Markov chain-based relationships, are able to predict the longest duration, LT, and the largest magnitude, MT. The product moment and L-moment ratio analyses indicate that the monthly and weekly flows in the Canadian rivers fit the gamma probability distribution function (pdf) reasonably well, whereas annual flows can be regarded to follow the normal pdf. The threshold level chosen in the analysis is the long-term median of SHI sequences for the annual flows. For the monthly and weekly flows, the threshold level represents the median of the respective month or week and hence is time varying. The runs of deficit in the SHI sequences are treated as drought episodes and thus the theory of runs formed an essential tool for analysis. This paper indicates that the Markov chain-based methodology works well for predicting LT on annual, monthly, and weekly SHI sequences. Markov chains of zero order (MC0), first order (MC1), and second order (MC2) turned out to be satisfactory on annual, monthly, and weekly scales, respectively. The drought magnitude, MT, was predicted satisfactorily via the model MT = Id × Lc, where Id stands for drought intensity and Lc is a characteristic drought length related to LT through a scaling parameter, ɸ (= 0.5). The Id can be deemed to follow a truncated normal pdf, whose mean and variance when combined implicitly with Lc proved prudent for predicting MT at all time scales in the aforesaid relationship. Full article
(This article belongs to the Section Statistical Hydrology)
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23 pages, 4334 KiB  
Article
Evaluation and Adjustment of Historical Hydroclimate Data: Improving the Representation of Current Hydroclimatic Conditions in Key California Watersheds
by Andrew Schwarz, Z. Q. Richard Chen, Alejandro Perez and Minxue He
Hydrology 2025, 12(2), 22; https://doi.org/10.3390/hydrology12020022 - 22 Jan 2025
Viewed by 410
Abstract
The assumption of stationarity in historical hydroclimatic data, fundamental to traditional water resource planning models, is increasingly challenged by the impacts of climate change. This discrepancy can lead to inaccurate model outputs and misinformed management decisions. This study addresses this challenge by developing [...] Read more.
The assumption of stationarity in historical hydroclimatic data, fundamental to traditional water resource planning models, is increasingly challenged by the impacts of climate change. This discrepancy can lead to inaccurate model outputs and misinformed management decisions. This study addresses this challenge by developing a novel monthly data adjustment approach, the Runoff Curve Year–Type–Monthly (RC-YTM) method. The application of this method is exemplified at five key California watersheds. The RC-YTM method accounts for the increasing variability and shifts in seasonal runoff timing observed in the historical data (1922–2021), aligning it with the contemporary climate conditions represented by the period from 1992 to 2021 at the study watersheds. This method adjusts both annual and monthly streamflow values using a combination of precipitation–runoff relationships, quantile mapping, and water year classification. The adjusted data, reflecting current climatic conditions more accurately than the raw historical data, serve as valuable inputs for operational water resource planning models like CalSim3, commonly used in California for water management. This approach, demonstrably effective in capturing the observed climate change impacts on streamflow at monthly timesteps, enhances the reliability of model simulations representing contemporary conditions, which can lead to better-informed decision-making in water management, infrastructure investment, drought and flood risk assessment, and adaptation strategies. While focused on specific California watersheds, this study’s findings and the adaptable RC-YTM method hold significant implications for water resource management in other regions facing similar hydroclimatic challenges in a changing climate. Full article
(This article belongs to the Special Issue Runoff Modelling under Climate Change)
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20 pages, 8692 KiB  
Article
Forecasting Model for Danube River Water Temperature Using Artificial Neural Networks
by Cristina-Sorana Ionescu, Ioana Opriș, Daniela-Elena Gogoașe Nistoran and Constantin-Alexandru Baciu
Hydrology 2025, 12(2), 21; https://doi.org/10.3390/hydrology12020021 - 21 Jan 2025
Viewed by 313
Abstract
The objective of this paper is to propose an artificial neural network (ANN) model to forecast the Danube River temperature at Chiciu–Călărași, Romania, bordered by Romanian and Bulgarian ecological sites, and situated upstream of the Cernavoda nuclear power plant. Given the temperature increase [...] Read more.
The objective of this paper is to propose an artificial neural network (ANN) model to forecast the Danube River temperature at Chiciu–Călărași, Romania, bordered by Romanian and Bulgarian ecological sites, and situated upstream of the Cernavoda nuclear power plant. Given the temperature increase trend, the potential of thermal pollution is rising, impacting aquatic and terrestrial ecosystems. The available data covered a period of eight years, between 2008 and 2015. Using as input data actual air and water temperatures, and discharge, as well as air temperature data provided by weather forecasts, the ANN model predicts the Danube water temperature one week in advance with a root mean square deviation (RMSE) of 0.954 °C for training and 0.803 °C for testing. The ANN uses the Levenberg–Marquardt feedforward backpropagation algorithm. This feature is useful for the irrigation systems and for the power plants in the area that use river water for different purposes. The results are encouraging for developing similar studies in other locations and extending the ANN model to include more parameters that can have a significant influence on water temperature. Full article
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23 pages, 5265 KiB  
Article
High-Resolution Flow and Phosphorus Forecasting Using ANN Models, Catering for Extremes in the Case of the River Swale (UK)
by Elisabeta Cristina Timis, Horia Hangan, Vasile Mircea Cristea, Norbert Botond Mihaly and Michael George Hutchins
Hydrology 2025, 12(2), 20; https://doi.org/10.3390/hydrology12020020 - 21 Jan 2025
Viewed by 448
Abstract
The forecasting of river flows and pollutant concentrations is essential in supporting mitigation measures for anthropogenic and climate change effects on rivers and their environment. This paper addresses two aspects receiving little attention in the literature: high-resolution (sub-daily) data-driven modeling and the prediction [...] Read more.
The forecasting of river flows and pollutant concentrations is essential in supporting mitigation measures for anthropogenic and climate change effects on rivers and their environment. This paper addresses two aspects receiving little attention in the literature: high-resolution (sub-daily) data-driven modeling and the prediction of phosphorus compounds. It presents a series of artificial neural networks (ANNs) to forecast flows and the concentrations of soluble reactive phosphorus (SRP) and total phosphorus (TP) under a wide range of conditions, including low flows and storm events (0.74 to 484 m3/s). Results show correct forecast along a stretch of the River Swale (UK) with an anticipation of up to 15 h, at resolutions of up to 3 h. The concentration prediction is improved compared to a previous application of an advection–dispersion model. Full article
(This article belongs to the Special Issue Hydrodynamics and Water Quality of Rivers and Lakes)
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