Hydrology

24 pages, 2384 KB

Open AccessFeature PaperEditor’s ChoiceArticle

An Application of the Ecosystem Services Assessment Approach to the Provision of Groundwater for Human Supply and Aquifer Management Support

by Malgorzata Borowiecka, Mar Alcaraz and Marisol Manzano

Hydrology 2025, 12(6), 137; https://doi.org/10.3390/hydrology12060137 - 3 Jun 2025

Viewed by 3054

Abstract

Increasing pressures on groundwater in the last decades have led to a deterioration in the quality of groundwater for human consumption around the world. Beyond the essential evaluation of groundwater dynamics and quality, analyzing the situation from the perspective of the Ecosystem Services [...] Read more.

Increasing pressures on groundwater in the last decades have led to a deterioration in the quality of groundwater for human consumption around the world. Beyond the essential evaluation of groundwater dynamics and quality, analyzing the situation from the perspective of the Ecosystem Services Assessment (ESA) approach can be useful to support aquifer management plans aiming to recover aquifers’ capacity to provide good quality water. This work illustrates how to implement the ESA using groundwater flow and nitrate transport modelling for evaluating future trends of the provisioning service Groundwater of Good Quality for Human Supply. It has been applied to the Medina del Campo Groundwater Body (Spain), where the intensification of agricultural activities and groundwater exploitation since the 1970s caused severe nitrate pollution. Nitrate status and future trends under different fertilizer and aquifer exploitation scenarios were modelled with MT3DMS coupled to a MODFLOW model calibrated with piezometric time series. Historical land use and fertilizer data were compiled to assess nitrogen loadings. Besides the uncertainties of the model, the results clearly show that: (i) managing fertilizer loads is more effective than managing aquifer exploitation; and (ii) only the cessation of nitrogen application by the year 2030 would improve the evaluated provisioning service in the long term. The study illustrates how the ESA can be incorporated to evaluate the expected relative impact of different management actions aimed at improving significant groundwater services to humans. Full article

(This article belongs to the Special Issue Hydrological Modelling for the Sustainable Management of Water Resources in River Basins)

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25 pages, 6290 KB

Open AccessEditor’s ChoiceArticle

Precipitation-Related Atmospheric Nutrient Deposition in Farmington Bay: Analysis of Spatial and Temporal Patterns

by Gustavious P. Williams, A. Woodruff Miller, Amin Aghababaei, Abin Raj Chapagain, Pitamber Wagle, Yubin Baaniya, Rachel H. Magoffin, Xueyi Li, Taylor Miskin, Peter D. Oldham, Samuel J. Oldham, Tyler Peterson, Lyle Prince, Kaylee B. Tanner, Anna C. Cardall and Daniel P. Ames

Hydrology 2025, 12(6), 131; https://doi.org/10.3390/hydrology12060131 - 27 May 2025

Cited by 2 | Viewed by 1865

Abstract

This study quantifies the atmospheric deposition (AD) of nutrient loads into the Farmington Bay ecosystem via wet deposition over a three-year period. We analyzed nutrient concentrations from 509 total phosphorus (TP), 507 orthophosphate (OP), and 511 total nitrogen (TN) samples collected at seven [...] Read more.

This study quantifies the atmospheric deposition (AD) of nutrient loads into the Farmington Bay ecosystem via wet deposition over a three-year period. We analyzed nutrient concentrations from 509 total phosphorus (TP), 507 orthophosphate (OP), and 511 total nitrogen (TN) samples collected at seven locations around the Bay. We estimated AD loads using two different spatial interpolation methods, Kriging and Inverse Distance Weighting (IDW), as well as average concentrations. The loads computed using Kriging and IDW were similar, but the loads computed using sample averages were about 70% smaller. We estimated that annual atmospherically deposited nutrient loads range from 306 to 594 Mg for TN, 73 to 195 Mg for TP, and 43 to 144 Mg for OP. The loads in 2023 were significantly higher than those in 2021 and 2022, a phenomenon we attribute to higher precipitation and a major loading event that occurred on 13 April 2023. Based on comparison with studies concerning nearby Utah Lake, the total loads could be two to three times larger than our estimates. These studies suggest that fine particulate matter may significantly contribute to AD nutrient loads, but these loads are not captured by our sampling method. However, the inclusion of non-water surfaces in Farmington Bay may mitigate this difference. Full article

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26 pages, 9116 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Automated Calibration of SWMM for Improved Stormwater Model Development and Application

by Hossein Ahmadi, Durelle Scott, David J. Sample and Mina Shahed Behrouz

Hydrology 2025, 12(6), 129; https://doi.org/10.3390/hydrology12060129 - 25 May 2025

Cited by 10 | Viewed by 6411

Abstract

The fast pace of urban development and increasing intensity of precipitation events have made managing urban stormwater an increasingly difficult challenge. Hydrologic models are commonly used to predict flows and assess the performance of stormwater controls, often based on a hypothetical yet standardized [...] Read more.

The fast pace of urban development and increasing intensity of precipitation events have made managing urban stormwater an increasingly difficult challenge. Hydrologic models are commonly used to predict flows and assess the performance of stormwater controls, often based on a hypothetical yet standardized design storm. The Storm Water Management Model (SWMM) is widely used for simulating runoff in urban watersheds. However, calibration of SWMM, as with all hydrologic models, is often plagued with issues such as subjectivity, and an abundance of model parameters, leading to delays and inefficiencies in model development and application. Further development of modeling and simulation tools to aid in design is critical in improving the function of stormwater management systems. To address these issues, we developed an integration of PySWMM (a Python wrapper (tool) for SWMM) and Pymoo (a Python package for multi-objective optimization) to automate the SWMM calibration process. The tool was tested using a case study urban watershed in Fredericksburg, VA. This tool can employ either a single-objective or multi-objective approach to calibrate a SWMM model by minimizing the error between prediction and observed values. This tool uses performance metrics including Nash-Sutcliffe Efficiency (NSE), Percent Bias (PBIAS), and Root Mean Square Error (RMSE) Standardized Ratio (RSR) for both single-event and long-term continuous rainfall-runoff processes. During multi-objective optimization calibration, the model achieved NSE, PBIAS, and RSR values of 0.73, 17.1, and 0.52, respectively; while the validation period recorded values of 0.86, 13.1, and 0.37, respectively. Additionally, in the single-objective optimization test case, the model yielded NSE values of 0.68 and 0.73 for the calibration and validation, respectively. The tool also supports parallelized optimization algorithms and utilizes Application Programming Interfaces (APIs) to dynamically update SWMM model parameters, accelerating both model execution and convergence. The tool successfully calibrated the SWMM model, delivering reliable results with suitable computational performance. Full article

(This article belongs to the Special Issue Advances in Urban Hydrology and Stormwater Management)

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24 pages, 3815 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Using High-Resolution Flood Hazard and Urban Heat Island Maps for High-Priority BGI Placement at the City Scale

by Stefan Reinstaller, Albert Wilhelm König and Dirk Muschalla

Hydrology 2025, 12(5), 125; https://doi.org/10.3390/hydrology12050125 - 20 May 2025

Cited by 2 | Viewed by 3305

Abstract

This study presents a general workflow for creating a priority map for blue–green infrastructure (BGI) placement at the city scale, incorporating model-based benefit analysis. This workflow generates a BGI priority map, combining flood hazard and urban heat island maps, that guarantees multi-functional requirements [...] Read more.

This study presents a general workflow for creating a priority map for blue–green infrastructure (BGI) placement at the city scale, incorporating model-based benefit analysis. This workflow generates a BGI priority map, combining flood hazard and urban heat island maps, that guarantees multi-functional requirements are met. This approach was applied at a small study site in Feldbach, Austria. In the second part, we used the priority map generated to implement six BGI strategies in an integrated 1D-2D urban flood model and a semi-distributed hydrological model at high-priority and low-priority locations. The use of the efficiency index (EI_mod) enabled a multi-objective assessment. The results indicate that all the strategies led to a higher EI_mod when implemented in high-priority locations compared to low-priority locations. Our findings demonstrate that priority maps support decision making regarding where strategies should be implemented, providing remarkable benefits for water management objectives. Additionally, the findings highlight the importance of incorporating potential flooding areas to enhance prioritisation regarding flood hazard indicators. In future assessments, economic parameters, such as cost considerations, should also be integrated in order to optimise BGI placement efficiency. Full article

(This article belongs to the Special Issue Advances in Urban Hydrology and Stormwater Management)

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26 pages, 27036 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Managed Aquifer Recharge (MAR) in Semiarid Regions: Water Quality Evaluation and Dynamics from the Akrotiri MAR System, Cyprus

by Maria Achilleos, Ourania Tzoraki and Evangelos Akylas

Hydrology 2025, 12(5), 123; https://doi.org/10.3390/hydrology12050123 - 19 May 2025

Cited by 10 | Viewed by 4653

Abstract

Managed Aquifer Recharge (MAR) is increasingly being adopted across Europe to enhance water security in semiarid regions, with over 230 operational sites. The Akrotiri MAR system in Limassol, Cyprus, comprises 17 recharge ponds operating since 2016 to counteract saltwater intrusion. This study evaluates [...] Read more.

Managed Aquifer Recharge (MAR) is increasingly being adopted across Europe to enhance water security in semiarid regions, with over 230 operational sites. The Akrotiri MAR system in Limassol, Cyprus, comprises 17 recharge ponds operating since 2016 to counteract saltwater intrusion. This study evaluates MAR effectiveness by analyzing spatial and temporal variations in water quality from 2016 to 2020. Parameters analyzed include nutrients, metals, pesticides, pharmaceuticals, fecal indicators, physicochemical characteristics, recharge and pumping volumes, and groundwater levels. The results show that soil aquifer treatment (SAT) generally improves groundwater quality but certain boreholes exhibited elevated nitrate (range 12.70–31 mg/L), electrical conductivity (range 936–10,420 μs/cm), and chloride concentrations (range 117–1631 mg/L), attributed to recharge water quality, seawater intrusion, and nearby agricultural activities. Tertiary treated wastewater used for recharge occasionally exceeds permissible limits, particularly in E. coli (up to 2420/100 mL), chloride (up to 385 mg/L), and nitrogen (up to 41 mg/L). Supplementing recharge with dam-supplied freshwater improves groundwater quality and raises water levels. These findings underline the importance of continuous monitoring and effective management, adopting sustainable farming practices, and the strict control of recharge water quality. The study offers valuable insights for optimizing MAR systems and supports integrating MAR into circular water management frameworks to mitigate pollution and seawater intrusion, enhancing long-term aquifer sustainability. Full article

(This article belongs to the Special Issue Groundwater Pollution: Sources, Mechanisms, and Prevention (Second Edition))

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43 pages, 8825 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Regional Analysis of the Dependence of Peak-Flow Quantiles on Climate with Application to Adjustment to Climate Trends

by Thomas Over, Mackenzie Marti and Hannah Podzorski

Hydrology 2025, 12(5), 119; https://doi.org/10.3390/hydrology12050119 - 14 May 2025

Viewed by 2301

Abstract

Standard flood-frequency analysis methods rely on an assumption of stationarity, but because of growing understanding of climatic persistence and concern regarding the effects of climate change, the need for methods to detect and model nonstationary flood frequency has become widely recognized. In this [...] Read more.

Standard flood-frequency analysis methods rely on an assumption of stationarity, but because of growing understanding of climatic persistence and concern regarding the effects of climate change, the need for methods to detect and model nonstationary flood frequency has become widely recognized. In this study, a regional statistical method for estimating the effects of climate variations on annual maximum (peak) flows that allows for the effect to vary by quantile is presented and applied. The method uses a panel–quantile regression framework based on a location-scale model with two fixed effects per basin. The model was fitted to 330 selected gauged basins in the midwestern United States, filtered to remove basins affected by reservoir regulation and urbanization. Precipitation and discharge simulated using a water-balance model at daily and annual time scales were tested as climate variables. Annual maximum daily discharge was found to be the best predictor of peak flows, and the quantile regression coefficients were found to depend monotonically on annual exceedance probability. Application of the models to gauged basins is demonstrated by estimating the peak-flow distributions at the end of the study period (2018) and, using the panel model, to the study basins as-if-ungauged by using leave-one-out cross validation, estimating the fixed effects using static basin characteristics, and parameterizing the water-balance model discharge using median parameters. The errors of the quantiles predicted as-if-ungauged approximately doubled compared to the errors of the fitted panel model. Full article

(This article belongs to the Special Issue Runoff Modelling under Climate Change)

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29 pages, 8155 KB

Open AccessEditor’s ChoiceArticle

Time-Series Analysis of Monitoring Data from Springs to Assess the Hydrodynamic Characteristics of a Coastal Discharge Zone: Example of Jurjevska Žrnovnica Springs in Croatia

by Andrej Stroj, Jasmina Lukač Reberski, Louise D. Maurice and Ben P. Marchant

Hydrology 2025, 12(5), 118; https://doi.org/10.3390/hydrology12050118 - 13 May 2025

Cited by 1 | Viewed by 2398

Abstract

This study assesses the functioning of the karst aquifer system located on the Croatian coast of the Adriatic Sea, where saltwater intrusion often presents a major problem for freshwater supply. We use two years of sensor data collected from two coastal springs to [...] Read more.

This study assesses the functioning of the karst aquifer system located on the Croatian coast of the Adriatic Sea, where saltwater intrusion often presents a major problem for freshwater supply. We use two years of sensor data collected from two coastal springs to conduct a range of time-invariant and time-variant statistical analyses over various timescales. We perform separate analyses of the within-day and longer-term variation in the data as well as the interactions between the spring levels, salinity, rainfall, and sea levels. Such comprehensive analyses provide a greater understanding into the inner functioning of the intricate, heavily karstified aquifers. Time-invariant time-series analyses of the hourly data indicate that the spring levels and salinity are strongly controlled by sea levels. Furthermore, time-variant wavelet analyses demonstrate that the variation in spring levels in both springs has two modes defined by flow regime. Increases in the delay of the spring response to sea level indicate that aquifer diffusivity decreases in low flow conditions. Analyses facilitated the development of a conceptual model of the karst subsurface in the discharge zone. Using daily data, we constructed a linear mixed model of the spring levels. This model identified long-term sea level changes, rainfall from previous weeks, and seasonal recharge patterns as the primary factors influencing longer-term spring dynamics. Full article

(This article belongs to the Special Issue Characterization and Monitoring of Coastal Hydrological Environment for Assessing the Impact of Seawater Intrusion on Coastal Aquifers)

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27 pages, 7434 KB

Open AccessEditor’s ChoiceArticle

Baseflow Index Trends in Iowa Rivers and the Relationships to Other Hydrologic Metrics

by Elliot S. Anderson and Keith E. Schilling

Hydrology 2025, 12(5), 116; https://doi.org/10.3390/hydrology12050116 - 10 May 2025

Cited by 3 | Viewed by 3272

Abstract

The US state of Iowa has experienced profound historical changes in its streamflow and baseflow. While several studies have noted increasing baseflow and baseflow index (BFI) values throughout the 20th century, analyses quantifying BFI trends in recent years or exploring spatial differences in [...] Read more.

The US state of Iowa has experienced profound historical changes in its streamflow and baseflow. While several studies have noted increasing baseflow and baseflow index (BFI) values throughout the 20th century, analyses quantifying BFI trends in recent years or exploring spatial differences in watersheds marked by varying land use and geologic properties have not been conducted. This study calculated annual values for BFI (and several other hydrologic metrics) using flow records from 42 Iowa stream gauges containing at least 50 years of uninterrupted measurements. While BFI overwhelmingly rose throughout the mid-1900s, circa 1990 it began to level off. In some areas of Iowa (e.g., the southwest), BFI has continued to rise over the past 30 years—albeit at a slower rate; in other regions, it has become stationary or declined. One site failed to follow this trend (Walnut Cr), the only basin to experience large-scale urbanization. Furthermore, BFI demonstrated a strong negative correlation to streamflow flashiness, indicating that rising baseflow has also made Iowa streams less dynamic. BFI was largely independent of overall streamflow. These results may suggest the increased influence of conservation practices and the diminishing impacts of tile drainage on the delivery of water to Iowa’s rivers. Full article

(This article belongs to the Special Issue Hydrological Processes in Agricultural Watersheds)

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22 pages, 7460 KB

Open AccessEditor’s ChoiceArticle

Surface and Subsurface Heatwaves in the Hypersaline Dead Sea Caused by Severe Dust Intrusion

by Pavel Kishcha, Isaac Gertman and Boris Starobinets

Hydrology 2025, 12(5), 114; https://doi.org/10.3390/hydrology12050114 - 6 May 2025

Cited by 2 | Viewed by 2325

Abstract

The relationship between global warming and heatwaves contributes to environmental risks. We investigate lake heatwaves (LHWs) in the Eastern Mediterranean, where dust intrusions are frequently observed. The dust intrusions are characterized by the arrival of warm air masses containing dust pollution from the [...] Read more.

The relationship between global warming and heatwaves contributes to environmental risks. We investigate lake heatwaves (LHWs) in the Eastern Mediterranean, where dust intrusions are frequently observed. The dust intrusions are characterized by the arrival of warm air masses containing dust pollution from the desert. In saline lakes, LHWs caused by dust intrusions have not been investigated in previous studies. In our study we focus on this point. It was found for the first time that, in the hypersaline Dead Sea, a severe dust intrusion (aerosol optical depth of over 3) caused the formation of LHWs, as appeared in September 2015. At the water surface, the LHWs were represented by abnormally high daily maximal and minimal surface water temperature (SWT) in comparison with their seasonally varied 90th percentile thresholds for 10 consecutive days (7–17 September). The surface LHWs’ intensity was up to 3 °C. Satellite (MODIS-Terra and METEOSAT) SWT did not detect the LHWs. Surface LHWs were accompanied by subsurface LHWs down to a depth of 20 m. The subsurface LHWs lasted longer (16 days) than the surface LHWs (10 days). There was a 4-day delay between the first date of the surface LHWs (7 September) and the start date of the subsurface LHWs (11 September). The maximal intensity of the subsurface LHWs decreased with depth from 1 m (0.6 °C) down to 5 m (0.3 °C), followed by an increase (up to 0.6 °C) at the deeper layers (from 10 m to 20 m). Taking into account that, over the Eastern Mediterranean, desert dust has increased during the past several decades, one can expect frequent occurrence of dust-related intense persistent heatwaves in the Dead Sea in the coming years. This will contribute to additional water heating and further drying up of the Dead Sea. Full article

(This article belongs to the Special Issue Lakes as Sensitive Indicators of Hydrology, Environment, and Climate)

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32 pages, 3423 KB

Open AccessEditor’s ChoiceArticle

Investigation of Sediment Characteristics and Nutrient Content in Relation to Pilot Dredging at Kis-Balaton Water Protection System (Hungary)

by Hilda Hernádi, András Makó, Zsófia Lovász, Sándor Szoboszlay, Péter Harkai, Judit Háhn, Mihály Kocsis, Eszter Schöphen, Zoltán Tóth, András Bidló, Márk Rékási, Árpád Ferincz, Gábor Csitári and Gyöngyi Barna

Hydrology 2025, 12(5), 112; https://doi.org/10.3390/hydrology12050112 - 6 May 2025

Cited by 3 | Viewed by 3425

Abstract

The internal nutrient load of natural and artificial lakes is a worldwide problem. To minimize its potential risks, the dredging of the highly eutrophic shallow first reservoir of Kis-Balaton (Lake Hídvégi) is planned in the near future. Our study aimed to evaluate the [...] Read more.

The internal nutrient load of natural and artificial lakes is a worldwide problem. To minimize its potential risks, the dredging of the highly eutrophic shallow first reservoir of Kis-Balaton (Lake Hídvégi) is planned in the near future. Our study aimed to evaluate the potential effects of dredging and desiccation on water and sediment quality. Experimental dredging was carried out in the northernmost part of Lake Hídvégi (2023). The physical and chemical characteristics of the sediment and nutrient loss during desiccation were examined in a column experiment. The relationships between the properties of leachate and sediment were identified using principal component analysis (SPSS). Spatial variations in sediment particle size distribution, nutrient content, and other chemical parameters (e.g., organic matter) suggest that deeper core sampling than the depth of preliminary dredging is necessary for a more comprehensive assessment of potential impacts. We found that spatiotemporally varying the dominance of chemical and biological processes affects the amount of and changes in phosphorus fractions under lake-/sediment-specific conditions. The readily available calcium- and iron-bound phosphorus, texture, and organic matter content of the sediment play an important role in phosphorus fixation/release. Based on our results, dredging and desiccation are feasible within the intended operating parameters. The sediment’s composition does not preclude potential agricultural disposal. Full article

(This article belongs to the Section Surface Waters and Groundwaters)

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20 pages, 1702 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Estimation of Hydraulic Properties of Growing Media from Numerical Inversion of Mini Disk Infiltrometer Data

by Hadi Hamaaziz Muhammed, Ruediger Anlauf and Diemo Daum

Hydrology 2025, 12(5), 100; https://doi.org/10.3390/hydrology12050100 - 22 Apr 2025

Cited by 4 | Viewed by 2795

Abstract

Accurately determining the hydraulic properties of soilless growing media is essential for optimizing water management in container-based horticulture and agriculture. The very rapid estimation of hydraulic properties using a Mini Disk Infiltrometer has great potential for practical use compared to the very time-consuming [...] Read more.

Accurately determining the hydraulic properties of soilless growing media is essential for optimizing water management in container-based horticulture and agriculture. The very rapid estimation of hydraulic properties using a Mini Disk Infiltrometer has great potential for practical use compared to the very time-consuming standard methods. The objectives of this study were (1) to calibrate simulated cumulative stepwise infiltration under different suctions with the measured data from Mini Disk Infiltrometer, (2) to evaluate the efficiency of the Hydrus-2D inverse model to predict water dynamics through substrates, (3) to compare the substrate hydraulic parameters obtained through the numerical inversion model to those obtained via laboratory methods, and (4) to provide recommendations on how to effectively use the MDI-based method for practical applications. This study employs numerical inversion of Mini Disk Infiltrometer (MDI) data to estimate the hydraulic parameters of three different growing media, namely white peat, thermally treated wood fibre (WF4), and Seedling substrate. Infiltration experiments were conducted under suction-controlled conditions using varying initial moisture contents, followed by numerical simulations using the Hydrus-2D model and the Van Genuchten equation to describe the hydraulic parameters. The results demonstrated strong agreement between observed and simulated infiltration data, particularly under moistened conditions, with high R² > 0.9 values indicating the model’s effectiveness. However, discrepancies were observed for substrates in their initial dry state, suggesting limitations in capturing early-stage infiltration dynamics. The findings highlighted the potential of numerical inversion methods for estimating substrate hydraulic properties but also revealed the need for methodological refinements. Modifying the Van Genuchten model or exploring alternative approaches such as the Brooks and Corey model may enhance accuracy. Extending the suction range of measurement techniques is also recommended to improve parameter estimation. This study provides important evidence that the inverse method based on MDI is an effective tool for rapidly determining the hydraulic functions of substrates, which are important in promoting sustainable horticultural practices. Future research should focus on refining parameter estimation methods and addressing model limitations to enhance the reliability of hydraulic property assessments in soilless growing media. Full article

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24 pages, 3748 KB

Open AccessEditor’s ChoiceArticle

Leveraging Recurrent Neural Networks for Flood Prediction and Assessment

by Elnaz Heidari, Vidya Samadi and Abdul A. Khan

Hydrology 2025, 12(4), 90; https://doi.org/10.3390/hydrology12040090 - 16 Apr 2025

Cited by 14 | Viewed by 3551

Abstract

Recent progress in Artificial Intelligence and Machine Learning (AIML) has accelerated improvements in the prediction performance of many hydrological processes. Yet, flood prediction remains a challenging task due to its complex nature. Two common challenges afflicting the task are flood volatility and the [...] Read more.

Recent progress in Artificial Intelligence and Machine Learning (AIML) has accelerated improvements in the prediction performance of many hydrological processes. Yet, flood prediction remains a challenging task due to its complex nature. Two common challenges afflicting the task are flood volatility and the sensitivity and complexity of flood generation attributes. This study explores the application of Recurrent Neural Networks (RNNs)—specifically Vanilla Recurrent Neural Networks (VRNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—in flood prediction and assessment. By integrating catchment-specific hydrological and meteorological variables, the RNN models leverage sequential data processing to capture the temporal dynamics and seasonal patterns characteristic of flooding. These models were employed across diverse terrains, including mountainous watersheds in the state of South Carolina, USA, to examine their robustness and adaptability. To identify significant hydrological events for flash flood analysis, a discharge frequency analysis was conducted using the Pearson Type III distribution. The 1-year and 2-year return period flows were estimated based on this analysis, and the 1-year return flow was selected as a conservative threshold for flash flood event identification to ensure a sufficient number of training instances. Comparative benchmarking with the National Water Model (NWM v3.0) revealed that the RNN-based approaches offer notable enhancements in capturing the intensity and timing of flood events, particularly for short-duration and high-magnitude floods (flash floods). Comparison of predicted disharges with the discharge recorded at the gauges revealed that GRU had the best performance as it achieved the highest mean NSE values and exhibited low variability across diverse watersheds. LSTM results were slightly less consistent compared to the GRU albeit achieving satisfactory performance, proving its value in hydrological forecasting. In contrast, VRNN had the highest variability and the lowest NSE values among the three. The NWM model trailed the machine learning-based models. The study highlights the efficacy of the RNN models in advancing hydrological predictions. Full article

(This article belongs to the Section Water Resources and Risk Management)

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19 pages, 12753 KB

Open AccessEditor’s ChoiceArticle

Impact Assessment of Floating Photovoltaic Systems on the Water Quality of Kremasta Lake, Greece

by Angeliki Mentzafou, Elias Dimitriou, Ioannis Karaouzas and Stamatis Zogaris

Hydrology 2025, 12(4), 92; https://doi.org/10.3390/hydrology12040092 - 16 Apr 2025

Cited by 10 | Viewed by 5550

Abstract

Floating photovoltaic systems (FPV) are one of the emerging technologies that are able to support the “green” energy transition. In Greece, the environmental impact assessment of such projects is still under early development. The scope of the present study was to provide insights [...] Read more.

Floating photovoltaic systems (FPV) are one of the emerging technologies that are able to support the “green” energy transition. In Greece, the environmental impact assessment of such projects is still under early development. The scope of the present study was to provide insights into the potential impacts of a small-scale FPV system on the water quality of the oligotrophic Kremasta Lake, an artificial reservoir. For this reason, a hydrodynamic and water quality model was employed. The results showed that the water quality parameter variations were insignificant and limited only in the immediate area of the FPV construction and gradually disappeared toward the shoreline. Likewise, this variation was restricted to the first few meters of depth of the water column and was eliminated onwards. The water temperature slightly decreased only in the area of close proximity to the installation. Average annual dissolved oxygen, chlorophyll-a, and nutrient concentrations were predicted not to change considerably after the panels’ construction. FPV systems can provide an attractive alternative for energy production in artificial reservoirs, especially in regions of land use conflicts that are associated with land allocation for alternative energy development. Given the limited data on the long-term impact of such projects, robust monitoring programs are essential. These initiatives rely on public support, making collaboration between stakeholders and the local community crucial. Full article

(This article belongs to the Special Issue Novel Procedures and Methodologies for Surface and Underground Water Quality Analysis: Theory and Application)

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18 pages, 5008 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Tracking Nitrate Sources in the Lower Kagera River in the Lake Victoria Basin: Insights from Hydrochemistry, Isotopes, and the MixSIAR Model

by Catherine Mathenge, Stephen Mureithi, Pascal Boeckx, Benjamin Nyilitya and Cargele Masso

Hydrology 2025, 12(4), 84; https://doi.org/10.3390/hydrology12040084 - 11 Apr 2025

Cited by 4 | Viewed by 3480

Abstract

Nitrate contamination poses a significant global environmental threat, impacting the water quality in surface and groundwater systems. Despite its considerable impact, there remains a lack of comprehensive understanding of nitrate sources and discharge patterns, particularly in the Lake Victoria basin of East Africa. [...] Read more.

Nitrate contamination poses a significant global environmental threat, impacting the water quality in surface and groundwater systems. Despite its considerable impact, there remains a lack of comprehensive understanding of nitrate sources and discharge patterns, particularly in the Lake Victoria basin of East Africa. To address this gap, a study was conducted in the Kagera River basin, responsible for 33% of Lake Victoria’s surface inflow. This study utilized δ¹⁵N and δ¹⁸O isotope analysis in nitrate, hydrochemistry, and the Bayesian mixing model (MixSIAR) to identify and quantify nitrate sources. Spatiotemporal data were collected across three seasons: long rains, dry season, and short rains, in areas with diverse land uses. Nitrate isotopic data from water and potential sources were integrated into a Bayesian mixing model to determine the relative contributions of various nitrate sources. Notable spatial variations were observed at sampling sites with concentrations ranging from 0.004 to 3.31 mg L⁻¹. Spatially and temporally, δ¹⁵N-NO₃⁻ values ranged from +6.0% to +10.2‰, whereas δ¹⁸O-NO₃⁻ displayed significant spatial differences with mean ranges from −1% to +7‰. MixSIAR analysis revealed important contributions from manure and sewage sources ranging between 49% and 73%. A boron analysis revealed manure was the main source of nitrates in the manure and sewage. These results show that it is necessary to implement improved manure and sewage management practices, especially through proper waste treatment and disposal systems, to enable informed policy decisions to enhance nitrogen management strategies in riparian East Africa, and to safeguard the region’s water resources and ecosystems. Full article

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29 pages, 15893 KB

Open AccessEditor’s ChoiceArticle

Application of Temporal Fusion Transformers to Run-Of-The-River Hydropower Scheduling

by Rafael Francisco, José Pedro Matos, Rui Marinheiro, Nuno Lopes, Maria Manuela Portela and Pedro Barros

Hydrology 2025, 12(4), 81; https://doi.org/10.3390/hydrology12040081 - 3 Apr 2025

Cited by 6 | Viewed by 4504

Abstract

This study explores the application of Temporal Fusion Transformers (TFTs) to improve the predictability of hourly potential hydropower production for a small run–of–the–river hydropower plant in Portugal. Accurate hourly power forecasts are essential for optimizing participation in the spot electricity market, where deviations [...] Read more.

This study explores the application of Temporal Fusion Transformers (TFTs) to improve the predictability of hourly potential hydropower production for a small run–of–the–river hydropower plant in Portugal. Accurate hourly power forecasts are essential for optimizing participation in the spot electricity market, where deviations incur penalties. This research introduces the novel application of the TFT, a deep–learning model tailored for time series forecasting and uncovering complex patterns, to predict hydropower production based on meteorological data, historical production records, and plant capacity. Key challenges such as filtering observed hydropower outputs (to remove strong, and unpredictable human influence) and adapting the historical series to installed capacity increases are discussed. An analysis of meteorological information from several sources, including ground information, reanalysis, and forecasting models, was also undertaken. Regarding the latter, precipitation forecasts from the European Centre for Medium–Range Weather Forecasts (ECMWF) proved to be more accurate than those of the Global Forecast System (GFS). When combined with ECMWF data, the TFT model achieved significantly higher accuracy in potential hydropower production predictions. This work provides a framework for integrating advanced machine learning models into operational hydropower scheduling, aiming to reduce classical modeling efforts while maximizing energy production efficiency, reliability, and market performance. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: The Flood Estimation and Forecasting Chain: Meteorological–Hydrological–Hydraulic Forecasts and Predictive Uncertainty towards Operational Decisions)

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15 pages, 8054 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Seasonal and Spatial Dynamics of Surface Water Resources in the Tropical Semi-Arid Area of the Letaba Catchment: Insights from Google Earth Engine, Landscape Metrics, and Sentinel-2 Imagery

by Makgabo Johanna Mashala, Timothy Dube and Kingsley Kwabena Ayisi

Hydrology 2025, 12(4), 68; https://doi.org/10.3390/hydrology12040068 - 24 Mar 2025

Cited by 6 | Viewed by 3613

Abstract

Understanding the spatial and seasonal dynamics of surface water bodies is imperative for addressing water security challenges in water-scarce regions. This study aimed to evaluate the efficacy of multi-date Sentinel-2-derived spectral indices, specifically the normalized difference water index (NDWI), modified normalized difference water [...] Read more.

Understanding the spatial and seasonal dynamics of surface water bodies is imperative for addressing water security challenges in water-scarce regions. This study aimed to evaluate the efficacy of multi-date Sentinel-2-derived spectral indices, specifically the normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and Sentinel 2 Water Index (SWI), in conjunction with landscape metrics for mapping spatial and seasonal fluctuations in surface water bodies. Google Earth Engine (GEE) was employed for this assessment. The research achieved impressive overall accuracies, ranging from 96 to 100% for both dry and wet seasons, highlighting the robustness of the methodology. The study revealed significant differences in water bodies in terms of size and coverage between the dry and wet seasons. Surprisingly, the dry season exhibited a higher prevalence of water bodies when compared to the wet season, indicating unexpected patterns of water availability in the region and the substantial heterogeneity of water bodies. Meanwhile, the wet season was characterized by extensive coverage. These findings challenge conventional assumptions about water resource availability during different seasons. Based on the findings, the study recommends that water resource management strategies in semi-arid regions consider the observed seasonal variability in water bodies. Policymakers and stakeholders should adopt adaptive management approaches to address the unique challenges posed by differing water body dynamics in dry and wet seasons. Future research endeavors should explore the underlying factors driving these seasonal fluctuations and assess the potential long-term impacts on water availability. This can help to develop more resilient and sustainable water security strategies to cope with changing climate conditions in semi-arid tropical environments. Full article

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24 pages, 4146 KB

Open AccessEditor’s ChoiceArticle

Assessing the Effects of Bioenergy Cropping Scenarios on the Surface Water and Groundwater of an Intensively Agricultural Basin in Central Greece

by Maria Sismanidi, Lamprini Kokkinaki, Sofia Kavalieratou, Haralampos Georgoussis, Kyriakos D. Giannoulis, Elias Dimitriou and Yiannis Panagopoulos

Hydrology 2025, 12(4), 66; https://doi.org/10.3390/hydrology12040066 - 22 Mar 2025

Cited by 3 | Viewed by 3568

Abstract

Pinios river basin constitutes the most important agricultural production area in Greece but contributes to the degradation of the quality and quantity of surface water and groundwater bodies. Bioenergy crops implemented as part of the existing cropping systems could be a novel and [...] Read more.

Pinios river basin constitutes the most important agricultural production area in Greece but contributes to the degradation of the quality and quantity of surface water and groundwater bodies. Bioenergy crops implemented as part of the existing cropping systems could be a novel and efficient mitigation strategy against water degradation, contributing to the production of energy through renewable sources. This study uses the Soil and Water Assessment Tool (SWAT) to first develop a representative model of Pinios river basin and evaluate its current state with respect to water availability and nitrate water pollution. A low-input perennial bioenergy crop, switchgrass, is then simulated closely to the Greek conditions to investigate its potential effects on water in three implementation scenarios: the installation and growth of switchgrass in the entire irrigated cropland, exclusively in irrigated sloping (slopes > 1.5%) cropland, and exclusively in irrigated non-sloping cropland. The simulated results demonstrate that under all scenarios, the water quality improvements with respect to the nitrate loads entering surface water and groundwater bodies were significant, with their reduction being directly affected by the extent to which switchgrass replaced resource-demanding conventional crops. Specifically, the reduction in the annual nitrate loads in the surface water under these three scenarios varied from 7% to 18% at the river basin scale, while in certain cropland areas, the respective reduction even exceeded a level of 80%. The potential to improve the water status was also considerable, as the implementation of the bioenergy crop reduced the irrigation water used annually in the basin by 10% (64 Mm³) when switchgrass replaced the conventional crops only on the sloping land and by almost 30% (187 Mm³) when it replaced them throughout the irrigated land. At the same time, significant biomass production above 18 t/ha/y applied in all of the simulations. This study also highlights the contribution of the bioenergy crop to the rehabilitation of the groundwater levels across the basin, with the possibility of increasing them by >50% compared to the baseline, implying that the adoption of switchgrass could be a promising means against water scarcity. Full article

(This article belongs to the Special Issue Hydrological Modelling for the Sustainable Management of Water Resources in River Basins)

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15 pages, 1869 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Molecular Composition of Stream Dissolved Organic Matter in Cool-Temperate Forest Headwaters with Landslides, Northern Japan

by Jun’ichiro Ide, Kenta Hara, Yohei Arata, Izuki Endo, Mizue Ohashi, Hiroshi Nishimura and Takashi Gomi

Hydrology 2025, 12(3), 63; https://doi.org/10.3390/hydrology12030063 - 19 Mar 2025

Viewed by 2129

Abstract

Vegetation and subsequent ecosystem services can recover over time in forest headwaters devastated by massive disasters. However, in cold regions, their recovery rates are typically slow and often imperceptible, which makes it difficult to evaluate how much ecosystem services have recovered. This study [...] Read more.

Vegetation and subsequent ecosystem services can recover over time in forest headwaters devastated by massive disasters. However, in cold regions, their recovery rates are typically slow and often imperceptible, which makes it difficult to evaluate how much ecosystem services have recovered. This study targeted dissolved organic matter (DOM), which plays a central role in biogeochemical processes in forest ecosystems, and aimed to examine whether vegetation conditions affect the quality of stream DOM from cool-temperate forest headwaters in northern Japan. To achieve this, hydrological observations and stream water sampling were conducted monthly from May to December 2021 in three small forest catchments with different landslide coverage. Dissolved organic carbon (DOC) concentration in stream water was measured, and the molecular composition of DOM was analyzed using ultrahigh-resolution mass spectrometry and compared among the three catchments. The peak-intensity-weighted average aromaticity index (AI_wa) increased with DOC concentration. We found that AI_wa was the highest in the undisturbed catchment, followed by the catchments with landslide coverages of 16% and 52% at a given DOC level. These results indicate that the quality of DOM could dramatically change depending not only on DOC concentration but also on vegetation disturbance in cool-temperate forest headwaters. Full article

(This article belongs to the Section Surface Waters and Groundwaters)

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45 pages, 3649 KB

Open AccessFeature PaperEditor’s ChoiceReview

Protocols for Water and Environmental Modeling Using Machine Learning in California

by Minxue He, Prabhjot Sandhu, Peyman Namadi, Erik Reyes, Kamyar Guivetchi and Francis Chung

Hydrology 2025, 12(3), 59; https://doi.org/10.3390/hydrology12030059 - 14 Mar 2025

Cited by 7 | Viewed by 8369

Abstract

The recent surge in popularity of generative artificial intelligence (GenAI) tools like ChatGPT has reignited global interest in AI, a technology with a well-established history spanning several decades. The California Department of Water Resources (DWR) has been at the forefront of this field, [...] Read more.

The recent surge in popularity of generative artificial intelligence (GenAI) tools like ChatGPT has reignited global interest in AI, a technology with a well-established history spanning several decades. The California Department of Water Resources (DWR) has been at the forefront of this field, leveraging Artificial Neural Networks (ANNs), a core technique in machine learning (ML), which is a subfield of AI, for water and environmental modeling (WEM) since the early 1990s. While protocols for WEM exist in California, they were designed primarily for traditional statistical or process-based models that rely on predefined equations and physical principles. In contrast, ML models learn patterns from data and require different development methodologies, which existing protocols do not address. This study, drawing on DWR’s extensive experience in ML, addresses this gap by developing standardized protocols for the development and implementation of ML models in WEM in California. The proposed protocols cover four key phases of ML development and implementation: (1) problem definition, ensuring clear objectives and contextual understanding; (2) data preparation, emphasizing standardized collection, quality control, and accessibility; (3) model development, advocating for a progression from simple models to hybrid and ensemble approaches while integrating domain knowledge for improved accuracy; and (4) model deployment, highlighting documentation, training, and open-source practices to enhance transparency and collaboration. A case study is provided to demonstrate the practical application of these protocols step by step. Once implemented, these protocols can help achieve standardization, quality assurance, interoperability, and transparency in water and environmental modeling using machine learning in California. Full article

(This article belongs to the Special Issue Advancing Hydrological Science Through Artificial Intelligence: Innovations and Applications)

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25 pages, 10057 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Machine Learning Analysis of Hydrological and Hydrochemical Data from the Abelar Pilot Basin in Abegondo (Coruña, Spain)

by Javier Samper-Pilar, Javier Samper-Calvete, Alba Mon, Bruno Pisani and Antonio Paz-González

Hydrology 2025, 12(3), 49; https://doi.org/10.3390/hydrology12030049 - 6 Mar 2025

Cited by 2 | Viewed by 4451

Abstract

The Abelar pilot basin in Coruña (northwestern Spain) has been monitored for hydrological and hydrochemical data to assess the effects of eucalyptus plantation and manure applications on water resources, water quality, and nitrate contamination. Here, we report the machine learning analysis of hydrological [...] Read more.

The Abelar pilot basin in Coruña (northwestern Spain) has been monitored for hydrological and hydrochemical data to assess the effects of eucalyptus plantation and manure applications on water resources, water quality, and nitrate contamination. Here, we report the machine learning analysis of hydrological and hydrochemical data from the Abelar basin. K-means cluster analysis (CA) is used to relate nitrate concentrations at the outlet of the basin with daily interflows and groundwater flows calculated with a hydrological balance. CA identifies three linearly separable clusters. Times series Gaussian process regression (TS-GPR) is employed to predict surface water nitrate concentration by incorporating hydrological variables as additional input parameters using a time series shifting. TS-GPR allows modelling nitrate concentrations based on shifted interflows and groundwater flows and chemical concentrations with R² = 0.82 and 0.80 for training and testing, respectively. Groundwater flow from five days prior to the current date,

Q_{g 5}

, is the most important input parameter of the TS-GPR model. Interaction effects between the variables are found. TS-GPR validation with recent data provides results consistent with those of testing (R² = 0.85). Model inspection by permutation feature importance and partial dependence plots shows interactions between

Q_{g 5}

and Cl, and between Ca and Mg. Full article

(This article belongs to the Special Issue Novel Procedures and Methodologies for Surface and Underground Water Quality Analysis: Theory and Application)

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Graphical abstract

41 pages, 24123 KB

Open AccessFeature PaperEditor’s ChoiceArticle

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

Cited by 15 | Viewed by 6994

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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25 pages, 9673 KB

Open AccessEditor’s ChoiceArticle

A Systematic Modular Approach for the Coupling of Deep-Learning-Based Models to Forecast Urban Flooding Maps in Early Warning Systems

by Juliana Koltermann da Silva, Benjamin Burrichter, Andre Niemann and Markus Quirmbach

Hydrology 2024, 11(12), 215; https://doi.org/10.3390/hydrology11120215 - 12 Dec 2024

Cited by 2 | Viewed by 3330

Abstract

Deep learning (DL) approaches to forecast precipitation and inundation areas in the short-term forecast horizon have up until now been treated as independent research problems from the model development perspective. However, for the urban hydrology area, the coupling of these models is necessary [...] Read more.

Deep learning (DL) approaches to forecast precipitation and inundation areas in the short-term forecast horizon have up until now been treated as independent research problems from the model development perspective. However, for the urban hydrology area, the coupling of these models is necessary in order to forecast the upcoming inundation area maps and is, therefore, of the utmost importance for successful flood risk management. In this paper, three deep-learning-based models are coupled in a systematic modular approach with the aim to analyze the performance of this model chain in an operative setup for urban pluvial flooding nowcast: precipitation nowcasting with an adapted version of the NowcastNet model, the forecast of manhole overflow hydrographs with a Seq2Seq model, and the generation of a spatiotemporal sequence of inundation areas in an urban catchment for the upcoming hour with an encoder–decoder model. It can be concluded that the forecast quality still largely depends on the accuracy of the precipitation nowcasting model. With the increasing development of DL models for both precipitation and flood nowcasting, the presented modular approach for model coupling enables the substitution of individual blocks for better and newer models in the model chain without jeopardizing the operation of the flooding forecast system. Full article

(This article belongs to the Special Issue New Perspectives in the Flood Forecasting Chain (Weather Prediction, Rainfall-Runoff Modeling, and Communication with Stakeholders))

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22 pages, 13992 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Simulation of Seawater Intrusion and Upconing Processes in Mediterranean Aquifer in Response to Climate Change (Plana de Castellón, Spain)

by Barbara del R. Almazan-Benitéz, Maria V. Esteller-Alberich, Arianna Renau-Pruñonosa and José L. Expósito-Castillo

Hydrology 2024, 11(12), 205; https://doi.org/10.3390/hydrology11120205 - 28 Nov 2024

Cited by 9 | Viewed by 3733

Abstract

In coastal regions, groundwater is often the only freshwater resource available for human consumption, agriculture, and other productive activities. From a management point of view, it is essential to understand the processes that occur in a coastal aquifer affected by seawater intrusion and [...] Read more.

In coastal regions, groundwater is often the only freshwater resource available for human consumption, agriculture, and other productive activities. From a management point of view, it is essential to understand the processes that occur in a coastal aquifer affected by seawater intrusion and upconing processes and evaluate their potential response to climate change as these scenarios usually indicate a decrease in aquifer recharge. Therefore, the dynamics of seawater intrusion and the upconing process in the Plana de Castellón aquifer on the Mediterranean coast were analysed by building and calibrating a new numerical model of flow and transport using the MODFLOW and SEAWAT codes. The model was used to examine two Shared Socioeconomic Pathway (SSP) climate change scenarios (SSP1–2.6 and SSP5–8.5) when considering field data with constant extraction conditions. The results suggest that by 2050, groundwater levels could rise by 0.18 m (on average) in the SSP1–2.6 scenario and by 0.12 m for the SSP5–8.5 scenario. In these cases, aquifer recharge and groundwater discharge to the sea could increase compared to the historical period, as precipitation is not expected to decrease significantly during this timeframe, even in the most unfavourable scenario (SSP5–8.5). The result would be the attenuation of seawater intrusion and a decrease in the volume of the aquifer that is affected by the upconing process, resulting in total dissolved solids values below 2000 mg/L. The innovation of this research lies in the fact that the numerical model allowed the dynamics of seawater intrusion and the upconing process to be adequately represented, especially in the latter process, as it was not possible to model it with real data in another study. These results can improve and facilitate decision-making for the management of the aquifer and contribute to plans for future exploitation strategies. Full article

(This article belongs to the Section Hydrology–Climate Interactions)

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17 pages, 5575 KB

Open AccessEditor’s ChoiceArticle

The Importance of Solving Subglaciar Hydrology in Modeling Glacier Retreat: A Case Study of Hansbreen, Svalbard

by Eva De Andrés, José M. Muñoz-Hermosilla, Kaian Shahateet and Jaime Otero

Hydrology 2024, 11(11), 193; https://doi.org/10.3390/hydrology11110193 - 12 Nov 2024

Cited by 3 | Viewed by 3264

Abstract

Arctic tidewater glaciers are retreating, serving as key indicators of global warming. This study aims to assess how subglacial hydrology affects glacier front retreat by comparing two glacier–fjord models of the Hansbreen glacier: one incorporating a detailed subglacial hydrology model and another simplifying [...] Read more.

Arctic tidewater glaciers are retreating, serving as key indicators of global warming. This study aims to assess how subglacial hydrology affects glacier front retreat by comparing two glacier–fjord models of the Hansbreen glacier: one incorporating a detailed subglacial hydrology model and another simplifying the subglacial discharge to a single channel centered in the flow line. We first validate the subglacial hydrology model by comparing its discharge channels with observations of plume activity. Simulations conducted from April to December 2010 revealed that the glacier front position aligns more closely with the observations in the coupled model than in the simplified version. Furthermore, the mass loss due to calving and submarine melting is greater in the coupled model, with the calving mass loss reaching 6 Mt by the end of the simulation compared to 4 Mt in the simplified model. These findings highlight the critical role of subglacial hydrology in predicting glacier dynamics and emphasize the importance of detailed modeling in understanding the responses of Arctic tidewater glaciers to climate change. Full article

(This article belongs to the Section Hydrological and Hydrodynamic Processes and Modelling)

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18 pages, 3661 KB

Open AccessEditor’s ChoiceArticle

Estimation of Reservoir Storage Capacity Using the Gould-Dincer Formula with the Aid of Possibility Theory

by Nikos Mylonas, Christos Tzimopoulos, Basil Papadopoulos and Nikiforos Samarinas

Hydrology 2024, 11(10), 172; https://doi.org/10.3390/hydrology11100172 - 11 Oct 2024

Cited by 1 | Viewed by 3516

Abstract

This paper presents a method for estimating reservoir storage capacity using the Gould–Dincer normal formula (G-DN), enhanced by the possibility theory. The G-DN equation is valuable for regional studies of reservoir reliability, particularly under climate change scenarios, using regional statistics. However, because the [...] Read more.

This paper presents a method for estimating reservoir storage capacity using the Gould–Dincer normal formula (G-DN), enhanced by the possibility theory. The G-DN equation is valuable for regional studies of reservoir reliability, particularly under climate change scenarios, using regional statistics. However, because the G-DN formula deals with measured data, it introduces a degree of uncertainty and fuzziness that traditional probability theory struggles to address. Possibility theory, an extension of fuzzy set theory, offers a suitable framework for managing this uncertainty and fuzziness. In this study, the G-DN formula is adapted to incorporate fuzzy logic, and the possibilistic nature of reservoir capacity is translated into a probabilistic framework using α-cuts from the possibility theory. These α-cuts approximate probability confidence intervals with high confidence. Applying the proposed methodology, in the present crisp case with the storage capacity D = 0.75, the value of the capacity C was found to be

1271 \times 10^{6} m^{3}

, and that for D = 0.5 was

634.5 \times 10^{6} m^{3}

. On the other hand, in the fuzzy case using the possibility theory, the value of the capacity for D = 0.75 is the internal

[315, 5679] \times 10^{6} m^{3}

and for D = 0.5 the value is interval

[158, 2839] \times 10^{6} m^{3}

, with a probability of ≥95% and a risk level of α = 5% for both cases. The proposed approach could be used as a robust tool in the toolkit of engineers working on irrigation, drainage, and water resource projects, supporting informed and effective engineering decisions. Full article

(This article belongs to the Special Issue Water Resources Management under Uncertainty and Climate Change)

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17 pages, 27193 KB

Open AccessFeature PaperEditor’s ChoiceArticle

A Machine Learning Approach to Map the Vulnerability of Groundwater Resources to Agricultural Contamination

by Victor Gómez-Escalonilla and Pedro Martínez-Santos

Hydrology 2024, 11(9), 153; https://doi.org/10.3390/hydrology11090153 - 13 Sep 2024

Cited by 15 | Viewed by 5245

Abstract

Groundwater contamination poses a major challenge to water supplies around the world. Assessing groundwater vulnerability is crucial to protecting human livelihoods and the environment. This research explores a machine learning-based variation of the classic DRASTIC method to map groundwater vulnerability. Our approach is [...] Read more.

Groundwater contamination poses a major challenge to water supplies around the world. Assessing groundwater vulnerability is crucial to protecting human livelihoods and the environment. This research explores a machine learning-based variation of the classic DRASTIC method to map groundwater vulnerability. Our approach is based on the application of a large number of tree-based machine learning algorithms to optimize DRASTIC’s parameter weights. This contributes to overcoming two major issues that are frequently encountered in the literature. First, we provide an evidence-based alternative to DRASTIC’s aprioristic approach, which relies on static ratings and coefficients. Second, the use of machine learning approaches to compute DRASTIC vulnerability maps takes into account the spatial distribution of groundwater contaminants, which is expected to improve the spatial outcomes. Despite offering moderate results in terms of machine learning metrics, the machine learning approach was more accurate in this case than a traditional DRASTIC application if appraised as per the actual distribution of nitrate data. The method based on supervised classification algorithms was able to produce a mapping in which about 45% of the points with high nitrate concentrations were located in areas predicted as high vulnerability, compared to 6% shown by the original DRASTIC method. The main difference between using one method or the other thus lies in the availability of sufficient nitrate data to train the models. It is concluded that artificial intelligence can lead to more robust results if enough data are available. Full article

(This article belongs to the Section Surface Waters and Groundwaters)

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20 pages, 3519 KB

Open AccessEditor’s ChoiceArticle

The Implementation of Multimodal Large Language Models for Hydrological Applications: A Comparative Study of GPT-4 Vision, Gemini, LLaVa, and Multimodal-GPT

by Likith Anoop Kadiyala, Omer Mermer, Dinesh Jackson Samuel, Yusuf Sermet and Ibrahim Demir

Hydrology 2024, 11(9), 148; https://doi.org/10.3390/hydrology11090148 - 11 Sep 2024

Cited by 46 | Viewed by 12437

Abstract

Large Language Models (LLMs) combined with visual foundation models have demonstrated significant advancements, achieving intelligence levels comparable to human capabilities. This study analyzes the latest Multimodal LLMs (MLLMs), including Multimodal-GPT, GPT-4 Vision, Gemini, and LLaVa, with a focus on hydrological applications such as [...] Read more.

Large Language Models (LLMs) combined with visual foundation models have demonstrated significant advancements, achieving intelligence levels comparable to human capabilities. This study analyzes the latest Multimodal LLMs (MLLMs), including Multimodal-GPT, GPT-4 Vision, Gemini, and LLaVa, with a focus on hydrological applications such as flood management, water level monitoring, agricultural water discharge, and water pollution management. We evaluated these MLLMs on hydrology-specific tasks, testing their response generation and real-time suitability in complex real-world scenarios. Prompts were designed to enhance the models’ visual inference capabilities and contextual comprehension from images. Our findings reveal that GPT-4 Vision demonstrated exceptional proficiency in interpreting visual data, providing accurate assessments of flood severity and water quality. Additionally, MLLMs showed potential in various hydrological applications, including drought prediction, streamflow forecasting, groundwater management, and wetland conservation. These models can optimize water resource management by predicting rainfall, evaporation rates, and soil moisture levels, thereby promoting sustainable agricultural practices. This research provides valuable insights into the potential applications of advanced AI models in addressing complex hydrological challenges and improving real-time decision-making in water resource management Full article

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21 pages, 2741 KB

Open AccessEditor’s ChoiceArticle

Continental Scale Regional Flood Frequency Analysis: Combining Enhanced Datasets and a Bayesian Framework

by Duy Anh Alexandre, Chiranjib Chaudhuri and Jasmin Gill-Fortin

Hydrology 2024, 11(8), 119; https://doi.org/10.3390/hydrology11080119 - 11 Aug 2024

Cited by 6 | Viewed by 3940

Abstract

Flood frequency analysis at large scales, essential for the development of flood risk maps, is hindered by the scarcity of gauge flow data. Suitable methods are thus required to predict flooding in ungauged basins, a notoriously complex problem in hydrology. We develop a [...] Read more.

Flood frequency analysis at large scales, essential for the development of flood risk maps, is hindered by the scarcity of gauge flow data. Suitable methods are thus required to predict flooding in ungauged basins, a notoriously complex problem in hydrology. We develop a Bayesian hierarchical model (BHM) based on the generalized extreme value (GEV) and the generalized Pareto distribution for regional flood frequency analysis at high resolution across a large part of North America. Our model leverages annual maximum flow data from ≈20,000 gauged stations and a dataset of 130 static catchment-specific covariates to predict extreme flows at all catchments over the continent as well as their associated statistical uncertainty. Additionally, a modification is made to the data layer of the BHM to include peaks over threshold flow data when available, which improves the precision of the discharge level estimates. We validated the model using a hold-out approach and found that its predictive power is very good for the GEV distribution location and scale parameters and improvable for the shape parameter, which is notoriously hard to estimate. The resulting discharge return levels yield a satisfying agreement when compared with the available design peak discharge from various government sources. The assessment of the covariates’ contributions to the model is also informative with regard to the most relevant underlying factors influencing flood-inducing peak flows. According to the developed aggregate importance score, the key covariates in our model are temperature-related bioindicators, the catchment drainage area and the geographical location. Full article

(This article belongs to the Section Water Resources and Risk Management)

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37 pages, 2150 KB

Open AccessEditor’s ChoiceArticle

Monitoring Slope Movement and Soil Hydrologic Behavior Using IoT and AI Technologies: A Systematic Review

by Md Jobair Bin Alam, Luis Salgado Manzano, Rahul Debnath and Ahmed Abdelmoamen Ahmed

Hydrology 2024, 11(8), 111; https://doi.org/10.3390/hydrology11080111 - 24 Jul 2024

Cited by 41 | Viewed by 10798

Abstract

Landslides or slope failure pose a significant risk to human lives and infrastructures. The stability of slopes is controlled by various hydrological processes such as rainfall infiltration, soil water dynamics, and unsaturated soil behavior. Accordingly, soil hydrological monitoring and tracking the displacement of [...] Read more.

Landslides or slope failure pose a significant risk to human lives and infrastructures. The stability of slopes is controlled by various hydrological processes such as rainfall infiltration, soil water dynamics, and unsaturated soil behavior. Accordingly, soil hydrological monitoring and tracking the displacement of slopes become crucial to mitigate such risks by issuing early warnings to the respective authorities. In this context, there have been advancements in monitoring critical soil hydrological parameters and slope movement to ensure potential causative slope failure hazards are identified and mitigated before they escalate into disasters. With the advent of the Internet of Things (IoT), artificial intelligence, and high-speed internet, the potential to use such technologies for remotely monitoring soil hydrological parameters and slope movement is becoming increasingly important. This paper provides an overview of existing hydrological monitoring systems using IoT and AI technologies, including soil sampling, deploying on-site sensors such as capacitance, thermal dissipation, Time-Domain Reflectometers (TDRs), geophysical applications, etc. In addition, we review and compare the traditional slope movement detection systems, including topographic surveys for sophisticated applications such as terrestrial laser scanners, extensometers, tensiometers, inclinometers, GPS, synthetic aperture radar (SAR), LiDAR, and Unmanned Aerial Vehicles (UAVs). Finally, this interdisciplinary research from both Geotechnical Engineering and Computer Science perspectives provides a comprehensive state-of-the-art review of the different methodologies and solutions for monitoring landslides and slope failures, along with key challenges and prospects for potential future study. Full article

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18 pages, 5340 KB

Open AccessEditor’s ChoiceArticle

Measurement and Calculation of Sediment Transport on an Ephemeral Stream

by Loukas Avgeris, Konstantinos Kaffas and Vlassios Hrissanthou

Hydrology 2024, 11(7), 96; https://doi.org/10.3390/hydrology11070096 - 30 Jun 2024

Cited by 4 | Viewed by 5157

Abstract

Sediment transport remains a significant challenge for researchers due to the intricate nature of the physical processes involved and the diverse characteristics of watercourses worldwide. A type of watercourse that is of particular interest for study is the ephemeral streams, found primarily in [...] Read more.

Sediment transport remains a significant challenge for researchers due to the intricate nature of the physical processes involved and the diverse characteristics of watercourses worldwide. A type of watercourse that is of particular interest for study is the ephemeral streams, found primarily in semiarid and arid regions. Due to their unique nature, a new measurement algorithm was created and a modified bed load sampler was built. Measurement of the bed load transport rate and calculation of the water discharge were conducted in an ephemeral stream in Northeastern Greece, where the mean calculated streamflow rate ranged from 0.019 to 0.314 m³/s, and the measured sediment load transport rates per unit width varied from 0.00001 to 0.00213 kg/m/s. The sediment concentration was determined through various methods, including nonlinear regression equations and formulas developed by Yang, with the coefficients of these formulas calibrated accordingly. The results demonstrated that the equations derived from Yang’s multiple regression analysis offered a superior fit compared to the original equations. As a result, two modified versions of Yang’s stream sediment transport formulas were developed and are presented to the readership. To assess the accuracy of the modified formulas, a comparison was conducted between the calculated total sediment concentrations and the measured total sediment concentrations based on various statistical criteria. The analysis shows that none of Yang’s original formulas fit the available data well, but after optimization, both modified formulas can be applied to the specific ephemeral stream. The results indicate also that the formulas derived from the nonlinear regression can be successfully used for the determination of the total sediment concentration in the ephemeral stream and have a better fit compared to Yang’s formulas. The correlation from the nonlinear regression equations suggests that total sediment transport is primarily influenced by water discharge and rainfall intensity, with the latter showing a high correlation coefficient of 0.998. Full article

(This article belongs to the Special Issue Advances in Catchments Hydrology and Sediment Dynamics)

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18 pages, 12564 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Climate Change Projections of Potential Evapotranspiration for the North American Monsoon Region

by Eylon Shamir, Lourdes Mendoza Fierro, Sahar Mohsenzadeh Karimi, Norman Pelak, Emilie Tarouilly, Hsin-I Chang and Christopher L. Castro

Hydrology 2024, 11(6), 83; https://doi.org/10.3390/hydrology11060083 - 14 Jun 2024

Cited by 7 | Viewed by 6182

Abstract

We assessed and quantified future projected changes in terrestrial evaporative demand by calculating Potential Evapotranspiration (PET) for the North American Monsoon region in the Southwestern U.S. and Mexico. The PET projections were calculated using the daily Penman–Monteith equation. The terrestrial meteorological variables needed [...] Read more.

We assessed and quantified future projected changes in terrestrial evaporative demand by calculating Potential Evapotranspiration (PET) for the North American Monsoon region in the Southwestern U.S. and Mexico. The PET projections were calculated using the daily Penman–Monteith equation. The terrestrial meteorological variables needed for the equation (i.e., minimum and maximum daily temperature, specific humidity, wind speed, incoming shortwave radiation, and pressure) were obtained from the North American–CORDEX initiative. We used dynamically downscaled projections of three CMIP5 GCMs for RCP8.5 emission scenarios (i.e., HadGEM2-ES, MPI-ESM-LR, and GFDL-ESM2M), and each was dynamically downscaled to ~25 km by two RCMs (i.e., WRF and regCM4). All terrestrial annual PET projections showed a statistically significant increase when comparing the historical period (1986–2005) to future projections (2020–2039 and 2040–2059). The regional spatial average of the six GCM-RCM combinations projected an increase in the annual PET of about +4% and +8% for 2020–2039 and 2040–2059, respectively. The projected average 20-year annual changes over the study area range for the two projection periods were +1.4%–+8.7% and +3%–+14.2%, respectively. The projected annual PET increase trends are consistent across the entire region and for the six GCM-RCM combinations. Higher annual changes are projected in the northeast part of the region, while smaller changes are projected along the pacific coast. The main drivers for the increase are the projected warming and increase in the vapor pressure deficit. The projected changes in PET, which represent the changes in the atmospheric evaporative demand, are substantial and likely to impact vegetation and the hydrometeorological regime in the area. Quantitative assessments of the projected PET changes provided by this study should be considered in upcoming studies to develop resilience plans and adaptation strategies for mitigating the projected future changes. Full article

(This article belongs to the Special Issue Advances in Evaporation and Evaporative Demand: Part II)

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18 pages, 5743 KB

Open AccessEditor’s ChoiceArticle

Trend Analysis of Hydro-Meteorological Variables in the Wadi Ouahrane Basin, Algeria

by Mohammed Achite, Tommaso Caloiero, Andrzej Wałęga, Alessandro Ceppi and Abdelhak Bouharira

Hydrology 2024, 11(6), 77; https://doi.org/10.3390/hydrology11060077 - 31 May 2024

Cited by 8 | Viewed by 3954

Abstract

In recent decades, a plethora of natural disasters, including floods, storms, heat waves, droughts, and various other weather-related events, have brought destruction worldwide. In particular, Algeria is facing several natural hydrometeorological and geological hazards. In this study, meteorological parameters (precipitation, temperature, relative humidity, [...] Read more.

In recent decades, a plethora of natural disasters, including floods, storms, heat waves, droughts, and various other weather-related events, have brought destruction worldwide. In particular, Algeria is facing several natural hydrometeorological and geological hazards. In this study, meteorological parameters (precipitation, temperature, relative humidity, wind speed, and sunshine) and runoff data were analyzed for the Wadi Ouahrane basin (northern Algeria), into which drains much of the surrounding agricultural land and is susceptible to floods. In particular, a trend analysis was performed using the Mann–Kendall (MK) test, the Sen’s slope estimator, and the Innovative Trend Analysis (ITA) method to detect possible trends in the time series over the period 1972/73–2017/2018. The results revealed significant trends in several hydro-meteorological variables. In particular, neither annual nor monthly precipitation showed a clear tendency, thus failing to indicate potential changes in the rainfall patterns. Temperature evidenced a warming trend, indicating a potential shift in the local climate, while streamflow revealed a decreasing trend, reflecting the complex interaction between precipitation and other hydrological factors. Full article

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24 pages, 6357 KB

Open AccessEditor’s ChoiceArticle

Fuzzy Finite Elements Solution Describing Recession Flow in Unconfined Aquifers

by Christos Tzimopoulos, Kyriakos Papadopoulos, Nikiforos Samarinas, Basil Papadopoulos and Christos Evangelides

Hydrology 2024, 11(4), 47; https://doi.org/10.3390/hydrology11040047 - 30 Mar 2024

Cited by 3 | Viewed by 3176

Abstract

In this work, a novel fuzzy FEM (Finite Elements Method) numerical solution describing the recession flow in unconfined aquifers is proposed. In general, recession flow and drainage problems can be described by the nonlinear Boussinesq equation, while the introduced hydraulic parameters (Conductivity K [...] Read more.

In this work, a novel fuzzy FEM (Finite Elements Method) numerical solution describing the recession flow in unconfined aquifers is proposed. In general, recession flow and drainage problems can be described by the nonlinear Boussinesq equation, while the introduced hydraulic parameters (Conductivity K and Porosity S) present significant uncertainties for various reasons (e.g., spatial distribution, human errors, etc.). Considering the general lack of in situ measurements for these parameters as well as the certain spatial variability that they present in field scales, a fuzzy approach was adopted to include the problem uncertainties and cover the disadvantage of ground truth missing data. The overall problem is encountered with a new approximate fuzzy FEM numerical solution, leading to a system of crisp boundary value problems. To prove the validity and efficiency of the new fuzzy FEM, a comparative analysis between the proposed approach and other well-known and tested approximations was carried out. According to the results, the proposed FEM numerical solution agrees with Karadinumerical method for the crisp case and is in close agreement with the original analytical solution proposed by Boussinesq in 1904 with the absolute reduced error to be 4.6‰. Additionally, the possibility theory is applied, enabling the engineers and designers of irrigation, drainage, and water resources projects to gain knowledge of hydraulic properties (e.g., water level, outflow volume) and make the right decisions for rational and productive engineering studies. Full article

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21 pages, 7741 KB

Open AccessEditor’s ChoiceArticle

A Thermal Regime and a Water Circulation in a Very Deep Lake: Lake Tazawa, Japan

by Kazuhisa A. Chikita, Hideo Oyagi and Kazuhiro Amita

Hydrology 2024, 11(3), 40; https://doi.org/10.3390/hydrology11030040 - 16 Mar 2024

Cited by 2 | Viewed by 4824

Abstract

A thermal system in the very deep Lake Tazawa (maximum depth, 423 m) was investigated by estimating the heat budget. In the heat budget estimate, the net heat input at the lake’s surface and the heat input by river inflow and groundwater inflow [...] Read more.

A thermal system in the very deep Lake Tazawa (maximum depth, 423 m) was investigated by estimating the heat budget. In the heat budget estimate, the net heat input at the lake’s surface and the heat input by river inflow and groundwater inflow were considered. Then, the heat loss by snowfall onto the lake’s surface was taken into account. Meanwhile, the lake water temperature was monitored at 0.2 m to the bottom by mooring temperature loggers for more than two years. The heat storage change of the lake from the loggers was calibrated by frequent vertical measurements of water temperature at every 0.1 m pitch by a profiler with high accuracy (±0.01 °C). The heat storage change (W/m²) obtained by the temperature loggers reasonably accorded to that from the heat budget estimate. In the heat budget, the net heat input at lake surface dominated the heat storage change, but significant heat loss by river inflow sporadically occurred, caused by the relatively large discharge from a reservoir in the upper region. How deeply the vertical water circulation in the lake occurs in winter was judged according to the differences between water temperatures at 0.2 m depth and at the bottom and between vertical profiles of dissolved oxygen over winter. It is strongly suggested that the whole water circulation process does not occur every winter, and if it does, it is very weak. A consistent increase in the water temperature at the bottom is probably due to the conservation of geothermal heat by high frequency of incomplete vertical water circulation. Full article

(This article belongs to the Topic Climate Change and Human Impact on Freshwater Water Resources: Rivers and Lakes)

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18 pages, 4007 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Agricultural Water Footprints and Productivity in the Colorado River Basin

by George B. Frisvold and Dari Duval

Hydrology 2024, 11(1), 5; https://doi.org/10.3390/hydrology11010005 - 30 Dec 2023

Cited by 10 | Viewed by 9070

Abstract

The Colorado River provides water to 40 million people in the U.S. Southwest, with river basin spanning 250,000 square miles (647,497 km²). Quantitative water rights assigned to U.S. states, Mexico, and tribes in the Colorado Basin exceed annual streamflows. Climate change [...] Read more.

The Colorado River provides water to 40 million people in the U.S. Southwest, with river basin spanning 250,000 square miles (647,497 km²). Quantitative water rights assigned to U.S. states, Mexico, and tribes in the Colorado Basin exceed annual streamflows. Climate change is expected to limit streamflows further. To balance water demands with supplies, unprecedented water-use cutbacks have been proposed, primarily for agriculture, which consumes more than 60% of the Basin’s water. This study develops county-level, Basin-wide measures of agricultural economic water productivity, water footprints, and irrigation cash rent premiums, to inform conservation programs and compensation schemes. These measures identify areas where conservation costs in terms of foregone crop production or farm income are high or low. Crop sales averaged USD 814 per acre foot (AF) (USD 0.66/m³) of water consumed in the Lower Basin and 131 USD/AF (USD 0.11/m³) in the Upper Basin. Crop sales minus crop-specific input costs averaged 485 USD/AF (USD 0.39/m³) in the Lower Basin and 93 USD/AF (USD 0.08 per m³) in the Upper Basin. The blue water footprint (BWF) was 1.2 AF/USD 1K (1480 m³/USD1K) of water per thousand dollars of crop sales in the Lower Basin and 7.6 AF/USD 1K (9374 m³/USD1K) in the Upper Basin. Counties with higher water consumption per acre have a lower BWF. Full article

(This article belongs to the Special Issue Water Resources Management under Uncertainty and Climate Change)

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25 pages, 33171 KB

Open AccessEditor’s ChoiceArticle

Spatial Estimation of Snow Water Equivalent for Glaciers and Seasonal Snow in Iceland Using Remote Sensing Snow Cover and Albedo

by Andri Gunnarsson and Sigurdur M. Gardarsson

Hydrology 2024, 11(1), 3; https://doi.org/10.3390/hydrology11010003 - 26 Dec 2023

Cited by 5 | Viewed by 5416

Abstract

Efficient water resource management in glacier- and snow-dominated basins requires accurate estimates of the snow water equivalent (SWE) in late winter and spring and melt onset timing and intensity. To understand the high spatio-temporal variability of snow and glacier ablation, a spatially distributed [...] Read more.

Efficient water resource management in glacier- and snow-dominated basins requires accurate estimates of the snow water equivalent (SWE) in late winter and spring and melt onset timing and intensity. To understand the high spatio-temporal variability of snow and glacier ablation, a spatially distributed energy balance model combining satellite-based retrievals of albedo and snow cover was applied. Incoming short-wave energy, contributing to daily estimates of melt energy, was constrained by remotely sensed surface albedo for snow-covered surfaces. Fractional snow cover was used for non-glaciated areas, as it provides estimates of snow cover for each pixel to better constrain snow melt. Thus, available daily estimates of melt energy in a given area were the product of the possible melt energy and the fractional snow cover of the area or pixel for non-glaciated areas. This provided daily estimates of melt water to determine seasonal snow and glacier ablation in Iceland for the period 2000–2019. Observations from snow pits on land and glacier summer mass balance were used for evaluation, and observations from land and glacier-based automatic weather stations were used to evaluate model inputs for the energy balance model. The results show that the interannual SWE variability was generally high both for seasonal snow and glaciers. For seasonal snow, the largest SWE (>1000 mm) was found in mountainous and alpine areas close to the coast, notably in the East- and Westfjords, Tröllaskaga, and in the vicinity of glacier margins. Lower SWE values were observed in the central highlands, flatter inland areas, and at lower elevations. For glaciers, more SWE (glacier ablation) was associated with lower glacier elevations while less melt was observed at higher elevations. For the impurity-rich bare-ice areas that are exposed annually, observed SWE was more than 3000 mm. Full article

(This article belongs to the Topic Hydrology and Water Resources Management)

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32 pages, 6394 KB

Open AccessEditor’s ChoiceArticle

Evaluating the Benefits of Flood Warnings in the Management of an Urban Flood-Prone Polder Area

by Felipe Duque, Greg O’Donnell, Yanli Liu, Mingming Song and Enda O’Connell

Hydrology 2023, 10(12), 238; https://doi.org/10.3390/hydrology10120238 - 13 Dec 2023

Cited by 3 | Viewed by 4798

Abstract

Polders are low-lying areas located in deltas, surrounded by embankments to prevent flooding (river or tidal floods). They rely on pumping systems to remove water from the inner rivers (artificial rivers inside the polder area) to the outer rivers, especially during storms. Urbanized [...] Read more.

Polders are low-lying areas located in deltas, surrounded by embankments to prevent flooding (river or tidal floods). They rely on pumping systems to remove water from the inner rivers (artificial rivers inside the polder area) to the outer rivers, especially during storms. Urbanized polders are especially vulnerable to pluvial flooding if the drainage, storage, and pumping capacity of the polder is inadequate. In this paper, a Monte Carlo (MC) framework is proposed to evaluate the benefits of rainfall threshold-based flood warnings when mitigating pluvial flooding in an urban flood-prone polder area based on 24 h forecasts. The framework computes metrics that give the potential waterlogging duration, maximum inundated area, and pump operation costs by considering the full range of potential storms. The benefits of flood warnings are evaluated by comparing the values of these metrics across different scenarios: the no-warning, perfect, deterministic, and probabilistic forecast scenarios. Probabilistic forecasts are represented using the concept of “predictive uncertainty” (PU). A polder area located in Nanjing was chosen for the case study. The results show a trade-off between the metrics that represent the waterlogging and the pumping costs, and that probabilistic forecasts of rainfall can considerably enhance these metrics. The results can be used to design a rainfall threshold-based flood early warning system (FEWS) for a polder area and/or evaluate its benefits. Full article

(This article belongs to the Section Hydrological and Hydrodynamic Processes and Modelling)

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15 pages, 1852 KB

Open AccessEditor’s ChoiceArticle

Evaluating Non-Stationarity in Precipitation Intensity-Duration-Frequency Curves for the Dallas–Fort Worth Metroplex, Texas, USA

by Binita Ghimire, Gehendra Kharel, Esayas Gebremichael and Linyin Cheng

Hydrology 2023, 10(12), 229; https://doi.org/10.3390/hydrology10120229 - 2 Dec 2023

Cited by 5 | Viewed by 6430

Abstract

Extreme precipitation has become more frequent and intense with time and space. Infrastructure design tools such as Intensity-Duration-Frequency (IDF) curves still rely on historical precipitation and stationary assumptions, risking current and future urban infrastructure. This study developed IDF curves by incorporating non-stationarity trends [...] Read more.

Extreme precipitation has become more frequent and intense with time and space. Infrastructure design tools such as Intensity-Duration-Frequency (IDF) curves still rely on historical precipitation and stationary assumptions, risking current and future urban infrastructure. This study developed IDF curves by incorporating non-stationarity trends in precipitation annual maximum series (AMS) for Dallas–Fort Worth, the fourth-largest metropolitan region in the United States. A Pro-NEVA tool was used to develop non-stationary IDF curves, taking historical precipitation AMS for seven stations that showed a non-stationary trend with time as a covariate. Four statistical indices—the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE)—were used as the model goodness of fit evaluation. The lower AIC, BIC, and RMSE values and higher NSE values for non-stationary models indicated a better performance compared to the stationary models. Compared to the traditional stationary assumption, the non-stationary IDF curves showed an increase (up to 75%) in the 24 h precipitation intensity for the 100-year return period. Using the climate change adaptive non-stationary IDF tool for the DFW metroplex and similar urban regions could enable decision makers to make climate-informed choices about infrastructure investments, emergency preparedness measures, and long-term urban development and water resource management planning. Full article

(This article belongs to the Special Issue Trends and Variations in Hydroclimatic Variables)

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11 pages, 752 KB

Open AccessEditor’s ChoiceReview

Stream-Aquifer Systems in Semi-Arid Regions: Hydrologic, Legal, and Management Issues

by Neil S. Grigg, Ryan T. Bailey and Ryan G. Smith

Hydrology 2023, 10(12), 224; https://doi.org/10.3390/hydrology10120224 - 29 Nov 2023

Cited by 2 | Viewed by 3558

Abstract

Integrated solutions to groundwater management problems require effective analysis of stream-aquifer connections, especially in irrigated semi-arid regions where groundwater pumping affects return flows and causes streamflow depletion. Scientific research can explain technical issues, but legal and management solutions are difficult due to the [...] Read more.

Integrated solutions to groundwater management problems require effective analysis of stream-aquifer connections, especially in irrigated semi-arid regions where groundwater pumping affects return flows and causes streamflow depletion. Scientific research can explain technical issues, but legal and management solutions are difficult due to the complexities of hydrogeology, the expense of data collection and model studies, and the inclination of water users not to trust experts, regulatory authorities, and in some cases, their management organizations. The technical, legal, and management issues are reviewed, and experiences with integrated management of stream-aquifer systems are used to illustrate how governance authorities can approach engineering, legal, regulatory, and management challenges incrementally. The situations in three basins of the State of Colorado with over-appropriated water resources are explained to identify modeling and control issues confronting regulators and managers of water rights. Water rights administration in the state follows the strict appropriation method and a workable technical-legal approach to establishing regulatory and management strategies has been developed. The explanations show how models and data management are improving, but the complexities of hydrogeology and institutional systems must be confronted on a case-by-case basis. Stream-aquifer systems will require more attention in the future, better data will be needed, model developers must prove superiority over simpler methods, and organizational arrangements will be needed to facilitate successful collective action amidst inevitable legal challenges. Continued joint research between technical, legal, and management communities will also be needed. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Integrated Surface Water and Groundwater Resources Management)

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23 pages, 3457 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Calibration of Land-Use-Dependent Evaporation Parameters in Distributed Hydrological Models Using MODIS Evaporation Time Series Data

by Markus C. Casper, Zoé Salm, Oliver Gronz, Christopher Hutengs, Hadis Mohajerani and Michael Vohland

Hydrology 2023, 10(12), 216; https://doi.org/10.3390/hydrology10120216 - 21 Nov 2023

Cited by 4 | Viewed by 3840

Abstract

The land-use-specific calibration of evapotranspiration parameters in hydrologic modeling is challenging due to the lack of appropriate reference data. We present a MODIS-based calibration approach of vegetation-related evaporation parameters for two mesoscale catchments in western Germany with the physically based distributed hydrological model [...] Read more.

The land-use-specific calibration of evapotranspiration parameters in hydrologic modeling is challenging due to the lack of appropriate reference data. We present a MODIS-based calibration approach of vegetation-related evaporation parameters for two mesoscale catchments in western Germany with the physically based distributed hydrological model WaSiM-ETH. Time series of land-use-specific actual evapotranspiration (ETa) patterns were generated from MOD16A2 evapotranspiration and CORINE land-cover data from homogeneous image pixels for the major land-cover types in the region. Manual calibration was then carried out for 1D single-cell models, each representing a specific land-use type based on aggregated 11-year mean ETa values using SKout and PBIAS as objective functions (SKout > 0.8, |PBIAS| < 5%). The spatio-temporal evaluation on the catchment scale was conducted by comparing the simulated ETa pattern to six daily ETa grids derived from LANDSAT data. The results show a clear overall improvement in the SPAEF (spatial efficiency metric) for most land-use types, with some deficiencies for two scenes in spring and late summer due to phenological variation and a particularly dry hydrological system state, respectively. The presented method demonstrates a significant improvement in the simulation of ETa regarding both time and spatial scale. Full article

(This article belongs to the Special Issue Water Resources Management under Uncertainty and Climate Change)

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17 pages, 10228 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Locating Potential Groundwater Pathways in a Fringing Reef Using Continuous Electrical Resistivity Profiling

by Matthew W. Becker, Francine M. Cason and Benjamin Hagedorn

Hydrology 2023, 10(11), 206; https://doi.org/10.3390/hydrology10110206 - 25 Oct 2023

Cited by 4 | Viewed by 3943

Abstract

Groundwater discharge from high tropical islands can have a significant influence on the biochemistry of reef ecosystems. Recent studies have suggested that a portion of groundwater may underflow the reefs to be discharged, either through the reef flat or toward the periphery of [...] Read more.

Groundwater discharge from high tropical islands can have a significant influence on the biochemistry of reef ecosystems. Recent studies have suggested that a portion of groundwater may underflow the reefs to be discharged, either through the reef flat or toward the periphery of the reef system. Understanding of this potential discharge process is limited by the characterization of subsurface reef structures in these environments. A geophysical method was used in this study to profile the reef surrounding the high volcanic island of Mo’orea, French Polynesia. Boat-towed continuous resistivity profiling (CRP) revealed electrically resistive features at about 10–15 m depth, ranging in width from 30 to 200 m. These features were repeatable in duplicate survey lines, but resolution was limited by current-channeling through the seawater column. Anomalous resistivity could represent the occurrence of freshened porewater confined within the reef, but a change in porosity due to secondary cementation cannot be ruled out. Groundwater-freshened reef porewater has been observed near-shore on Mo’orea and suggested elsewhere using similar geophysical surveys, but synthetic models conducted as part of this study demonstrate that CRP alone is insufficient to draw these conclusions. These CRP surveys suggest reefs surrounding high islands may harbor pathways for terrestrial groundwater flow, but invasive sampling is required to demonstrate the role of groundwater in terrestrial runoff. Full article

(This article belongs to the Topic Monitoring Inland Water Quality and Ecological Status)

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16 pages, 2147 KB

Open AccessEditor’s ChoiceArticle

Efficient Flood Early Warning System for Data-Scarce, Karstic, Mountainous Environments: A Case Study

by Evangelos Rozos, Vasilis Bellos, John Kalogiros and Katerina Mazi

Hydrology 2023, 10(10), 203; https://doi.org/10.3390/hydrology10100203 - 19 Oct 2023

Cited by 10 | Viewed by 4816

Abstract

This paper presents an efficient flood early warning system developed for the city of Mandra, Greece which experienced a devastating flood event in November 2017 resulting in significant loss of life. The location is of particular interest due to both its small-sized water [...] Read more.

This paper presents an efficient flood early warning system developed for the city of Mandra, Greece which experienced a devastating flood event in November 2017 resulting in significant loss of life. The location is of particular interest due to both its small-sized water basin (20 km

^{2}

upstream of the studied cross-section), necessitating a rapid response time for effective flood warning calculations, and the lack of hydrometric data. To address the first issue, a database of pre-simulated flooding events with a 2D hydrodynamic model corresponding to synthetic precipitations with different return periods was established. To address the latter issue, the hydrological model was calibrated using qualitative information collected after the catastrophic event, compensating for the lack of hydrometric data. The case study demonstrates the establishment of a hybrid (online–offline) flood early warning system in data-scarce environments. By utilizing pre-simulated events and qualitative information, the system provides valuable insights for flood forecasting and aids in decision-making processes. This approach can be applied to other similar locations with limited data availability, contributing to improved flood management strategies and enhanced community resilience. Full article

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25 pages, 6833 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Nutrient Loadings to Utah Lake from Precipitation-Related Atmospheric Deposition

by Mitchell M. Brown, Justin T. Telfer, Gustavious P. Williams, A. Woodruff Miller, Robert B. Sowby, Riley C. Hales and Kaylee B. Tanner

Hydrology 2023, 10(10), 200; https://doi.org/10.3390/hydrology10100200 - 11 Oct 2023

Cited by 9 | Viewed by 3742

Abstract

Atmospheric deposition (AD) is a less understood and quantified source of nutrient loading to waterbodies. AD occurs via settling (large particulates), contact (smaller particulates and gaseous matter), and precipitation (rain, snow) transport pathways. Utah Lake is a shallow eutrophic freshwater lake located in [...] Read more.

Atmospheric deposition (AD) is a less understood and quantified source of nutrient loading to waterbodies. AD occurs via settling (large particulates), contact (smaller particulates and gaseous matter), and precipitation (rain, snow) transport pathways. Utah Lake is a shallow eutrophic freshwater lake located in central Utah, USA, with geophysical characteristics that make it particularly susceptible to AD-related nutrient loading. Studies have shown AD to be a significant contributor to the lake’s nutrient budget. This study analyzes nutrient samples from nine locations around the lake and four precipitation gauges over a 6-year study period using three different methods to estimate AD from the precipitation transport pathway. The methods used are simple averaging, Thiessen polygons, and inverse distance weighting, which we use to spatially interpolate point sample data to estimate nutrient lake loads. We hold that the inverse distance weighting method produces the most accurate results. We quantify, present, and compare nutrient loads and nutrient loading rates for total phosphorus (TP), total inorganic nitrogen (TIN), and ortho phosphate (OP) from precipitation events. We compute loading rates for the calendar year (Mg/yr) from each of the three analysis methods along with monthly loading rates where Mg is 10⁶ g. Our estimated annual precipitation AD loads for TP, OP, and TIN are 120.96 Mg/yr (132.97 tons/yr), 60.87 Mg/yr (67.1 tons/yr), and 435 Mg/yr (479.5 tons/yr), respectively. We compare these results with published data on total AD nutrient loads and show that AD from precipitation is a significant nutrient source for Utah Lake, contributing between 25% and 40% of the total AD nutrient load to the lake. Full article

(This article belongs to the Topic Monitoring Inland Water Quality and Ecological Status)

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17 pages, 4960 KB

Open AccessFeature PaperEditor’s ChoiceArticle

A Soil Moisture Profile Conceptual Framework to Identify Water Availability and Recovery in Green Stormwater Infrastructure

by Matina Shakya, Amanda Hess, Bridget M. Wadzuk and Robert G. Traver

Hydrology 2023, 10(10), 197; https://doi.org/10.3390/hydrology10100197 - 6 Oct 2023

Cited by 9 | Viewed by 6537

Abstract

The recovery of soil void space through infiltration and evapotranspiration processes within green stormwater infrastructure (GSI) is key to continued hydrologic function. As such, soil void space recovery must be well understood to improve the design and modeling and to provide realistic expectations [...] Read more.

The recovery of soil void space through infiltration and evapotranspiration processes within green stormwater infrastructure (GSI) is key to continued hydrologic function. As such, soil void space recovery must be well understood to improve the design and modeling and to provide realistic expectations of GSI performance. A novel conceptual framework of soil moisture behavior was developed to define the soil moisture availability at pre-, during, and post-storm conditions. It uses soil moisture measurements and provides seven critical soil moisture points (A, B, C, D, E, F, F″) that describe the soil–water void space recovery after a storm passes through a GSI. The framework outputs a quantification of a GSI subsurface hydrology, including average soil moisture, the duration of saturation, soil moisture recession, desaturation time, infiltration rates, and evapotranspiration (ET) rates. The outputs the framework provide were compared to the values that were obtained through more traditional measurements of infiltration (through spot field infiltration testing), ET (through a variety of methods to quantify GSI ET), soil moisture measurements (through the soil water characteristics curve), and the duration of saturation/desaturation time (through a simulated runoff test), all which provided a strong justification to the framework. This conceptual framework has several applications, including providing an understanding of a system’s ability to hold water, the post-storm recovery process, GSI unit processes (ET and infiltration), important water contents that define the soil–water relationship (such as field capacity and saturation), and a way to quantify long-term changes in performance all through minimal monitoring with one or more soil moisture sensors. The application of this framework to GSI design promotes a deeper understanding of the subsurface hydrology and site-specific soil conditions, which is a key advancement in the understanding of long-term performance and informing GSI design and maintenance. Full article

(This article belongs to the Special Issue Soil–Water–City Nexus in Urban Environment: Experimental Investigations and Numerical Analysis in Urban Hydrology Science)

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22 pages, 8605 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Assessing Terrestrial Water Storage Variations in Southern Spain Using Rainfall Estimates and GRACE Data

by Eulogio Pardo-Igúzquiza, Jean-Philippe Montillet, José Sánchez-Morales, Peter A. Dowd, Juan Antonio Luque-Espinar, Neda Darbeheshti and Francisco Javier Rodríguez-Tovar

Hydrology 2023, 10(9), 187; https://doi.org/10.3390/hydrology10090187 - 15 Sep 2023

Cited by 4 | Viewed by 4057

Abstract

This paper investigates the relationship between rainfall, groundwater and Gravity Recovery and Climate Experiment (GRACE) data to generate regional-scale estimates of terrestrial water storage variations in the Andalucía region of southern Spain. These estimates can provide information on groundwater depletion (caused by periods [...] Read more.

This paper investigates the relationship between rainfall, groundwater and Gravity Recovery and Climate Experiment (GRACE) data to generate regional-scale estimates of terrestrial water storage variations in the Andalucía region of southern Spain. These estimates can provide information on groundwater depletion (caused by periods of low rainfall or droughts) and groundwater recovery. The spatial distribution of groundwater bodies in southern Spain is complex and current in situ groundwater monitoring methods are deficient, particularly in terms of obtaining representative samples and in implementing and maintaining groundwater monitoring networks. The alternative approach proposed here is to investigate the relationship between precipitation time series and changes in the terrestrial water storage estimated from GRACE observations. The results were validated against the estimated fluctuation in regional groundwater. The maximum correlation between the mean groundwater level and the GRACE observations is 0.69 and this occurs at a lag of one month because the variation in gravity is immediate, but rainfall water requires around one month to travel across the vadose zone before it reaches the groundwater table. Using graphical methods of accumulated deviations from the mean, we show that, in general, groundwater storage follows the smooth, multi-year trends of terrestrial water storage but with less short-term trends; the same is true of rainfall, for which the local trends are more pronounced. There is hysteresis-like behaviour in the variations in terrestrial water storage and in the variations of groundwater. In practical terms, this study shows that, despite the abnormal dryness of the Iberian Peninsula during the 2004–2010 drought, the depleted groundwater storage in Andalucía recovered almost to its pre-drought level by 2016. In addition, groundwater storage and terrestrial water storage show very similar trends but with a delay in the groundwater trend. Full article

(This article belongs to the Section Hydrology–Climate Interactions)

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19 pages, 9333 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Urban Flood Modelling under Extreme Rainfall Conditions for Building-Level Flood Exposure Analysis

by Christos Iliadis, Panagiota Galiatsatou, Vassilis Glenis, Panagiotis Prinos and Chris Kilsby

Hydrology 2023, 10(8), 172; https://doi.org/10.3390/hydrology10080172 - 17 Aug 2023

Cited by 37 | Viewed by 9430

Abstract

The expansion of urban areas and the increasing frequency and magnitude of intense rainfall events are anticipated to contribute to the widespread escalation of urban flood risk across the globe. To effectively mitigate future flood risks, it is crucial to combine a comprehensive [...] Read more.

The expansion of urban areas and the increasing frequency and magnitude of intense rainfall events are anticipated to contribute to the widespread escalation of urban flood risk across the globe. To effectively mitigate future flood risks, it is crucial to combine a comprehensive examination of intense rainfall events in urban areas with the utilization of detailed hydrodynamic models. This study combines extreme value analysis techniques applied to rainfall data ranging from sub-hourly to daily durations with a high-resolution flood modelling analysis at the building level in the centre of Thessaloniki, Greece. A scaling procedure is employed to rainfall return levels assessed by applying the generalised extreme value (GEV) distribution to annual maximum fine-temporal-scale data, and these scaling laws are then applied to more reliable daily rainfall return levels estimated by means of the generalised Pareto distribution (GPD), in order to develop storm profiles with durations of 1 h and 2 h. The advanced flood model, CityCAT, is then used for the simulation of pluvial flooding, providing reliable assessments of building-level exposure to flooding hazards. The results of the analysis conducted provide insights into flood depths and water flowpaths in the city centre of Thessaloniki, identifying major flowpaths along certain main streets resulting in localised flooding, and identifying around 165 and 186 buildings highly exposed to inundation risk in the study area for 50-year storm events with durations of 1 h and 2 h, respectively. For the first time in this study area, a detailed analysis of extreme rainfall events is combined with a high-resolution Digital Terrain Model (DTM), used as an input into the advanced and fully featured CityCAT hydrodynamic model, to assess critical flowpaths and buildings at high flood risk. The results of this study can aid in the planning and design of resilient solutions to combat urban flash floods, as well as contribute to targeted flood damage mitigation and flood risk reduction. Full article

(This article belongs to the Special Issue Urban Hydrology and Urban Water Works under Rapid Urbanisation and Climate Change Conditions in Metropolitan Areas and Mega Cities)

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22 pages, 9908 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Decoupling of Ecological and Hydrological Drought Conditions in the Limpopo River Basin Inferred from Groundwater Storage and NDVI Anomalies

by Kyung Y. Kim, Todd Scanlon, Sophia Bakar and Venkataraman Lakshmi

Hydrology 2023, 10(8), 170; https://doi.org/10.3390/hydrology10080170 - 12 Aug 2023

Cited by 8 | Viewed by 6176

Abstract

Droughts are projected to increase in intensity and frequency with the rise of global mean temperatures. However, not all drought indices equally capture the variety of influences that each hydrologic component has on the duration and magnitude of a period of water deficit. [...] Read more.

Droughts are projected to increase in intensity and frequency with the rise of global mean temperatures. However, not all drought indices equally capture the variety of influences that each hydrologic component has on the duration and magnitude of a period of water deficit. While such indices often agree with one another due to precipitation being the major input, heterogeneous responses caused by groundwater recharge, soil moisture memory, and vegetation dynamics may lead to a decoupling of identifiable drought conditions. As a semi-arid basin, the Limpopo River Basin (LRB) is a severely water-stressed region associated with unique climate patterns that regularly affect hydrological extremes. In this study, we find that vegetation indices show no significant long-term trends (S-statistic 9; p-value 0.779), opposing that of the modeled groundwater anomalies (S-statistic -57; p-value 0.05) in the growing season for a period of 18 years (2004–2022). Although the Mann-Kendall time series statistics for NDVI and drought indices are non-significant when basin-averaged, spatial heterogeneity further reveals that such a decoupling trend between vegetation and groundwater anomalies is indeed significant (p-value < 0.05) in colluvial, low-land aquifers to the southeast, while they remain more coupled in the central-west LRB, where more bedrock aquifers dominate. The conclusions of this study highlight the importance of ecological conditions with respect to water availability and suggest that water management must be informed by local vegetation species, especially in the face of depleting groundwater resources. Full article

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26 pages, 7730 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Improvements and Evaluation of the Agro-Hydrologic VegET Model for Large-Area Water Budget Analysis and Drought Monitoring

by Gabriel B. Senay, Stefanie Kagone, Gabriel E. L. Parrish, Kul Khand, Olena Boiko and Naga M. Velpuri

Hydrology 2023, 10(8), 168; https://doi.org/10.3390/hydrology10080168 - 10 Aug 2023

Cited by 11 | Viewed by 5939

Abstract

We enhanced the agro-hydrologic VegET model to include snow accumulation and melt processes and the separation of runoff into surface runoff and deep drainage. Driven by global weather datasets and parameterized by land surface phenology (LSP), the enhanced VegET model was implemented in [...] Read more.

We enhanced the agro-hydrologic VegET model to include snow accumulation and melt processes and the separation of runoff into surface runoff and deep drainage. Driven by global weather datasets and parameterized by land surface phenology (LSP), the enhanced VegET model was implemented in the cloud to simulate daily soil moisture (SM), actual evapotranspiration (ETa), and runoff (R) for the conterminous United States (CONUS) and the Greater Horn of Africa (GHA). Evaluation of the VegET model with independent data showed satisfactory performance, capturing the temporal variability of SM (Pearson correlation r: 0.22–0.97), snowpack (r: 0.86–0.88), ETa (r: 0.41–0.97), and spatial variability of R (r: 0.81–0.90). Absolute magnitudes showed some biases, indicating the need of calibrating the model for water budget analysis. The seasonal Landscape Water Requirement Satisfaction Index (L-WRSI) for CONUS and GHA showed realistic depictions of drought hazard extent and severity, indicating the usefulness of the L-WRSI for the convergence of an evidence toolkit used by the Famine Early Warning System Network to monitor potential food insecurity conditions in different parts of the world. Using projected weather datasets and landcover-based LSP, the VegET model can be used not only for global monitoring of drought conditions, but also for evaluating scenarios on the effect of a changing climate and land cover on agriculture and water resources. Full article

(This article belongs to the Topic Hydrology and Water Resources in Agriculture and Ecology)

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24 pages, 10756 KB

Open AccessEditor’s ChoiceArticle

Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery

by Kevin J. Wienhold, Dongfeng Li, Wenzhao Li and Zheng N. Fang

Hydrology 2023, 10(8), 158; https://doi.org/10.3390/hydrology10080158 - 28 Jul 2023

Cited by 25 | Viewed by 7329

Abstract

The identification of flood hazards during emerging public safety crises such as hurricanes or flash floods is an invaluable tool for first responders and managers yet remains out of reach in any comprehensive sense when using traditional remote-sensing methods, due to cloud cover [...] Read more.

The identification of flood hazards during emerging public safety crises such as hurricanes or flash floods is an invaluable tool for first responders and managers yet remains out of reach in any comprehensive sense when using traditional remote-sensing methods, due to cloud cover and other data-sourcing restrictions. While many remote-sensing techniques exist for floodwater identification and extraction, few studies demonstrate an up-to-day understanding with better techniques in isolating the spectral properties of floodwaters from collected data, which vary for each event. This study introduces a novel method for delineating near-real-time inundation flood extent and depth mapping for storm events, using an inexpensive unmanned aerial vehicle (UAV)-based multispectral remote-sensing platform, which was designed to be applicable for urban environments, under a wide range of atmospheric conditions. The methodology is demonstrated using an actual flooding-event—Hurricane Zeta during the 2020 Atlantic hurricane season. Referred to as the UAV and Floodwater Inundation and Depth Mapper (FIDM), the methodology consists of three major components, including aerial data collection, processing, and flood inundation (water surface extent) and depth mapping. The model results for inundation and depth were compared to a validation dataset and ground-truthing data, respectively. The results suggest that UAV-FIDM is able to predict inundation with a total error (sum of omission and commission errors) of 15.8% and produce flooding depth estimates that are accurate enough to be actionable to determine road closures for a real event. Full article

(This article belongs to the Special Issue Flood Inundation Mapping in Hydrological Systems)

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19 pages, 3306 KB

Open AccessFeature PaperEditor’s ChoiceArticle

ANN-Based Predictors of ASR Well Recovery Effectiveness in Unconfined Aquifers

by Saeid Masoudiashtiani and Richard C. Peralta

Hydrology 2023, 10(7), 151; https://doi.org/10.3390/hydrology10070151 - 19 Jul 2023

Cited by 2 | Viewed by 3223

Abstract

In this study, we present artificial neural networks (ANNs) to aid in a reconnaissance evaluation of an aquifer storage and recovery (ASR) well. Recovery effectiveness (REN) is the proportion of ASR-injected water recovered during subsequent extraction from the same well. ANN-based predictors allow [...] Read more.

In this study, we present artificial neural networks (ANNs) to aid in a reconnaissance evaluation of an aquifer storage and recovery (ASR) well. Recovery effectiveness (REN) is the proportion of ASR-injected water recovered during subsequent extraction from the same well. ANN-based predictors allow rapid REN prediction without requiring preparation for and execution of solute transport simulations. REN helps estimate blended water quality resulting from a conservative solute in an aquifer, extraction for environmental protection, and other uses, respectively. Assume that into an isotropic homogenous portion of an unconfined, one-layer aquifer, extra surface water is injected at a steady rate during two wet months (61 days) through a fully penetrating ASR well. And then, water is extracted from the well at the same steady rate during three dry months (91-day period of high demand). The presented dimensionless input parameters were designed to be calibrated within the ANNs to match REN values. The values result from groundwater flow and solute transport simulations for ranges of impact factors of unconfined aquifers. The ANNs calibrated the weighting coefficients associated with the input parameters to predict the achievable REN of an ASR well. The ASR steadily injects extra surface water during periods of water availability and, subsequently, steadily extracts groundwater for use. The total extraction volume equaled the total injection volume at the end of extraction day 61. Subsequently, continuing extraction presumes a pre-existing groundwater right. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Integrated Surface Water and Groundwater Resources Management)

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