Hydrology

18 pages, 3793 KiB

Open AccessArticle

Continuous Simulations for Predicting Green Roof Hydrologic Performance for Future Climate Scenarios

by Komal Jabeen, Giovanna Grossi, Michele Turco, Arianna Dada, Stefania A. Palermo, Behrouz Pirouz, Patrizia Piro, Ilaria Gnecco and Anna Palla

Hydrology 2025, 12(2), 41; https://doi.org/10.3390/hydrology12020041 - 19 Feb 2025

Cited by 1 | Viewed by 653

Abstract

Urban green spaces, including green roofs (GRs), are vital infrastructure for climate resilience, retaining water in city landscapes and supporting ecohydrological processes. Quantifying the hydrologic performance of GRs in the urban environment for future climate scenarios is the original contribution of this research [...] Read more.

Urban green spaces, including green roofs (GRs), are vital infrastructure for climate resilience, retaining water in city landscapes and supporting ecohydrological processes. Quantifying the hydrologic performance of GRs in the urban environment for future climate scenarios is the original contribution of this research developed within the URCA! project. For this purpose, a continuous modelling approach is undertaken to evaluate the hydrological performance of GRs expressed by means of the runoff volume and peak flow reduction at the event scale for long data series (at least 20 years). To investigate the prediction of GRs performance in future climates, a simple methodological approach is proposed, using monthly projection factors for the definition of future rainfall and temperature time series, and transferring the system parametrization of the current model to the future one. The proposed approach is tested for experimental GR sites in Genoa and Rende, located in Northern and Southern Italy, respectively. Referring to both the Genoa and Rende experimental sites, simulation results are analysed to demonstrate how the GR performance varies with respect to rainfall event characteristics, including total depth, maximum rainfall intensity and ADWP for current and future scenarios. 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: 2nd Edition)

► Show Figures

Graphical abstract

12 pages, 1273 KiB

Open AccessFeature PaperArticle

Leaf Water Storage Capacity Among Eight US Hardwood Tree Species: Differences in Seasonality and Methodology

by Natasha Scavotto, Courtney M. Siegert, Heather D. Alexander and J. Morgan Varner

Hydrology 2025, 12(2), 40; https://doi.org/10.3390/hydrology12020040 - 18 Feb 2025

Viewed by 517

Abstract

Canopy hydrology and forest water inputs are directly linked to the physical properties of tree crowns (e.g., foliar and woody surfaces), which determine a tree’s capacity to intercept and retain incident rainfall. The changing forest structure, notably the decline of oak’s (Quercus [...] Read more.

Canopy hydrology and forest water inputs are directly linked to the physical properties of tree crowns (e.g., foliar and woody surfaces), which determine a tree’s capacity to intercept and retain incident rainfall. The changing forest structure, notably the decline of oak’s (Quercus) dominance and encroachment of non-oak species in much of the upland hardwood forests of the eastern United States, challenges our understanding of how species-level traits scale up to control the forest hydrologic budget. The objective of this study was to determine how the leaf water storage capacity varies across species and canopy layers, and how these relationships change throughout the growing season. We measured the leaf water storage capacity of overstory and midstory trees of native deciduous oaks (Q. alba, Q. falcata, Q. stellata) and non-oak species (Carya tomentosa, Acer rubrum, Ulmus alata, Liquidambar styraciflua, Nyssa sylvatica) using two methods (water displacement and rainfall simulation). Overstory Q. alba leaves retained 0.5 times less water per unit leaf area than other overstory species (p < 0.001) in the early growing season, while in the late growing season, C. tomentosa leaves had the lowest storage capacity (p = 0.024). Quercus falcata leaves displayed a minimal change in storage between seasons, while Q. alba and Q. stellata leaves had higher water storage in the late growing season. Midstory U. alata leaves had 3.5 times higher water storage capacity in the early growing season compared to all the other species (p < 0.001), but this difference diminished in the late growing season. Furthermore, the water storage capacities from the simulated rainfall experiments were up to two times higher than those in the water displacement experiments, particularly during the early growing season. These results underscore the complexity of leaf water storage dynamics, the methodology, and the implications for forest hydrology and species interactions. Broader efforts to understand species-level controls on canopy water portioning through leaf and other crown characteristics are necessary. Full article

(This article belongs to the Section Ecohydrology)

► Show Figures

Figure 1

39 pages, 2991 KiB

Open AccessFeature PaperReview

Event-Based vs. Continuous Hydrological Modeling with HEC-HMS: A Review of Use Cases, Methodologies, and Performance Metrics

by Golden Odey and Younghyun Cho

Hydrology 2025, 12(2), 39; https://doi.org/10.3390/hydrology12020039 - 17 Feb 2025

Viewed by 2618

Abstract

This study critically examines the applications of the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) in hydrological research from 2000 to 2023, with a focus on its use in event-based and continuous simulations. A bibliometric analysis reveals a steady growth in research productivity and [...] Read more.

This study critically examines the applications of the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) in hydrological research from 2000 to 2023, with a focus on its use in event-based and continuous simulations. A bibliometric analysis reveals a steady growth in research productivity and identifies key thematic areas, including hydrologic modeling, climate change impact assessment, and land use analysis. Event-based modeling, employing methods such as the SCS curve number (CN) and SCS unit hydrograph, demonstrates exceptional performance in simulating short-term hydrological responses, particularly in flood risk management and stormwater applications. In contrast, continuous modeling excels in capturing long-term processes, such as soil moisture dynamics and groundwater contributions, using methodologies like soil moisture accounting and linear reservoir baseflow approaches, which are critical for water resource planning and climate resilience studies. This review highlights the adaptability of HEC-HMS, showcasing its successful integration of event-based precision and continuous process modeling through hybrid approaches, enabling robust analyses across temporal scales. By synthesizing methodologies, performance metrics, and case studies, this study offers practical insights for selecting appropriate modeling techniques tailored to specific hydrological objectives. Moreover, it identifies critical research gaps, including the need for advanced calibration methods, enhanced parameter sensitivity analyses, and improved integration with hydraulic models. These findings highlight HEC-HMS’s critical role in improving hydrological research and give a thorough foundation for its use in addressing current water resource concerns. Full article

► Show Figures

Figure 1

25 pages, 8413 KiB

Open AccessArticle

Flood Exposure Dynamics and Quantitative Evaluation of Low-Cost Flood Control Measures in the Bengawan Solo River Basin of Indonesia

by Badri Bhakta Shrestha, Mohamed Rasmy and Daisuke Kuribayashi

Hydrology 2025, 12(2), 38; https://doi.org/10.3390/hydrology12020038 - 17 Feb 2025

Viewed by 1017

Abstract

The frequent occurrence of floods puts additional pressure on people to change their activities and alter land use practices, consequently making exposed lands more vulnerable to floods. It is thus crucial to investigate dynamic changes in flood exposures and conduct quantitative evaluations of [...] Read more.

The frequent occurrence of floods puts additional pressure on people to change their activities and alter land use practices, consequently making exposed lands more vulnerable to floods. It is thus crucial to investigate dynamic changes in flood exposures and conduct quantitative evaluations of flood risk-reduction strategies to minimize damage to exposed items. This study quantitatively assessed dynamics of flood exposure and flood risk, and evaluated the effectiveness of flood control measures in the Bengawan Solo River basin, Indonesia. The Water and Energy Budget-Based Rainfall–Runoff–Inundation Model was employed for flood simulation for different return periods, and then dynamics of flood exposures and flood risk were assessed. After that, the effectiveness of flood control measures was quantitively evaluated. The results show that settlement/built-up areas and population are increasing in flood-prone areas. The flood-exposed paddy field and settlement areas for 100-year flood were estimated to be more than 950 and 212.58 km², respectively. The results also show that the dam operation for flood control in the study area reduces the flood damage to buildings, contents, and agriculture by approximately 21.2%, 20.9%, and 25.1%, respectively. The river channel improvements were also found effective to reduce flood damage in the study area. The flood damage can be reduced by more than 60% by implementing a combination of a flood control dam and river channel improvements. The findings can be useful for planning and implementing effective flood risk reduction measures. Full article

► Show Figures

Figure 1

29 pages, 14374 KiB

Open AccessArticle

Assessment of Heavy Metals in Surface Waters of the Santiago–Guadalajara River Basin, Mexico

by Rosa Leonor González-Díaz, José de Anda, Harvey Shear, Luis Eduardo Padilla-Tovar, Ofelia Yadira Lugo-Melchor and Luis Alberto Olvera-Vargas

Hydrology 2025, 12(2), 37; https://doi.org/10.3390/hydrology12020037 - 17 Feb 2025

Viewed by 1490

Abstract

The Santiago–Guadalajara River Basin has an area of 10,016.46 km². The Metropolitan Area of Guadalajara, within the basin, is the second-largest city in the country, with more than 5 million inhabitants. The growth of the urban population, as well as industrial [...] Read more.

The Santiago–Guadalajara River Basin has an area of 10,016.46 km². The Metropolitan Area of Guadalajara, within the basin, is the second-largest city in the country, with more than 5 million inhabitants. The growth of the urban population, as well as industrial and agricultural activities with insufficient infrastructure for the sanitation of wastewater and its reuse, have caused environmental deterioration of surface waters and gradual depletion of groundwater resources. To assess the level of contamination in surface waters from the presence of heavy metals in the basin, a monthly monitoring campaign was carried out at 25 sampling stations located in the main and tributary streams from July 2021 to April 2022. The following decreasing sequence was found according to the mean concentration values: Fe > Al > Mn > B > Ba > Zn > As > Cu > Cr > Ni > Pb > Cd. The Heavy Metal Pollution Index (HPI) method was applied to assess the level of risk to aquatic life, finding an average global HPI value of 305.522 for the basin, which classifies it as in the critical contamination range. The results also reflect health risks due to the presence of As, Cd, and Ni in some monitored stations. It will be necessary to expand the monitoring network, identify the point and non-point sources of contamination, and implement measures for pollution control to protect aquatic life and human health due to the presence of heavy metals in the river. Full article

(This article belongs to the Special Issue Hydrodynamics and Water Quality of Rivers and Lakes)

► Show Figures

Figure 1

22 pages, 2068 KiB

Open AccessArticle

Determination of the Total Phosphorus Decay Coefficient Based on Hydrological Models in an Artificial Reservoir in the Brazilian Semi-Arid Region

by Francisco Josivan de Oliveira Lima, Fernando Bezerra Lopes, Daniel Antônio Camelo Cid, Iran Eduardo Lima Neto, Renan Vieira Rocha, Alyson Brayner Sousa Estácio, Isabel Cristina da Silva Araújo, Nayara Rochelli de Sousa Luna, Michele Cunha Pontes, Arthur Costa Tomaz de Souza and Eunice Maia de Andrade

Hydrology 2025, 12(2), 36; https://doi.org/10.3390/hydrology12020036 - 16 Feb 2025

Viewed by 672

Abstract

Phosphorus input into surface water is a global concern due to its role in eutrophication, which is especially critical in semi-arid regions with their challenging climatic conditions. This study evaluated the best model for estimating the phosphorus decay coefficient (k) in semi-arid lakes, [...] Read more.

Phosphorus input into surface water is a global concern due to its role in eutrophication, which is especially critical in semi-arid regions with their challenging climatic conditions. This study evaluated the best model for estimating the phosphorus decay coefficient (k) in semi-arid lakes, using flows from the Soil Moisture Accounting Procedure (SMAP), model of Génie Rural à 4 paramètres Journalier (GR4J), and reverse water balance hydrological models. Conducted at the Orós reservoir with 37 sampling campaigns from 2008 to 2017, it compared decay rates for temperate, tropical, and semi-arid climates. Some analyses also used phosphorus concentrations measured at the reservoir inlet. Model efficiency was assessed with bias, mean relative error, mean squared error, root mean squared error, and standard deviation. from the best models, water quality classes were classified based on phosphorus concentrations with the use of a confusion matrix to calculate accuracy, precision, recall, and F1 score. The findings demonstrated that the decay rate tailored for semi-arid regions, when combined with GR4J flow data, offered the highest accuracy in estimating phosphorus concentrations (bias = 0.0012, RMSE = 0.0326, EMR = 60.6134, STD = 0.0312). In contrast, the decay rate calibrated for tropical conditions with SMAP-derived flows proved superior for classifying water quality categories (classes defined by CONAMA Resolution 357/05). Therefore, the GR4J model for semi-arid conditions stands out for concentration estimation, while the tropical decay rate with SMAP flows is preferable for effective classification of water quality status. Full article

(This article belongs to the Special Issue Hydrodynamics and Water Quality of Rivers and Lakes)

► Show Figures

Figure 1

27 pages, 7459 KiB

Open AccessArticle

Flood Modelling of the Zhabay River Basin Under Climate Change Conditions

by Aliya Nurbatsina, Zhanat Salavatova, Aisulu Tursunova, Iulii Didovets, Fredrik Huthoff, María-Elena Rodrigo-Clavero and Javier Rodrigo-Ilarri

Hydrology 2025, 12(2), 35; https://doi.org/10.3390/hydrology12020035 - 15 Feb 2025

Cited by 1 | Viewed by 918

Abstract

Flood modelling in snow-fed river basins is critical for understanding the impacts of climate change on hydrological extremes. The Zhabay River in northern Kazakhstan exemplifies a basin highly vulnerable to seasonal floods, which pose significant risks to infrastructure, livelihoods, and water resource management. [...] Read more.

Flood modelling in snow-fed river basins is critical for understanding the impacts of climate change on hydrological extremes. The Zhabay River in northern Kazakhstan exemplifies a basin highly vulnerable to seasonal floods, which pose significant risks to infrastructure, livelihoods, and water resource management. Traditional flood forecasting in Central Asia still relies on statistical models developed during the Soviet era, which are limited in their ability to incorporate non-stationary climate and anthropogenic influences. This study addresses this gap by applying the Soil and Water Integrated Model (SWIM) to project climate-driven changes in the hydrological regime of the Zhabay River. The study employs a process-based, high-resolution hydrological model to simulate flood dynamics under future climate conditions. Historical hydrometeorological data were used to calibrate and validate the model at the Atbasar gauge station. Future flood scenarios were simulated using bias-corrected outputs from an ensemble of General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5 for the periods 2011–2040, 2041–2070, and 2071–2099. This approach enables the assessment of seasonal and interannual variability in flood magnitudes, peak discharges, and their potential recurrence intervals. Findings indicate a substantial increase in peak spring floods, with projected discharge nearly doubling by mid-century under both climate scenarios. The study reveals a 1.8-fold increase in peak discharge between 2010 and 2040, and a twofold increase from 2041 to 2070. Under the RCP 4.5 scenario, extreme flood events exceeding a 100-year return period (2000 m³/s) are expected to become more frequent, whereas the RCP 8.5 scenario suggests a stabilization of extreme event occurrences beyond 2071. These findings underscore the growing flood risk in the region and highlight the necessity for adaptive water resource management strategies. This research contributes to the advancement of climate-resilient flood forecasting in Central Asian river basins. The integration of process-based hydrological modelling with climate projections provides a more robust framework for flood risk assessment and early warning system development. The outcomes of this study offer crucial insights for policymakers, hydrologists, and disaster management agencies in mitigating the adverse effects of climate-induced hydrological extremes in Kazakhstan. Full article

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

► Show Figures

Figure 1

24 pages, 10495 KiB

Open AccessArticle

Dependence of Soil Moisture and Strength on Topography and Vegetation Varies Within a SMAP Grid Cell

by Joseph R. Bindner, Holly Proulx, Kevin Wickham, Jeffrey D. Niemann, Joseph Scalia IV, Timothy R. Green and Peter J. Grazaitis

Hydrology 2025, 12(2), 34; https://doi.org/10.3390/hydrology12020034 - 15 Feb 2025

Viewed by 474

Abstract

Off-road vehicle mobility assessments rely on fine-resolution (~10 m) estimates of soil moisture and strength across the region of interest. Such estimates are often produced by downscaling soil moisture from a microwave satellite like SMAP, then using the soil moisture in a soil [...] Read more.

Off-road vehicle mobility assessments rely on fine-resolution (~10 m) estimates of soil moisture and strength across the region of interest. Such estimates are often produced by downscaling soil moisture from a microwave satellite like SMAP, then using the soil moisture in a soil strength model. Soil moisture downscaling methods typically assume consistent relationships between the moisture and topographic, vegetation, and soil composition characteristics within the microwave satellite grid cells. The objective of this study is to examine whether soil moisture and strength exhibit heterogenous dependencies on topography, vegetation, and soil composition characteristics within a SMAP grid cell. Soil moisture and strength data were collected at four geographically separated regions within a 9 km SMAP grid cell in the Front Range foothills of northern Colorado. Laboratory methods and pedotransfer functions were used to characterize soil attributes, and remote sensing data were used to determine topographic and vegetation attributes. Pearson correlation analyses were used to quantify the direction, strength, and significance of the relationships of both soil moisture and strength with topography, vegetation, and soil composition. Contrary to the common assumption, spatial variations in the slope and correlation of the relationships are observed for both soil moisture and strength. The findings indicate that improved predictions of soil moisture and soil strength may be achievable by soil moisture downscaling procedures that use spatially variable parameters across the downscaling extent. Full article

(This article belongs to the Section Soil and Hydrology)

► Show Figures

Figure 1

21 pages, 4483 KiB

Open AccessArticle

DEM Generation Incorporating River Channels in Data-Scarce Contexts: The “Fluvial Domain Method”

by Jairo R. Escobar Villanueva, Jhonny I. Pérez-Montiel and Andrea Gianni Cristoforo Nardini

Hydrology 2025, 12(2), 33; https://doi.org/10.3390/hydrology12020033 - 14 Feb 2025

Cited by 1 | Viewed by 1413

Abstract

This paper presents a novel methodology to generate Digital Elevation Models (DEMs) in flat areas, incorporating river channels from relatively coarse initial data. The technique primarily utilizes filtered dense point clouds derived from SfM-MVS (Structure from Motion-Multi-View Stereo) photogrammetry of available crewed aerial [...] Read more.

This paper presents a novel methodology to generate Digital Elevation Models (DEMs) in flat areas, incorporating river channels from relatively coarse initial data. The technique primarily utilizes filtered dense point clouds derived from SfM-MVS (Structure from Motion-Multi-View Stereo) photogrammetry of available crewed aerial imagery datasets. The methodology operates under the assumption that the aerial survey was carried out during low-flow or drought conditions so that the dry (or almost dry) riverbed is detected, although in an imprecise way. Direct interpolation of the detected elevation points yields unacceptable river channel bottom profiles (often exhibiting unrealistic artifacts) and even distorts the floodplain. In our Fluvial Domain Method, channel bottoms are represented like “highways”, perhaps overlooking their (unknown) detailed morphology but gaining in general topographic consistency. For instance, we observed an 11.7% discrepancy in the river channel long profile (with respect to the measured cross-sections) and a 0.38 m RMSE in the floodplain (with respect to the GNSS-RTK measurements). Unlike conventional methods that utilize active sensors (satellite and airborne LiDAR) or classic topographic surveys—each with precision, cost, or labor limitations—the proposed approach offers a more accessible, cost-effective, and flexible solution that is particularly well suited to cases with scarce base information and financial resources. However, the method’s performance is inherently limited by the quality of input data and the simplification of complex channel morphologies; it is most suitable for cases where high-resolution geomorphological detail is not critical or where direct data acquisition is not feasible. The resulting DEM, incorporating a generalized channel representation, is well suited for flood hazard modeling. A case study of the Ranchería river delta in the Northern Colombian Caribbean demonstrates the methodology. Full article

(This article belongs to the Special Issue Hydrological Modeling and Sustainable Water Resources Management)

► Show Figures

Figure 1

16 pages, 3161 KiB

Open AccessArticle

Eutrophication Conditions in Two High Mountain Lakes: The Influence of Climate Conditions and Environmental Pollution

by Fátima Goretti García-Miranda, Claudia Muro, Yolanda Alvarado, José Luis Expósito-Castillo and Héctor Víctor Cabadas-Báez

Hydrology 2025, 12(2), 32; https://doi.org/10.3390/hydrology12020032 - 13 Feb 2025

Viewed by 933

Abstract

The lakes known as El Sol and La Luna are high mountain water deposits located in Mexico within an inactive volcanic system. These lakes are of ecological importance because they are unique in Mexico. However, currently, the lakes have experienced changes in their [...] Read more.

The lakes known as El Sol and La Luna are high mountain water deposits located in Mexico within an inactive volcanic system. These lakes are of ecological importance because they are unique in Mexico. However, currently, the lakes have experienced changes in their shape and an increase in algae blooms, coupled with the degradation of the basin, which has alerted government entities to the need to address the lakes’ problems. To address the environmental status of El Sol and La Luna, a trophic study was conducted during the period of 2021–2023, including an analysis of the influence of climatic variables, lake water quality, and eutrophication conditions. The trophic state was established based on the eutrophication index. The Pearson correlations defined the eutrophication interrelation between the distinct factors influencing the lakes’ status. El Sol registered higher eutrophication conditions than La Luna. El Sol was identified as seasonal eutrophic and La Luna as transitioning from oligotrophic to mesotrophic, showing high levels of chlorophyll, total phosphorus, and total nitrogen and low water transparency. The principal factors altering the eutrophic conditions were water pollution and climatic variables (precipitation and ambient temperature). Eutrophication was the prime factor impacting perimeter loss at El Sol, whereas at La Luna, it was due to a decline in precipitation. Full article

(This article belongs to the Topic Advances in Hydrological Remote Sensing)

► Show Figures

Figure 1

24 pages, 6615 KiB

Open AccessFeature PaperArticle

Creation and Comparison of High-Resolution Daily Precipitation Gridded Datasets for Greece Using a Variety of Interpolation Techniques

by Giorgos Ntagkounakis, Panagiotis Nastos, John Kapsomenakis and Kostas Douvis

Hydrology 2025, 12(2), 31; https://doi.org/10.3390/hydrology12020031 - 10 Feb 2025

Viewed by 780

Abstract

This study investigates a range of precipitation interpolation techniques with the objective of generating high-resolution gridded daily precipitation datasets for the Greek region. The study utilizes a comprehensive station dataset, incorporating geographical variables derived from satellite-based elevation data and integrating precipitation data from [...] Read more.

This study investigates a range of precipitation interpolation techniques with the objective of generating high-resolution gridded daily precipitation datasets for the Greek region. The study utilizes a comprehensive station dataset, incorporating geographical variables derived from satellite-based elevation data and integrating precipitation data from the ERA5 reanalysis. A total of three different modeling approaches are developed. Firstly, we utilize a General Additive Model in conjunction with an Indicator Kriging model using only station data and limited geographical variables. In the second iteration of the model, we blend ERA5 reanalysis data in the interpolation methodology and incorporate more geographical variables. Finally, we developed a novel modeling framework that integrates ERA5 data, a variety of geographical data, and a multi-model interpolation process which utilizes different models to predict precipitation at distinct thresholds. Our results show that using the ERA5 data can increase the accuracy of the interpolated precipitation when the station dataset used is sparse. Additionally, the implementation of multi-model interpolation techniques which use distinct models for different precipitation thresholds can improve the accuracy of precipitation and extreme precipitation modeling, addressing important limitations of previous modeling approaches. Full article

(This article belongs to the Special Issue Advances in the Measurement, Utility and Evaluation of Precipitation Observations)

► Show Figures

Figure 1

10 pages, 2722 KiB

Open AccessFeature PaperArticle

Stable Isotope Investigations of Icicle Formation and Evolution

by Thomas Brubaker and R. V. Krishnamurthy

Hydrology 2025, 12(2), 30; https://doi.org/10.3390/hydrology12020030 - 9 Feb 2025

Viewed by 753

Abstract

Icicles are elongated structures formed from water flowing over hangings and crystallizing in sub-freezing conditions. These features are ubiquitous in several parts of the world that experience severe to moderate winter seasons. It has been suggested that they could be a source of [...] Read more.

Icicles are elongated structures formed from water flowing over hangings and crystallizing in sub-freezing conditions. These features are ubiquitous in several parts of the world that experience severe to moderate winter seasons. It has been suggested that they could be a source of recharge to groundwater. Icicles are presumed to affect groundwater quality via incorporation of atmospheric and roof top contaminants. Relatively little attention has been paid to these wintry features, insofar as only a few theoretical models have attempted to describe their formation. Stable isotope measurements (δ¹⁸O and δ²H) of icicles that were melted stepwise into fractions are presented as support for the models that invoke the rapid formation of icicles. Icicles exhibit minimal fraction to fraction isotope variation, suggesting a lack of isotope equilibrium and that kinetic effects dominate the freezing process. Deviations from the Global Meteoric Water Line (GMWL), which is similar to the Local Meteoric Water Line (LMWL), indicate that post-depositional processes, namely sublimation, may occur throughout the freezing process. Isotopic evidence lends support to a “growth-cessation-growth” variation of the already proposed methods of rapid icicle formation, where a cessation period occurs between pulses of rapid freezing during icicle growth. Full article

(This article belongs to the Special Issue Isotope Hydrology in the U.S.)

► Show Figures

Figure 1

19 pages, 6001 KiB

Open AccessArticle

Policy Measures to Lead Sustainable Development of Agriculture Catchment: Socio-Hydrology Modeling Insights

by Mahendran Roobavannan, Jaya Kandasamy and Saravanamuthu Vigneswaran

Hydrology 2025, 12(2), 29; https://doi.org/10.3390/hydrology12020029 - 9 Feb 2025

Cited by 1 | Viewed by 737

Abstract

Achieving sustainable development in agricultural catchments requires well-designed policy measures. This study examines the intricate interactions between social dynamics and hydrological processes within agricultural systems to propose targeted policy interventions. By employing socio-hydrology models that integrate socio-economic and hydrological data, the research provides [...] Read more.

Achieving sustainable development in agricultural catchments requires well-designed policy measures. This study examines the intricate interactions between social dynamics and hydrological processes within agricultural systems to propose targeted policy interventions. By employing socio-hydrology models that integrate socio-economic and hydrological data, the research provides valuable insights into the feedback loops and interdependencies that influence catchment sustainability. In this study, we find that policies on population management should aim to balance natural growth rates with the carrying capacity of the basin. Strategies such as education, healthcare access, and family planning can help manage demographic pressures. Migration policies should consider the economic and environmental impacts of population influx and support balanced regional development to distribute the demographic pressures more evenly. Wage growth should be aligned with economic productivity to prevent unemployment and inequality. Policies that promote equitable wage structures and enhance labor mobility between sectors can mitigate disparities. The findings emphasize the necessity of adaptive policies that address both environmental and societal factors, advocating for interdisciplinary approaches in water resource management and agricultural policy development. This study also highlights the pivotal role of technological innovations and the societal values and norms that shape sustainability and resilience in agricultural catchments. Full article

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

► Show Figures

Figure 1

21 pages, 4960 KiB

Open AccessArticle

Evaluating Expert Opinion-Based Reservoir Operation in Cfa/Csa Climatic Conditions

by Mahdi Sedighkia and Bithin Datta

Hydrology 2025, 12(2), 28; https://doi.org/10.3390/hydrology12020028 - 6 Feb 2025

Viewed by 621

Abstract

This study evaluates the application of an expert opinion-based fuzzy method for reservoir operation in humid subtropical climate/hot-summer Mediterranean climatic classes (Cfa/Csa in the Köppen–Geiger climate classification system), which are characterized by humid subtropical to Mediterranean conditions with ample rainfall and seasonal water [...] Read more.

This study evaluates the application of an expert opinion-based fuzzy method for reservoir operation in humid subtropical climate/hot-summer Mediterranean climatic classes (Cfa/Csa in the Köppen–Geiger climate classification system), which are characterized by humid subtropical to Mediterranean conditions with ample rainfall and seasonal water availability challenges. Effective reservoir management in these regions is critical for balancing water storage and downstream release and maintaining ecosystem health under variable hydrological conditions. The performance of the fuzzy method was compared to two meta-heuristic algorithms: gravitational search algorithm (GSA) and shuffled frog leaping algorithm (SFLA). System performance was assessed using key indices such as the reliability index as a measure of meeting water demands. The fuzzy method achieved the highest reliability index of 0.690, outperforming GSA (0.677) and SFLA (0.688), demonstrating its superior ability to ensure consistent water supply downstream. The fuzzy method, leveraging expert knowledge, not only enhanced downstream water supply reliability but also reduced computational time compared to the meta-heuristic approaches. The incorporation of expert opinions provides a practical, robust, and efficient framework for reservoir management in challenging climate conditions such as Cfa/Csa classes. Additionally, the fuzzy solution demonstrated superior adaptability to diverse hydrological conditions, balancing ecological and water supply needs effectively. These findings highlight the potential of using expert opinions to support sustainable reservoir operations by achieving optimal trade-offs between competing objectives and addressing challenges in water resource management under varying climatic conditions. Full article

(This article belongs to the Special Issue Natural or Artificial Water Reservoirs' Impacts on Mitigating the Intensities of Floods and Droughts)

► Show Figures

Figure 1

20 pages, 5362 KiB

Open AccessArticle

Investigating the Water, Ecosystem, and Agriculture Nexus in Three Inland River Basins of the Arid Hexi Corridor, China, Using Integrated Hydrological Modeling

by Yuan Chen and Yong Tian

Hydrology 2025, 12(2), 27; https://doi.org/10.3390/hydrology12020027 - 6 Feb 2025

Cited by 1 | Viewed by 743

Abstract

The Water–Ecosystem–Agriculture (WEA) relationship is pivotal to the sustainable development of arid and semi-arid areas. The WEA nexus in these areas is essential for making policies towards sustainable development. This study aims to explore the WEA nexus in three large inland river basins [...] Read more.

The Water–Ecosystem–Agriculture (WEA) relationship is pivotal to the sustainable development of arid and semi-arid areas. The WEA nexus in these areas is essential for making policies towards sustainable development. This study aims to explore the WEA nexus in three large inland river basins (Heihe River Basin, Shiyang River Basin, and Shule River Basin) in the Hexi Corridor, Northwest China, using an integrated hydrological modeling approach. The integrated model was calibrated and validated against observed streamflow data, achieving Nash–Sutcliffe Efficiencies ranging from 0.83 to 0.94 in the validation period. The major findings are as follows. First, altering the amount of irrigation water significantly affects hydrological and ecological processes in both midstream and downstream areas, influencing the WEA nexus. For example, a 20% reduction in irrigation demand led to a 0.46 billion m³/year recovery in midstream groundwater storage and a 4.3% increase in downstream ecosystem health, but resulted in a 5.4% decrease in midstream agricultural productivity. Second, intense trade-offs among agricultural productivity, ecosystem health, and groundwater sustainability were identified. These trade-offs are highly sensitive to water management strategies, particularly those affecting groundwater sustainability. Third, implementing stricter groundwater-level drawdown constraints significantly improved groundwater sustainability and ecosystem health. Fourth, this study highlighted unique WEA nexus characteristics in each of the three basins. This study provides insights into the understanding the complex WEA nexus, and the quantitative results underscore the trade-offs and synergies within the WEA nexus, providing a foundation for informed decision-making in water resource management. Full article

► Show Figures

Figure 1

16 pages, 4882 KiB

Open AccessArticle

Effects of Inconsistency in Drought Event Definitions on Drought Characteristics

by Frank Joseph Wambura

Hydrology 2025, 12(2), 26; https://doi.org/10.3390/hydrology12020026 - 5 Feb 2025

Viewed by 747

Abstract

Drought, as one of the hazards exacerbated by climate change, has attracted the attention of many scientists. Many drought studies have used different drought event definitions (DEDs). However, little is known about the effects of these definitions on drought characteristics. This study investigated [...] Read more.

Drought, as one of the hazards exacerbated by climate change, has attracted the attention of many scientists. Many drought studies have used different drought event definitions (DEDs). However, little is known about the effects of these definitions on drought characteristics. This study investigated the effects of DEDs on drought characteristics using the standardized precipitation evapotranspiration index (SPEI) in the Upper Pangani Basin in northeast Tanzania. First, rainfall and air temperature data from the Climatic Research Unit database were used to compute the SPEI. Then, four different types of DEDs were used to identify drought events in the SPEI time series. The identified drought events were examined for agreements and correlations using Kappa and Phi coefficients, respectively, and finally characterized. The findings show that different DEDs produced different types and frequencies of drought events. The patterns of drought events for these DEDs had agreements ranging from 52 to 78% and correlations ranging from 79% to 95%. Different DEDs also led to different drought intensities, ranging from mild to extreme, although the overall drought intensities were either mild or moderate. From this study, we can infer that using suitable DEDs is essential for identifying drought events, as they enable accurate comparisons of droughts across regions and periods, consequently reducing errors and biases in evaluating drought hazards. Full article

► Show Figures

Figure 1

41 pages, 24123 KiB

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 2 | Viewed by 1694

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

► Show Figures

Figure 1

19 pages, 4538 KiB

Open AccessFeature PaperArticle

The Use of Fluorescent Organic Matter as a Natural Transit Time Tracer in the Unsaturated Zone of the Fontaine De Vaucluse Karst System

by Leïla Serène, Naomi Mazzilli, Christelle Batiot-Guilhe, Christophe Emblanch, Milanka Babic, Julien Dupont, Roland Simler and Matthieu Blanc

Hydrology 2025, 12(2), 24; https://doi.org/10.3390/hydrology12020024 - 1 Feb 2025

Cited by 1 | Viewed by 626

Abstract

The fluorescence index called the Transit Time index (TTi) is based on the fluorescence of natural organic matter in order to qualitatively assess the transit time of karst groundwater, using springs affected by human activities. This study aims to further evaluate the potential [...] Read more.

The fluorescence index called the Transit Time index (TTi) is based on the fluorescence of natural organic matter in order to qualitatively assess the transit time of karst groundwater, using springs affected by human activities. This study aims to further evaluate the potential of fluorescent compounds as a natural tracer of transit time when applied to unsaturated zone flows with natural catchments, in contrast to the first study. For this purpose, a bi-monthly sampling of one year of monitoring for organic matter fluorescence, TOC, major elements and water-stable isotopes was performed. A conceptual model of the sources and fates of fluorescent compounds is built, emphasizing the allochthonous origin of humic-like C compounds, and the autochthonous production of humic-like M and protein-like compounds within the unsaturated zone. Fluorescent compound intensity interpretation according to this model reveals consistent relative transit times with flow behavior and also provides complementary information. The results also show the TTi’s ability to summarize fluorescent compounds, its consistency with relative transit time, and its higher sensitivity as compared to other natural tracers. However, prior to its use, a thorough assessment of soil organic matter, microbial activity, and potential anthropogenic contamination is required, encouraging interdisciplinary collaboration between hydrogeologists, microbiologists and soil scientists. Full article

► Show Figures

Figure 1

18 pages, 1162 KiB

Open AccessArticle

Modelling Hydrological Droughts in Canadian Rivers Based on Markov Chains Using the Standardized Hydrological Index as a Platform

by Tribeni C. Sharma and Umed S. Panu

Hydrology 2025, 12(2), 23; https://doi.org/10.3390/hydrology12020023 - 31 Jan 2025

Viewed by 645

Abstract

The standardized hydrological index (SHI) is the standardized but not normalized (normal probability variate) value of the streamflow used to characterize a hydrological drought, akin to the standardized precipitation index (SPI, which is both standardized and normalized) in the [...] Read more.

The standardized hydrological index (SHI) is the standardized but not normalized (normal probability variate) value of the streamflow used to characterize a hydrological drought, akin to the standardized precipitation index (SPI, which is both standardized and normalized) in the realm of the meteorological drought. The time series of the SHI can be used as a platform for deriving the longest duration, L_T, and the largest magnitude, M_T (in standardized form), of a hydrological drought over a desired return period of T time units (year, month, or week). These parameters are predicted based on the SHI series derived from the annual, monthly, and weekly flow sequences of Canadian rivers. An important point to be reckoned with is that the monthly and weekly sequences are non-stationary compared to the annual sequences, which fulfil the conditions of stochastic stationarity. The parameters, such as the mean, standard deviation (or coefficient of variation), lag 1 autocorrelation, and conditional probabilities from SHI sequences, when used in Markov chain-based relationships, are able to predict the longest duration, L_T, and the largest magnitude, M_T. The product moment and L-moment ratio analyses indicate that the monthly and weekly flows in the Canadian rivers fit the gamma probability distribution function (pdf) reasonably well, whereas annual flows can be regarded to follow the normal pdf. The threshold level chosen in the analysis is the long-term median of SHI sequences for the annual flows. For the monthly and weekly flows, the threshold level represents the median of the respective month or week and hence is time varying. The runs of deficit in the SHI sequences are treated as drought episodes and thus the theory of runs formed an essential tool for analysis. This paper indicates that the Markov chain-based methodology works well for predicting L_T on annual, monthly, and weekly SHI sequences. Markov chains of zero order (MC0), first order (MC1), and second order (MC2) turned out to be satisfactory on annual, monthly, and weekly scales, respectively. The drought magnitude, M_T, was predicted satisfactorily via the model M_T = I_d × L_c, where I_d stands for drought intensity and L_c is a characteristic drought length related to L_T through a scaling parameter, ɸ (= 0.5). The I_d can be deemed to follow a truncated normal pdf, whose mean and variance when combined implicitly with L_c proved prudent for predicting M_T at all time scales in the aforesaid relationship. Full article

(This article belongs to the Section Statistical Hydrology)

► Show Figures

Figure 1

23 pages, 4334 KiB

Open AccessArticle

Evaluation and Adjustment of Historical Hydroclimate Data: Improving the Representation of Current Hydroclimatic Conditions in Key California Watersheds

by Andrew Schwarz, Z. Q. Richard Chen, Alejandro Perez and Minxue He

Hydrology 2025, 12(2), 22; https://doi.org/10.3390/hydrology12020022 - 22 Jan 2025

Viewed by 1044

Abstract

The assumption of stationarity in historical hydroclimatic data, fundamental to traditional water resource planning models, is increasingly challenged by the impacts of climate change. This discrepancy can lead to inaccurate model outputs and misinformed management decisions. This study addresses this challenge by developing [...] Read more.

The assumption of stationarity in historical hydroclimatic data, fundamental to traditional water resource planning models, is increasingly challenged by the impacts of climate change. This discrepancy can lead to inaccurate model outputs and misinformed management decisions. This study addresses this challenge by developing a novel monthly data adjustment approach, the Runoff Curve Year–Type–Monthly (RC-YTM) method. The application of this method is exemplified at five key California watersheds. The RC-YTM method accounts for the increasing variability and shifts in seasonal runoff timing observed in the historical data (1922–2021), aligning it with the contemporary climate conditions represented by the period from 1992 to 2021 at the study watersheds. This method adjusts both annual and monthly streamflow values using a combination of precipitation–runoff relationships, quantile mapping, and water year classification. The adjusted data, reflecting current climatic conditions more accurately than the raw historical data, serve as valuable inputs for operational water resource planning models like CalSim3, commonly used in California for water management. This approach, demonstrably effective in capturing the observed climate change impacts on streamflow at monthly timesteps, enhances the reliability of model simulations representing contemporary conditions, which can lead to better-informed decision-making in water management, infrastructure investment, drought and flood risk assessment, and adaptation strategies. While focused on specific California watersheds, this study’s findings and the adaptable RC-YTM method hold significant implications for water resource management in other regions facing similar hydroclimatic challenges in a changing climate. Full article

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

► Show Figures

Figure 1

20 pages, 8692 KiB

Open AccessArticle

Forecasting Model for Danube River Water Temperature Using Artificial Neural Networks

by Cristina-Sorana Ionescu, Ioana Opriș, Daniela-Elena Gogoașe Nistoran and Constantin-Alexandru Baciu

Hydrology 2025, 12(2), 21; https://doi.org/10.3390/hydrology12020021 - 21 Jan 2025

Viewed by 967

Abstract

The objective of this paper is to propose an artificial neural network (ANN) model to forecast the Danube River temperature at Chiciu–Călărași, Romania, bordered by Romanian and Bulgarian ecological sites, and situated upstream of the Cernavoda nuclear power plant. Given the temperature increase [...] Read more.

The objective of this paper is to propose an artificial neural network (ANN) model to forecast the Danube River temperature at Chiciu–Călărași, Romania, bordered by Romanian and Bulgarian ecological sites, and situated upstream of the Cernavoda nuclear power plant. Given the temperature increase trend, the potential of thermal pollution is rising, impacting aquatic and terrestrial ecosystems. The available data covered a period of eight years, between 2008 and 2015. Using as input data actual air and water temperatures, and discharge, as well as air temperature data provided by weather forecasts, the ANN model predicts the Danube water temperature one week in advance with a root mean square deviation (RMSE) of 0.954 °C for training and 0.803 °C for testing. The ANN uses the Levenberg–Marquardt feedforward backpropagation algorithm. This feature is useful for the irrigation systems and for the power plants in the area that use river water for different purposes. The results are encouraging for developing similar studies in other locations and extending the ANN model to include more parameters that can have a significant influence on water temperature. Full article

► Show Figures

Figure 1

23 pages, 5265 KiB

Open AccessArticle

High-Resolution Flow and Phosphorus Forecasting Using ANN Models, Catering for Extremes in the Case of the River Swale (UK)

by Elisabeta Cristina Timis, Horia Hangan, Vasile Mircea Cristea, Norbert Botond Mihaly and Michael George Hutchins

Hydrology 2025, 12(2), 20; https://doi.org/10.3390/hydrology12020020 - 21 Jan 2025

Viewed by 955

Abstract

The forecasting of river flows and pollutant concentrations is essential in supporting mitigation measures for anthropogenic and climate change effects on rivers and their environment. This paper addresses two aspects receiving little attention in the literature: high-resolution (sub-daily) data-driven modeling and the prediction [...] Read more.

The forecasting of river flows and pollutant concentrations is essential in supporting mitigation measures for anthropogenic and climate change effects on rivers and their environment. This paper addresses two aspects receiving little attention in the literature: high-resolution (sub-daily) data-driven modeling and the prediction of phosphorus compounds. It presents a series of artificial neural networks (ANNs) to forecast flows and the concentrations of soluble reactive phosphorus (SRP) and total phosphorus (TP) under a wide range of conditions, including low flows and storm events (0.74 to 484 m³/s). Results show correct forecast along a stretch of the River Swale (UK) with an anticipation of up to 15 h, at resolutions of up to 3 h. The concentration prediction is improved compared to a previous application of an advection–dispersion model. Full article

(This article belongs to the Special Issue Hydrodynamics and Water Quality of Rivers and Lakes)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Hydrology, Volume 12, Issue 2 (February 2025) – 22 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI