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A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrological and Hydrodynamic Processes and Modelling".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 29009

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Special Issue Editors

Dr. Minxue He

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Guest Editor

California Department of Water Resources, 1416 9th Street, Sacramento, CA 95814, USA
Interests: hydrological; hydraulic; hydrodynamic; and water quality modeling;climate change;stochastic modeling; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Seong Jin Noh

E-Mail Website
Guest Editor

Department of Civil Engineering, Kumoh National Institute of Technology (KIT), Gumi, Republic of Korea
Interests: integrated hydrologic modeling; data assimilation; urban flood and water cycle; climate-adaptive water resources management
Special Issues, Collections and Topics in MDPI journals

Dr. Haksu Lee

E-Mail Website
Guest Editor

LEN Technologies, Oak Hill, VA 20171, USA
Interests: hydrology; hydrometeorology; ensemble forecasting; data assimilation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to draw your attention to a call for papers for the Special Issue “Recent Advances in Hydrological Modeling” in the journal Hydrology (https://www.mdpi.com/journal/hydrology; CiteScore = 3.4).

Hydrological models are important tools that are widely used in water resources operations, planning, and management practices. These models can be classified into different types according to their structure (data-driven/statistical, conceptual, physical, hybrid) or spatial representation (lumped, distributed). Model calibration, validation/verification, sensitivity analysis, and uncertainty analysis are integral parts of the modeling process. In operations, field (in situ and remote sensing) observations are often merged into hydrological models to update model states and/or parameters for improved model performance. Model outputs, particularly ensemble-based outputs, are typically post-processed before being utilized to inform decision-making.

Advances in computing (e.g., cloud-based and parallelization) and information (e.g., artificial intelligence) technologies provide new opportunities for improved hydrological modeling. In addition, ongoing and projected environmental changes (e.g., global warming, intensified extreme events) pose new challenges for hydrological modeling, and call for innovative modeling approaches and integrating modeling across different disciplines. In this context, this Special Issue invites studies covering, but not limited to, the following areas:

New conceptual, physical, data-driven, or hybrid models that advance our understanding of hydrological processes under a changing climate, at large scales (e.g., national/continental/global), or in ungauged areas.
Methodological advances and case studies on merging in-situ and remote sensing observations into hydrological models for improved model performance and uncertainty diagnosis.
Novel approaches and applications on verification or post-processing hydrological model results.
Ensemble-based approaches to modeling extreme events, climate change, and urban flooding.
Utilizing multiple techniques for the improved performance of hydrological models (e.g., the combination of data assimilation, machine learning, and pre- or post-processing).
Novel methodologies to analyze uncertainty, sensitivity, or structural inadequacy associated with hydrological modeling.
Integrated modeling across different landscapes (e.g., surface/subsurface), multidisciplinary subjects (meteorology, hydrology, hydraulics, hydrodynamics, water quality, etc.), and time scales (minutes to decades).

Dr. Minxue He
Dr. Seong Jin Noh
Dr. Haksu Lee
Guest Editors

Manuscript Submission Information

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

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

hydrological modeling
data assimilation
sensitivity analysis
uncertainty analysis
machine learning
integrated modeling
verification
pre- and post-processing
climate change

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Published Papers (8 papers)

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Editorial

Jump to: Research

5 pages, 170 KiB

Open AccessEditorial

Editorial to the Special Issue “Recent Advances in Hydrological Modeling”

by Minxue He, Seong Jin Noh and Haksu Lee

Hydrology 2024, 11(7), 108; https://doi.org/10.3390/hydrology11070108 - 22 Jul 2024

Viewed by 1858

Abstract

Hydrological models play a crucial role as essential tools in the realms of water resources operations, planning, and management practices [...] Full article

(This article belongs to the Special Issue Recent Advances in Hydrological Modeling)

Research

Jump to: Editorial

15 pages, 4376 KiB

Open AccessArticle

Analysis of Changes in Water Flow after Passing through the Planned Dam Reservoir Using a Mixture Distribution in the Face of Climate Change: A Case Study of the Nysa Kłodzka River, Poland

by Łukasz Gruss, Mirosław Wiatkowski, Maksymilian Połomski, Łukasz Szewczyk and Paweł Tomczyk

Hydrology 2023, 10(12), 226; https://doi.org/10.3390/hydrology10120226 - 1 Dec 2023

Cited by 2 | Viewed by 2453

Abstract

Climate change and extreme weather events have the potential to increase the occurrences of flooding and hydrological droughts. Dam reservoir operation can mitigate or aggravate this impact. This study aims to evaluate the influence of the planned Kamieniec Ząbkowicki dam reservoir on the flow patterns of the Nysa Kłodzka river in the context of changing hydrological conditions and climate change. In the study, a 40-year observational series of hydrological data was used to simulate changes in water flow through the river valley in a numerical model. This simulation was conducted both for the natural river valley and for the same river valley but with the added reservoir dam. Flow simulations revealed that dam operation increased downstream flow values, reducing variability in extreme high-flow events. Addition, the mixture log-normal distribution shows that the operation of the dam resulted in a reduction in the variability of both low flows and extreme high-flow events. Furthermore, the model illustrates that moderate-flow conditions remain relatively stable and similar before and after dam construction. The Mann–Kendall trend test, Sen slope trend test and Innovative Trend Analysis indicated that the dam had a significant impact on flow trends, reducing the negative trend. This hydrotechnical structure stabilizes and regulates flows, especially in response to climate-induced changes. These findings highlight the effectiveness of the dam in mitigating flood risk and supporting water resource management. It is essential to consider the role of the dam in adapting to changing hydrological conditions influenced by climate change. For practical application, efficient flow regulation by reservoir administration is crucial. Full article

(This article belongs to the Special Issue Recent Advances in Hydrological Modeling)

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25 pages, 3668 KiB

Open AccessFeature PaperArticle

Simulating Phosphorus Load Reductions in a Nested Catchment Using a Flow Pathway-Based Modeling Approach

by Russell Adams and Paul Quinn

Hydrology 2023, 10(9), 184; https://doi.org/10.3390/hydrology10090184 - 14 Sep 2023

Cited by 1 | Viewed by 2238

Abstract

Catchment models are essential tools to identify and predict water quality problems linked to excessive nutrient applications (in this case phosphorus (P)). The Catchment Runoff Attenuation Flux Tool (CRAFT) has been successfully used to model nutrient fluxes and concentrations in north-western European catchments. The model is extremely parsimonious due to the relatively small number of parameters. However, an improvement to the representation of soluble P and particulate P fluxes in the fast-subsurface and surface runoff flow pathways was required. A case study in the north of Ireland applied the original and the new, enhanced (Dynamic) version of the CRAFT to the trans-border Blackwater catchment (UK and Republic of Ireland) covering nearly 1500 km², with the land use predominantly livestock grazing. The larger size of the Blackwater also required a nested modeling approach to be implemented using a multiple sub-catchment variant (MultiCRAFT). P load reductions in the different sub-catchments were first identified using a simple approach based on the gap between the Water Framework Directive (WFD) limits for “Good” ecological status for soluble reactive P (SRP) concentrations and the recently observed concentrations. Modeling of different mitigation scenarios was then conducted using the MultiCRAFT framework with the best-performing variant of the CRAFT model embedded. The catchment was found to have flashy, episodic delivery of high concentrations of SRP and PP during runoff events which will require different sources (i.e., diffuse and point) of P to be targeted to achieve the WFD targets by the end of the decade. The modeling results thus showed that the required SRP load reductions could be best achieved using a combined scenario of mitigation measures that targeted diffuse sources contributing to both the surface runoff and fast-subsurface flow pathways, with point sources also identified as needing reduction in some sub-catchments. Full article

(This article belongs to the Special Issue Recent Advances in Hydrological Modeling)

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32 pages, 34914 KiB

Open AccessFeature PaperArticle

Modeling Hydrodynamic Behavior of the Ottawa River: Harnessing the Power of Numerical Simulation and Machine Learning for Enhanced Predictability

by Jean Cardi, Antony Dussel, Clara Letessier, Isa Ebtehaj, Silvio Jose Gumiere and Hossein Bonakdari

Hydrology 2023, 10(9), 177; https://doi.org/10.3390/hydrology10090177 - 24 Aug 2023

Cited by 2 | Viewed by 2961

Abstract

The Ottawa River Watershed is a vast area that stretches across Ontario and Quebec and holds great importance for Canada’s people, economy, and collective history, both in the present and the future. The river has faced numerous floods in recent years due to climate change. The most significant flood occurred in 2019, surpassing a 100-year flood event, and serves as a stark reminder of how climate change impacts our environment. Considering the limitations of machine learning (ML) models, which heavily rely on historical data used during training, they may struggle to accurately predict such “non-experienced” or “unseen” floods that were not encountered during the training process. To tackle this challenge, our study has utilized a combination of numerical modeling and ML to create an integrated methodology. Indeed, a comprehensive dataset of river flow discharge was generated using a numerical model, encompassing a wide range of potential future floods. This significantly improved the ML training process to generalize the accuracy of results. Utilizing this dataset, a novel ML model called the Expanded Framework of Group Method of Data Handling (EFGMDH) has been developed. Its purpose is to provide decision-makers with explicit equations for estimating three crucial hydrodynamic characteristics of the Ottawa River: floodplain width, flow velocity, and river flow depth. These predictions rely on various inputs, including the location of the desired cross-section, river slope, Manning roughness coefficient at different river sections (right, left, and middle), and river flow discharge. To establish practical models for each of the aforementioned hydrodynamic characteristics of the Ottawa River, different input combinations were tested to identify the most optimal ones. The EFGMDH model demonstrated high accuracy throughout the training and testing stages, achieving an R² value exceeding 0.99. The proposed model’s exceptional performance demonstrates its reliability and practical applications for the study area. Full article

(This article belongs to the Special Issue Recent Advances in Hydrological Modeling)

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19 pages, 6120 KiB

Open AccessArticle

Decline in Seasonal Snow during a Projected 20-Year Dry Spell

by Benjamin J. Hatchett, Alan M. Rhoades and Daniel J. McEvoy

Hydrology 2022, 9(9), 155; https://doi.org/10.3390/hydrology9090155 - 26 Aug 2022

Cited by 6 | Viewed by 3133

Abstract

Snowpack loss in midlatitude mountains is ubiquitously projected by Earth system models, though the magnitudes, persistence, and time horizons of decline vary. Using daily downscaled hydroclimate and snow projections, we examine changes in snow seasonality across the U.S. Pacific Southwest region during a simulated severe 20-year dry spell in the 21st century (2051–2070) developed as part of the 4th California Climate Change Assessment to provide a “stress test” for water resources. Across California’s mountains, substantial declines (30–100% loss) in median peak annual snow water equivalent accompany changes in snow seasonality throughout the region compared to the historic period. We find that 80% of historic seasonal snowpacks transition to ephemeral conditions. Subsetting empirical-statistical wildfire projections for California by snow seasonality transition regions indicates a two-to-four-fold increase in the area burned, consistent with recent observations of high elevation wildfires following extended drought conditions. By analyzing six of the major California snow-fed river systems, we demonstrate snowpack reductions and seasonality transitions result in concomitant declines in annual runoff (47–58% of historical values). The negative impacts to statewide water supply reliability by the projected dry spell will likely be magnified by changes in snowpack seasonality and increased wildfire activity. Full article

(This article belongs to the Special Issue Recent Advances in Hydrological Modeling)

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11 pages, 3397 KiB

Open AccessArticle

Evaluation of the DRAINMOD Model’s Performance Using Different Time Steps in Evapotranspiration Computations

by Ahmed Awad, Mustafa El-Rawy, Mohmed Abdalhi and Nadhir Al-Ansari

Hydrology 2022, 9(2), 40; https://doi.org/10.3390/hydrology9020040 - 18 Feb 2022

Cited by 3 | Viewed by 3103

Abstract

The DRAINMOD model is a superior tool used to predict the changes in farmland water balance under different agricultural drainage layouts, fields, weather conditions, and management practices. In the present study, we assessed the sensitivity of the DRAINMOD predictions in farmland water balance to the time step (hourly or daily) in daily evapotranspiration (ET₀) computations for 12-hectares of farmland located at the lower reaches of the Yangtze River basin. The model was calibrated and validated and then was applied twice under two sets of daily ET₀ values, computed using the standardized ASCE Penman–Monteith model (one using the hourly time step (HTS) and the other using the daily time step (DTS)). Regarding daily computed ET₀ values, results show that abrupt diurnal changes in the weather always result in significant differences between daily ET₀ values when computed based on DTS and HTS. DRAINMOD simulations show that such differences between daily computed ET₀ values affected the model’s predictions of the “water fate” in the study area; e.g., adopting HTS rather than DTS resulted in a 4.8% increase, and a 3.1% and 1% decrease in the models’ cumulative predictions of runoff, drainage, and infiltration, respectively. Therefore, for a particular study area, it is critical to pay attention when deciding the best time step in ET₀ computations to ensure accurate DRAINMOD simulations, thereby ensuring better utilization of agricultural water alongside high agricultural productivity. Full article

(This article belongs to the Special Issue Recent Advances in Hydrological Modeling)

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14 pages, 2233 KiB

Open AccessEditor’s ChoiceArticle

Adaptive Conditional Bias-Penalized Kalman Filter for Improved Estimation of Extremes and Its Approximation for Reduced Computation

by Haojing Shen, Haksu Lee and Dong-Jun Seo

Hydrology 2022, 9(2), 35; https://doi.org/10.3390/hydrology9020035 - 17 Feb 2022

Cited by 7 | Viewed by 2492

Abstract

Kalman filter (KF) and its variants and extensions are wildly used for hydrologic prediction in environmental science and engineering. In many data assimilation applications of Kalman filter (KF) and its variants and extensions, accurate estimation of extreme states is often of great importance. When the observations used are uncertain, however, KF suffers from conditional bias (CB) which results in consistent under- and overestimation of extremes in the right and left tails, respectively. Recently, CB-penalized KF, or CBPKF, has been developed to address CB. In this paper, we present an alternative formulation based on variance-inflated KF to reduce computation and algorithmic complexity, and describe adaptive implementation to improve unconditional performance. For theoretical basis and context, we also provide a complete self-contained description of CB-penalized Fisher-like estimation and CBPKF. The results from one-dimensional synthetic experiments for a linear system with varying degrees of nonstationarity show that adaptive CBPKF reduces the root-mean-square error at the extreme tail ends by 20 to 30% over KF while performing comparably to KF in the unconditional sense. The alternative formulation is found to approximate the original formulation very closely while reducing computing time to 1.5 to 3.5 times of that for KF depending on the dimensionality of the problem. Hence, adaptive CBPKF offers a significant addition to the dynamic filtering methods for general application in data assimilation when the accurate estimation of extremes is of importance. Full article

(This article belongs to the Special Issue Recent Advances in Hydrological Modeling)

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17 pages, 2826 KiB

Open AccessEditor’s ChoiceArticle

Machine Learning in Assessing the Performance of Hydrological Models

by Evangelos Rozos, Panayiotis Dimitriadis and Vasilis Bellos

Hydrology 2022, 9(1), 5; https://doi.org/10.3390/hydrology9010005 - 27 Dec 2021

Cited by 34 | Viewed by 7195

Abstract

Machine learning has been employed successfully as a tool virtually in every scientific and technological field. In hydrology, machine learning models first appeared as simple feed-forward networks that were used for short-term forecasting, and have evolved into complex models that can take into account even the static features of catchments, imitating the hydrological experience. Recent studies have found machine learning models to be robust and efficient, frequently outperforming the standard hydrological models (both conceptual and physically based). However, and despite some recent efforts, the results of the machine learning models require significant effort to interpret and derive inferences. Furthermore, all successful applications of machine learning in hydrology are based on networks of fairly complex topology that require significant computational power and CPU time to train. For these reasons, the value of the standard hydrological models remains indisputable. In this study, we suggest employing machine learning models not as a substitute for hydrological models, but as an independent tool to assess their performance. We argue that this approach can help to unveil the anomalies in catchment data that do not fit in the employed hydrological model structure or configuration, and to deal with them without compromising the understanding of the underlying physical processes. Full article

(This article belongs to the Special Issue Recent Advances in Hydrological Modeling)

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