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Water
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Advances in Watershed Hydrology: Integrating Process Understanding and Predictive Modeling
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Advances in Watershed Hydrology: Integrating Process Understanding and Predictive Modeling

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

Deadline for manuscript submissions: 25 August 2026 | Viewed by 1562

Special Issue Editors

Dr. Gabriel Perez

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, Oklahoma State University, Stillwater, OK, USA
Interests: hydroclimate extremes; process-based hydrologic modeling; extreme event statistics; hydro-geomorphological characterization; flood risk management; high-performance computing in hydrology; hydroclimate impact assessment; flood frequency analysis
Dr. Nicolás Velásquez

E-Mail Website
Guest Editor Assistant
Mechanical and Civil Engineering Department, Florida Institute of Technology, Melbourne, FL, USA
Interests: flood forecasting; streamflow water temperature; model development; evaluation metrics

Special Issue Information

Dear Colleagues,

For this Special Issue, we welcome contributions that address key challenges related to water quantity and quality at the watershed scale, aiming to advance our understanding of watershed hydrology. Despite progress in the field, critical knowledge gaps remain in areas such as hydrological flux partitioning, process representation, sediment transport, flood forecasting, and uncertainty quantification. We invite the submission of studies that focus on watershed modeling, the development of new tools and frameworks for watershed-scale simulation, novel methods for analyzing hydrological signals, and integrated approaches for evaluating surface and subsurface hydrological processes. Submissions using physically based models, data-driven techniques, or hybrid approaches are particularly encouraged.

Dr. Gabriel Perez
Guest Editor

Dr. Nicolás Velásquez
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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

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

Keywords

  • watershed hydrology
  • hydrological modeling
  • uncertainty analysis
  • surface and subsurface processes
  • sediment transport
  • flood forecasting
  • machine learning in hydrology
  • hydrological signal analysis
  • model development and calibration
  • watershed-scale simulation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

25 pages, 5483 KB  
Article
Urban Expansion and Flood-Relevant Runoff Responses in Data-Limited Catchments
by Tropikë Agaj, Ewelina Janicka-Kubiak, Anna Budka and Valbon Bytyqi
Water 2026, 18(5), 639; https://doi.org/10.3390/w18050639 - 8 Mar 2026
Viewed by 727
Abstract
Rapid land-cover transformations associated with urban expansion have increasingly altered hydrological processes, modifying runoff generation and flood response at the catchment scale. This study applied the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) to examine rainfall–runoff dynamics in the Prosna River catchment (Poland) and [...] Read more.
Rapid land-cover transformations associated with urban expansion have increasingly altered hydrological processes, modifying runoff generation and flood response at the catchment scale. This study applied the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) to examine rainfall–runoff dynamics in the Prosna River catchment (Poland) and the Morava e Binçës River catchment (Kosovo) for 2006–2021. Land-use changes were quantified using CORINE Land Cover (CLC) data from 2006, 2012, and 2018, and their hydrological effects were evaluated through changes in the Curve Number (CN) parameter. The model was calibrated and validated for the Prosna catchment, achieving satisfactory performance (NSE = 0.72 during calibration and 0.56 during validation), confirming its reliability under varying hydrometeorological conditions. Due to the lack of continuous discharge data in Kosovo, a parameter-transfer approach was used, applying calibrated parameters from the Prosna to the Morava e Binçës. Scenario-based simulations assessed the combined effects of urban growth and meteorological variability. Under wetter conditions, increased precipitation and expanded impervious surfaces markedly amplified simulated discharge, with maximum daily differences reaching 86.9 m3 s−1. These findings underscore the sensitivity of catchment response to interacting land-use and precipitation changes and highlight the need for improved hydrological monitoring in data-scarce regions. Full article
(This article belongs to the Special Issue Advances in Watershed Hydrology: Integrating Process Understanding and Predictive Modeling)
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17 pages, 2806 KB  
Article
Daily Runoff Forecasting in the Middle Yangtze River Using a Long Short-Term Memory Network Optimized by the Sparrow Search Algorithm
by Qi Zhang, Yaoyao Dong, Chesheng Zhan, Yueling Wang, Hongyan Wang and Hongxia Zou
Water 2026, 18(3), 364; https://doi.org/10.3390/w18030364 - 31 Jan 2026
Viewed by 422
Abstract
To address the challenge of predicting runoff processes in the middle reaches of the Yangtze River under the influence of complex river–lake relationships and human disturbances, this paper proposes a coupled model based on the Sparrow Search Algorithm-optimized Long Short-Term Memory neural network [...] Read more.
To address the challenge of predicting runoff processes in the middle reaches of the Yangtze River under the influence of complex river–lake relationships and human disturbances, this paper proposes a coupled model based on the Sparrow Search Algorithm-optimized Long Short-Term Memory neural network (SSA-LSTM) for daily runoff forecasting at the Jiujiang Hydrological Station. The input data were preprocessed through feature selection and sequence decomposition. Subsequently, the Sparrow Search Algorithm (SSA) was utilized to perform automated of key hyperparameters of the Long Short-Term Memory (LSTM) model, thereby enhancing the model’s adaptability under complex hydrological conditions. Experimental results based on multi-station hydrological and meteorological data of the middle reaches of the Yangtze River from 2009 to 2016 show that the SSA-LSTM achieves a Nash–Sutcliffe Efficiency (NSE) of 0.98 during the testing period (2016). The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are reduced by 49.3% and 51.3%, respectively, compared to the standard LSTM. A comprehensive evaluation across different flow levels, utilizing Taylor diagrams and error distribution analysis, further confirms the model’s robustness. The model demonstrates robust performance across different flow regimes: compared to the standard LSTM model, SSA-LSTM improves the NSE from 0.45 to 0.88 in high-flow scenarios, exhibiting excellent capabilities in peak flow prediction and flood process characterization. In low-flow scenarios, the NSE is improved from −0.77 to 0.72, indicating more reliable prediction of baseflow mechanisms. The study demonstrates that SSA-LSTM can effectively capture hydrological nonlinear characteristics under strong river–lake backwater and human disturbances, providing a high-precision and high-efficiency data-driven method for runoff prediction in complex basins. Full article
(This article belongs to the Special Issue Advances in Watershed Hydrology: Integrating Process Understanding and Predictive Modeling)
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