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Application of Various Hydrological Modeling Techniques and Methods in River Basin Management, 2nd Edition

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

Deadline for manuscript submissions: 25 October 2025 | Viewed by 467

Special Issue Editors

Dr. Ankur Srivastava

E-Mail Website
Guest Editor
Faculty of Science, University of Technology Sydney, Sydney, Australia
Interests: evapotranspiration; soil moisture; irrigation; hydrological modeling; ecohydrology; remote sensing of vegetation; solar radiation; landscape evolution; water resources; net radiation
Special Issues, Collections and Topics in MDPI journals
Dr. Venkataramana Sridhar

E-Mail Website
Guest Editor
Department of Biological Systems Engineering, Virginia Polytechnic University, Blacksburg, VA 24061, USA
Interests: evapotranspiration; earth system modeling; climate impacts on hydrology on water resources; land–atmosphere interactions
Special Issues, Collections and Topics in MDPI journals
Dr. Nikul Kumari

E-Mail Website
Guest Editor
Faculty of Science, University of Technology Sydney, Sydney, Australia
Interests: hydrology; ecohydrology; ecogeomorphology; remote-sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Freshwater scarcity and freshwater mismanagement are increasingly common challenges that pose a serious threat to the socio-economic development of today’s world. The rising demand for water in different parts of the world necessitates better management of freshwater resources for agricultural purposes, irrigation, water resources management, and climate feedback mechanisms. With advancements in modeling and computing techniques, hydrological models act as a boon for predicting extreme events like floods and droughts. Hydrological models (conceptual, semi-distributed, or fully distributed) are valuable and informative tools in determining and finding different ways to combat environment-related problems and stabilize the water balance of the watershed. In addition, machine learning algorithms (MLAs) have great potential and have been promising in simulating hydrologic processes. For instance, streamflow estimation is crucial for efficient water management and decision-making in any given catchment, especially for drought and flood hydrology, crop modeling, flood forecasting, crop water requirement, major reservoir operations, freshwater allocation, as well as freshwater utilization and management. The complex nature of hydrological processes such as evapotranspiration, soil moisture, and baseflow among the land–water–plant ecosystems hinders the accurate streamflow estimation at the watershed scale. The present Special Issue of Water focuses on the developments in new techniques and perspectives in catchment modeling, the adaptation of remotely sensed data for hydrological modeling, and the application of MLAs in predicting water balance components.

Dr. Ankur Srivastava
Dr. Venkataramana Sridhar
Dr. Nikul Kumari
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. 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

  • freshwater
  • hydrological model
  • machine learning
  • streamflow
  • evapotranspiration
  • soil moisture
  • remote sensing
  • land use/land cover change
  • drought
  • flood
  • crop water requirement
  • irrigation

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Related Special Issue

Published Papers (2 papers)

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Research

29 pages, 5037 KiB  
Article
Amalgamation of Drainage Area Ratio and Nearest Neighbors Methods for Predicting Stream Flows in British Columbia, Canada
by Muhammad Uzair Qamar, Courtney Turner and Cameron Stooshnoff
Water 2025, 17(10), 1502; https://doi.org/10.3390/w17101502 - 16 May 2025
Viewed by 82
Abstract
British Columbia, Canada, is recognized for its abundant natural resources, including agricultural and aquaculture products, sustained by its diverse climate and geography. Water resource allocation in BC is governed by the Water Sustainability Act, enacted on 29 February 2016, replacing the historic Water [...] Read more.
British Columbia, Canada, is recognized for its abundant natural resources, including agricultural and aquaculture products, sustained by its diverse climate and geography. Water resource allocation in BC is governed by the Water Sustainability Act, enacted on 29 February 2016, replacing the historic Water Act. However, limited gauging of streams across the province poses challenges for ensuring water allocation while meeting Environmental Flow Needs. Overallocated watersheds and data-scarce watersheds in need of licensing highlight the need for robust streamflow prediction methods. To address these challenges, we developed a methodology that integrates the Drainage Area Ratio and Nearest Neighbors techniques to predict streamflows efficiently, without incurring additional financial costs. We utilized Digital Elevation Models and flow data from provincially and municipally managed hydrometric stations, as well as from the Water Survey of Canada, to normalize streamflows based on area, slope, and elevation. This approach ensures hydrological predictions that account for variability in hydrological processes resulting from differences in lumped-scale watershed characteristics. The method was validated using streamflow data from hydrometric stations maintained by the aforementioned entities. For validation, each station was iteratively treated as ungauged by temporarily removing it from the dataset and then predicting its streamflow using the proposed methodologies. The results demonstrated that the amalgamated Drainage Area Ratio–Nearest Neighbors approach outperformed the traditional Drainage Area Ratio method, offering reliable predictions for diverse watersheds. This study provides an adaptable and cost-effective framework for enhancing water resource management across BC. Full article
(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management, 2nd Edition)
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23 pages, 6374 KiB  
Article
Evaluating Wildfire-Induced Changes in a Water-Yield Ecosystem Service at the Local Scale Using the InVEST Model
by Ye Inn Kim, Bernie Engel, Won Seok Jang and Young Jo Yun
Water 2025, 17(9), 1260; https://doi.org/10.3390/w17091260 - 23 Apr 2025
Viewed by 189
Abstract
This study evaluates the applicability of the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model for assessing wildfire-induced changes in water-related ecosystem services at a localized scale. Wildfires significantly alter hydrological processes by reducing vegetation cover, which in turn affects water-yield dynamics. [...] Read more.
This study evaluates the applicability of the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model for assessing wildfire-induced changes in water-related ecosystem services at a localized scale. Wildfires significantly alter hydrological processes by reducing vegetation cover, which in turn affects water-yield dynamics. To quantify these changes, we applied the InVEST annual water-yield model to a 4.95 km2 wildfire-affected area and validated its outputs against the physically based SWAT model. The study utilized Sentinel-2 imagery to create pre- and post-wildfire land cover maps, which served as key inputs for the InVEST model. The results showed a 7.05% increase in water yield after the wildfire. Validation using SWAT confirmed that InVEST could capture localized hydrological changes with accuracy. While InVEST simplifies hydrological processes by relying primarily on land cover data, it remains a valuable tool for rapid and low-resource assessments in wildfire-prone regions. This study highlights the potential of InVEST for rapid post-fire evaluations, offering a practical decision-support model for post-fire land and water resource management in the context of climate change. Full article
(This article belongs to the Special Issue Application of Various Hydrological Modeling Techniques and Methods in River Basin Management, 2nd Edition)
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