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Database Integration and Conceptual Frameworks in Hydrological Research
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Database Integration and Conceptual Frameworks in Hydrological Research

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

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 1863

Special Issue Editors

Dr. Mahesh Lal Maskey

E-Mail Website
Guest Editor
Water Systems Management Laboratory, School of Engineering, University of California, Merced, 5200 North Lake Road, Merced, CA 95340, USA
Interests: hydrology; hydroinformatics; fractals; hydroeconomics; climate change; groundwater and surface water hydrology; stochastic hydrology; machine learning; APEX; WRF-hydro; SWAT; MODFLOW
Prof. Dr. Upaka Rathnayake

E-Mail Website
Guest Editor
Department of Civil Engineering and Construction, Faculty of Engineering and Design, Atlantic Technological University, F91 YW50 Sligo, Ireland
Interests: watershed modeling; hydrology; urban water management; climate adaptation; AI-driven engineering solution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in computational tools and high-performance computing have greatly expanded the availability and diversity of hydrological data and models. The management of water resources now draws on satellite climate indicators, field measurements, groundwater and surface water records, as well as agricultural and socio-environmental information. However, challenges like fragmented data, inconsistencies, and a lack of clear integration frameworks limit the effective use of this wealth of information across disciplines and time scales.

This Special Issue, entitled "Database Integration and Conceptual Frameworks in Hydrological Research," seeks to address these gaps by encouraging scholarship that builds strong foundations for organizing, synthesizing, and applying hydrological data. We invite contributions such as literature reviews, conceptual frameworks, and original research focused on improving data integration, modeling frameworks, and the reusability of information in sustainable water management.

Key goals include promoting FAIR data principles, bridging disciplines like agro-hydrology and socio-hydrology through data architecture, exploring ethical and legal aspects of data governance, and developing standards for metadata and interoperability. By fostering clarity, connectivity, and credibility in hydrological research, this Special Issue aims to guide future advancements toward more integrated and sustainable water resource management.

Dr. Mahesh Lal Maskey
Prof. Dr. Upaka Rathnayake
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 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 modeling
  • hydrology
  • urban water management
  • climate adaptation
  • AI-driven engineering solution

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Published Papers (3 papers)

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Research

17 pages, 3512 KB  
Article
Statistical Evaluation of Observed Precipitation from INMET Meteorological Stations and MERGE Estimates in the Eastern Amazon
by Priscila da S. Batista, Júlio T. da Silva, Ana Carla dos S. Gomes, Jéssica A. de J. Corrêa, Gabriel Brito Costa, Antônio Marcos D. de Andrade, Carlos T. S. Dias, Leila S. S. Lisboa and Lucietta Guerreiro Martorano
Water 2026, 18(8), 898; https://doi.org/10.3390/w18080898 - 9 Apr 2026
Viewed by 462
Abstract
Accurate precipitation data are essential for understanding hydrological processes and supporting environmental and water resource management in the Amazon, where observational networks remain sparse and spatially uneven. This study evaluates the performance of the MERGE (Merge of Satellite and Gauge Precipitation Data) dataset, [...] Read more.
Accurate precipitation data are essential for understanding hydrological processes and supporting environmental and water resource management in the Amazon, where observational networks remain sparse and spatially uneven. This study evaluates the performance of the MERGE (Merge of Satellite and Gauge Precipitation Data) dataset, developed by CPTEC/INPE, in representing rainfall variability in the Eastern Amazon. Daily precipitation data from five INMET meteorological stations were compared with MERGE estimates over a 20-year period (1998–2017) using a multi-metric statistical framework, including correlation, regression, error metrics, efficiency indices, and clustering analysis. The results indicate strong agreement between observed and estimated precipitation, with Pearson correlation coefficients ranging from 0.94 to 0.99 and Nash–Sutcliffe efficiency values between 0.87 and 0.97. Regression analyses show coefficients of determination between 0.89 and 0.98, indicating that MERGE effectively reproduces the magnitude and temporal variability of precipitation. Monthly and interannual analyses confirm consistent representation of seasonal patterns and rainfall dynamics across the evaluated stations. The boxplot analysis reveals that MERGE accurately captures the overall distribution of precipitation but tends to underestimate higher precipitation values, particularly during months associated with intense rainfall. This behavior reflects limitations in representing localized convective events and spatial variability. Overall, the results demonstrate that MERGE provides a reliable representation of precipitation variability in the Eastern Amazon and represents a valuable dataset for hydroclimatic analyses in regions with limited observational coverage. Full article
(This article belongs to the Special Issue Database Integration and Conceptual Frameworks in Hydrological Research)
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14 pages, 2509 KB  
Article
Extractable Water Index (EWI): Towards a Universal Metric for Sustainable River Extraction
by Attidiyage Don Shashika Iresh, Bandunee C. L. Athapattu, W. C. D. Kumari Fernando, Jayantha T. B. Obeysekera and Upaka Rathnayake
Water 2026, 18(6), 707; https://doi.org/10.3390/w18060707 - 18 Mar 2026
Viewed by 446
Abstract
Sustainable river management depends on indices that balance human water demands with ecological flow requirements while accounting for hydrological variability. Existing water scarcity and withdrawal indices are largely based on monthly or annual aggregates, often neglecting daily variability and the effects of drought [...] Read more.
Sustainable river management depends on indices that balance human water demands with ecological flow requirements while accounting for hydrological variability. Existing water scarcity and withdrawal indices are largely based on monthly or annual aggregates, often neglecting daily variability and the effects of drought buffering. This study introduces the Extractable Water Index (EWI), a novel, dimensionless metric that quantifies the sustainable potential for water extraction using daily flow records. The EWI integrates mean available flow, flow variability, low-flow thresholds, and storage contributions into a single expression, thereby capturing both hydrological dynamics and ecological protections. Two scenarios were evaluated, (i) no-storage and (ii) with-storage, with the latter employing a semi-analytical approximation to represent a reservoir or pond. The EWI was applied to 20 daily river flow series for 16 river basins in Sri Lanka. Under no-storage conditions, thresholds were defined as follows: EWI < 0.45 indicates low extraction potential; 0.45 < EWI < 0.60 indicates moderate extraction potential; and EWI > 0.75 indicates high extraction potential. The results demonstrate that even modest storage can substantially enhance sustainable withdrawals. The EWI provides a transparent, reproducible decision-support tool that complements environmental flow standards and prioritizes rivers based on extractability. The EWI provides a valuable tool for estimating water extraction potential within the Sri Lankan context. This index can be applied across diverse hydroclimatic regimes and, when combined with threshold validation, can predict extraction requirements under varying seasonal flow conditions. Full article
(This article belongs to the Special Issue Database Integration and Conceptual Frameworks in Hydrological Research)
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25 pages, 9491 KB  
Article
Determination of the Surface Watercourse Velocities by Using the Propeller Current Meter, Unmanned Aerial Vehicle, and Mobile Phone
by Sanja Šamanović, Bojan Đurin, Vlado Cetl and Farhad Bahmanpouri
Water 2026, 18(2), 273; https://doi.org/10.3390/w18020273 - 21 Jan 2026
Viewed by 502
Abstract
According to existing procedures for defining the velocity distribution across cross profile sections of watercourses (e.g., Entropy theory and Power Law theory), surface velocity is a key input parameter, together with cross-sectional bathymetry. Field measurements to obtain velocity values and their distributions are [...] Read more.
According to existing procedures for defining the velocity distribution across cross profile sections of watercourses (e.g., Entropy theory and Power Law theory), surface velocity is a key input parameter, together with cross-sectional bathymetry. Field measurements to obtain velocity values and their distributions are often difficult due to limited equipment, unreliable data, missing data, or hazardous conditions such as flooding and inaccessible locations. This creates a strong need for alternative approaches to measuring surface velocities in rivers. The application of unmanned aerial vehicles (UAVs), mobile phones, and traditional field instruments such as the Propeller Current Meter (PCM) can significantly improve measurement efficiency, especially in situations where conventional methods are not feasible. This paper presents an algorithm for comparing these measurement approaches and quantifying their differences. The methodology is demonstrated using a real case study on the Bednja River in Croatia, which flows through alluvial deposits. The results show that video-based surface velocity estimation using UAV and mobile phone imagery is feasible under real river conditions. Still, its accuracy depends strongly on flow conditions and surface characteristics. While UAV recordings provide reliable results in fast and turbulent flows, mobile phone videos yield more stable performance in smoother flow conditions, where additional surface texture is available from natural tracers. Full article
(This article belongs to the Special Issue Database Integration and Conceptual Frameworks in Hydrological Research)
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