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Advances in Hydroinformatics and Geo/Statistics for Modelling and Risk Assessment of Water Systems

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

Deadline for manuscript submissions: 20 December 2026 | Viewed by 3763

Special Issue Editors

Dr. Santiago Zazo

E-Mail Website
Guest Editor
Hydraulic Engineering Area, IGA Research Group, Higher Polytechnic School of Ávila, University of Salamanca, Avda. Hornos Caleros 50, 05003 Ávila, Spain
Interests: flood modelling; river hydraulic engineering; assessment of the geometric uncertainty of hydraulic flood models; water sustainability; water resource management; drought; hydrological modelling; Bayesian causality analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. José-Luis Molina

E-Mail Website
Guest Editor
Hydraulic Engineering Area, IGA Research Group, Higher Polytechnic School of Ávila, University of Salamanca, Avda. Hornos Caleros 50, 05003 Ávila, Spain
Interests: hydrological engineering and science; applied statistics; AI; machine learning; data science; data-driven modelling; software development; hydraulic engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Carmen Patino-Alonso

E-Mail Website
Guest Editor
Department of Statistics, IGA Research Group, University of Salamanca, Campus Miguel de Unamuno, C/Alfonso X El Sabio s/n, 37007 Salamanca, Spain
Interests: data analysis; applied statistics; water circularity; hydro statistics; hydrometeorological modelling; multivariate analysis; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Fernando Espejo

E-Mail Website
Guest Editor Assistant
Hydraulic Engineering Area, Higher Polytechnic School of Ávila, University of Salamanca, Avda. Hornos Caleros 50, 05003 Ávila, Spain
Interests: urban water systems; water balance; hydraulic engineering; river hydraulic engineering; hydrological modelling; flood modelling; water sustainability; water infrastructure management programs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Guaranteeing security in order to cope with water risks (mainly floods and droughts) is essential not only to preserve and maintain the environment but also to protect socio-economic activity. However, population growth and its associated economic needs, as well as the evident modifications of the hydrological cycle patterns resulting from increasing climate variability, are seriously compromising the achievement of these objectives. In the face of this global challenge, new approaches and advances in the fields of hydroinformatics and geostatistics can help to improve the assessment of these increasingly systemic natural hazards.

This Special Issue is focused on improving risk assessment in water systems, covering a wide range of topics, including the spatio-temporal hydrological–hydraulic analysis of risk induced by sudden and extreme events, advances in the modelling of water systems, advances in the forest restoration and/or development of nature-based solutions to mitigate the effects of floods, innovative methodologies for time series analysis, new developments based on EPIC responses, or drought characterization, among other things.

Dr. Santiago Zazo
Prof. Dr. José-Luis Molina
Dr. Carmen Patino-Alonso
Guest Editors

Dr. Fernando Espejo
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

  • flood modelling
  • river hydraulic
  • geometric uncertainty of flood models
  • water sustainability
  • advances in forest restoration
  • nature-based solutions
  • advanves on time series analysis
  • advances of geostatistics

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Published Papers (3 papers)

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Research

22 pages, 5921 KB  
Article
Streamflow Simulation Based on a Hybrid Morphometric–Satellite Methodological Framework
by Devis A. Pérez-Campo, Fernando Espejo and Santiago Zazo
Water 2026, 18(7), 786; https://doi.org/10.3390/w18070786 - 26 Mar 2026
Viewed by 613
Abstract
This research investigates the relationships between the parameters of the GR4J hydrological model and a set of morphometric descriptors, climatic indices, land-cover characteristics, and soil properties across the Caquetá River Basin (Colombia). Twelve limnimetric–limnographic gauges with consistent records for the period 2001–2022 were [...] Read more.
This research investigates the relationships between the parameters of the GR4J hydrological model and a set of morphometric descriptors, climatic indices, land-cover characteristics, and soil properties across the Caquetá River Basin (Colombia). Twelve limnimetric–limnographic gauges with consistent records for the period 2001–2022 were selected for model calibration and validation. The corresponding sub-watersheds were delineated and characterized in terms of geomorphometry, vegetation cover, and soil permeability. According to that, the morphometric assessment focused on estimating key geomorphometric parameters, while land-cover descriptions utilized NDVI data. Soil type identification was based on the average approximate permeability across each analyzed sub-watershed. Model calibration was performed using the Differential Evolution Markov Chain (DE-MC) algorithm with 8000 simulations, forced by CHIRPS satellite precipitation and ERA5 potential evaporation data. Relationships between GR4J parameters and watershed attributes were assessed using Spearman’s rank correlation and curve-fitting analyses. The results reveal strong and consistent relationships between GR4J parameters (X1–X4) and key morphometric variables, including basin perimeter, circularity ratio, main channel length, and channel slope. Coefficients of determination ranged from 0.80 to 0.98, highlighting the potential for parameter regionalization based on physiographic and environmental descriptors. Full article
(This article belongs to the Special Issue Advances in Hydroinformatics and Geo/Statistics for Modelling and Risk Assessment of Water Systems)
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19 pages, 2575 KB  
Article
Assessing Urban Flood Susceptibility Using Random Forest Machine Learning and Geospatial Technologies: Application to the Bonoumin-Palmeraie Watershed, Abidjan (Côte d’Ivoire)
by Jean Homian Danumah, Wilfred Ahoumodom Ataba, Valère Carin Jofack Sokeng, You Lucette Akpa, Mahaman Bachir Saley and Andrew Ogilvie
Water 2026, 18(3), 402; https://doi.org/10.3390/w18030402 - 4 Feb 2026
Cited by 1 | Viewed by 1011
Abstract
Recurrent flooding poses a persistent and growing threat to West African watersheds facing rapid urbanization and climate change. Despite advances in machine learning and geospatial datasets, urban planning and flood prevention often rely on limited datasets and traditional analysis. This study addresses this [...] Read more.
Recurrent flooding poses a persistent and growing threat to West African watersheds facing rapid urbanization and climate change. Despite advances in machine learning and geospatial datasets, urban planning and flood prevention often rely on limited datasets and traditional analysis. This study addresses this research gap in the Bonoumin-Palmeraie watershed (Abidjan, Côte d’Ivoire) by developing an integrated approach leveraging remote sensing, Geographic Information Systems (GIS), and the Random Forest algorithm to assess and map flood susceptibility. Twelve conditioning factors related to topography, hydrology, land use, and climate were derived from Sentinel-1, ALOS PALSAR, and multi-source earth observation datasets. Historical flood extents were mapped in Google Earth Engine to train the Random Forest model in a Google Colab environment. The model demonstrated high discriminatory power, yielding an Area Under the Curve of 0.94 and Overall Accuracy of 0.83. Drainage density, rainfall, and altitude were identified as the primary explanatory drivers. The resulting flood susceptibility map indicates that 39% of the watershed exhibits medium to very high susceptibility, with critical hotspots in the neighborhoods of Palmeraie, Attoban, Akouedo, Djorogobité, and Riviera-Sogefiha. While limited by the exclusion of certain anthropogenic variables and ground truth constraints, the study provides a reproducible, data-driven framework for flood risk assessment in tropical urban environments. These findings offer essential scientific support for urban planners and decision-makers to enhance territorial planning and sustainable flood management in Abidjan. Full article
(This article belongs to the Special Issue Advances in Hydroinformatics and Geo/Statistics for Modelling and Risk Assessment of Water Systems)
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23 pages, 4509 KB  
Article
Data Assimilation for a Simple Hydrological Partitioning Model Using Machine Learning
by Changhwi Jeon, Chaelim Lee, Suhyung Jang and Sangdan Kim
Water 2025, 17(22), 3204; https://doi.org/10.3390/w17223204 - 9 Nov 2025
Viewed by 1452
Abstract
Predicting streamflow is a core element of efficient water resource management. Traditional hydrological models are constructed based on historical observational data, leading to cumulative prediction errors over time. To address this issue, this study proposes an Artificial Intelligence Filter (AIF) that integrates machine [...] Read more.
Predicting streamflow is a core element of efficient water resource management. Traditional hydrological models are constructed based on historical observational data, leading to cumulative prediction errors over time. To address this issue, this study proposes an Artificial Intelligence Filter (AIF) that integrates machine learning (ML) techniques into a data assimilation framework. The AIF learns the relationship between simulated streamflow and state variables (soil moisture, aquifer water level) and updates the state based on observed streamflow. This study applied the Simple Hydrologic Partitioning Model (SHPM) to four dam basins in southeastern Korea (Andong, Hapcheon, Miryang, Namgang). Model parameters were estimated using the Markov Chain Monte Carlo (MCMC) method, and results were compared with Open Loop (OL) simulations. After applying AIF, R2 and NSE increased by an average of approximately 0.02–0.04, representing a 2–5% improvement, achieving enhanced performance in most basins. KGE decreased slightly in some basins but improved by an average of about 2%. These results demonstrate that AIF not only enhances the accuracy of hydrological models but also contributes to securing the reliability of water resource forecasts through data assimilation and supports efficient management decision-making. Full article
(This article belongs to the Special Issue Advances in Hydroinformatics and Geo/Statistics for Modelling and Risk Assessment of Water Systems)
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