Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.7
3.5
Journals
Water
Special Issues
Hydroclimate Risk Assessment and Management: Data, Models and Remote Sensing...
water-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Water Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Hydroclimate Risk Assessment and Management: Data, Models and Remote Sensing Approaches

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

Deadline for manuscript submissions: 31 March 2027 | Viewed by 1442

Editors

Dr. Md Shahid Latif

E-Mail Website
Guest Editor
Canada Research Chair in Statistical Hydro-Climatology, National Institute of Scientific Research (INRS-ETE), Quebec City, QC G1K 9A9, Canada
Interests: statistical and stochastic hydro-climatology; compound extreme event modelling; probabilistic deep learning in hydro-climatology frequency analysis; hydrology and water resources; teleconnection and climate variability; nonstationary frequency analysis; application of copula-based methodology in hydro-climatological studies; risk and uncertainty analysis; extreme value analysis; climate change; statistical linking between meteorological extremes with community wellness and public health; time-series forecasting and modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Shaik Rehana

E-Mail Website
Guest Editor
International Institute of Information Technology Hyderabad, Hyderabad, India
Interests: hydrologic impacts of climate change; statistical downscaling; regional hydrologic modeling; river water quality management; reservoir operation; irrigation planning and management; drought assessment; climate extremes analysis; hydroinformatics; machine learning applications in water resources engineering
Dr. Fatih Tosunoğlu

E-Mail Website
Guest Editor
Department of Civil Engineering, Erzurum Technical University, 25050 Erzurum, Turkey
Interests: hydrology; flood and drought hydrology; statistical hydrology; water resources; non-stationary frequency analysis; trend assessment; application of multivariate models in hydrology and water resources; climate projections

Special Issue Information

Dear Colleagues,

Hydroclimate risks are intensifying and diversifying under climate change, land-use change, urbanization and growing water demands. Extremes such as floods, droughts, heatwaves, wildfires, storm surges, snow and ice loss, and water-quality crises now interact with long-term shifts in water availability, groundwater storage, ecosystems, and the water–energy–food nexus. This Special Issue invites broad, interdisciplinary contributions that use data, models and remote sensing to monitor, attribute, project and manage hydroclimate risks from local to global scales. We welcome studies that draw on in situ observations; satellite, airborne and ground-based remote sensing; reanalysis products; and climate projections (e.g., CMIP5/CMIP6, CORDEX, convection-permitting and impact-oriented regional simulations). Submissions may employ process-based hydrological, hydraulic, land-surface, groundwater, cryospheric, coastal and socio-hydrological models; statistical and extreme-value methods; multivariate and copula frameworks; and system-dynamics and agent-based approaches; as well as machine learning, deep learning, hybrid physics–ML and data-assimilation or ensemble techniques. Topics include (but are not limited to) hazard and impact mapping; early warning, improved forecasting of hydroclimatological extremes such as flood, droughts, water quality assessment, and climate services; compound and cascading events; downscaling and bias adjustment of climate projections; detection and attribution of trends; nature-based solutions; and decision-support tools for adaptation planning, design standards and risk governance. We particularly encourage work that links physical hazards with exposure, vulnerability, socio-economic pathways, and governance, spanning urban and rural systems, agricultural and ecological impacts, and data-scarce or rapidly changing regions. Original research articles, methodological and benchmark papers, operational and agency case studies, and critical reviews are all welcome. Contributions that provide open datasets, models, or tools, or that are co-developed with practitioners and stakeholders, are especially encouraged. Overall, this Special Issue seeks to turn hydroclimate information into actionable strategies for risk reduction and resilience.

Dr. Md Shahid Latif
Dr. Shaik Rehana
Dr. Fatih Tosunoğlu
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-anonymized 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

  • hydroclimate risk and extremes
  • climate change
  • CMIP5/CMIP6 and regional projections
  • remote sensing
  • reanalysis and in situ observations
  • hydrological
  • hydraulic
  • groundwater and socio-hydrological modelling
  • floods
  • droughts
  • heatwaves
  • wildfires and compound events
  • downscaling
  • bias adjustment and data assimilation
  • machine learning
  • deep learning and hybrid modelling
  • multi-hazard impact modelling and climate services
  • water–energy–food–ecosystem nexus
  • adaptation
  • resilience and decision-support systems
  • AI applications in improving hydroclimato-logical forecasting
  • flood
  • drought
  • water quality assessment

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 1995 KB  
Article
Early Identification of Subtle Deformations in Potential Debris Flow Source Areas Using Phase-Unwrapped Convolutional Neural Networks and Long-Time-Series InSAR Technology
by Jianwei Ren, Dan Xu, Qinzheng Lang, Na He, Guangyu Chen, Ying Zhou and Filip Gurkalo
Water 2026, 18(15), 1883; https://doi.org/10.3390/w18151883 - 2 Aug 2026
Viewed by 243
Abstract
Mudslides are sudden and highly destructive; their source areas typically undergo slow, millimeter-scale creep over a period of months or even years before destabilization. If these precursor signals can be detected, valuable time can be gained for disaster prevention and mitigation. However, in [...] Read more.
Mudslides are sudden and highly destructive; their source areas typically undergo slow, millimeter-scale creep over a period of months or even years before destabilization. If these precursor signals can be detected, valuable time can be gained for disaster prevention and mitigation. However, in the weathered crust and residual deposits of potential debris flow source areas, the long-term coupled action of freeze–thaw cycles and rainfall causes continuous reorganization of internal particle contact force chains, generating weak, metastable creep signals. The high-order nonlinearity and spatial heterogeneity of the interference phase gradient in low-coherence regions lead to pixel-spanning jumps in the unwrapped phase that are blurred by integer multiples of π. The high rate of phase jumps between adjacent pixels severely hampers the early detection of weak deformation. To address this, we propose a method for the early detection of weak deformation in potential debris flow source areas based on phase-unwrapping convolutional neural networks and long-time-series InSAR technology. First, we use long-time-series InSAR technology to construct a spatiotemporal map of interferogram sequences and establish feature propagation paths between high- and low-coherence interferogram pairs using the coherence coefficient as an edge weight. Second, we design a phase-unwrapping graph convolutional network that aggregates phase gradient information from neighboring nodes through two graph convolutional layers to correct the unwrapping results of low-coherence interferogram pairs and suppress cross-pixel jumps caused by π-integer-multiple blurring. Finally, by combining a dual-criterion classification approach based on temporal attention scores and deformation acceleration, the method captures the complete evolutionary process from stable creep to accelerated deformation. Experimental results show that the maximum phase jump rate of this method is approximately 0.02, effectively resolving the phase jump issue caused by high-order nonlinear gradients; in some areas of the study region, where deformation ranges from −1 mm to −9 mm, the inversion error is consistently controlled within ±1 mm. A total of five potential debris flow source areas were identified, classified by creep stage as follows: one in the accelerated deformation stage, two in the stable creep stage, and two in the early creep stage. No significant surface failure occurred in any of these source areas. This method provides reliable technical support and a decision-making basis for refined early warning, disaster prevention, and mitigation of debris flow hazards and holds significant engineering application value. Full article
(This article belongs to the Special Issue Hydroclimate Risk Assessment and Management: Data, Models and Remote Sensing Approaches)
►▼ Show Figures

Figure 1

32 pages, 3764 KB  
Article
Assessment of Compound Hydrological–Thermal Extremes over Indian River Systems
by Jaya Bharat Reddy Buchupalle, Satish Kumar Mummidivarapu, Shaik Rehana, Shahid Latif and Taha B. M. J. Ouarda
Water 2026, 18(8), 896; https://doi.org/10.3390/w18080896 - 9 Apr 2026
Viewed by 698
Abstract
River water quality assessment has traditionally been conducted using univariate or threshold-based approaches; however, the exploration of extremes assessment under bivariate water quality variables has been limited by many studies. Understanding the compound extremes of low river discharge (Q) and elevated river water [...] Read more.
River water quality assessment has traditionally been conducted using univariate or threshold-based approaches; however, the exploration of extremes assessment under bivariate water quality variables has been limited by many studies. Understanding the compound extremes of low river discharge (Q) and elevated river water temperatures (RWTs) resulting from climatic variability is essential for effective water quality management and protection of the river. This study investigates the joint behaviour of RWTs and Q in six Indian rivers: Kaveri, Mahi, Sabarmati, Vardha, Bhadra, and Yamuna. The Weibull-3P and Generalised Extreme Value (GEV-3P) distributions best fit for Q and RWTs, respectively. The adequacy of eighteen different parametric copula classes was evaluated. The Gaussian copula provided the best fit for the Vardha River, the Frank copula for Bhadra, and the BB8 copula for the Yamuna River. The evaluation of joint return periods (RPs) and conditional distributions has identified notable spatial variability in compound hydrological and thermal extreme hazards. The semi-arid Vardha River showed the shortest RPs for simultaneous low Q and high RWTs, indicating a greater likelihood of combined extremes. Conversely, the monsoon-fed Bhadra River displayed moderate hazard levels, while the Himalayan-fed Yamuna River had the longest joint RPs and the lowest conditional probabilities. This suggests that simultaneous extreme drought and heat events are less likely in the Yamuna basin, although significant risks remain for less severe thresholds. Full article
(This article belongs to the Special Issue Hydroclimate Risk Assessment and Management: Data, Models and Remote Sensing Approaches)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop