Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.4
Journals
Water
Special Issues
Statistical Methods and Hydroinformatics Applied in Water Resources
share announcement

Statistical Methods and Hydroinformatics Applied in Water Resources

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

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 8251

Special Issue Editors

Dr. Milan Gocić

E-Mail Website
Guest Editor
Faculty of Civil Engineering and Architecture, University of Nis, Aleksandra Medvedeva 14, 18000 Nis, Serbia
Interests: hydroinformatics; drought risk analysis; data analysis; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Michael Tritthart

E-Mail Website
Guest Editor
Institute of Hydraulic Engineering and River Research, Department of Water-Atmosphere-Environment, BOKU – University of Natural Resources and Life Sciences, Muthgasse 107, 1190 Vienna, Austria
Interests: numerical modelling; hydrodynamics; sediment transport; hydraulic engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Charalampos Skoulikaris

E-Mail Website
Guest Editor
Department of Civil Engineering, School of Engineering, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece
Interests: integrated water resources management; hydrology and hydrological modelling; simulation of hydroelectric projects; assessment of climate change to national and transboundary basins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Implementation of statistical methods and soft computing techniques enables new possibilities in water resources management. Development of innovative and more efficient solutions based on application of hydroinformatic methods can lead to the achievement of sustainability of water-related goals in the changing environment. Thus, the importance of hydroinformatics as a scientific field that combines hydraulic and hydrologic knowledge is in solving a wider spectrum of various issues in water resources. 

The main purpose of this Special Issue is to compile a collection of selected original papers presenting the state-of-the-art research on statistical methods and hydroinformatics applied in water resources. We welcome contributions that emphasize and present the latest advances on issues, such as data-driven modelling, application of physically-based simulation methods, virtual and augmented reality, coupling and nesting of different models, development of models’ bridging interfaces (either with programming or with existing tools, e.g., GIS)  and smart water monitoring systems.

Dr. Charalampos Skoulikaris
Prof. Dr. Michael Tritthart
Dr. Milan Gocić
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

  • data-driven modelling
  • numerical methods
  • soft computing techniques
  • physically-based simulation
  • applied hydroinformatics
  • virtual and augmented reality
  • models coupling and bridging programming
  • smart water monitoring systems

Published Papers (3 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

22 pages, 1719 KiB  
Article
Joint Modelling of Flood Hydrograph Peak, Volume and Duration Using Copulas—Case Study of Sava and Drava River in Croatia, Europe
by Martina Lacko, Kristina Potočki, Kristina Ana Škreb and Nejc Bezak
Water 2022, 14(16), 2481; https://doi.org/10.3390/w14162481 - 12 Aug 2022
Cited by 2 | Viewed by 2074
Abstract
Morphodynamic changes in the riverbed may be accelerated by the climate change-induced effects, mostly through the increase of the frequency of extreme climatic events such as floods. This can lead to scouring of the riverbed around the bridge substructure and consequently reduces its [...] Read more.
Morphodynamic changes in the riverbed may be accelerated by the climate change-induced effects, mostly through the increase of the frequency of extreme climatic events such as floods. This can lead to scouring of the riverbed around the bridge substructure and consequently reduces its overall stability. In order to better understand hydromorphological processes at the local scale, the influence of floods on bridge scour requires a detailed analysis of several interacting flood hydrograph characteristics. This paper presents a multivariate analysis of the annual maximum (AM) flood discharge data at four gauging stations on the Drava and Sava Rivers in Croatia (Europe). As part of the hydrograph analysis, multiple baseflow separation methods were tested. Flood volumes and durations were derived after extracting the baseflow from measured discharge data. Suitable marginal distribution functions were fitted to the peak discharge (Q), flood volume (V) and duration (D) data. Bivariate copula analyses were conducted for the next pairs: peak discharge and volume (Q–V), hydrograph volume and duration (V–D) and peak discharge and hydrograph duration (Q–D). The results of the bivariate copula analyses were used to derive joint return periods for different flood variable combinations, which may serve as a preliminary analysis for the pilot bridges of the R3PEAT project where the aim is to investigate the influences on the riverbed erosion around bridges with installed scour countermeasures. Hence, a design hydrograph was derived that could be used as input data in the hydraulic model for the investigation of the bridge scour dynamics within the project and a preliminary methodology is proposed to be applied. The results indicate that bivariate frequency analysis can be very sensitive to the selected baseflow separation methodology. Therefore, future studies should test multiple baseflow separation methods and visually inspect the performance. Full article
(This article belongs to the Special Issue Statistical Methods and Hydroinformatics Applied in Water Resources)
►▼ Show Figures

Figure 1

20 pages, 5519 KiB  
Article
A Novel Method of Design Flood Hydrographs Estimation for Flood Hazard Mapping
by Wiesław Gądek, Beata Baziak, Tamara Tokarczyk and Wiwiana Szalińska
Water 2022, 14(12), 1856; https://doi.org/10.3390/w14121856 - 09 Jun 2022
Cited by 5 | Viewed by 2095
Abstract
Flood hazard mapping requires knowledge of peak flow as well as flood wave volume and shape, usually represented as a design flood hydrograph (DFH). Statistical approaches for DFH development include nonparametric and parametric methods. The former are developed from long-term flow observations and [...] Read more.
Flood hazard mapping requires knowledge of peak flow as well as flood wave volume and shape, usually represented as a design flood hydrograph (DFH). Statistical approaches for DFH development include nonparametric and parametric methods. The former are developed from long-term flow observations and are thus related to the physio-hydro-climatological catchment properties, but not applicable for ungauged catchments. The alternative parametric DFH can be estimated for any river cross-section, but its links with catchment characteristics are limited. The goal of this study was to introduce a novel hybrid approach for DFH estimation, where the parametric DFH is estimated from the selected properties of the nonparametric DFH (hydrograph width at the levels of 50% and 75% of the peak flow and skewness coefficient) that can be related to the catchment characteristics. The model that offers effective parameter estimation and best correspondence to the reference observation-based hydrograph was selected from among Gamma distribution, Strupczewski and Baptista candidates. The method was validated for 34 catchments of the upper Vistula River and Middle Odra water regions (Poland) based on data from the 1964–2010 period. The Baptista method was found to provide the best model for hybrid DFH construction according to the applied quality measures. Full article
(This article belongs to the Special Issue Statistical Methods and Hydroinformatics Applied in Water Resources)
►▼ Show Figures

Figure 1

18 pages, 3664 KiB  
Article
Spatio-Temporal Interpolation and Bias Correction Ordering Analysis for Hydrological Simulations: An Assessment on a Mountainous River Basin
by Charalampos Skoulikaris, Panagiota Venetsanou, Georgia Lazoglou, Christina Anagnostopoulou and Konstantinos Voudouris
Water 2022, 14(4), 660; https://doi.org/10.3390/w14040660 - 20 Feb 2022
Cited by 8 | Viewed by 3126
Abstract
Triggering hydrological simulations with climate change gridded datasets is one of the prevailing approaches in climate change impact assessment at a river basin scale, with bias correction and spatio-temporal interpolation being functions routinely used on the datasets preprocessing. The research object is to [...] Read more.
Triggering hydrological simulations with climate change gridded datasets is one of the prevailing approaches in climate change impact assessment at a river basin scale, with bias correction and spatio-temporal interpolation being functions routinely used on the datasets preprocessing. The research object is to investigate the dilemma arisen when climate datasets are used, and shed light on which process—i.e., bias correction or spatio-temporal interpolation—should go first in order to achieve the maximum hydrological simulation accuracy. In doing so, the fifth generation of the European Centre for Medium Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) temperature and precipitation products of 9 × 9 km spatial resolution, which are considered as the reference data, are initially compared with the same hindcast variables of a regional climate model of 12.5 × 12.5 km spatial resolution over a specific case study basin and for a 10-year period (1991–2000). Thereafter, the climate model’s variables are (a) bias corrected followed by their spatial interpolation at the reference resolution of 9 × 9 km with the use of empirical quantile mapping and spatio-temporal kriging methods respectively, and (b) spatially downscaled and then bias corrected by using the same methods as before. The derived outputs from each of the produced dataset are not only statistically analyzed at a climate variables level, but they are also used as forcings for the hydrological simulation of the river runoff. The simulated runoffs are compared through statistical performance measures, and it is established that the discharges attributed to the bias corrected climate data followed by the spatio-temporal interpolation present a high degree of correlation with the reference ones. The research is considered a useful roadmap for the preparation of gridded climate change data before being used in hydrological modeling. Full article
(This article belongs to the Special Issue Statistical Methods and Hydroinformatics Applied in Water Resources)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop