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Special Issue "Stochastic Modelling of Hydrometeorological Processes for Engineering Applications"

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

Deadline for manuscript submissions: 1 December 2019

Special Issue Editors

Guest Editor
Dr. Panayiotis Dimitriadis

National Technical University of Athens; National Observatory of Athens, Athens, Greece
Website | E-Mail
Interests: stochastic framework; hydrodynamics; uncertainty analysis; experimental turbulence; water/energy nexus
Guest Editor
Dr. Ioannis Tsoukalas

Department of Water Resources, School of Civil Engineering, National Technical University of Athens, Athens, Greece
Website | E-Mail
Interests: stochastic and statistical hydrology; optimization methods; surrogate modelling; hydroinformatics; water resources management
Guest Editor
Dr. Demetris Koutsoyiannis

Department of Water Resources, School of Civil Engineering, National Technical University of Athens, Athens, Greece
Website | E-Mail
Interests: civil engineering; water resources engineering; hydrology; stochastics; climatology

Special Issue Information

Dear Colleagues,

Hydrometeorological inputs are a key ingredient and simultaneously one of the main sources of uncertainty of every hydrology-related study. This type of uncertainty is referred to as hydrometeorological uncertainty, and is of utmost importance in risk-based engineering works. This is highlighted by the profound relationship that exists between climate and water-related engineering works and operations, with human life and security. Therefore, embracing the existence of stochasticity can be regarded as a first step towards the development of uncertainty-aware, Monte Carlo-based methodologies and frameworks for the design, management, and operation of hydrological and water resources engineering works.

Considering hydrometeorological observations (i.e., time series) as realizations of stochastic processes allows their analysis, modelling, simulation, and forecasting as such. This is an assumption that essentially enables the use of statistical concepts, probability laws, and stochastics in an effort to describe their spatiotemporal evolution and dynamics.

The aim of this Special Issue is to provide a collection of innovative contributions related to:

  • Modelling and simulation of hydrometeorological processes across multiple statiotemporal scales.
  • Statistical/stochastic methods and frameworks for hydrometeorological extremes.
  • Hydrodynamic uncertainty in flood risk management.
  • Stochastic similarities among hydrometeorological processes.
  • Novel temporal or spatial downscaling approaches based on a stochastic framework.
  • The use of stochastics within hydrological and water resources engineering applications.
  • Bridging the gap between research and real-world engineering though open-source software implementations.

Dr. Demetris Koutsoyiannis
Dr. Panayiotis Dimitriadis
Dr. Ioannis Tsoukalas
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 papers will be 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 monthly 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 1600 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.


  • stochastic modelling and simulation
  • hydrological design under uncertainty
  • uncertainty propagation
  • stochastic forecasting models
  • hydrometeorological extremes
  • large-scale variability
  • flood (or drought) risk management
  • simulation of water systems under uncertainty
  • hydrometeorological processes (e.g., precipitation, temperature)

Published Papers

This special issue is now open for submission, see below for planned papers.

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: A Stochastic Eco-Hydrological Framework for Reservoir Design
Authors: A. Koskinas, P. Tsira and A. Tegos
Abstract: The concept of Environmental Flow Assessment (EFA) is a crucial element of modified ecosystems featuring large infrastructure such as dams and reservoirs and affecting the infrastructure from design to its operation. While the need to maintain a constant streamflow has been well documented throughout the years or more recently the seasonal varying outflows, the relatively short length of available inputs raises questions about the reliability of any given environmental flow scheme. However, with the use of stochastics, it is possible to quantify these uncertainties, and present a solution that incorporates long-term persistence into a balanced reservoir simulation model. In this work, an attempt is made to determine a scheme that can produce a reliable timeseries of fluctuating environmental flows matching original river conditions to best accommodate local wildlife, in accordance with newer studies. The primary goal is to ensure the best possible conditions for the ecosystem, and then secondarily to allow a steady supply of water for other uses. Using a synthetic timeseries based on historical inputs, it is possible to determine essential statistical flow characteristics, and use these to detect the optimal reservoir storage and the optimal flow scheme that maximizes environmental and human demand reliability.
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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