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Stochastic Modelling of Hydrometeorological Processes for Engineering Applications
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Stochastic Modelling of Hydrometeorological Processes for Engineering Applications

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 27904

Special Issue Editors

Dr. Panayiotis Dimitriadis

E-Mail Website
Guest Editor
1. Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, Athens, Greece
2. University of West Attica, Aigaleo, Greece
Interests: stochastics; hydrodynamics; uncertainty analysis; experimental turbulence; water/energy nexus
Special Issues, Collections and Topics in MDPI journals
Dr. Ioannis Tsoukalas

E-Mail Website
Guest Editor
Department of Water Resources and Environmental Engineering, National Technical University of Athens, Athens, Greece
Interests: stochastic simulation; temporal and spatial downscaling/disaggregation; copulas; hydroinformatics; hydrometeorological extremes; uncertainty quantification; optimization algorithms; water resources management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Demetris Koutsoyiannis

E-Mail Website
Guest Editor
Department of Water Resources, School of Civil Engineering, National Technical University of Athens, Athens, Greece
Interests: civil engineering; water resources engineering; hydrology; stochastics; climatology
Special Issues, Collections and Topics in MDPI journals

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 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

  • 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)

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Published Papers (7 papers)

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Research

19 pages, 4983 KiB  
Article
Stochastic Modeling of Hydroclimatic Processes Using Vine Copulas
by George Pouliasis, Gina Alexandra Torres-Alves and Oswaldo Morales-Napoles
Water 2021, 13(16), 2156; https://doi.org/10.3390/w13162156 - 5 Aug 2021
Cited by 5 | Viewed by 3086
Abstract
The generation of synthetic time series is important in contemporary water sciences for their wide applicability and ability to model environmental uncertainty. Hydroclimatic variables often exhibit highly skewed distributions, intermittency (that is, alternating dry and wet intervals), and spatial and temporal dependencies that [...] Read more.
The generation of synthetic time series is important in contemporary water sciences for their wide applicability and ability to model environmental uncertainty. Hydroclimatic variables often exhibit highly skewed distributions, intermittency (that is, alternating dry and wet intervals), and spatial and temporal dependencies that pose a particular challenge to their study. Vine copula models offer an appealing approach to generate synthetic time series because of their ability to preserve any marginal distribution while modeling a variety of probabilistic dependence structures. In this work, we focus on the stochastic modeling of hydroclimatic processes using vine copula models. We provide an approach to model intermittency by coupling Markov chains with vine copula models. Our approach preserves first-order auto- and cross-dependencies (correlation). Moreover, we present a novel framework that is able to model multiple processes simultaneously. This method is based on the coupling of temporal and spatial dependence models through repetitive sampling. The result is a parsimonious and flexible method that can adequately account for temporal and spatial dependencies. Our method is illustrated within the context of a recent reliability assessment of a historical hydraulic structure in central Mexico. Our results show that by ignoring important characteristics of probabilistic dependence that are well captured by our approach, the reliability of the structure could be severely underestimated. Full article
(This article belongs to the Special Issue Stochastic Modelling of Hydrometeorological Processes for Engineering Applications)
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22 pages, 14731 KiB  
Article
Clustering Simultaneous Occurrences of the Extreme Floods in the Neckar Catchment
by Ehsan Modiri and András Bárdossy
Water 2021, 13(4), 399; https://doi.org/10.3390/w13040399 - 4 Feb 2021
Cited by 1 | Viewed by 3843
Abstract
Flood protection is crucial for making socioeconomic policies due to the high losses of extreme floods. So far, the synchronous occurrences of flood events have not been deeply investigated. In this paper, multivariate analysis was implemented to reveal the interconnection between these floods [...] Read more.
Flood protection is crucial for making socioeconomic policies due to the high losses of extreme floods. So far, the synchronous occurrences of flood events have not been deeply investigated. In this paper, multivariate analysis was implemented to reveal the interconnection between these floods in spatiotemporal resolution. The discharge measurements of 46 gauges with a continuous daily time series for 55 years were taken over the Neckar catchment. Initially, the simultaneous floods were identified. The Kendall correlation between the pair sets of peaks was determined to scrutinize the similarities between the simultaneous events. Agglomerative hierarchical clustering tree (AHCT) and multidimensional scaling (MDS) were employed, and obtained clusters were compared and evaluated with the Silhouette verification method. AHCT shows that the Average and Ward algorithms are appropriate to detect reasonable clusters. The Neckar catchment has been divided into three major clusters: the first cluster mainly covers the western part and is bounded by the Black Forest and Swabian Alps. The second cluster is mostly located in the eastern part of the upper Neckar. The third cluster contains the remaining lowland areas of the Neckar basin. The results illustrate that the clusters act relatively as a function of topography, geology, and anthropogenic alterations of the catchment. Full article
(This article belongs to the Special Issue Stochastic Modelling of Hydrometeorological Processes for Engineering Applications)
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17 pages, 3271 KiB  
Article
Integrating Climatic and Physical Information in a Bayesian Hierarchical Model of Extreme Daily Precipitation
by Charlotte A. Love, Brian E. Skahill, John F. England, Gregory Karlovits, Angela Duren and Amir AghaKouchak
Water 2020, 12(8), 2211; https://doi.org/10.3390/w12082211 - 6 Aug 2020
Cited by 2 | Viewed by 2815
Abstract
Extreme precipitation events are often localized, difficult to predict, and available records are often sparse. Improving frequency analysis and describing the associated uncertainty are essential for regional hazard preparedness and infrastructure design. Our primary goal is to evaluate incorporating Bayesian model averaging (BMA) [...] Read more.
Extreme precipitation events are often localized, difficult to predict, and available records are often sparse. Improving frequency analysis and describing the associated uncertainty are essential for regional hazard preparedness and infrastructure design. Our primary goal is to evaluate incorporating Bayesian model averaging (BMA) within a spatial Bayesian hierarchical model framework (BHM). We compare results from two distinct regions in Oregon with different dominating rainfall generation mechanisms, and a region of overlap. We consider several Bayesian hierarchical models from relatively simple (location covariates only) to rather complex (location, elevation, and monthly mean climatic variables). We assess model predictive performance and selection through the application of leave-one-out cross-validation; however, other model assessment methods were also considered. We additionally conduct a comprehensive assessment of the posterior inclusion probability of covariates provided by the BMA portion of the model and the contribution of the spatial random effects term, which together characterize the pointwise spatial variation of each model’s generalized extreme value (GEV) distribution parameters within a BHM framework. Results indicate that while using BMA may improve analysis of extremes, model selection remains an important component of tuning model performance. The most complex model containing geographic and information was among the top performing models in western Oregon (with relatively wetter climate), while it performed among the worst in the eastern Oregon (with relatively drier climate). Based on our results from the region of overlap, site-specific predictive performance improves when the site and the model have a similar annual maxima climatology—winter storm dominated versus summer convective storm dominated. The results also indicate that regions with greater temperature variability may benefit from the inclusion of temperature information as a covariate. Overall, our results show that the BHM framework with BMA improves spatial analysis of extremes, especially when relevant (physical and/or climatic) covariates are used. Full article
(This article belongs to the Special Issue Stochastic Modelling of Hydrometeorological Processes for Engineering Applications)
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19 pages, 7964 KiB  
Article
Identification of the Optimum Rain Gauge Network Density for Hydrological Modelling Based on Radar Rainfall Analysis
by Yeboah Gyasi-Agyei
Water 2020, 12(7), 1906; https://doi.org/10.3390/w12071906 - 3 Jul 2020
Cited by 22 | Viewed by 4358
Abstract
Rain gauges continue to be sources of rainfall data despite progress made in precipitation measurements using radar and satellite technology. There has been some work done on assessing the optimum rain gauge network density required for hydrological modelling, but without consensus. This paper [...] Read more.
Rain gauges continue to be sources of rainfall data despite progress made in precipitation measurements using radar and satellite technology. There has been some work done on assessing the optimum rain gauge network density required for hydrological modelling, but without consensus. This paper contributes to the identification of the optimum rain gauge network density, using scaling laws and bias-corrected 1 km × 1 km grid radar rainfall records, covering an area of 28,371 km2 that hosts 315 rain gauges in south-east Queensland, Australia. Varying numbers of radar pixels (rain gauges) were repeatedly sampled using a unique stratified sampling technique. For each set of rainfall sampled data, a two-dimensional correlogram was developed from the normal scores obtained through quantile-quantile transformation for ordinary kriging which is a stochastic interpolation. Leave-one-out cross validation was carried out, and the simulated quantiles were evaluated using the performance statistics of root-mean-square-error and mean-absolute-bias, as well as their rates of change. A break in the scaling of the plots of these performance statistics against the number of rain gauges was used to infer the optimum rain gauge network density. The optimum rain gauge network density varied from 14 km2/gauge to 38 km2/gauge, with an average of 25 km2/gauge. Full article
(This article belongs to the Special Issue Stochastic Modelling of Hydrometeorological Processes for Engineering Applications)
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41 pages, 14180 KiB  
Article
Simulation of Non-Gaussian Correlated Random Variables, Stochastic Processes and Random Fields: Introducing the anySim R-Package for Environmental Applications and Beyond
by Ioannis Tsoukalas, Panagiotis Kossieris and Christos Makropoulos
Water 2020, 12(6), 1645; https://doi.org/10.3390/w12061645 - 8 Jun 2020
Cited by 27 | Viewed by 5405
Abstract
Stochastic simulation has a prominent position in a variety of scientific domains including those of environmental and water resources sciences. This is due to the numerous applications that can benefit from it, such as risk-related studies. In such domains, stochastic models are typically [...] Read more.
Stochastic simulation has a prominent position in a variety of scientific domains including those of environmental and water resources sciences. This is due to the numerous applications that can benefit from it, such as risk-related studies. In such domains, stochastic models are typically used to generate synthetic weather data with the desired properties, often resembling those of hydrometeorological observations, which are then used to drive deterministic models of the understudy system. However, generating synthetic weather data with the desired properties is not an easy task. This is due to the peculiarities of such processes, i.e., non-Gaussianity, intermittency, dependence, and periodicity, and the limited availability of open-source software for such purposes. This work aims to simplify the synthetic data generation procedure by providing an R-package called anySim, specifically designed for the simulation of non-Gaussian correlated random variables, stochastic processes at single and multiple temporal scales, and random fields. The functionality of the package is demonstrated through seven simulation studies, accompanied by code snippets, which resemble real-world cases of stochastic simulation (i.e., generation of synthetic weather data) of hydrometeorological processes and fields (e.g., rainfall, streamflow, temperature, etc.), across several spatial and temporal scales (ranging from annual down to 10-min simulations). Full article
(This article belongs to the Special Issue Stochastic Modelling of Hydrometeorological Processes for Engineering Applications)
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22 pages, 2927 KiB  
Article
Innovative Variance Corrected Sen’s Trend Test on Persistent Hydrometeorological Data
by Wenpeng Wang, Yuelong Zhu, Bo Liu, Yuanfang Chen and Xu Zhao
Water 2019, 11(10), 2119; https://doi.org/10.3390/w11102119 - 12 Oct 2019
Cited by 16 | Viewed by 3313
Abstract
Trend detection in observations helps one to identify anthropogenic forces on natural hydrological and climatic systems. Hydrometeorological data are often persistent over time that deviates from the assumption of independence used by many statistical methods. A recently proposed Sen’s trend test claimed to [...] Read more.
Trend detection in observations helps one to identify anthropogenic forces on natural hydrological and climatic systems. Hydrometeorological data are often persistent over time that deviates from the assumption of independence used by many statistical methods. A recently proposed Sen’s trend test claimed to be free of this problem and thereby received widespread attention. However, both theoretical derivation and stochastic simulation of the current study implies that persistence inflates the trend significance, leading to false trends. To tackle this problem, we incorporate the feature of persistence into the variance of the trend test statistic, whereby an innovative variance-corrected Sen’s trend test is developed. Two theoretical variances of the trend test statistic are newly derived to account for short-term and long-term persistent behavior. The original variance for independent data is also corrected because of its negative bias. A stepwise procedure, including steps to specify the underlying persistent behavior and to test trend with new statistic, is outlined for performing the new test on factual data. Variance-corrected Sen’s trend test can effectively restore the inflated trend significance back to its nominal state. This study may call for the reassessment of published results of the original Sen’s trend test on data with persistence. Full article
(This article belongs to the Special Issue Stochastic Modelling of Hydrometeorological Processes for Engineering Applications)
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Graphical abstract

14 pages, 2360 KiB  
Article
Development of a Non-Parametric Stationary Synthetic Rainfall Generator for Use in Hourly Water Resource Simulations
by Ziwen Yu, Stephanie Miller, Franco Montalto and Upmanu Lall
Water 2019, 11(8), 1728; https://doi.org/10.3390/w11081728 - 20 Aug 2019
Cited by 3 | Viewed by 4160
Abstract
This paper presents a new non-parametric, synthetic rainfall generator for use in hourly water resource simulations. Historic continuous precipitation time series are discretized into sequences of dry and wet events separated by an inter-event dry period at least equal to four hours. A [...] Read more.
This paper presents a new non-parametric, synthetic rainfall generator for use in hourly water resource simulations. Historic continuous precipitation time series are discretized into sequences of dry and wet events separated by an inter-event dry period at least equal to four hours. A first-order Markov Chain model is then used to generate synthetic sequences of alternating wet and dry events. Sequential events in the synthetic series are selected based on couplings of historic wet and dry events, using nearest neighbor and moving window methods. The new generator is used to generate synthetic sequences of rainfall for New York (NY), Syracuse (NY), and Miami (FL) using over 50 years of observations. Monthly precipitation differences (e.g., seasonality) are well represented in the synthetic series generated for all three cities. The synthetic New York results are also shown to reproduce realistic event sequences proved by a deep event-based analysis. Full article
(This article belongs to the Special Issue Stochastic Modelling of Hydrometeorological Processes for Engineering Applications)
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