E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Quantifying Uncertainty in Integrated Catchment Studies"

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

Deadline for manuscript submissions: closed (31 January 2018)

Special Issue Editors

Guest Editor
Dr. Franz Tscheikner-Gratl

Section of Sanitary Engineering, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands
Website | E-Mail
Interests: rehabilitation of aging infrastructure; asset management; uncertainty analysis; urban water management; modelling; climate change adaptation
Guest Editor
Dr. Vasilis Bellos

CH2M, Burderop Park, Swindon SN4 0QD, UK
Website | E-Mail
Interests: hydrodynamic modelling; flood modelling; urban catchment studies; integrated modelling; hydrological modelling; model reduction

Special Issue Information

Dear Colleagues,

Integrated catchment modelling is defined as the simulation of the linkage between the several sub-models, simulating processes of the water cycle (rural and urban) starting from the meteorological input, until the final recipient. These integrated catchment studies can be used to plan projects, to optimise systems, as well as to evaluate the need of certain measures. However, the stepwise process of abstraction from reality to model representation with its simplifications and idealisations of the real systems comes with the unavoidable occurrence of uncertainties. The definition, recognition and consideration of these uncertainties is, therefore, of the utmost importance for applying such models and for the interpretation of model results, in real world problems.

In this Special Issue we would like to invite research on integrated catchment studies for both quantity and quality modelling, specially focusing on the quantification of the uncertainty. Manuscripts which are coping with the following topics are specifically invited:

  • quantification and the propagation of uncertainty at significant temporal and spatial scales in catchments,

  • approaches for minimising uncertainties in integrated models,

  • techniques for model reduction of computationally expensive models,

  • real world case studies on integrated catchment modelling,

  • tools, which can be deployed by end users considering all aspects of modelling uncertainty and hence they are able to be used in the context of the decision-making process.

Dr. Franz Tscheikner-Gratl
Dr. Vasilis Bellos
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.

Keywords

  • uncertainty quantification

  • water cycle

  • integrated catchment modelling

  • water quality

  • decision making

  • uncertainty propagation

Published Papers (8 papers)

View options order results:
result details:
Displaying articles 1-8
Export citation of selected articles as:

Research

Jump to: Review

Open AccessArticle
stUPscales: An R-Package for Spatio-Temporal Uncertainty Propagation across Multiple Scales with Examples in Urban Water Modelling
Water 2018, 10(7), 837; https://doi.org/10.3390/w10070837
Received: 1 February 2018 / Revised: 13 June 2018 / Accepted: 14 June 2018 / Published: 23 June 2018
PDF Full-text (3733 KB) | HTML Full-text | XML Full-text
Abstract
Integrated environmental modelling requires coupling sub-models at different spatial and temporal scales, thus accounting for change of support procedures (aggregation and disaggregation). We introduce the R-package spatio-temporal Uncertainty Propagation across multiple scales, stUPscales, which constitutes a contribution [...] Read more.
Integrated environmental modelling requires coupling sub-models at different spatial and temporal scales, thus accounting for change of support procedures (aggregation and disaggregation). We introduce the R-package spatio-temporal Uncertainty Propagation across multiple scales, stUPscales, which constitutes a contribution to state-of-the-art open source tools that support uncertainty propagation analysis in temporal and spatio-temporal domains. We illustrate the tool with an uncertainty propagation example in environmental modelling, specifically in the urban water domain. The functionalities of the class setup and the methods and functions MC.setup, MC.sim, MC.analysis and Agg.t are explained, which are used for setting up, running and analysing Monte Carlo uncertainty propagation simulations, and for spatio-temporal aggregation. We also show how the package can be used to model and predict variables that vary in space and time by using a spatio-temporal variogram model and space-time ordinary kriging. stUPscales takes uncertainty characterisation and propagation a step further by including temporal and spatio-temporal auto- and cross-correlation, resulting in more realistic (spatio-)temporal series of environmental variables. Due to its modularity, the package allows the implementation of additional methods and functions for spatio-temporal disaggregation of model inputs and outputs, when linking models across multiple space-time scales. Full article
(This article belongs to the Special Issue Quantifying Uncertainty in Integrated Catchment Studies)
Figures

Graphical abstract

Open AccessArticle
EmiStatR: A Simplified and Scalable Urban Water Quality Model for Simulation of Combined Sewer Overflows
Water 2018, 10(6), 782; https://doi.org/10.3390/w10060782
Received: 20 January 2018 / Revised: 30 May 2018 / Accepted: 4 June 2018 / Published: 13 June 2018
Cited by 1 | PDF Full-text (3594 KB) | HTML Full-text | XML Full-text
Abstract
Many complex urban drainage quality models are computationally expensive. Complexity and computing times may become prohibitive when these models are used in a Monte Carlo (MC) uncertainty analysis of long time series, in particular for practitioners. Computationally scalable and fast “surrogate” models may [...] Read more.
Many complex urban drainage quality models are computationally expensive. Complexity and computing times may become prohibitive when these models are used in a Monte Carlo (MC) uncertainty analysis of long time series, in particular for practitioners. Computationally scalable and fast “surrogate” models may reduce the overall computation time for practical applications in which often large data sets would be needed otherwise. We developed a simplified semi-distributed urban water quality model, EmiStatR, which brings uncertainty and sensitivity analyses of urban drainage water quality models within reach of practitioners. Its lower demand in input data and its scalability allow for simulating water volume and pollution loads in combined sewer overflows in several catchments fast and efficiently. The scalable code implemented in EmiStatR reduced the computation time significantly (by a factor of around 24 when using 32 cores). EmiStatR can be applied efficiently to test hypotheses by using MC uncertainty studies or long-term simulations. Full article
(This article belongs to the Special Issue Quantifying Uncertainty in Integrated Catchment Studies)
Figures

Figure 1

Open AccessFeature PaperArticle
Effects of Input Data Content on the Uncertainty of Simulating Water Resources
Water 2018, 10(5), 621; https://doi.org/10.3390/w10050621
Received: 1 March 2018 / Revised: 4 May 2018 / Accepted: 8 May 2018 / Published: 10 May 2018
Cited by 3 | PDF Full-text (1930 KB) | HTML Full-text | XML Full-text
Abstract
The widely used, partly-deterministic Soil and Water Assessment Tool (SWAT) requires a large amount of spatial input data, such as a digital elevation model (DEM), land use, and soil maps. Modelers make an effort to apply the most specific data possible for the [...] Read more.
The widely used, partly-deterministic Soil and Water Assessment Tool (SWAT) requires a large amount of spatial input data, such as a digital elevation model (DEM), land use, and soil maps. Modelers make an effort to apply the most specific data possible for the study area to reflect the heterogeneous characteristics of landscapes. Regional data, especially with fine resolution, is often preferred. However, such data is not always available and can be computationally demanding. Despite being coarser, global data are usually free and available to the public. Previous studies revealed the importance for single investigations of different input maps. However, it remains unknown whether higher-resolution data can lead to reliable results. This study investigates how global and regional input datasets affect parameter uncertainty when estimating river discharges. We analyze eight different setups for the SWAT model for a catchment in Luxembourg, combining different land-use, elevation, and soil input data. The Metropolis–Hasting Markov Chain Monte Carlo (MCMC) algorithm is used to infer posterior model parameter uncertainty. We conclude that our higher resolved DEM improves the general model performance in reproducing low flows by 10%. The less detailed soil-map improved the fit of low flows by 25%. In addition, more detailed land-use maps reduce the bias of the model discharge simulations by 50%. Also, despite presenting similar parameter uncertainty (P-factor ranging from 0.34 to 0.41 and R-factor from 0.41 to 0.45) for all setups, the results show a disparate parameter posterior distribution. This indicates that no assessment of all sources of uncertainty simultaneously is compensated by the fitted parameter values. We conclude that our result can give some guidance for future SWAT applications in the selection of the degree of detail for input data. Full article
(This article belongs to the Special Issue Quantifying Uncertainty in Integrated Catchment Studies)
Figures

Figure 1

Open AccessArticle
A Heuristic Method for Measurement Site Selection in Sewer Systems
Water 2018, 10(2), 122; https://doi.org/10.3390/w10020122
Received: 21 December 2017 / Revised: 19 January 2018 / Accepted: 25 January 2018 / Published: 29 January 2018
Cited by 3 | PDF Full-text (8424 KB) | HTML Full-text | XML Full-text
Abstract
Although calibration of a hydrodynamic model depends on the availability of measurement data representing the system behavior, advice for the planning of necessary measurement campaigns for model calibration is scarce. This work tries to address this question of efficient measurement site selection on [...] Read more.
Although calibration of a hydrodynamic model depends on the availability of measurement data representing the system behavior, advice for the planning of necessary measurement campaigns for model calibration is scarce. This work tries to address this question of efficient measurement site selection on a network scale for the objective of calibrating a hydrodynamic model case study in Austria. For this, a model-based approach is chosen, as the method should be able to be used before measurement data is available. An existing model is assumed to represent the real system behavior. Based on this extended availability of “measurement data” in every point of the system, different approaches are established to heuristically assess the suitability of one or more pipes in combination as calibration point(s). These approaches intend to find suitable answers to the question of measurement site selection for this specific case study within a relatively short time and with a reasonable computational effort. As a result, the relevance of the spatial distribution of calibration points is highlighted. Furthermore, particular efficient calibration points are identified and further measurement sites in the underlying network are recommended. Full article
(This article belongs to the Special Issue Quantifying Uncertainty in Integrated Catchment Studies)
Figures

Figure 1

Open AccessFeature PaperArticle
Quantifying Roughness Coefficient Uncertainty in Urban Flooding Simulations through a Simplified Methodology
Water 2017, 9(12), 944; https://doi.org/10.3390/w9120944
Received: 10 October 2017 / Revised: 29 November 2017 / Accepted: 30 November 2017 / Published: 4 December 2017
Cited by 4 | PDF Full-text (5338 KB) | HTML Full-text | XML Full-text
Abstract
A methodology is presented which can be used in the evaluation of parametric uncertainty in urban flooding simulation. Due to the fact that such simulations are time consuming, the following methodology is proposed: (a) simplification of the description of the physical process; (b) [...] Read more.
A methodology is presented which can be used in the evaluation of parametric uncertainty in urban flooding simulation. Due to the fact that such simulations are time consuming, the following methodology is proposed: (a) simplification of the description of the physical process; (b) derivation of a training data set; (c) development of a data-driven surrogate model; (d) use of a forward uncertainty propagation scheme. The simplification comprises the following steps: (a) unit hydrograph derivation using a 2D hydrodynamic model; (b) calculation of the losses in order to determine the effective rainfall depth; (c) flood event simulation using the principle of the proportionality and superposition. The above methodology was implemented in an urban catchment located in the city of Athens, Greece. The model used for the first step of the simplification was FLOW-R2D, whereas the well-known SWMM software (US Environmental Protection Agency, Washington, DC, USA) was used for the second step of the simplification. For the training data set derivation, an ensemble of 100 Unit Hydrographs was derived with the FLOW-R2D model. The parameters which were modified in order to produce this ensemble were the Manning coefficients in the two friction zones (residential and urban open space areas). The surrogate model used to replicate the unit hydrograph derivation, using the Manning coefficients as an input, was based on the Polynomial Chaos Expansion technique. It was found that, although the uncertainties in the derived results have to be taken into account, the proposed methodology can be a fast and efficient way to cope with dynamic flood simulation in an urban catchment. Full article
(This article belongs to the Special Issue Quantifying Uncertainty in Integrated Catchment Studies)
Figures

Figure 1

Open AccessArticle
Impact of Spatiotemporal Characteristics of Rainfall Inputs on Integrated Catchment Dissolved Oxygen Simulations
Water 2017, 9(12), 926; https://doi.org/10.3390/w9120926
Received: 23 October 2017 / Revised: 17 November 2017 / Accepted: 23 November 2017 / Published: 28 November 2017
Cited by 2 | PDF Full-text (6364 KB) | HTML Full-text | XML Full-text
Abstract
Integrated Catchment Modelling aims to simulate jointly urban drainage systems, wastewater treatment plant and rivers. The effect of rainfall input uncertainties in the modelling of individual urban drainage systems has been discussed in several studies already. However, this influence changes when simultaneously simulating [...] Read more.
Integrated Catchment Modelling aims to simulate jointly urban drainage systems, wastewater treatment plant and rivers. The effect of rainfall input uncertainties in the modelling of individual urban drainage systems has been discussed in several studies already. However, this influence changes when simultaneously simulating several urban drainage subsystems and their impact on receiving water quality. This study investigates the effect of the characteristics of rainfall inputs on a large-scale integrated catchment simulator for dissolved oxygen predictions in the River Dommel (The Netherlands). Rainfall products were generated with varying time-aggregation (10, 30 and 60 min) deriving from different sources of data with increasing spatial information: (1) Homogeneous rainfall from a single rain gauge; (2) block kriging from 13 rain gauges; (3) averaged C-Band radar estimation and (4) kriging with external drift combining radar and rain gauge data with change of spatial support. The influence of the different rainfall inputs was observed at combined sewer overflows (CSO) and dissolved oxygen (DO) dynamics in the river. Comparison of the simulations with river monitoring data showed a low sensitivity to temporal aggregation of rainfall inputs and a relevant impact of the spatial scale with a link to the storm characteristics to CSO and DO concentration in the receiving water. Full article
(This article belongs to the Special Issue Quantifying Uncertainty in Integrated Catchment Studies)
Figures

Figure 1

Open AccessArticle
Optimal Temporal Resolution of Rainfall for Urban Applications and Uncertainty Propagation
Water 2017, 9(10), 762; https://doi.org/10.3390/w9100762
Received: 29 June 2017 / Revised: 20 September 2017 / Accepted: 22 September 2017 / Published: 4 October 2017
Cited by 3 | PDF Full-text (5054 KB) | HTML Full-text | XML Full-text
Abstract
The optimal temporal resolution for rainfall applications in urban hydrological models depends on different factors. Accumulations are often used to reduce uncertainty, while a sufficiently fine resolution is needed to capture the variability of the urban hydrological processes. Merging radar and rain gauge [...] Read more.
The optimal temporal resolution for rainfall applications in urban hydrological models depends on different factors. Accumulations are often used to reduce uncertainty, while a sufficiently fine resolution is needed to capture the variability of the urban hydrological processes. Merging radar and rain gauge rainfall is recognized to improve the estimation accuracy. This work explores the possibility to merge radar and rain gauge rainfall at coarser temporal resolutions to reduce uncertainty, and to downscale the results. A case study in the UK is used to cross-validate the methodology. Rainfall estimates merged and downscaled at different resolutions are compared. As expected, coarser resolutions tend to reduce uncertainty in terms of rainfall estimation. Additionally, an example of urban application in Twenterand, the Netherlands, is presented. The rainfall data from four rain gauge networks are merged with radar composites and used in an InfoWorks model reproducing the urban drainage system of Vroomshoop, a village in Twenterand. Fourteen combinations of accumulation and downscaling resolutions are tested in the InfoWorks model and the optimal is selected comparing the results to water level observations. The uncertainty is propagated in the InfoWorks model with ensembles. The results show that the uncertainty estimated by the ensemble spread is proportional to the rainfall intensity and dependent on the relative position between rainfall cells and measurement points. Full article
(This article belongs to the Special Issue Quantifying Uncertainty in Integrated Catchment Studies)
Figures

Figure 1

Review

Jump to: Research

Open AccessReview
Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment
Water 2017, 9(10), 796; https://doi.org/10.3390/w9100796
Received: 31 July 2017 / Revised: 3 October 2017 / Accepted: 13 October 2017 / Published: 17 October 2017
Cited by 4 | PDF Full-text (547 KB) | HTML Full-text | XML Full-text
Abstract
A thorough review has been performed on interpolation methods to fill gaps in time-series, efficiency criteria, and uncertainty quantifications. On one hand, there are numerous available methods: interpolation, regression, autoregressive, machine learning methods, etc. On the other hand, there are many methods and [...] Read more.
A thorough review has been performed on interpolation methods to fill gaps in time-series, efficiency criteria, and uncertainty quantifications. On one hand, there are numerous available methods: interpolation, regression, autoregressive, machine learning methods, etc. On the other hand, there are many methods and criteria to estimate efficiencies of these methods, but uncertainties on the interpolated values are rarely calculated. Furthermore, while they are estimated according to standard methods, the prediction uncertainty is not taken into account: a discussion is thus presented on the uncertainty estimation of interpolated/extrapolated data. Finally, some suggestions for further research and a new method are proposed. Full article
(This article belongs to the Special Issue Quantifying Uncertainty in Integrated Catchment Studies)
Figures

Figure 1

Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top