E-Mail Alert

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

Journal Browser

Journal Browser

Special Issue "Uncertainty Analysis and Modeling in Hydrological Forecasting"

A special issue of Water (ISSN 2073-4441).

Deadline for manuscript submissions: closed (31 May 2016)

Special Issue Editors

Guest Editor
Prof. Dr. Paolo Reggiani

Department of Civil Engineering, University of Siegen, 57068 Siegen, Germany
Website | E-Mail
Guest Editor
Prof. Dr. Ezio Todini

University of Bologna, Piazza Porta San Donato 1, 40126 Bologna, Italy
Website | E-Mail
Interests: hydrological and hydraulic modelling; real-time flood forecasting; predictive uncertainty estimation and water resources

Special Issue Information

Dear Colleagues,

Hydrological extreme events are at the origin of a range of natural hazards, such as destructive inundation disasters caused by floods or famine due to water and food scarcity caused by prolonged drought conditions. Assessing future scenarios to enable decision-makers to implement mitigating structural or non-structural actions requires forecasting water levels and/or discharges and/or water volumes with sufficient lead time, as well as predicting the probabilities of occurrences of critical hydrological events. Accurate, deterministic forecasts of relevant variables, such as flood levels, discharges, and water volumes, are near-impossible to be achieved. Simulated hydrological responses of river basins remain highly uncertain, due to the presence of a broad variety of schematizations, erroneous measurements, and prior assumptions. Although great improvements have been achieved in the last decades in hydrological modeling, today, it has been recognized that the most important source of uncertainty remains with the meteorological forcing due to the high, uncertain spatial distribution and intensity of precipitation. Errors in either conceptual or physically-based models' representations of  watershed processes and the lack of knowledge of initial and boundary conditions (such as, for instance, the antecedent soil moisture conditions or non-monitored lateral inflows) also strongly affect the quality of forecasts. Systematic uncertainty analysis aims at quantifying these sources of uncertainty in order to improve model structures and parameterizations.  Alternatively, Bayesian approaches aim at assessing predictive probability distributions for quantities of interest, thus providing quantitative tools for decision-makers in assessing the likelihood of an event and the most appropriate alleviation measure. Contributors are encouraged to present state-of-the art research to help the wider user community to include uncertainty in hydrological forecasts and to use such forecasts in supporting the decision-making process.

Prof. Dr. Paolo Reggiani
Prof. Dr. Ezio Todini
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 1400 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

Probabilistic Forecast Streamflow predictions Hydrological uncertainty Meteorological uncertainty Validation uncertainty Sensitivity analysis Output post-processing Input pre-processing Predictive Uncertainty Ensemble predictions

Published Papers (9 papers)

View options order results:
result details:
Displaying articles 1-9
Export citation of selected articles as:

Research

Open AccessArticle Case Study: A Real-Time Flood Forecasting System with Predictive Uncertainty Estimation for the Godavari River, India
Water 2016, 8(10), 463; doi:10.3390/w8100463
Received: 27 June 2016 / Revised: 6 October 2016 / Accepted: 12 October 2016 / Published: 18 October 2016
Cited by 2 | PDF Full-text (4803 KB) | HTML Full-text | XML Full-text
Abstract
This work presents the application of the multi-temporal approach of the Model Conditional Processor (MCP-MT) for predictive uncertainty (PU) estimation in the Godavari River basin, India. MCP-MT is developed for making probabilistic Bayesian decision. It is the most appropriate approach if the uncertainty
[...] Read more.
This work presents the application of the multi-temporal approach of the Model Conditional Processor (MCP-MT) for predictive uncertainty (PU) estimation in the Godavari River basin, India. MCP-MT is developed for making probabilistic Bayesian decision. It is the most appropriate approach if the uncertainty of future outcomes is to be considered. It yields the best predictive density of future events and allows determining the probability that a critical warning threshold may be exceeded within a given forecast time. In Bayesian decision-making, the predictive density represents the best available knowledge on a future event to address a rational decision-making process. MCP-MT has already been tested for case studies selected in Italian river basins, showing evidence of improvement of the effectiveness of operative real-time flood forecasting systems. The application of MCP-MT for two river reaches selected in the Godavari River basin, India, is here presented and discussed by considering the stage forecasts provided by a deterministic model, STAFOM-RCM, and hourly dataset based on seven monsoon seasons in the period 2001–2010. The results show that the PU estimate is useful for finding the exceedance probability for a given hydrometric threshold as function of the forecast time up to 24 h, demonstrating the potential usefulness for supporting real-time decision-making. Moreover, the expected value provided by MCP-MT yields better results than the deterministic model predictions, with higher Nash–Sutcliffe coefficients and lower error on stage forecasts, both in term of mean error and standard deviation and root mean square error. Full article
(This article belongs to the Special Issue Uncertainty Analysis and Modeling in Hydrological Forecasting)
Figures

Figure 1

Open AccessArticle Predictive Uncertainty Estimation on a Precipitation and Temperature Reanalysis Ensemble for Shigar Basin, Central Karakoram
Water 2016, 8(6), 263; doi:10.3390/w8060263
Received: 13 February 2016 / Revised: 2 June 2016 / Accepted: 4 June 2016 / Published: 21 June 2016
Cited by 1 | PDF Full-text (22427 KB) | HTML Full-text | XML Full-text
Abstract
The Upper Indus Basin (UIB) and the Karakoram Range are the subject of ongoing hydro-glaciological studies to investigate possible glacier mass balance shifts due to climatic change. Because of the high altitude and remote location, the Karakoram Range is difficult to access and,
[...] Read more.
The Upper Indus Basin (UIB) and the Karakoram Range are the subject of ongoing hydro-glaciological studies to investigate possible glacier mass balance shifts due to climatic change. Because of the high altitude and remote location, the Karakoram Range is difficult to access and, therefore, remains scarcely monitored. In situ precipitation and temperature measurements are only available at valley locations. High-altitude observations exist only for very limited periods. Gridded precipitation and temperature data generated from the spatial interpolation of in situ observations are unreliable for this region because of the extreme topography. Besides satellite measurements, which offer spatial coverage, but underestimate precipitation in this area, atmospheric reanalyses remain one of the few alternatives. Here, we apply a proven approach to quantify the uncertainty associated with an ensemble of monthly precipitation and temperature reanalysis data for 1979–2009 in Shigar Basin, Central Karakoram. A Model-Conditional Processor (MCP) of uncertainty is calibrated on precipitation and temperature in situ data measured in the proximity of the study region. An ensemble of independent reanalyses is processed to determine the predictive uncertainty of monthly observations. As to be expected, the informative gain achieved by post-processing temperature reanalyses is considerable, whereas significantly less gain is achieved for precipitation post-processing. The proposed approach indicates a systematic assessment procedure for predictive uncertainty through probabilistic weighting of multiple re-forecasts, which are bias-corrected on ground observations. The approach also supports an educated reconstruction of gap-filling for missing in situ observations. Full article
(This article belongs to the Special Issue Uncertainty Analysis and Modeling in Hydrological Forecasting)
Open AccessArticle Predictive Uncertainty Estimation of Hydrological Multi-Model Ensembles Using Pair-Copula Construction
Water 2016, 8(4), 125; doi:10.3390/w8040125
Received: 21 December 2015 / Revised: 4 March 2016 / Accepted: 22 March 2016 / Published: 31 March 2016
Cited by 3 | PDF Full-text (3764 KB) | HTML Full-text | XML Full-text
Abstract
Predictive uncertainty (PU) is defined as the probability of occurrence of an observed variable of interest, conditional on all available information. In this context, hydrological model predictions and forecasts are considered to be accessible but yet uncertain information. To estimate the PU of
[...] Read more.
Predictive uncertainty (PU) is defined as the probability of occurrence of an observed variable of interest, conditional on all available information. In this context, hydrological model predictions and forecasts are considered to be accessible but yet uncertain information. To estimate the PU of hydrological multi-model ensembles, we apply a method based on the use of copulas which enables modelling the dependency structures between variates independently of their marginal distributions. Given that the option to employ copula functions imposes certain limitations in the multivariate case, we model the multivariate distribution as a cascade of bivariate copulas by using the pair-copula construction. We apply a mixture of probability distributions to estimate the marginal densities and distributions of daily flow rates for various meteorological and hydrological situations. The proposed method is applied to a multi-model ensemble involving two hydrological and one statistical flow models at two gauge stations in the Moselle river basin. Verification and inter-comparison with other PU assessment methods show that copulas are well-suited for this scope and constitute a valid approach for predictive uncertainty estimation of hydrological multi-model predictions. Full article
(This article belongs to the Special Issue Uncertainty Analysis and Modeling in Hydrological Forecasting)
Open AccessArticle Post-Processing of Stream Flows in Switzerland with an Emphasis on Low Flows and Floods
Water 2016, 8(4), 115; doi:10.3390/w8040115
Received: 16 December 2015 / Revised: 8 March 2016 / Accepted: 16 March 2016 / Published: 24 March 2016
Cited by 2 | PDF Full-text (677 KB) | HTML Full-text | XML Full-text
Abstract
Post-processing has received much attention during the last couple of years within the hydrological community, and many different methods have been developed and tested, especially in the field of flood forecasting. Apart from the different meanings of the phrase “post-processing” in meteorology and
[...] Read more.
Post-processing has received much attention during the last couple of years within the hydrological community, and many different methods have been developed and tested, especially in the field of flood forecasting. Apart from the different meanings of the phrase “post-processing” in meteorology and hydrology, in this paper, it is regarded as a method to correct model outputs (predictions) based on meteorological (1) observed input data, (2) deterministic forecasts (single time series) and (3) ensemble forecasts (multiple time series) and to derive predictive uncertainties. So far, the majority of the research has been related to floods, how to remove bias and improve the forecast accuracy and how to minimize dispersion errors. Given that global changes are driving climatic forces, there is an urgent need to improve the quality of low-flow predictions, as well, even in regions that are normally less prone to drought. For several catchments in Switzerland, different post-processing methods were tested with respect to low stream flow and flooding conditions. The complexity of the applied procedures ranged from simple AR processes to more complex methodologies combining wavelet transformations and Quantile Regression Neural Networks (QRNN) and included the derivation of predictive uncertainties. Furthermore, various verification methods were tested in order to quantify the possible improvements that could be gained by applying these post-processing procedures based on different stream flow conditions. Preliminary results indicate that there is no single best method, but with an increase of complexity, a significant improvement of the quality of the predictions can be achieved. Full article
(This article belongs to the Special Issue Uncertainty Analysis and Modeling in Hydrological Forecasting)
Open AccessArticle Objective Classification of Rainfall in Northern Europe for Online Operation of Urban Water Systems Based on Clustering Techniques
Water 2016, 8(3), 87; doi:10.3390/w8030087
Received: 18 December 2015 / Revised: 19 February 2016 / Accepted: 29 February 2016 / Published: 4 March 2016
PDF Full-text (1820 KB) | HTML Full-text | XML Full-text
Abstract
This study evaluated methods for automated classification of rain events into groups of “high” and “low” spatial and temporal variability in offline and online situations. The applied classification techniques are fast and based on rainfall data only, and can thus be applied by,
[...] Read more.
This study evaluated methods for automated classification of rain events into groups of “high” and “low” spatial and temporal variability in offline and online situations. The applied classification techniques are fast and based on rainfall data only, and can thus be applied by, e.g., water system operators to change modes of control of their facilities. A k-means clustering technique was applied to group events retrospectively and was able to distinguish events with clearly different temporal and spatial correlation properties. For online applications, techniques based on k-means clustering and quadratic discriminant analysis both provided a fast and reliable identification of rain events of “high” variability, while the k-means provided the smallest number of rain events falsely identified as being of “high” variability (false hits). A simple classification method based on a threshold for the observed rainfall intensity yielded a large number of false hits and was thus outperformed by the other two methods. Full article
(This article belongs to the Special Issue Uncertainty Analysis and Modeling in Hydrological Forecasting)
Open AccessArticle Probabilistic Forecasting of Drought Events Using Markov Chain- and Bayesian Network-Based Models: A Case Study of an Andean Regulated River Basin
Water 2016, 8(2), 37; doi:10.3390/w8020037
Received: 3 October 2015 / Revised: 11 January 2016 / Accepted: 11 January 2016 / Published: 23 January 2016
Cited by 3 | PDF Full-text (1717 KB) | HTML Full-text | XML Full-text
Abstract
The scarcity of water resources in mountain areas can distort normal water application patterns with among other effects, a negative impact on water supply and river ecosystems. Knowing the probability of droughts might help to optimize a priori the planning and management of
[...] Read more.
The scarcity of water resources in mountain areas can distort normal water application patterns with among other effects, a negative impact on water supply and river ecosystems. Knowing the probability of droughts might help to optimize a priori the planning and management of the water resources in general and of the Andean watersheds in particular. This study compares Markov chain- (MC) and Bayesian network- (BN) based models in drought forecasting using a recently developed drought index with respect to their capability to characterize different drought severity states. The copula functions were used to solve the BNs and the ranked probability skill score (RPSS) to evaluate the performance of the models. Monthly rainfall and streamflow data of the Chulco River basin, located in Southern Ecuador, were used to assess the performance of both approaches. Global evaluation results revealed that the MC-based models predict better wet and dry periods, and BN-based models generate slightly more accurately forecasts of the most severe droughts. However, evaluation of monthly results reveals that, for each month of the hydrological year, either the MC- or BN-based model provides better forecasts. The presented approach could be of assistance to water managers to ensure that timely decision-making on drought response is undertaken. Full article
(This article belongs to the Special Issue Uncertainty Analysis and Modeling in Hydrological Forecasting)
Open AccessArticle Uncertainty Analysis in the Evaluation of Extreme Rainfall Trends and Its Implications on Urban Drainage System Design
Water 2015, 7(12), 6931-6945; doi:10.3390/w7126667
Received: 5 October 2015 / Accepted: 1 December 2015 / Published: 5 December 2015
Cited by 9 | PDF Full-text (752 KB) | HTML Full-text | XML Full-text
Abstract
Future projections provided by climate models suggest that the occurrence of extreme rainfall events will increase and this is evidence that the climate is changing. Because the design of urban drainage systems is based on the statistical analysis of past events, variations in
[...] Read more.
Future projections provided by climate models suggest that the occurrence of extreme rainfall events will increase and this is evidence that the climate is changing. Because the design of urban drainage systems is based on the statistical analysis of past events, variations in the intensity and frequency of extreme rainfall represent a critical issue for the estimation of rainfall. For this reason, the design criteria of drainage systems should take into account the trends in the past and the future climate changes projections. To this end, a Bayesian procedure was proposed to update the parameters of depth–duration–frequency (DDF) curves to assess the uncertainty related to the estimation of these values, once the evidence of annual maximum rainfall trends was verified. Namely, in the present study, the historical extreme rainfall series with durations of 1, 3, 6, 12 and 24 h for the period of 1950–2008, recorded by the rain gauges located near the Paceco urban area (southern Italy), were analyzed to detect statistically significant trends using the non‐parametric Mann‐Kendall test. Based on the rainfall trends, the parameters of the DDF curves for a five‐year return period were updated to define some climate scenarios. Finally, the implications of the uncertainty related to the DDF parameters estimation on the design of a real urban drainage system was assessed to provide an evaluation of its performance under the assumption of climate change. Results showed that the future increase of annual maximum precipitation in the area of study would affect the analyzed drainage system, which could face more frequent episodes of surcharge. Full article
(This article belongs to the Special Issue Uncertainty Analysis and Modeling in Hydrological Forecasting)
Figures

Open AccessArticle On Approaches to Analyze the Sensitivity of Simulated Hydrologic Fluxes to Model Parameters in the Community Land Model
Water 2015, 7(12), 6810-6826; doi:10.3390/w7126662
Received: 9 September 2015 / Revised: 24 November 2015 / Accepted: 27 November 2015 / Published: 4 December 2015
Cited by 2 | PDF Full-text (4322 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model
[...] Read more.
Effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash–Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalized linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization. Full article
(This article belongs to the Special Issue Uncertainty Analysis and Modeling in Hydrological Forecasting)
Figures

Open AccessFeature PaperArticle Uncertainty Analysis of Multi-Model Flood Forecasts
Water 2015, 7(12), 6788-6809; doi:10.3390/w7126654
Received: 3 September 2015 / Revised: 31 October 2015 / Accepted: 17 November 2015 / Published: 1 December 2015
Cited by 1 | PDF Full-text (2224 KB) | HTML Full-text | XML Full-text
Abstract
This paper demonstrates, by means of a systematic uncertainty analysis, that the use of outputs from more than one model can significantly improve conditional forecasts of discharges or water stages, provided the models are structurally different. Discharge forecasts from two models and the
[...] Read more.
This paper demonstrates, by means of a systematic uncertainty analysis, that the use of outputs from more than one model can significantly improve conditional forecasts of discharges or water stages, provided the models are structurally different. Discharge forecasts from two models and the actual forecasted discharge are assumed to form a three-dimensional joint probability density distribution (jpdf), calibrated on long time series of data. The jpdf is decomposed into conditional probability density distributions (cpdf) by means of Bayes formula, as suggested and explored by Krzysztofowicz in a series of papers. In this paper his approach is simplified to optimize conditional forecasts for any set of two forecast models. Its application is demonstrated by means of models developed in a study of flood forecasting for station Stung Treng on the middle reach of the Mekong River in South-East Asia. Four different forecast models were used and pairwise combined: forecast with no model, with persistence model, with a regression model, and with a rainfall-runoff model. Working with cpdfs requires determination of dependency among variables, for which linear regressions are required, as was done by Krzysztofowicz. His Bayesian approach based on transforming observed probability distributions of discharges and forecasts into normal distributions is also explored. Results obtained with his method for normal prior and likelihood distributions are identical to results from direct multiple regressions. Furthermore, it is shown that in the present case forecast accuracy is only marginally improved, if Weibull distributed basic data were converted into normally distributed variables. Full article
(This article belongs to the Special Issue Uncertainty Analysis and Modeling in Hydrological Forecasting)

Journal Contact

MDPI AG
Water Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
E-Mail: 
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Water Edit a special issue Review for Water
logo
loading...
Back to Top