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Forecast of Extreme Events in the Water Cycle—Data, Models and Uncertainties

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

Deadline for manuscript submissions: closed (31 March 2017) | Viewed by 88976

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Special Issue Editors

Dr. Yunqing Xuan

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Zienkiewicz Centre for Computational Engineering, College of Engineering, Swansea University, Singleton Park, Swansea SA2 8PP, UK
Interests: hydro-meteorological modeling; extreme hydrological events; climate change impact; flood risk management; uncertainty modeling
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Prof. Dr. Harshinie Karunarathna

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Energy and Environment Research Group, Zienkiewicz Centre for Computational Engineering, College of Engineering, ESRI Building, Bay Campus, Swansea University, Swansea SA1 8EN, UK
Interests: coastal/estuarine sediment transport and morphodynamics; climate change impacts on ocean waves and morphology; renewable energy; coastal erosion and flooding; computational modelling
Special Issues, Collections and Topics in MDPI journals

Dr. Adrián Pedrozo-Acuña

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Department of Hydraulics, Institute of Engineering, National Autonomous University of Mexico, Mexico City, Mexico
Interests: flood risk; uncertainty; hydrology; coastal engineering; extreme events

Special Issue Information

Dear Colleagues,

Recent years have seen an increasing research interest in extreme events in the Water Cycle, especially for those in the context of climate change impact, such as heavy precipitation, storm surges, severe floods, and droughts. While a substantial number of studies have revealed that a more volatile climate may eventually contribute to more extreme events, there has been a lack of published quantitative evidence, which is necessary to systematically quantify the magnitudes of the changes, or, to a lesser degree, to determine the likelihoods of occurrence of them.

With rapid development in computational models, computing power, as well as new technology for Earth observations, there is a significant scope that this need can be successfully addressed. This Special Issue of Water is designed to fill the gap of publications in this field. We cordially invite you to publish your up-to-date research outcomes in the areas listed (but certainly not limited to):

1) New data or techniques for extreme event analysis;

2) Model development that is aimed to address the forecasts of extreme events;

3) Techniques that quantify, manifest, and/or reduce uncertainties in extreme events forecasts;

4) Climate change impacts on engineering design standards of infrastructure.

We look forward to an inspirational collection of papers on the latest advances in these research areas.

Dr. Yunqing Xuan
Dr. Harshinie Karunarathna
Dr. Adrián Pedrozo-Acuña
Guest Editors

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Published Papers (12 papers)

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3566 KiB

Open AccessFeature PaperArticle

Modelling Extreme Wave Overtopping at Aberystwyth Promenade

by Daniel A. Thompson, Harshinie Karunarathna and Dominic E. Reeve

Water 2017, 9(9), 663; https://doi.org/10.3390/w9090663 - 14 Sep 2017

Cited by 16 | Viewed by 7164

Abstract

The work presents a methodology to assess the coastal impacts during a storm event which caused significant damage along the promenade at Aberystwyth, Wales on the 3 January 2014. Overtopping was analysed in detail for a section of promenade by downscaling offshore wave conditions to force a surf zone hydrodynamic model, NEWRANS. Overtopping discharges are computed and were in qualitative agreement with published discharges for the level of damage observed along the promenade. Peak storm conditions were observed to arrive just before and during high tide at Aberystwyth, which in addition to a storm surge and wave-setup, contributed to the damage observed. A high frequency of overtopping occurs during peak high tide, with overtopping also occurring in the hour leading up to and following high tide. Finally, comparisons to design methods for the estimation of overtopping discharge were made. Current empirical formulae underestimated the peak overtopping event at high tide. The methodology applied is generic and applicable to any location. Full article

(This article belongs to the Special Issue Forecast of Extreme Events in the Water Cycle—Data, Models and Uncertainties)

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3867 KiB

Open AccessArticle

Streamflow Forecasting Using Empirical Wavelet Transform and Artificial Neural Networks

by Tian Peng, Jianzhong Zhou, Chu Zhang and Wenlong Fu

Water 2017, 9(6), 406; https://doi.org/10.3390/w9060406 - 7 Jun 2017

Cited by 98 | Viewed by 8029

Abstract

Accurate and reliable streamflow forecasting plays an important role in various aspects of water resources management such as reservoir scheduling and water supply. This paper shows the development of a novel hybrid model for streamflow forecasting and demonstrates its efficiency. In the proposed hybrid model for streamflow forecasting, the empirical wavelet transform (EWT) is firstly employed to eliminate the redundant noises from the original streamflow series. Secondly, the partial autocorrelation function (PACF) values are explored to identify the inputs for the artificial neural network (ANN) models. Thirdly, the weights and biases of the ANN architecture are tuned and optimized by the multi-verse optimizer (MVO) algorithm. Finally, the simulated streamflow is obtained using the well-trained MVO-ANN model. The proposed hybrid model has been applied to annual streamflow observations from four hydrological stations in the upper reaches of the Yangtze River, China. Parallel experiments using non-denoising models, the back propagation neural network (BPNN) and the ANN optimized by the particle swarm optimization algorithm (PSO-ANN) have been designed and conducted to compare with the proposed model. Results obtained from this study indicate that the proposed hybrid model can capture the nonlinear characteristics of the streamflow time series and thus provides more accurate forecasting results. Full article

(This article belongs to the Special Issue Forecast of Extreme Events in the Water Cycle—Data, Models and Uncertainties)

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1014 KiB

Open AccessArticle

Estimation of Instantaneous Peak Flow Using Machine-Learning Models and Empirical Formula in Peninsular Spain

by Patricia Jimeno-Sáez, Javier Senent-Aparicio, Julio Pérez-Sánchez, David Pulido-Velazquez and José María Cecilia

Water 2017, 9(5), 347; https://doi.org/10.3390/w9050347 - 15 May 2017

Cited by 37 | Viewed by 8521

Abstract

The design of hydraulic structures and flood risk management is often based on instantaneous peak flow (IPF). However, available flow time series with high temporal resolution are scarce and of limited length. A correct estimation of the IPF is crucial to reducing the consequences derived from flash floods, especially in Mediterranean countries. In this study, empirical methods to estimate the IPF based on maximum mean daily flow (MMDF), artificial neural networks (ANN), and adaptive neuro-fuzzy inference system (ANFIS) have been compared. These methods have been applied in 14 different streamflow gauge stations covering the diversity of flashiness conditions found in Peninsular Spain. Root-mean-square error (RMSE), and coefficient of determination (R²) have been used as evaluation criteria. The results show that: (1) the Fuller equation and its regionalization is more accurate and has lower error compared with other empirical methods; and (2) ANFIS has demonstrated a superior ability to estimate IPF compared to any empirical formula. Full article

(This article belongs to the Special Issue Forecast of Extreme Events in the Water Cycle—Data, Models and Uncertainties)

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2577 KiB

Open AccessArticle

Applicability of Zero-Inflated Models to Fit the Torrential Rainfall Count Data with Extra Zeros in South Korea

by Cheol-Eung Lee and Sang Ug Kim

Water 2017, 9(2), 123; https://doi.org/10.3390/w9020123 - 16 Feb 2017

Cited by 7 | Viewed by 5423

Abstract

Several natural disasters occur because of torrential rainfalls. The change in global climate most likely increases the occurrences of such downpours. Hence, it is necessary to investigate the characteristics of the torrential rainfall events in order to introduce effective measures for mitigating disasters such as urban floods and landslides. However, one of the major problems is evaluating the number of torrential rainfall events from a statistical viewpoint. If the number of torrential rainfall occurrences during a month is considered as count data, their frequency distribution could be identified using a probability distribution. Generally, the number of torrential rainfall occurrences has been analyzed using the Poisson distribution (POI) or the Generalized Poisson Distribution (GPD). However, it was reported that POI and GPD often overestimated or underestimated the observed count data when additional or fewer zeros were included. Hence, in this study, a zero-inflated model concept was applied to solve this problem existing in the conventional models. Zero-Inflated Poisson (ZIP) model, Zero-Inflated Generalized Poisson (ZIGP) model, and the Bayesian ZIGP model have often been applied to fit the count data having additional or fewer zeros. However, the applications of these models in water resource management have been very limited despite their efficiency and accuracy. The five models, namely, POI, GPD, ZIP, ZIGP, and Bayesian ZIGP, were applied to the torrential rainfall data having additional zeros obtained from two rain gauges in South Korea, and their applicability was examined in this study. In particular, the informative prior distributions evaluated via the empirical Bayes method using ten rain gauges were developed in the Bayesian ZIGP model. Finally, it was suggested to avoid using the POI and GPD models to fit the frequency of torrential rainfall data. In addition, it was concluded that the Bayesian ZIGP model used in this study provided the most accurate results for the count data having additional zeros. Moreover, it was recommended that the ZIP model could be an alternative from a practical viewpoint, as the Bayesian approach used in this study was considerably complex. Full article

(This article belongs to the Special Issue Forecast of Extreme Events in the Water Cycle—Data, Models and Uncertainties)

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2262 KiB

Open AccessArticle

Evaluation of TRMM Product for Monitoring Drought in the Kelantan River Basin, Malaysia

by Mou Leong Tan, Kok Chooi Tan, Vivien P. Chua and Ngai Weng Chan

Water 2017, 9(1), 57; https://doi.org/10.3390/w9010057 - 17 Jan 2017

Cited by 75 | Viewed by 9254

Abstract

Assessment of satellite precipitation products’ capability for monitoring drought is relatively new in tropical regions. The purpose of this paper is to evaluate the reliability of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B43 product in estimating the standardized precipitation index (SPI) in the Kelantan River Basin, Malaysia from 1998 to 2014, by comparing it with data from 42 rain gauges. Overall, the TMPA-3B43 performed well in the monthly precipitation estimation, but performed moderately in the seasonal scale. Better performance was found in the northeast monsoon (wet season) than in the southwest monsoon (dry season). The product is more reliable in the northern and north-eastern regions (coastal zone) compared to the central, southern and south-eastern regions (mountainous area). For drought assessment, the correlations between the TMPA-3B43 and ground observations are moderate at various time-scales (one to twelve months), with better performance at shorter time-scales. The TMPA-3B43 shows similar temporal drought behavior by capturing most of the drought events at various time-scales, except for the 2008–2009 drought. These findings show that the TMPA-3B43 is not suitable to be used directly for SPI estimation in this basin. More bias correction and algorithm improvement work are needed to improve the accuracy of the TMPA-3B43 in drought monitoring. Full article

(This article belongs to the Special Issue Forecast of Extreme Events in the Water Cycle—Data, Models and Uncertainties)

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6215 KiB

Open AccessArticle

Uncertainty Analysis in Data-Scarce Urban Catchments

by Héctor A. Ballinas-González, Victor H. Alcocer-Yamanaka and Adrián Pedrozo-Acuña

Water 2016, 8(11), 524; https://doi.org/10.3390/w8110524 - 10 Nov 2016

Cited by 6 | Viewed by 6863

Abstract

The evaluation of the uncertainties in model predictions is key for advancing urban drainage modelling practice. This paper investigates, for the first time in Mexico, the effect of parameter sensitivity and predictive uncertainty in an application of a well-known urban stormwater model. Two of the most common methods used for assessing hydrological model parameter uncertainties are used: the Generalised Likelihood Uncertainty Estimation (GLUE) and a multialgorithm, genetically adaptive multi-objective method (AMALGAM). The uncertainty is estimated from eight selected hydrologic parameters used in the setup of the rainfall-runoff model. To ensure the reliability of the model, four rainfall events varying from 20 mm to 120 mm from minor to major count classes were selected. The results show that, for the selected storms, both techniques generate results with similar effectiveness, as measured using well-known error metrics; GLUE was found to have a slightly better performance compared to AMALGAM. In particular, it was demonstrated that it is possible to obtain reliable models with an index of agreement (IAd) greater than 60 and average Absolute Percentage Error (EAP) less than 30 percent derived from the uncertainty analysis. Thus, the quantification of uncertainty enables the generation of more reliable flow predictions. Moreover, these methods show the impact of aggregation of errors arising from different sources, minimising the amount of subjectivity associated with the model’s predictions. Full article

(This article belongs to the Special Issue Forecast of Extreme Events in the Water Cycle—Data, Models and Uncertainties)

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2904 KiB

Open AccessArticle

The Influence of the Annual Number of Storms on the Derivation of the Flood Frequency Curve through Event-Based Simulation

by Alvaro Sordo-Ward, Paola Bianucci, Luis Garrote and Alfredo Granados

Water 2016, 8(8), 335; https://doi.org/10.3390/w8080335 - 5 Aug 2016

Cited by 10 | Viewed by 5651

Abstract

This study addresses the question of how to select the minimum set of storms that should be simulated each year in order to estimate an accurate flood frequency curve for return periods ranging between 1 and 1000 years. The Manzanares basin (Spain) was used as a study case. A continuous 100,000-year hourly rainfall series was generated using the stochastic spatial–temporal model RanSimV3. Individual storms were extracted from the series by applying the exponential method. For each year, the extracted storms were transformed into hydrographs by applying an hourly time-step semi-distributed event-based rainfall–runoff model, and the maximum peak flow per year was determined to generate the reference flood frequency curve. Then, different flood frequency curves were obtained considering the N storms with maximum rainfall depth per year, with 1 ≤ N ≤ total number of storms. Main results show that: (a) the degree of alignment between the calculated flood frequency curves and the reference flood frequency curve depends on the return period considered, increasing the accuracy for higher return periods; (b) for the analyzed case studies, the flood frequency curve for medium and high return period (50 ≤ return period ≤ 1000 years) can be estimated with a difference lower than 3% (compared to the reference flood frequency curve) by considering the three storms with the maximum total rainfall depth each year; (c) when considering only the greatest storm of the year, for return periods higher than 10 years, the difference for the estimation of the flood frequency curve is lower than 10%; and (d) when considering the three greatest storms each year, for return periods higher than 100 years, the probability of achieving simultaneously a hydrograph with the annual maximum peak flow and the maximum volume is 94%. Full article

(This article belongs to the Special Issue Forecast of Extreme Events in the Water Cycle—Data, Models and Uncertainties)

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9625 KiB

Open AccessArticle

The Use of TRMM 3B42 Product for Drought Monitoring in Mexico

by Aurea De Jesús, Jose Agustín Breña-Naranjo, Adrián Pedrozo-Acuña and Victor Hugo Alcocer Yamanaka

Water 2016, 8(8), 325; https://doi.org/10.3390/w8080325 - 2 Aug 2016

Cited by 46 | Viewed by 8974

Abstract

Drought has been a recurrent phenomenon in Mexico. For its assessment and monitoring, several studies have monitored meteorological droughts using standardized indices of precipitation deficits. Such conventional studies have mostly relied on rain gauge-based measurements, with the main limitation being the scarcity of rain gauge spatial coverage. This issue does not allow a robust spatial characterization of drought. A recent alternative for monitoring purposes can be found in satellite-based remote sensing of meteorological variables. The main objective of this study is to evaluate the standardized precipitation index (SPI) in Mexico during the period 1998 to 2013, using the Tropical Rainfall Measuring Mission (TRMM) satellite product 3B42. Results suggest that Mexico experienced the driest conditions during the great drought between 2011 and 2012; however, temporal variability in the SPI was found across different climatic regions. Nevertheless, a comparison of the SPI derived by TRMM against the rain gauge-based SPI computed by the official Mexican Drought Monitor showed low to medium correlation of the time series though both SPIs managed to capture the most relevant droughts at the national scale. We conclude that the TRMM product can properly monitor meteorological droughts despite its relative short dataset length (~15 years). Finally, we recommend an assimilation of rain gauge and satellite-based precipitation data to provide more robust estimates of meteorological drought severity. Full article

(This article belongs to the Special Issue Forecast of Extreme Events in the Water Cycle—Data, Models and Uncertainties)

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5084 KiB

Open AccessArticle

Fully Stochastic Distributed Methodology for Multivariate Flood Frequency Analysis

by Isabel Flores-Montoya, Álvaro Sordo-Ward, Luis Mediero and Luis Garrote

Water 2016, 8(6), 225; https://doi.org/10.3390/w8060225 - 27 May 2016

Cited by 11 | Viewed by 6236

Abstract

An adequate estimation of the extreme behavior of basin response is essential both for designing river structures and for evaluating their risk. The aim of this paper is to develop a new methodology to generate extreme hydrograph series of thousands of years using an event-based model. To this end, a spatial-temporal synthetic rainfall generator (RainSimV3) is combined with a distributed physically-based rainfall–runoff event-based model (RIBS). The use of an event-based model allows simulating longer hydrograph series with less computational and data requirements but need to characterize the initial basis state, which depends on the initial basin moisture distribution. To overcome this problem, this paper proposed a probabilistic calibration–simulation approach, which considers the initial state and the model parameters as random variables characterized by a probability distribution though a Monte Carlo simulation. This approach is compared with two other approaches, the deterministic and the semi-deterministic approaches. Both approaches use a unique initial state. The deterministic approach also uses a unique value of the model parameters while the semi-deterministic approach obtains these values from its probability distribution through a Monte Carlo simulation, considering the basin variability. This methodology has been applied to the Corbès and Générargues basins, in the Southeast of France. The results show that the probabilistic approach offers the best fit. That means that the proposed methodology can be successfully used to characterize the extreme behavior of the basin considering the basin variability and overcoming the basin initial state problem. Full article

(This article belongs to the Special Issue Forecast of Extreme Events in the Water Cycle—Data, Models and Uncertainties)

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5481 KiB

Open AccessArticle

Historical Trends in Mean and Extreme Runoff and Streamflow Based on Observations and Climate Models

by Behzad Asadieh, Nir Y. Krakauer and Balázs M. Fekete

Water 2016, 8(5), 189; https://doi.org/10.3390/w8050189 - 7 May 2016

Cited by 28 | Viewed by 8513

Abstract

To understand changes in global mean and extreme streamflow volumes over recent decades, we statistically analyzed runoff and streamflow simulated by the WBM-plus hydrological model using either observational-based meteorological inputs from WATCH Forcing Data (WFD), or bias-corrected inputs from five global climate models (GCMs) provided by the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP). Results show that the bias-corrected GCM inputs yield very good agreement with the observation-based inputs in average magnitude of runoff and streamflow. On global average, the observation-based simulated mean runoff and streamflow both decreased about 1.3% from 1971 to 2001. However, GCM-based simulations yield increasing trends over that period, with an inter-model global average of 1% for mean runoff and 0.9% for mean streamflow. In the GCM-based simulations, relative changes in extreme runoff and extreme streamflow (annual maximum daily values and annual-maximum seven-day streamflow) are slightly greater than those of mean runoff and streamflow, in terms of global and continental averages. Observation-based simulations show increasing trend in mean runoff and streamflow for about one-half of the land areas and decreasing trend for the other half. However, mean and extreme runoff and streamflow based on the GCMs show increasing trend for approximately two-thirds of the global land area and decreasing trend for the other one-third. Further work is needed to understand why GCM simulations appear to indicate trends in streamflow that are more positive than those suggested by climate observations, even where, as in ISI-MIP, bias correction has been applied so that their streamflow climatology is realistic. Full article

(This article belongs to the Special Issue Forecast of Extreme Events in the Water Cycle—Data, Models and Uncertainties)

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6779 KiB

Open AccessArticle

Building Damage Assessment Using Scenario Based Tsunami Numerical Analysis and Fragility Curves

by Khawar Rehman and Yong-Sik Cho

Water 2016, 8(3), 109; https://doi.org/10.3390/w8030109 - 19 Mar 2016

Cited by 14 | Viewed by 7284

Abstract

A combination of a deterministic approach and fragility analysis is applied to assess tsunami damage caused to buildings. The area selected to validate the model is Imwon Port in Korea. The deterministic approach includes numerical modeling of tsunami propagation in the East Sea following an earthquake on the western coast of Japan. The model is based on the linear shallow-water equations (LSWE) augmented with Boussinesq approximation to account for dispersion effects in wave propagation, and coastal wave run-up is modeled by non-linear shallow-water equations (NLSWE). The output from the deterministic model comprises inundation depth. The numerical output is used to perform fragility analysis for buildings vulnerable to flooding by tsunamis in the port area. Recently developed fragility curves—based on the ordinal regression method—are used for damage probability estimates. The extent of structural damage in the areas under a tsunami hazard is identified by the numerical modeling of tsunami features. Our numerical model offers high bathymetric resolution, which enables us to capture flow features at the individual structure level and results in improved estimation of damage probability. This approach can serve as a measure of assessing structure vulnerability for areas with little or no records of tsunami damage and provide planners with a better understanding of structure behavior when a tsunami strikes. Full article

(This article belongs to the Special Issue Forecast of Extreme Events in the Water Cycle—Data, Models and Uncertainties)

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Open AccessCase Report

Storm Flood Characteristics and Identification of Periodicity for Flood-Causing Rainstorms in the Second Songhua River Basin

by Hongyan Li, Shanshan Bao, Xiaojun Wang and Hang Lv

Water 2016, 8(12), 529; https://doi.org/10.3390/w8120529 - 1 Dec 2016

Cited by 7 | Viewed by 5334

Abstract

Rainstorm weather systems and storm flood characteristics were studied to explore the relationship between the rainstorm weather system, the type of rainstorm, the cause of the flood and the time of occurrence, and some basic characteristics law of storm floods are summarized in the Second Songhua River Basin (Northeastern China). Then, the periodicity of catastrophic years was identified using the commensurability method and is shown to have an average of 11 years. Compared with simple flood forecasting, forecasting of flood-causing precipitation has a longer forecast period, which can gain the requisite time to discharge a reservoir and regain storage capacity, lower the limitation level, and manage the occurrence of flooding. Full article

(This article belongs to the Special Issue Forecast of Extreme Events in the Water Cycle—Data, Models and Uncertainties)

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