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Special Issue "Statistical Analysis and Stochastic Modelling of Hydrological Extremes"

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

Deadline for manuscript submissions: 15 February 2019

Special Issue Editor

Guest Editor
Dr. Hossein Tabari

Department of Civil Engineering, University of Leuven (KU Leuven), Belgium
Website | E-Mail
Phone: +32 16 37 70 07
Interests: statistical analysis of hydrological extremes; climate change/variability impact assessment on hydrology and water resources; analysis of historical hydrological time series; hydroclimatological forecasting; monitoring and modelling of water availability and drought/water scarcity

Special Issue Information

Dear Colleagues,

Assessment of hydrological extremes is of paramount importance, as they have the potential to affect society in terms of human health and mortality, and also may have effects on the ecosystem and economy (e.g., infrastructure and agriculture). In the last few decades, millions of people have been affected by hydrological extremes. The risk of these hazards will increase in the future as a result of climate change, and as population and infrastructure continue to increase and occupy areas exposed to higher risks. This Special Issue invites original research articles, as well as review articles, that address statistical analysis and stochastic modelling of hydrological extremes under current and future climate conditions. We are particularly interested in studies related to innovative stochastic and statistical approaches to analyze hydrological extremes. Potential topics include, but are not limited to:

  • Decadal anomaly and trend analysis of historical hydrological extremes
  • Anthropogenic and atmospheric drivers
  • Assessment of uncertainties in hydrological projections and observations
  • Application of statistical and dynamical downscaling methods
  • Hydrological modeling under extreme conditions
  • Early warning and forecasting systems
  • Regional and global drought and flood analyses
  • Adaptation and mitigation strategies
  • Socio-environmental consequences of hydrological extremes

Dr. Hossein Tabari
Guest Editor

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

  • Hydrological extremes
  • Pluvial and fluvial floods
  • Hydrological drought
  • Forecasting models
  • Climate change
  • Decadal climate variability
  • Uncertainty analysis

Published Papers (6 papers)

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Research

Open AccessArticle Applicability of ε-Support Vector Machine and Artificial Neural Network for Flood Forecasting in Humid, Semi-Humid and Semi-Arid Basins in China
Water 2019, 11(1), 85; https://doi.org/10.3390/w11010085
Received: 23 November 2018 / Revised: 29 December 2018 / Accepted: 1 January 2019 / Published: 6 January 2019
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Abstract
The aim of this study was to develop hydrological models that can represent different geo-climatic system, namely: humid, semi-humid and semi-arid systems, in China. Humid and semi-humid areas suffer from frequent flood events, whereas semi-arid areas suffer from flash floods because of urbanization
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The aim of this study was to develop hydrological models that can represent different geo-climatic system, namely: humid, semi-humid and semi-arid systems, in China. Humid and semi-humid areas suffer from frequent flood events, whereas semi-arid areas suffer from flash floods because of urbanization and climate change, which contribute to an increase in runoff. This study applied ɛ-Support Vector Machine (ε-SVM) and artificial neural network (ANN) for the simulation and forecasting streamflow of three different catchments. The Evolutionary Strategy (ES) optimization method was used to optimize the ANN and SVM sensitive parameters. The relative performance of the two models was compared, and the results indicate that both models performed well for humid and semi-humid systems, and SVM generally perform better than ANN in the streamflow simulation of all catchments. Full article
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Open AccessArticle Improved Forecasting of Extreme Monthly Reservoir Inflow Using an Analogue-Based Forecasting Method: A Case Study of the Sirikit Dam in Thailand
Water 2018, 10(11), 1614; https://doi.org/10.3390/w10111614
Received: 24 September 2018 / Revised: 2 November 2018 / Accepted: 5 November 2018 / Published: 9 November 2018
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Abstract
Reservoir inflow forecasting is crucial for appropriate reservoir management, especially in the flood season. Forecasting for this season must be sufficiently accurate and timely to allow dam managers to release water gradually for flood control in downstream areas. Recently, several models and methodologies
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Reservoir inflow forecasting is crucial for appropriate reservoir management, especially in the flood season. Forecasting for this season must be sufficiently accurate and timely to allow dam managers to release water gradually for flood control in downstream areas. Recently, several models and methodologies have been developed and applied for inflow forecasting, with good results. Nevertheless, most were reported to have weaknesses in capturing the peak flow, especially rare extreme flows. In this study, an analogue-based forecasting method, designated the variation analogue method (VAM), was developed to overcome this weakness. This method, the wavelet artificial neural network (WANN) model, and the weighted mean analogue method (WMAM) were used to forecast the monthly reservoir inflow of the Sirikit Dam, located in the Nan River Basin, one of the eight sub-basins of the Chao Phraya River Basin in Thailand. It is one of four major dams in the Chao Phraya Basin, with a maximum storage of 10.64 km3, which supplies water to 22 provinces in this basin, covering an irrigation area of 1,513,465 hectares. Due to the huge extreme monthly inflow in August, with inflow of more than 3 km3 in 1985 and 2011, monthly or longer lead time inflow forecasting is needed for proper water and flood control management of this dam. The results of forecasting indicate that the WANN model provided good forecasting for whole-year forecasting including both low-flow and high-flow patterns, while the WMAM model provided only satisfactory results. The VAM showed the best forecasting performance and captured the extreme inflow of the Sirikit Dam well. For the high-flow period (July–September), the WANN model provided only satisfactory results, while those of the WMAM were markedly poorer than for the whole year. The VAM showed the best capture of flow in this period, especially for extreme flow conditions that the WANN and WMAM models could not capture. Full article
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Open AccessArticle A Multi-GCM Assessment of the Climate Change Impact on the Hydrology and Hydropower Potential of a Semi-Arid Basin (A Case Study of the Dez Dam Basin, Iran)
Water 2018, 10(10), 1458; https://doi.org/10.3390/w10101458
Received: 28 August 2018 / Revised: 8 October 2018 / Accepted: 8 October 2018 / Published: 16 October 2018
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Abstract
In this paper, the impact of climate change on the climate and discharge of the Dez Dam Basin and the hydropower potential of two hydropower plants (Bakhtiari and Dez) is investigated based on the downscaled outputs of six GCMs (General Circulation Models) and
[...] Read more.
In this paper, the impact of climate change on the climate and discharge of the Dez Dam Basin and the hydropower potential of two hydropower plants (Bakhtiari and Dez) is investigated based on the downscaled outputs of six GCMs (General Circulation Models) and three SRES (Special Report on Emission Scenarios) scenarios for the early, mid and late 21st century. Projections of all the scenarios and GCMs revealed a significant rise in temperature (up to 4.9 °C) and slight to moderate variation in precipitation (up to 18%). Outputs of the HBV hydrologic model, enforced by projected datasets, show a reduction of the annual flow by 33% under the climate change condition. Further, analyzing the induced changes in the inflow and hydropower generation potential of the Bakhtiari and Dez dams showed that both inflow and hydropower generation is significantly affected by climate change. For the Bakhtiari dam, this indicates a consistent reduction of inflow (up to 27%) and electricity generation (up to 32%). While, in the Dez dam case, the inflow is projected to decrease (up to 22%) and the corresponding hydropower is expected to slightly increase (up to 3%). This contrasting result for the Dez dam is assessed based on its reservoir and hydropower plant capacity, as well as other factors such as the timely releases to meet different demands and flow regime changes under climate change. The results show that the Bakhtiari reservoir and power plant will not meet the design-capacity outputs under the climate change condition as its large capacity cannot be fully utilized; while there is room for the further development of the Dez power plant. Comparing the results of the applied GCMs showed high discrepancies among the outputs of different models. Full article
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Open AccessArticle Wavelet-ANN versus ANN-Based Model for Hydrometeorological Drought Forecasting
Water 2018, 10(8), 998; https://doi.org/10.3390/w10080998
Received: 4 June 2018 / Revised: 29 June 2018 / Accepted: 10 July 2018 / Published: 27 July 2018
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Abstract
Malaysia is one of the countries that has been experiencing droughts caused by a warming climate. This study considered the Standard Index of Annual Precipitation (SIAP) and Standardized Water Storage Index (SWSI) to represent meteorological and hydrological drought, respectively. The study area is
[...] Read more.
Malaysia is one of the countries that has been experiencing droughts caused by a warming climate. This study considered the Standard Index of Annual Precipitation (SIAP) and Standardized Water Storage Index (SWSI) to represent meteorological and hydrological drought, respectively. The study area is the Langat River Basin, located in the central part of peninsular Malaysia. The analysis was done using rainfall and water level data over 30 years, from 1986 to 2016. Both of the indices were calculated in monthly scale, and two neural network-based models and two wavelet-based artificial neural network (W-ANN) models were developed for monthly droughts. The performance of the SIAP and SWSI models, in terms of the correlation coefficient (R), was 0.899 and 0.968, respectively. The application of a wavelet for preprocessing the raw data in the developed W-ANN models achieved higher correlation coefficients for most of the scenarios. This proves that the created model can predict meteorological and hydrological droughts very close to the observed values. Overall, this study helps us to understand the history of drought conditions over the past 30 years in the Langat River Basin. It further helps us to forecast drought and to assist in water resource management. Full article
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Open AccessArticle Drought Prediction for Areas with Sparse Monitoring Networks: A Case Study for Fiji
Water 2018, 10(6), 788; https://doi.org/10.3390/w10060788
Received: 18 May 2018 / Revised: 11 June 2018 / Accepted: 11 June 2018 / Published: 14 June 2018
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Abstract
Hybrid drought prediction models were developed for areas with limited monitoring gauges using the APEC Climate Center Multi-Model Ensemble seasonal climate forecast and machine learning models of Extra-Trees and Adaboost. The models provide spatially distributed detailed drought prediction data of the 6-month Standardized
[...] Read more.
Hybrid drought prediction models were developed for areas with limited monitoring gauges using the APEC Climate Center Multi-Model Ensemble seasonal climate forecast and machine learning models of Extra-Trees and Adaboost. The models provide spatially distributed detailed drought prediction data of the 6-month Standardized Precipitation Index for the case study area, Fiji. In order to overcome the limitation of a sparse monitoring network, both in-situ data and bias-corrected dynamic downscaling of historical climate data from the Weather Research Forecasting (WRF) model were used as reference data. Performance measures of the mean absolute error as well as classification accuracy were used. The WRF outputs reflect the topography of the area. Hybrid models showed better performance than simply bias corrected forecasts in most cases. Especially, the model based on Extra-Trees trained using the WRF model outputs performed the best in most cases. Full article
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Open AccessArticle Spatial Downscaling of Satellite Precipitation Data in Humid Tropics Using a Site-Specific Seasonal Coefficient
Water 2018, 10(4), 409; https://doi.org/10.3390/w10040409
Received: 11 February 2018 / Revised: 23 March 2018 / Accepted: 26 March 2018 / Published: 31 March 2018
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Abstract
This paper described the development of a spatial downscaling algorithm to produce finer grid resolution for satellite precipitation data (0.05°) in humid tropics. The grid resolution provided by satellite precipitation data (>0.25°) was unsuitable for practical hydrology and meteorology applications in the high
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This paper described the development of a spatial downscaling algorithm to produce finer grid resolution for satellite precipitation data (0.05°) in humid tropics. The grid resolution provided by satellite precipitation data (>0.25°) was unsuitable for practical hydrology and meteorology applications in the high hydrometeorological dynamics of Southeast Asia. Many downscaling algorithms have been developed based on significant seasonal relationships, without vegetation and climate conditions, which were inapplicable in humid, equatorial, and tropical regions. Therefore, we exploited the potential of the low variability of rainfall and monsoon characteristics (period, location, and intensity) on a local scale, as a proxy to downscale the satellite precipitation grid and its corresponding rainfall estimates. This study hypothesized that the ratio between the satellite precipitation and ground rainfall in the low-variance spatial rainfall pattern and seasonality region of humid tropics can be used as a coefficient (constant value) to spatially downscale future satellite precipitation datasets. The spatial downscaling process has two major phases: the first is the derivation of the high-resolution coefficient (0.05°), and the second is applying the coefficient to produce the high-resolution precipitation map. The first phase utilized the long-term bias records (1998–2008) between the high-resolution areal precipitation (0.05°) that was derived from dense network of ground precipitation data and re-gridded satellite precipitation data (0.05°) from the Tropical Rainfall Measuring Mission (TRMM) to produce the site-specific coefficient (SSC) for each individual pixel. The outcome of the spatial downscaling process managed to produce a higher resolution of the TRMM data from 0.25° to 0.05° with a lower bias (average: 18%). The trade-off for the process was a small decline in the correlation between TRMM and ground rainfall. Our results indicate that the SSC downscaled method can be used to spatially downscale satellite precipitation data in humid, tropical regions, where the seasonal rainfall is consistent. Full article
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Graphical abstract

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