Special Issue "Forecast of Extreme Events in the Water Cycle—Data, Models and Uncertainties"
A special issue of Water (ISSN 2073-4441).
Deadline for manuscript submissions: closed (31 March 2017)
Dr. Yunqing Xuan
Zienkiewicz Centre for Computational Engineering, College of Engineering, Swansea University, Singleton Park, Swansea SA2 8PP, United Kingdom
Interests: hydro-meteorological modeling; extreme hydrological events; climate change impact; flood risk management; uncertainty modeling
Dr. Harshinie Karunarathna
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
Manuscript Submission Information
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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.
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Title: A Climate Informed Predictive Model for Extreme Precipitation in Puerto Rico
Authors: Julio I. Vidal Salcedo 1, Naresh Devineni 2 and Reza Khanbilvardi 3
Affiliations: 1 Department of Civil Engineering, NOAA-Cooperative Remote Sensing Science and Technology Center, The City College of New York, NY, United States
2 Department of Civil Engineering, NOAA-Cooperative Remote Sensing Science and Technology Center, City University of New York, New York, NY, United States
3 Department of Civil Engineering, NOAA-Cooperative Remote Sensing Science and Technology Center, City University of New York, New York, NY, United States
Abstract: Precipitation modeling for Puerto Rico has been based on assumption of stationarity. Therefore probabilities do not vary across years. It is now recognized that in many places, these events are associated with specific climate states which may recur with non-uniform probability across years. Conditional on knowledge of the operating climate regime, the probability of an extreme rainfall of a certain magnitude can be higher or lower in a given year. A hierarchical Bayesian model is proposed to predict extreme rainfall of different durations, during the peak season, in Puerto Rico by fitting data to a generalized extreme value distribution with non-stationary parameters. Twenty one land-based stations were used in this study. The stations, located throughout Puerto Rico, have hourly rainfall records that extend back 40 years. Wavelet analysis applied to the extreme rainfall time series reveals 2-5 year dominant frequency mode coherent with El Nino Southern Oscillation. Field correlation analysis at different lags is also performed with sea surface temperatures. Results showed strong correlations with ENSO and Atlantic SST. A Hierarchal Bayesian model is developed for one seasonahead predictions of summer extreme rainfall using exogenous climate variables.