Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.4
Journals
Water
Special Issues
Statistical Analysis in Hydrology: Methods and Applications
share announcement

Statistical Analysis in Hydrology: Methods and Applications

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 3178

Special Issue Editor

Dr. Yu Zhang

E-Mail Website
Guest Editor
Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA
Interests: hydrologic prediction; frequency analysis; hydrometerology; risk assessment; data assimilation; ensemble postprocessing; extreme value statistics

Special Issue Information

Dear Colleagues,

I am writing to promote an upcoming Special Issue in the open access journal Water, titled “Statistical Analysis in Hydrology: Methods and Applications”.  The Special Issue will offer an opportunity for researchers in many subdisciplines of hydrology to share recent advances in the statistical techniques and practical applications. Example applications include hydrologic forecast, frequency analysis, and hydrologic product generation.  The following topics are especially welcomed:

  • statistical representations of hydro-climate extremes, including droughts, extreme rainfall and snowfall, and flash flooding, in space and time that account for nonstationarity;
  • methods for characterizing uncertainties in hydrologic predictions and retrospective analysis;
  • comparisons of conventional statistical approaches and machine learning techniques in predictions of impactful hydrologic events;
  • novel statistical techniques for downscaling the weather and climate model predictions and projections.

The Special Issue will mark a milestone in the statistical hydrology literature by highlighting the latest developments across the field.

Dr. Yu Zhang
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 submissions that pass pre-check are 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 semimonthly 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 2600 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

  • hydrologic predictions
  • statistical postprocessing
  • frequency estimates
  • spatial statistics
  • data assimilation
  • time series
  • nonstationarity

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 8936 KiB  
Article
Estimation of the Peak over Threshold-Based Design Rainfall and Its Spatial Variability in the Upper Vistula River Basin, Poland
by Katarzyna Kołodziejczyk and Agnieszka Rutkowska
Water 2023, 15(7), 1316; https://doi.org/10.3390/w15071316 - 27 Mar 2023
Cited by 2 | Viewed by 1351
Abstract
The proper assessment of design rainfalls with long return periods is very important because they are inputs for many flood studies. In this paper, estimations are performed on daily design rainfall totals from 16 meteorological stations located in the area of the Upper [...] Read more.
The proper assessment of design rainfalls with long return periods is very important because they are inputs for many flood studies. In this paper, estimations are performed on daily design rainfall totals from 16 meteorological stations located in the area of the Upper Vistula River Basin (UVB), Poland. The study material consists of a historical series of daily rainfall totals from the period of 1960–2021. The peak over threshold (POT) method is used, and the rainfall depth over threshold is assumed to follow the generalized Pareto distribution (GPD) with parameters estimated from Hill statistics. Alternatively, the competitive method based on annual maxima (AM) is applied. The theoretical distribution of AM is assumed to follow a theoretical distribution function selected by using the Akaike information criterion (AIC) from a family of seven candidate distributions, the parameters of which are estimated by using the maximum likelihood method. The two methods are compared by using the root mean square error (RMSE) and the mean deviation error (MDE) criteria. It is found that the POT-based method with GPD and Hill estimators outperform the AM-based method when considering the highest rainfall events. The confidence intervals of the design rainfalls, derived by using the Monte Carlo simulation method, reflects their large spatial diversity across the UVB. It is shown that the station’s altitude strongly correlates with the threshold, variance, and design rainfall depth of the GPD. This proves the advantage of the GPD with Hill estimates, namely that it can accurately reflect the spatial properties of rainfall and its variability in the UVB. Results can be applied in water-management applications related to floods. Full article
(This article belongs to the Special Issue Statistical Analysis in Hydrology: Methods and Applications)
►▼ Show Figures

Figure 1

20 pages, 8328 KiB  
Article
A Spatial-Reduction Attention-Based BiGRU Network for Water Level Prediction
by Kexin Bao, Jinqiang Bi, Ruixin Ma, Yue Sun, Wenjia Zhang and Yongchao Wang
Water 2023, 15(7), 1306; https://doi.org/10.3390/w15071306 - 26 Mar 2023
Cited by 3 | Viewed by 1504
Abstract
According to the statistics of ship traffic accidents on inland waterways, potential safety hazards such as stranding, hitting rocks, and suspending navigation are on the increase because of the sudden rise and fall of the water level, which may result in fatalities, environmental [...] Read more.
According to the statistics of ship traffic accidents on inland waterways, potential safety hazards such as stranding, hitting rocks, and suspending navigation are on the increase because of the sudden rise and fall of the water level, which may result in fatalities, environmental devastation, and massive economic losses. In view of this situation, the purpose of this paper is to propose a high-accuracy water-level-prediction model based on the combination of the spatial-reduction attention and bidirectional gate recurrent unit (SRA-BiGRU), which provides support for ensuring the safe navigation of ships, guiding the reasonable stowage of ships, and flood prevention. The first contribution of this model is that it makes use of its strong fitting ability to capture nonlinear characteristics, and it fully considers the time series of water-level data. Secondly, the bidirectional recurrent neural network structure makes full use of past and future water-level information in the mapping process between input and output sequences. Thirdly, and most importantly, the introduction of spatial-reduction attention on the basis of BiGRU can not only automatically capture the correlations between the hidden vectors generated by BiGRU to address the issue of precision degradation due to the extended time span in water-level-forecasting tasks but can also make full use of the spatial information between water-level stations by emphasizing the influence of significant features on the prediction results. It is noteworthy that comparative experiments gradually prove the superiority of GRU, bidirectional recurrent neural network structure, and spatial-reduction attention, demonstrating that SRA-BiGRU is a water-level-prediction model with high availability, high accuracy, and high robustness. Full article
(This article belongs to the Special Issue Statistical Analysis in Hydrology: Methods and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop