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Water
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Advances in Quantification and Modeling of Hydrological Droughts
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Advances in Quantification and Modeling of Hydrological Droughts

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

Deadline for manuscript submissions: 26 July 2024 | Viewed by 1103

Special Issue Editors

Prof. Dr. Umed S. Panu

E-Mail Website
Guest Editor
Department of Civil Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, Canada
Interests: drought analysis; reservoir sizing; drought forecasting; streamflow synthesis; data infilling procedures; pattern analysis and synthesis of hydrological data series
Dr. Tribeni C. Sharma

E-Mail Website
Guest Editor
Department of Civil Engineering, Lakehead University, Thunder Bay, ON, Canada
Interests: analysis and modelling of hydrological droughts; time series analysis of hydrological data; hydrology and water resources assessment of African catchments

Special Issue Information

Dear Colleagues,

Drought constitutes an important part of the scientific field of hydrology and water resources. Drought is considered one of the major natural hazards, with significant adverse impacts on the environment and consequently on a variety of economic sectors across the world. Because of the wide spatial and temporal diversity of droughts, there exists no universally accepted definition. Currently, drought draws worldwide attention primarily because of climate change, among other causative factors. The quantification of drought encompasses the study of deficiencies in precipitation, streamflow, water storage in surface waters, groundwater storage and soil water content. Accordingly, three major types of droughts are recognized: meteorological, hydrological and agricultural.

A hydrologic drought refers to a period of low flows in rivers, low water levels in surface reservoirs, ponds, wetlands, aquifers, etc., that lasts long enough to cause noticeable damage to sectors such as municipal water supply, agriculture industry, hydropower generation and navigation. The recent California drought and almost irreversible water levels in Lake Mead are grim reminders of this form of drought. The major cause of hydrologic droughts is a deficiency in precipitation (termed meteorological droughts), which translates into a deficiency in the aforesaid water bodies. The quantification of hydrological drought parameters (namely initiation and termination points on the time scale, duration, magnitude, intensity, frequency and regional spread) is crucial for planning suitable strategies for mitigation of drought impacts and management of water resources in a region. The role of hydrologic drought indices in the assessment of the severity of a drought is of paramount importance, although no universal index has yet been adopted. In addition, the quantification of drought magnitude and duration is an essential aspect of the sizing, operation and management of reservoirs for equitable distribution of water resources to multiple users during extended drought periods.

The other major component of hydrological drought analysis is modelling for prediction and forecasting. These components assume special significance in the wake of climate change. In particular, forecasting is more important as it deals with the warning signals of future droughts, which are likely to impact the availability of water for multiple uses in society.

The present Issue will focus on the above aspects of hydrological droughts, and papers are solicited which address the quantification of hydrological droughts based on the historical data, choice of suitable drought indices, stochastic characteristic of drought parameters, frequency and time domain analyses using traditional and machine learning algorithms (ANN, artificial intelligence, support vector regression, discrete wavelet transform, etc.) for prediction and forecasting. Papers emphasizing the role of climate change in the worsening scenario of hydrological droughts are encouraged, and case studies highlighting the applications of recent analytical and machine-learning-based modelling tools and methodologies shall be given prominence.

Prof. Dr. Umed S. Panu
Dr. Tribeni C. Sharma
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 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

  • approaches to the analysis and quantification of hydrologic droughts
  • hydrologic drought parameters (drought onset and termination, drought duration, drought intensity/severity, drought magnitude, etc.)
  • indices for the quantification of hydrologic droughts
  • machine learning methods such as artificial neural networks (ANNs), artificial intelligence, support vector regression (SVR) and discrete wavelet transform (DWT) in hydrologic drought modelling
  • association of hydrological droughts with meteorological and agricultural droughts
  • climate change and hydrological droughts
  • hydrological drought prediction/forecasting—traditional and machine-learning-based methodologies
  • early warning signals of hydrological drought
  • mitigation strategies for hydrological droughts
  • reservoir planning and assessment of storage volumes using hydrological drought models
  • impact of hydrological droughts on water quality of water bodies

Published Papers (1 paper)

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Review

23 pages, 606 KiB  
Review
Current State of Advances in Quantification and Modeling of Hydrological Droughts
by Tribeni C. Sharma and Umed S. Panu
Water 2024, 16(5), 729; https://doi.org/10.3390/w16050729 - 29 Feb 2024
Viewed by 707
Abstract
Hydrological droughts may be referred to as sustained and regionally extensive water shortages as reflected in streamflows that are noticeable and gauged worldwide. Hydrological droughts are largely analyzed using the truncation level approach to represent the desired flow condition such as the median, [...] Read more.
Hydrological droughts may be referred to as sustained and regionally extensive water shortages as reflected in streamflows that are noticeable and gauged worldwide. Hydrological droughts are largely analyzed using the truncation level approach to represent the desired flow condition such as the median, mean, or any other flow quantile of an annual, monthly, or weekly flow sequence. The quantification of hydrologic droughts is accomplished through indices, such as the standardized streamflow index (SSI) in tandem with the standardized precipitation index (SPI) commonly used in meteorological droughts. The runs of deficits in the SSI sequence below the truncation level are treated as drought episodes, and thus, the theory of runs forms an essential tool for analysis. The parameters of significance from the modeling perspective of hydrological droughts (or tantamount to streamflow droughts in this paper) are the longest duration and the largest magnitude over a desired return period of T-year (or month or week) of the streamflow sequences. It is to be stressed that the magnitude component of the hydrological drought is of paramount importance for the design and operation of water resource storage systems such as reservoirs. The time scales chosen for the hydrologic drought analysis range from daily to annual, but for most applications, a monthly scale is deemed appropriate. For modeling the aforesaid parameters, several methodologies are in vogue, i.e., the empirical fitting of the historical drought sequences through a known probability density function (pdf), extreme number theorem, Markov chain analysis, log-linear, copulas, entropy-based analyses, and machine learning (ML)-based methods such as artificial neural networks (ANN), wavelet transform (WT), support vector machines (SVM), adaptive neuro-fuzzy inference systems (ANFIS), and hybrid methods involving entropy, copulas, and machine learning-based methods. The forecasting of the hydrologic drought is rigorously conducted through machine learning-based methodologies. However, the traditional stochastic methods such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), copulas, and entropy-based methods are still popular. New techniques for flow simulation are based on copula and entropy-based concepts and machine learning methodologies such as ANN, WT, SVM, etc. The simulated flows could be used for deriving drought parameters in consonance with traditional Monte Carlo methods of data generation. Efforts are underway to use hydrologic drought models for reservoir sizing across rivers. The ML methods whilst combined in the hybrid form hold promise in drought forecasting for better management of existing water resources during the drought periods. Data mining and pre-processing techniques are expected to play a significant role in hydrologic drought modeling and forecasting in future. Full article
(This article belongs to the Special Issue Advances in Quantification and Modeling of Hydrological Droughts)
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