Special Issue "Computational Ecohydrology"

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

Deadline for manuscript submissions: closed (31 October 2021).

Special Issue Editor

Dr. A. Pouyan Nejadhashemi
E-Mail Website
Guest Editor
Michigan State University, USA
Interests: Ecohydrology; Sensitivity and uncertainty analysis of water quality mitigation approaches to address climate change; Environmental impact assessment; Soft computing applications in water resources; Development of decision support systems (DSSs) for the evaluation of human impact on ecosystem sustainability; Evaluation and development of watershed/water quality models
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Special Issue Information

Dear Colleagues,

In recent decades, access to more large data sets obtained from increased monitoring and novel modeling techniques has opened new opportunities to assess natural systems, particularly in the ecological and hydrological sciences. Ecohydrology is the nexus of these two fields, and has the goal of defining ecological patterns using the hydrological characteristics of streamflow. Computational ecohydrology harnesses big data in order to more effectively describe these inferences at increasingly large spatial and temporal scales. The aim of this Special Issue is to describe and synthesize some of the most recent research in this area. Topics of interest include but are not limited to:

  • Using advanced big data/statistical/machine learning approaches to derive novel insights into ecohydrological processes;
  • Addressing scale issues spanning regional to local scales using novel data and analytics approaches;
  • Using sensor networks, remote sensing, crowd sourcing, and/or model outputs for environmental impact assessments;
  • Analyzing satellite images to identify key features relevant to hydrologic modeling.

Dr. A. Pouyan Nejadhashemi
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 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 2000 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

  • ecohydrology
  • sensor networks
  • big data
  • crowd sourcing
  • remote sensing
  • forecasting
  • coupled modeling
  • water management and planning
  • uncertainties
  • environmental impact assessments

Published Papers (1 paper)

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Research

Article
Detecting Pattern Anomalies in Hydrological Time Series with Weighted Probabilistic Suffix Trees
Water 2020, 12(5), 1464; https://doi.org/10.3390/w12051464 - 21 May 2020
Cited by 2 | Viewed by 914
Abstract
Anomalous patterns are common phenomena in time series datasets. The presence of anomalous patterns in hydrological data may represent some anomalous hydrometeorological events that are significantly different from others and induce a bias in the decision-making process related to design, operation and management [...] Read more.
Anomalous patterns are common phenomena in time series datasets. The presence of anomalous patterns in hydrological data may represent some anomalous hydrometeorological events that are significantly different from others and induce a bias in the decision-making process related to design, operation and management of water resources. Hence, it is necessary to extract those “anomalous” knowledge that can provide valuable and useful information for future hydrological analysis and forecasting from hydrological data. This paper focuses on the problem of detecting anomalous patterns from hydrological time series data, and proposes an effective and accurate anomalous pattern detection approach, TFSAX_wPST, which combines the advantages of the Trend Feature Symbolic Aggregate approximation (TFSAX) and weighted Probabilistic Suffix Tree (wPST). Experiments with different hydrological real-world time series are reported, and the results indicate that the proposed methods are fast and can correctly detect anomalous patterns for hydrological time series analysis, and thus promote the deep analysis and continuous utilization of hydrological time series data. Full article
(This article belongs to the Special Issue Computational Ecohydrology)
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