Special Issue "Stochastic Modeling in Hydrology"

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

Deadline for manuscript submissions: 1 July 2021.

Special Issue Editors

Prof. Dr. Momcilo Markus
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Guest Editor
Prairie Research Institute, Illinois State Water Survey (ISWS), University of Illinois, Urbana, IL 61801, USA
Interests: stochastic hydrology; hydroclimatology; statistical hydrology; data mining; riverine nutrients; precipitation frequency; flood frequency; climate change
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Prof. Dr. Daeryong Park
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Guest Editor
Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Korea
Interests: hydrologic/water quality modeling; stormwater best management practice (BMP)/low-impact development (LID); uncertainty and reliability analysis; statistical data analysis; decision support systems
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Prof. Dr. Myoung-Jin Um
E-Mail Website
Guest Editor
Department of Civil Engineering, Kyonggi University, Suwon-si 16227, Korea
Interests: hydrology; environmental engineering; hydrological modeling; spatial-temporal analysis; hydro-meteorology; risk analysis; climate change impacts; statistical analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Since their advent in hydrology over a half-century ago, stochastic models have become an important tool in solving many important issues in hydrology, including system simulation, filling in missing data, real-time and extended hydrologic forecasting, synthetic data generation for the evaluation of management scenarios, downscaling climate variables, and so forth. Growing public awareness of climate change and other significant sources of hydrologic non-stationarity additionally highlights the importance of stochastic hydrology. Increasing recognition of the non-stationary nature of hydrologic phenomena in recent decades gives an additional impetus for developing and implementing nonstationary stochastic methods in hydrology and associated fields.

This Special Issue invites innovative contributions in the field of stochastic hydrology and related fields. Multidisciplinary manuscripts encompassing stochastic hydrology and other fields, including but not limited to hydroclimatology, nonstationary modeling, soft computing, and geospatial analysis, are particularly welcome. Applied stochastic hydrology studies are encouraged, including hydrologic system simulation and optimization; water quality and quantity forecasting in rivers, streams, and lakes; analysis of the effects of climate projections; and frequency analysis of hydrologic extremes.

New ideas and insightful applications from your contributions will help us familiarize the Water readership with the present research trends and trace future research directions in theoretical and applied stochastic analysis in hydrology.

Prof. Dr. Momcilo Markus
Prof. Dr. Daeryong Park
Prof. Dr. Myoung-Jin Um
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 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.


  • Time-series analysis
  • System analysis
  • State-space modeling
  • Hydroclimatology
  • Climate change
  • Extreme events
  • Nonstationarity
  • Uncertainty quantification
  • Artificial intelligence
  • Water quality

Published Papers (1 paper)

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Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) Method
Water 2020, 12(11), 3137; https://doi.org/10.3390/w12113137 - 09 Nov 2020
Cited by 1 | Viewed by 664
The uncertainty in traditional hydrological modeling is a challenge that has not yet been overcome. This research aimed to provide a new method called the hybrid causal–hydrological (HCH) method, which consists of the combination of traditional rainfall–runoff models with novel hydrological approaches based [...] Read more.
The uncertainty in traditional hydrological modeling is a challenge that has not yet been overcome. This research aimed to provide a new method called the hybrid causal–hydrological (HCH) method, which consists of the combination of traditional rainfall–runoff models with novel hydrological approaches based on artificial intelligence, called Bayesian causal modeling (BCM). This was implemented by building nine causal models for three sub-basins of the Barbate River Basin (SW Spain). The models were populated by gauging (observing) short runoff series and from long and short hydrological runoff series obtained from the Témez rainfall–runoff model (T-RRM). To enrich the data, all series were synthetically replicated using an ARMA model. Regarding the results, on the one hand differences in the dependence intensities between the long and short series were displayed in the dependence mitigation graphs (DMGs), which were attributable to the insufficient amount of data available from the hydrological records and to climate change processes. The similarities in the temporal dependence propagation (basin memory) and in the symmetry of DMGs validate the reliability of the hybrid methodology, as well as the results generated in this study. Consequently, water planning and management can be substantially improved with this approach. Full article
(This article belongs to the Special Issue Stochastic Modeling in Hydrology)
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