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Water
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Stochastic Modeling in Hydrology
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Stochastic Modeling in Hydrology

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 7199

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
Special Issues, Collections and Topics in MDPI journals
Dr. Daeryong Park

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Guest Editor
Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Republic of Korea
Interests: urban hydrology; urban green infrastructure; urban water availability; urban stormwater management; water quality modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Myoung-Jin Um

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Guest Editor
Department of Civil Engineering, Kyonggi University, Suwon-si 16227, Republic of Korea
Interests: hydrology; environmental engineering; hydrological modeling; spatial–temporal analysis; hydro-meteorology; risk analysis; climate change impacts; statistical analysis
Special Issues, Collections and Topics 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 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

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

Published Papers (2 papers)

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Research

18 pages, 6549 KiB  
Article
Improvement of Downstream Flow by Modifying SWAT Reservoir Operation Considering Irrigation Water and Environmental Flow from Agricultural Reservoirs in South Korea
by Jinuk Kim, Jiwan Lee, Jongyoon Park, Sehoon Kim and Seongjoon Kim
Water 2021, 13(18), 2543; https://0-doi-org.brum.beds.ac.uk/10.3390/w13182543 - 16 Sep 2021
Cited by 9 | Viewed by 2922
Abstract
This study aims to develop a reservoir operation rule adding downstream environmental flow release (EFR) to the exclusive use of irrigation water supply (IWS) from agricultural reservoirs through canals to rice paddy areas. A reservoir operation option was added in the Soil and [...] Read more.
This study aims to develop a reservoir operation rule adding downstream environmental flow release (EFR) to the exclusive use of irrigation water supply (IWS) from agricultural reservoirs through canals to rice paddy areas. A reservoir operation option was added in the Soil and Water Assessment Tool (SWAT) to handle both EFR and IWS. For a 366.5 km2 watershed including three agricultural reservoirs and a rice paddy irrigation area of 4744.7 ha, the SWAT was calibrated and validated using 21 years (1998–2018) of daily reservoir water levels and downstream flow data at Gongdo (GD) station. For reservoir water level and streamflow, the average root means square error (RMSE) ranged from 19.70 mm to 19.54 mm, and the coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) had no effect on the improved SWAT. By applying the new reservoir option, the EFR amount for a day was controlled by keeping the reservoir water level up in order to ensure that the IWS was definitely satisfied in any case. The downstream mean wet streamflow (Q95) decreased to 5.70 m3/sec from 5.71 m3/sec and the mean minimum flow (Q355) increased to 1.05 m3/sec from 0.94 m3/sec. Through the development of a SWAT reservoir operation module that satisfies multiple water supply needs such as IWR and EFR, it is possible to manage agricultural water in the irrigation period and control the environmental flow in non-irrigation periods. This study provides useful information to evaluate and understand the future impacts of various changes in climate and environmental flows at other sites. Full article
(This article belongs to the Special Issue Stochastic Modeling in Hydrology)
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26 pages, 6502 KiB  
Article
Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) Method
by Santiago Zazo, José-Luis Molina, Verónica Ruiz-Ortiz, Mercedes Vélez-Nicolás and Santiago García-López
Water 2020, 12(11), 3137; https://0-doi-org.brum.beds.ac.uk/10.3390/w12113137 - 09 Nov 2020
Cited by 14 | Viewed by 2550
Abstract
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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