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Water
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Advances in Streamflow and Flood Forecasting
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Advances in Streamflow and Flood Forecasting

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 6228

Special Issue Editors

Dr. Yen-Ming Chiang

E-Mail Website
Guest Editor
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
Interests: hydrological modeling; artificial intelligence; data merging; precipitation prediction; remote sensing; ensemble streamflow prediction
Special Issues, Collections and Topics in MDPI journals
Dr. Wen-Ping Tsai

E-Mail Website
Guest Editor
Department of Hydraulic and Ocean Engineering, National Cheng Kung University, Tainan, Taiwan
Interests: hydrologic model; machine learning/deep learning; big data; water resources managements; eco-hydrology; water-food-energy nexus
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rivers and streams experience flooding as a natural result of large rainstorms or spring snowmelt that may result in inundation or flooding disasters. Flooding is considered one of the biggest weather‐related killers in the world. Precipitation intensity has increased worldwide with global climate change, but this effect on streamflow and flood magnitude is difficult to pinpoint. Therefore, more accurate streamflow and flood forecasting methods are essential for hydrologists.

This Special Issue focuses on advanced approaches including the traditional approach of statistical and stochastic time-series modeling with their recent developments, stand-alone data-driven methods such as artificial intelligence (machine learning/ deep learning), and modern hybrid approaches where data-driven models are combined with preprocessing methods (or physically based hydrologic models) to improve the accuracy of streamflow and flood forecasting.

Dr. Yen-Ming Chiang
Dr. Wen-Ping Tsai
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

  • hydrologic modeling
  • artificial intelligence
  • machine learning
  • deep learning
  • remote sensing
  • climate change
  • uncertainty analysis

Published Papers (3 papers)

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Research

22 pages, 7805 KiB  
Article
Development of Flood Early Warning Frameworks for Small Streams in Korea
by Tae-Sung Cheong, Changwon Choi, Sung-Je Ye, Jihye Shin, Seojun Kim and Kang-Min Koo
Water 2023, 15(10), 1808; https://doi.org/10.3390/w15101808 - 9 May 2023
Cited by 2 | Viewed by 1722
Abstract
Currently, Korea is undergoing significant local extreme rainfall, which contributes to more than 80% of flood disasters. Additionally, there is an increasing occurrence of such extreme rainfall in small stream basins, accounting for over 60% of flood disasters. Consequently, it becomes imperative to [...] Read more.
Currently, Korea is undergoing significant local extreme rainfall, which contributes to more than 80% of flood disasters. Additionally, there is an increasing occurrence of such extreme rainfall in small stream basins, accounting for over 60% of flood disasters. Consequently, it becomes imperative to forecast runoff and water levels in advance to effectively mitigate flood disasters in small streams. The Flood Early Warning Framework (FEWF) presents one solution to reduce flood disasters by enabling the forecast of discharge and water levels during flood events. However, the application of FEWF in existing research is challenging due to the short flood travel time characteristic of small streams. This research proposes a methodology for constructing FEWF tailored to small streams using the nomograph and rating curve method. To evaluate the effectiveness of FEWF, a 6-year dataset from the Closed-circuit television-based Automatic Discharge Measurement Technique (CADMT) was utilized. The results indicate that FEWF successfully forecasts discharge and depth during flood events. By leveraging CADMT technology and real-time data, the development of precise and dependable FEWFs becomes possible. This advancement holds the potential to mitigate the consequences of extreme rainfall events and minimize flood-related casualties in small stream basins. Full article
(This article belongs to the Special Issue Advances in Streamflow and Flood Forecasting)
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16 pages, 7102 KiB  
Article
Comparative Study for Daily Streamflow Simulation with Different Machine Learning Methods
by Ruonan Hao and Zhixu Bai
Water 2023, 15(6), 1179; https://doi.org/10.3390/w15061179 - 18 Mar 2023
Cited by 12 | Viewed by 1989
Abstract
Rainfall–runoff modeling has been of great importance for flood control and water resource management. However, the selection of hydrological models is challenging to obtain superior simulation performance especially with the rapid development of machine learning techniques. Three models under different categories of machine [...] Read more.
Rainfall–runoff modeling has been of great importance for flood control and water resource management. However, the selection of hydrological models is challenging to obtain superior simulation performance especially with the rapid development of machine learning techniques. Three models under different categories of machine learning methods, including support vector regression (SVR), extreme gradient boosting (XGBoost), and the long-short term memory neural network (LSTM), were assessed for simulating daily runoff over a mountainous river catchment. The performances with different input scenarios were compared. Additionally, the joint multifractal spectra (JMS) method was implemented to evaluate the simulation performances during wet and dry seasons. The results show that: (1) LSTM always obtained a higher accuracy than XGBoost and SVR; (2) the impacts of the input variables were different for different machine learning methods, such as antecedent streamflow for XGBoost and rainfall for LSTM; (3) XGBoost showed a relatively high performance during dry seasons, and the classification of wet and dry seasons improved the simulation performance, especially for LSTM during dry seasons; (4) the JMS analysis indicated the advantages of a hybrid model combined with LSTM trained with wet-season data and XGBoost trained with dry-season data. Full article
(This article belongs to the Special Issue Advances in Streamflow and Flood Forecasting)
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23 pages, 2278 KiB  
Article
Forecasting the Ensemble Hydrograph of the Reservoir Inflow based on Post-Processed TIGGE Precipitation Forecasts in a Coupled Atmospheric-Hydrological System
by Mitra Tanhapour, Jaber Soltani, Bahram Malekmohammadi, Kamila Hlavcova, Silvia Kohnova, Zora Petrakova and Saeed Lotfi
Water 2023, 15(5), 887; https://doi.org/10.3390/w15050887 - 25 Feb 2023
Cited by 1 | Viewed by 1983
Abstract
The quality of precipitation forecasting is critical for more accurate hydrological forecasts, especially flood forecasting. The use of numerical weather prediction (NWP) models has attracted much attention due to their impact on increasing the flood lead time. It is vital to post-process raw [...] Read more.
The quality of precipitation forecasting is critical for more accurate hydrological forecasts, especially flood forecasting. The use of numerical weather prediction (NWP) models has attracted much attention due to their impact on increasing the flood lead time. It is vital to post-process raw precipitation forecasts because of their significant bias when they feed hydrological models. In this research, ensemble precipitation forecasts (EPFs) of three NWP models (National Centers for Environmental Prediction (NCEP), United Kingdom Meteorological Office (UKMO) (Exeter, UK), and Korea Meteorological Administration (KMA) (SEOUL, REPUBLIC OF KOREA)) were investigated for six historical storms leading to heavy floods in the Dez basin, Iran. To post-process EPFs, the raw output of every single NWP model was corrected using regression models. Then, two proposed models, the Group Method of Data Handling (GMDH) deep learning model and the Weighted Average–Weighted Least Square Regression (WA-WLSR) model, were employed to construct a multi-model ensemble (MME) system. The ensemble reservoir inflow was simulated using the HBV hydrological model under the two modeling approaches involving deterministic forecasts (simulation using observed precipitation data as input) and ensemble forecasts (simulation using post-processed EPFs as input). The results demonstrated that both GMDH and WA-WLSR models had a positive impact on improving the forecast skill of the NWP models, but more accurate results were obtained by the WA-WLSR model. Ensemble forecasts outperformed coupled atmospheric–hydrological modeling in comparison with deterministic forecasts to simulate inflow hydrographs. Our proposed approach lends itself to quantifying uncertainty of ensemble forecasts in hydrometeorological the models, making it possible to have more reliable strategies for extreme-weather event management. Full article
(This article belongs to the Special Issue Advances in Streamflow and Flood Forecasting)
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