Hydraulic Engineering and Ecohydrology

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

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 3196

Special Issue Editors

College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: ecohydrology; environmental water requirements; watershed management; rugulated rivers and lakes; ecological water diversion
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Guest Editor
Ecohydrology Laboratory, College of Agricultural Sciences, Oregon State University, Corvallis, OR, USA
Interests: ecohydrology; surface water–groundwater interactions; arid land hydrology; riparian systems; restoration

Special Issue Information

Dear Colleagues,

Hydraulic Engineering, especially reservoirs, plays a crucial role in watershed management through flood control, water supply, power generation, navigation, recreation, etc., and significantly contributes to social and economic development. However, hydraulic engineering inevitably affects the riverine, lacustrine, and wetland ecosystems in terms of water quality, aquatic community, and species diversity because of water corridor obstruction and interference with natural hydrological regimes. The environmental impact of hydraulic engineering on ecosystems is affected by the ecohydrological processes and mechanisms that maintain the structure and function of these ecosystems and will vary due to the project scale and operation strategy. Understanding the features, laws, and mechanisms behind the roles of hydraulic engineering in river, lake, and wetland ecosystems is critical to mitigating the adverse effects and improving adaptive management strategies for sustainable watershed development. This Special Issue will provide a platform for research that will clarify the role of hydraulic engineering in river, lake, and wetland ecosystems from an ecological perspective and provide specific implications for stakeholders and governors to enhance adaptive management. We seek theoretical, experimental, methodological, and application studies, and the topics covered by this Special Issue will include but are not limited to the following:

  • Ecohydrological processes and mechanisms driving riverine, lacustrine, and wetland ecosystems;
  • Environmental water requirements of riverine, lacustrine, and wetland ecosystems;
  • Environmental impact assessment of hydraulic engineering on riverine, lacustrine, and wetland ecosystems;
  • Ecohydrological model development for simulating the hydrological and ecological processes of riverine, lacustrine, and wetland ecosystems;
  • Ecological regulation strategies for mitigating the negative environmental impact of hydraulic engineering and sustainable management.

Dr. Feng Huang
Dr. Carlos G. Ochoa
Guest Editors

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Keywords

  • ecohydrological processes and mechanisms
  • environmental water requirements
  • environmental impact assessment
  • ecohydrological simulation
  • ecological regulation

Published Papers (4 papers)

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Research

16 pages, 6074 KiB  
Article
Dynamic Bayesian-Network-Based Approach to Enhance the Performance of Monthly Streamflow Prediction Considering Nonstationarity
by Wen Zhang, Pengcheng Xu, Chunming Liu, Hongyuan Fang, Jianchun Qiu and Changsheng Zhang
Water 2024, 16(7), 1064; https://doi.org/10.3390/w16071064 - 07 Apr 2024
Viewed by 397
Abstract
In recognizing the pervasive nonstationarity of hydrometeorological variables, a paradigm shift towards alternative analytical methodologies is imperative for refining hydroclimatic data modeling and prediction. We introduce a novel approach leveraging nonstationary Graphical Modeling and Bayesian Networks (NGM-BNs) tailored for hydrometeorological applications. Demonstrated through [...] Read more.
In recognizing the pervasive nonstationarity of hydrometeorological variables, a paradigm shift towards alternative analytical methodologies is imperative for refining hydroclimatic data modeling and prediction. We introduce a novel approach leveraging nonstationary Graphical Modeling and Bayesian Networks (NGM-BNs) tailored for hydrometeorological applications. Demonstrated through monthly streamflow forecasting in the Kashgar River Basin of China, our method illuminates the temporal evolution of network relationships, underscoring the dynamism inherent in both input variables and modeling parameters. The key to our approach is identifying the most suitable time horizon (MST) for model updates, which is intricately problem-specific and crucial for peak performance. This methodology not only unveils changing predictor significance across varying flow conditions but also elucidates the fluctuating temporal links between variables, especially under the lens of climate change, for instance, the growing impact of snowmelt on the Kashgar Basin’s streamflow. Compared to stationary counterparts, our nonstationary Bayesian framework excels in capturing extreme events by adeptly accommodating temporal shifts, outperforming traditional models including both stationary and nonstationary variants of Support Vector Regression (SVR) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Full article
(This article belongs to the Special Issue Hydraulic Engineering and Ecohydrology)
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15 pages, 3131 KiB  
Article
A Coupled Parameter Automation Calibration Module for Urban Stormwater Modelling
by Li Gu, Yingying Sun, Cheng Gao and Liangliang She
Water 2024, 16(6), 824; https://doi.org/10.3390/w16060824 - 12 Mar 2024
Viewed by 662
Abstract
In the context of accelerating urbanisation, the issue of urban stormwater flooding security has garnered increasing attention. Further development of urban stormwater management techniques is imperative to achieve more stable, precise, and expeditious simulation outcomes. The calibration of model parameters, which is a [...] Read more.
In the context of accelerating urbanisation, the issue of urban stormwater flooding security has garnered increasing attention. Further development of urban stormwater management techniques is imperative to achieve more stable, precise, and expeditious simulation outcomes. The calibration of model parameters, which is a pivotal phase in stormwater simulation endeavours, is hampered by challenges such as substantial subjectivity, time intensiveness, and low efficiency. Therefore, this study introduces a parameter calibration model coupled with the Non-dominated Sorting Genetic Algorithm III (NSGA-III). This model utilises the Nash–Sutcliffe efficiency (NSE) and peak relative error (PE) values for various rainfall events as objective functions to calibrate and assess the study target. The two rainfalls used for rate determination had NSE values greater than 0.9 and absolute PE values less than 0.17; the rainfall used for validation had NSE values greater than 0.9 and absolute PE values less than 0.27. Thus, the results of the model for the rate determination of the parameters are reliable. In addition, the inverted generation distance and hypervolume values indicate that the iterative process of the algorithm during population evolution demonstrated stable iterative outcomes and ensured sound population quality. Both reach relative stability after 40 iterations. In conclusion, the proposed multi-objective parameter calibration model integrated with NSGA-III offers dependable calibration results and robust computational efficacy, presenting novel avenues and perspectives for urban stormwater model parameter calibration and simulation. Full article
(This article belongs to the Special Issue Hydraulic Engineering and Ecohydrology)
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14 pages, 5459 KiB  
Article
Prediction of Tail Water Level under the Influence of Backwater Effect Based on Deep Learning Models: A Case Study in the Xiangjiaba Hydropower Station
by Sen Zhang, Shuai Xie, Yongqiang Wang, Yang Xu, Zheng Zhang and Benjun Jia
Water 2023, 15(21), 3854; https://doi.org/10.3390/w15213854 - 05 Nov 2023
Viewed by 1046
Abstract
Accurate forecasting of the tail water level (TWL) is of great importance for the safe and economic operation of hydropower stations. The prediction accuracy is significantly influenced by the backwater effect of downstream tributaries and the operation of adjacent hydropower stations, but the [...] Read more.
Accurate forecasting of the tail water level (TWL) is of great importance for the safe and economic operation of hydropower stations. The prediction accuracy is significantly influenced by the backwater effect of downstream tributaries and the operation of adjacent hydropower stations, but the explicit quantification method of the backwater effect is lacking. In this study, a deep-learning-model-based forecasting method for TWL predictions under the backwater effect is developed and applied in the Xiangjiaba (XJB) hydropower station, which is influenced by the backwater effect of downstream tributaries, including the Hengjiang River (HJR) and the Minjiang River (MJR). Firstly, the random forest algorithm was used to analyze the influence of HJR and MJR flows with different lag times on the TWL prediction error of the XJB hydropower station. The results show that the time lags of the backwater effect of HJR and MJR run offs on the TWL of the XJB are 5~7 h and 1~2 h, respectively. Then, the run off thresholds of the HJR and MJR for impacting the TWL of the XJB station are obtained through scenario comparison, and the results show that the run off thresholds of the HJR and the MJR are 700 m3/s and 7000 m3/s, respectively. Finally, based on the analysis of the time lag and the threshold of the backwater effect, a deep learning model (LSTM)-based TWL forecasting method is established and applied to predict the TWL of the XJB station. The results show that the forecasting model has a good predictive performance, with 98.22% of absolute errors less than 20 cm. The mean absolute error over the validation dataset is 5.27 cm, and the maximum absolute error is 63.35 cm. Compared with the LSTM-based prediction model without considering the backwater effect, the mean absolute error decreased by 31%, and the maximum absolute error decreased by 71%. Full article
(This article belongs to the Special Issue Hydraulic Engineering and Ecohydrology)
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15 pages, 14395 KiB  
Article
Differentiating the Effects of Streamflow and Topographic Changes on the Water Level of Dongting Lake, China, Using the LSTM Network and Scenario Analysis
by Jihu Zou, Feng Huang, Feier Yu, Xingzhi Shen, Shuai Han, Zhan Qian and Heng Jiang
Water 2023, 15(21), 3742; https://doi.org/10.3390/w15213742 - 26 Oct 2023
Viewed by 645
Abstract
Dongting Lake is the second largest freshwater lake in China and an internationally critical habitat for migratory birds. However, after 2004, the multiyear mean water levels of West Dongting Lake (WDL), South Dongting Lake (SDL), and East Dongting Lake (EDL) in the high-water [...] Read more.
Dongting Lake is the second largest freshwater lake in China and an internationally critical habitat for migratory birds. However, after 2004, the multiyear mean water levels of West Dongting Lake (WDL), South Dongting Lake (SDL), and East Dongting Lake (EDL) in the high-water stage decreased by 1.05 m, 1.15 m, and 1.32 m, respectively. Different areas of Dongting Lake experienced various degrees of shrinkage. It is necessary to study the dominant driving factors and their contributions to the falling water level. In this study, the water level changes in Dongting Lake were analyzed, and a long short-term memory neural network model was constructed to simulate the water level of Dongting Lake. Moreover, the contribution of changes in streamflow and topographic conditions to the water level changes in different areas of Dongting Lake was estimated with scenario analysis. The research results show that the changes in the streamflow were the main driving factors for the water level decline of WDL, SDL, and EDL in the high-water stage, and their contributions were 0.74 m, 0.97 m, and 1.16 m, respectively. The topographic changes had a great falling effect on the water level of Dongting Lake, and the falling effect on the water levels from October to June of the following year was the strongest in EDL (0.81 m), followed by WDL (0.49 m), and the weakest in SDL (0.3 m). These results can provide a scientific reference for the management of the water resources of Dongting Lake. Full article
(This article belongs to the Special Issue Hydraulic Engineering and Ecohydrology)
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