Managing Water Resources and Socio-Hydrologic Systems: New Understanding and Solutions

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

Deadline for manuscript submissions: 25 September 2024 | Viewed by 282

Special Issue Editors


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Guest Editor
School of Water Resources and Hydropower, Xi’an University of Technology, Xi’an 710048, China
Interests: watershed hydrological modeling; hydrological model; ecohydrological modeling; socio-hydrological modeling; ecohydrology
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor Assistant
School of Water Resources and Hydropower, Xi’an University of Technology, Xi’an 710048, China
Interests: hydrological modeling; stormwater management; watershed hydrology; integrated water resources management; geological hazard

Special Issue Information

Dear Colleagues,

As the increase of the impact of global climate change and human activities, the sustainable management of water resources has become a challenge in many river basins all over the world. The third IAHS Scientific Decade will be dedicated to local solutions under the global water crisis. The short name will be HELPING, and stand for Hydrology Engaging Local People IN one Global world. So Managing Water Resources and Socio-Hydrologic Systems will be an urgent topic for hydrologists, scientists, and decision-makers. Based on the progress of study on water resources management and socio-hydrologic systems, new understanding and solutions are vital for the sustainable management of water resources and socio-hydrologic systems to meet the challenge of the local water crisis.

We invite original research articles that contribute to new understanding and solutions for managing water resources and socio-hydrologic systems on the watershed scale or regional scale. Among the topics of interest for this Special Issue are:

  • new understanding of managing water resources
  • local solutions for water resources management at the watershed scale or regional scale
  • new understanding of socio-hydrologic systems
  • new understanding of interactions of social process and hydrologic process
  • new solutions to simulate socio-hydrologic processes
  • new solutions to predict the evolution of socio-hydrologic systems

Prof. Dr. Dengfeng Liu
Guest Editor

Dr. Yuanyuan Yang
Guest Editor Assistant

Manuscript Submission Information

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Keywords

  • water resources
  • socio-hydrology
  • socio-hydrologic systems
  • socio-hydrological model
  • socio-ecohydrological process
  • trade-off
  • watershed scale

Published Papers (1 paper)

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Research

17 pages, 2943 KiB  
Article
Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition
by Yuanyuan Yang, Weiyan Li and Dengfeng Liu
Water 2024, 16(11), 1552; https://doi.org/10.3390/w16111552 (registering DOI) - 28 May 2024
Viewed by 77
Abstract
Neural networks have become widely employed in streamflow forecasting due to their ability to capture complex hydrological processes and provide accurate predictions. In this study, we propose a framework for monthly runoff prediction using antecedent monthly runoff, water level, and precipitation. This framework [...] Read more.
Neural networks have become widely employed in streamflow forecasting due to their ability to capture complex hydrological processes and provide accurate predictions. In this study, we propose a framework for monthly runoff prediction using antecedent monthly runoff, water level, and precipitation. This framework integrates the discrete wavelet transform (DWT) for denoising, variational modal decomposition (VMD) for sub-sequence extraction, and gated recurrent unit (GRU) networks for modeling individual sub-sequences. Our findings demonstrate that the DWT–VMD–GRU model, utilizing runoff and rainfall time series as inputs, outperforms other models such as GRU, long short-term memory (LSTM), DWT–GRU, and DWT–LSTM, consistently exhibiting superior performance across various evaluation metrics. During the testing phase, the DWT–VMD–GRU model yielded RMSE, MAE, MAPE, NSE, and KGE values of 245.5 m3/s, 200.5 m3/s, 0.033, 0.997, and 0.978, respectively. Additionally, optimal sliding window durations for different input combinations typically range from 1 to 3 months, with the DWT–VMD–GRU model (using runoff and rainfall) achieving optimal performance with a one-month sliding window. The model’s superior accuracy enhances water resource management, flood control, and reservoir operation, supporting better-informed decisions and efficient resource allocation. Full article
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