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Hydrology and Water Resources in Agriculture and Ecology—2nd Edition
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State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, China
NUST Institute of Civil Engineering (NICE), School of Civil & Environmental Engineering (SCEE), National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan
HydroScience Consultancy, Dunedin 9018, New Zealand
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Hydrology and Water Resources in Agriculture and Ecology—2nd Edition

Abstract submission deadline
31 December 2024
Manuscript submission deadline
31 March 2025
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967

Topic Information

Dear Colleagues,

The agricultural sector uses the largest amount of water, accounting for over 60% of all water consumption worldwide, and this proportion is even higher in arid and semiarid regions. Consequently, agricultural hydrological processes are complicated by the influences of both natural and anthropogenic factors. Moreover, with the increasing water requirements for domestic and industrial use, the availability of water for agriculture and natural ecosystem is decreasing, which is further intensified by climate change. A systemic study on hydrology and water resources in agriculture and ecology will provide a basis for agricultural water security and ecosystem security.

The Volume II of the Topic, Hydrology and Water Resources in Agriculture and Ecology-II, will cover the following fields: water–heat–salt–nutrients transport in the soil–plant–atmosphere continuum (SAPC); agro-hydrological modeling; eco-hydrology; evapotranspiration modeling in cropland and irrigation district scales; agricultural drought assessment; water-salt balance and non-point source contamination modeling in an irrigation district; interaction between water-salt balance and crop yield; high-efficient use of water resources for agriculture; interactions among water, agriculture, and natural ecosystems; impact of climate change on agricultural hydrology and crop yield; and remote sensing application in agricultural and ecological hydrology.

We look forward to your contributions.

Dr. Songhao Shang
Prof. Dr. Hamza Gabriel
Dr. Magdy Mohssen
Topic Editors

Keywords

  • agricultural hydrology
  • eco-hydrology
  • agricultural water use
  • agro-hydrological modeling
  • irrigation district
  • water and salt balance
  • non-point source contamination
  • climate change
  • remote sensing

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Agronomy
agronomy 		3.7 5.2 2011 15.8 Days CHF 2600 Submit
Hydrology
hydrology 		3.2 4.1 2014 17.8 Days CHF 1800 Submit
Remote Sensing
remotesensing 		5.0 7.9 2009 23 Days CHF 2700 Submit
Sustainability
sustainability 		3.9 5.8 2009 18.8 Days CHF 2400 Submit
Water
water 		3.4 5.5 2009 16.5 Days CHF 2600 Submit

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Published Papers (1 paper)

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Article
Study on Real-Time Water Demand Prediction of Winter Wheat–Summer Corn Based on Convolutional Neural Network–Informer Combined Modeling
by Jianqin Ma, Yijian Chen, Xiuping Hao, Bifeng Cui and Jiangshan Yang
Sustainability 2024, 16(9), 3699; https://doi.org/10.3390/su16093699 - 28 Apr 2024
Viewed by 371
Abstract
The accurate prediction of crops’ water requirements is an important reference for real-time irrigation decisions on farmland. In order to achieve precise control of irrigation and improve irrigation water utilization, a real-time crop water requirement prediction model combining convolutional neural networks (CNNs) and [...] Read more.
The accurate prediction of crops’ water requirements is an important reference for real-time irrigation decisions on farmland. In order to achieve precise control of irrigation and improve irrigation water utilization, a real-time crop water requirement prediction model combining convolutional neural networks (CNNs) and the Informer model is presented in this paper, taking the real-time water demand of winter wheat–summer maize from 2017 to 2021 as the research object. The CNN model was used to extract the depth features of the day-by-day meteorological data of the crops, and the extracted feature values were inputted into the Informer model according to the time series for training and prediction to obtain the predicted water demand of winter wheat and summer maize. The results showed that the prediction accuracy of the constructed CNN–Informer combination model was higher compared to CNN, BP, and LSTM models, with an improvement of 1.2%, 25.1%, and 21.9% for winter wheat and 0.4%, 37.4%, and 20.3% for summer maize; based on the good performance of the model in capturing the long-term dependency relationship, the irrigation analysis using the model prediction data showed a significant water-saving effect compared with the traditional irrigation mode, with an average annual water saving of about 1004.3 m3/hm2, or 18.4%, which verified the validity of the model, and it can provide a basis for the prediction of crops’ water demand and sustainable agricultural development. Full article
(This article belongs to the Topic Hydrology and Water Resources in Agriculture and Ecology—2nd Edition)
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