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Exploring Progress in Agricultural Water Management under Changing Environments: Monitoring, Modelling, Performances, and Optimization with Applications

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: closed (26 July 2024) | Viewed by 1653

Special Issue Editors

Dr. Chongfeng Ren

E-Mail Website
Guest Editor
School of Water and Environment Department of Hydrology and Water Resources Engineering, Chang’An University, Xi’an, China
Interests: water resources management; water resources carrying capacity; agricultural water productivity; uncertainty; climate change; hydrological modeling
Dr. Jingyuan Xue

E-Mail Website
Guest Editor
Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu, China
Interests: agricultural water management; hydrological modeling; agricultural water produc-tivity; evapotranspiration; agricultural remote sensing; smart irrigation; non-point source pollution; climate change; food–water–environment sustainability

Special Issue Information

Dear Colleagues,

Rapid socioeconomic development coupled with climate change is likely to result in uncertainty in agricultural water use, making water resource management and regulation even more difficult. As a result, it is important to conduct research on water supply to irrigation districts and their operations, crop water requirement, and the balance between water supply and demand, as well as theories and methods for evaluating water resources’ carrying capacity in irrigation districts under a changing environment. In this regard, we welcome scholars to submit their research to this Special Issue entitled “Exploring progress in Agricultural Water Management under Changing Environments: Monitoring, Modelling, Performances, and Optimization with Applications”.

This Special Issue encourages research papers on agricultural water management that include knowledge of water resource optimization, mathematical models, hydrological modeling, agricultural water productivity, and other advanced techniques or approaches. Although preference is given to the fundamental issues, papers focusing on important unconventional or emerging applications of broad interest are also welcome.

Dr. Chongfeng Ren
Dr. Jingyuan Xue
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

  • hydrological modeling
  • water resource optimization
  • cropping pattern change
  • uncertainty
  • dynamic model
  • evap-otranspiration monitoring
  • agricultural water productivity
  • non-point source pollution
  • decision support system
  • decision making in agricultural water management

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Published Papers (2 papers)

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Research

25 pages, 7107 KiB  
Article
Enhancing Hydrological Variable Prediction through Multitask LSTM Models
by Yuguang Yan, Gan Li, Qingliang Li and Jinlong Zhu
Water 2024, 16(15), 2156; https://doi.org/10.3390/w16152156 - 30 Jul 2024
Viewed by 745
Abstract
Deep learning models possess the capacity to accurately forecast various hydrological variables, encompassing flow, temperature, and runoff, notably leveraging Long Short-Term Memory (LSTM) networks to exhibit exceptional performance in capturing long-term dynamics. Nonetheless, these deep learning models often fixate solely on singular predictive [...] Read more.
Deep learning models possess the capacity to accurately forecast various hydrological variables, encompassing flow, temperature, and runoff, notably leveraging Long Short-Term Memory (LSTM) networks to exhibit exceptional performance in capturing long-term dynamics. Nonetheless, these deep learning models often fixate solely on singular predictive tasks, thus overlooking the interdependencies among variables within the hydrological cycle. To address this gap, our study introduces a model that amalgamates Multitask Learning (MTL) and LSTM, harnessing inter-variable information to achieve high-precision forecasting across multiple tasks. We evaluate our proposed model on the global ERA5-Land dataset and juxtapose the results against those of a single-task model predicting a sole variable. Furthermore, experiments explore the impact of task weight allocation on the performance of multitask learning. The results indicate that when there is positive transfer among variables, multitask learning aids in enhancing predictive performance. When jointly forecasting first-layer soil moisture (SM1) and evapotranspiration (ET), the Nash–Sutcliffe Efficiency (NSE) increases by 19.6% and 4.1%, respectively, compared to the single-task baseline model; Kling–Gupta Efficiency (KGE) improves by 8.4% and 6.1%. Additionally, the model exhibits greater forecast stability when confronted with extreme data variations in tropical monsoon regions (AM). In conclusion, our study substantiates the applicability of multitask learning in the realm of hydrological variable prediction. Full article
(This article belongs to the Special Issue Exploring Progress in Agricultural Water Management under Changing Environments: Monitoring, Modelling, Performances, and Optimization with Applications)
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19 pages, 3904 KiB  
Article
The Factors Affecting Stability and Durability of Flow Diversion Simple Weirs in Muchinga Province of Zambia
by Alex Lushikanda Kabwe, Masahiro Hyodo, Hidehiko Ogata, Yoshihiro Sagawa, Yoshinao Adachi and Masayuki Ishii
Water 2024, 16(13), 1852; https://doi.org/10.3390/w16131852 - 28 Jun 2024
Viewed by 618
Abstract
The effectiveness of simple weirs and the ability of rural farmers to construct durable weirs are still areas of concern in rural areas in Zambia. The objective of this study is to investigate the factors contributing to the varying levels of durability of [...] Read more.
The effectiveness of simple weirs and the ability of rural farmers to construct durable weirs are still areas of concern in rural areas in Zambia. The objective of this study is to investigate the factors contributing to the varying levels of durability of the simple weir structures used for the diversion of river flows. This study was conducted in five districts where 33 simple weirs located in similar geographical zones were analyzed for their longevity. The research delved into catchment and climatic variables, as well as the social and psychological perception of simple weirs. This study conducted interviews with key informants who were familiar with the use of simple weirs in Muchinga Provinces between 26 December 2023 and 15 January 2024 using semi-structured questions. The findings of this study indicated that simple weirs constructed on relatively square-shaped catchments and narrow-shaped catchment areas were less vulnerable to damage and easy to operate and maintain. The study also found that climatic factors such as storm rainfall events had little impact on the operation and maintenance of these weirs in Muchinga province, as most sites are in the rainfall shadow while farmers’ views about the structures varied from site to site. Overall, planning is necessary for implementing small or large irrigation structures. Full article
(This article belongs to the Special Issue Exploring Progress in Agricultural Water Management under Changing Environments: Monitoring, Modelling, Performances, and Optimization with Applications)
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