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Water
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Advances in Water Use Efficiency in a Changing Environment
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Advances in Water Use Efficiency in a Changing Environment

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 11179

Special Issue Editors

Dr. Haibo Wang

E-Mail Website
Guest Editor
Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: terrestrial carbon cycle; carbon and water fluxes; remote sensing of vegetation; eddy covariance; ecological modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xufeng Wang

E-Mail Website
Guest Editor
Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: carbon cycle; vegetation phenology; remote sensing; carbon fluxes observation
Special Issues, Collections and Topics in MDPI journals
Dr. Yi Song

E-Mail Website
Guest Editor
State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
Interests: remote sensing estimation of evapotranspiration; hydro-thermal coupling simulation; vegetation change

Special Issue Information

Dear Colleagues,

For decades now, terrestrial ecosystems have been experiencing significant changes such as land degradation, deforestation and drought due to the impacts of global climate change and anthropogenic activities. Investigating the spatio-temporal dynamics of ecological processes at different spatio-temporal scales is of great importance in a changing environment. Water use efficiency (WUE) is an important characteristic of ecosystem function that refers to the connections between carbon cycles and water cycles. Clarifying the underlying mechanisms of WUE patterns is vital to the global water cycle.

The main goal of this Special Issue is to report the recent advances on the patterns and processes of WUE at different spatial and temporal scales in a changing environment. Submissions will address one or more of the following topics: carbon and water flux measurements, modelling of water and carbon fluxes, remote sensing estimation approach, land use and land cover change, and extreme climate events and their impacts. Other closely related topics are also welcome.

Dr. Haibo Wang
Dr. Xufeng Wang
Dr. Yi Song
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

  • evapotranspiration
  • water use efficiency
  • carbon and water fluxes
  • climate change
  • land use and land cover
  • extreme climate
  • arid and semi-arid areas
  • remote sensing

Published Papers (3 papers)

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Research

21 pages, 4267 KiB  
Article
Meteorological Data Fusion Approach for Modeling Crop Water Productivity Based on Ensemble Machine Learning
by Ahmed Elbeltagi, Aman Srivastava, Nand Lal Kushwaha, Csaba Juhász, János Tamás and Attila Nagy
Water 2023, 15(1), 30; https://doi.org/10.3390/w15010030 - 22 Dec 2022
Cited by 9 | Viewed by 2358
Abstract
Crop water productivity modeling is an increasingly popular rapid decision making tool to optimize water resource management in agriculture for the decision makers. This work aimed to model, predict, and simulate the crop water productivity (CWP) for grain yields of both wheat and [...] Read more.
Crop water productivity modeling is an increasingly popular rapid decision making tool to optimize water resource management in agriculture for the decision makers. This work aimed to model, predict, and simulate the crop water productivity (CWP) for grain yields of both wheat and maize. Climate datasets were collected over the period from 1969 to 2019, including: mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (H), solar radiation (SR), sunshine hours (Ssh), wind speed (WS), and day length (DL). Five machine learning (ML) methods were applied, including random forest (RF), support vector regression (SVM), bagged trees (BT), boosted trees (BoT), and matern 5/2 Gaussian process (MG). Models implemented by MG, including Tmean, SR, WS, and DL (Model 3); Tmax, Tmin, Tmean, SR, Ssh, WS, H, and DL (Model 8); Tmean, and SR (Model 9), were found optimal (r2 = 0.85) for forecasting CWP for wheat. Moreover, results of CWP for maize showed that the BT model, a combination of SR, WS, H, and Tmin data, achieved a high correlation coefficient of 0.82 compared to others. The outcomes demonstrated several high performance ML-based alternative CWP estimation methods in case of limited climatic data supporting decision making for designers, developers, and managers of water resources. Full article
(This article belongs to the Special Issue Advances in Water Use Efficiency in a Changing Environment)
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21 pages, 3339 KiB  
Article
Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin
by Huseyin Cagan Kilinc
Water 2022, 14(3), 490; https://doi.org/10.3390/w14030490 - 7 Feb 2022
Cited by 26 | Viewed by 4413
Abstract
Water, a renewable but limited resource, is vital for all living creatures. Increasing demand makes the sustainability of water resources crucial. River flow management, one of the key drivers of sustainability, will be vital to protect communities from the worst impacts on the [...] Read more.
Water, a renewable but limited resource, is vital for all living creatures. Increasing demand makes the sustainability of water resources crucial. River flow management, one of the key drivers of sustainability, will be vital to protect communities from the worst impacts on the environment. Modelling and estimating river flow in the hydrological process is crucial in terms of effective planning, management, and sustainable use of water resources. Therefore, in this study, a hybrid approach integrating long short-term memory networks (LSTM) and particle swarm algorithm (PSO) was proposed. For this purpose, three hydrological stations were utilized in the study along the Orontes River basin, Karasu, Demirköprü, and Samandağ, respectively. The timespan of Demirköprü and Karasu stations in the study was between 2010 and 2019. Samandağ station data were from 2009–2018. The datasets consisted of daily flow values. In order to validate the performance of the model, the first 80% of the data were used for training, and the remaining 20% were used for the testing of the three FMSs. Statistical methods such as linear regression and the more classical model autoregressive integrated moving average (ARIMA) were used during the comparison process to assess the proposed method’s performance and demonstrate its superior predictive ability. The estimation results of the models were evaluated with RMSE, MAE, MAPE, SD, and R2 statistical metrics. The comparison of daily streamflow predictions results revealed that the PSO-LSTM model provided promising accuracy results and presented higher performance compared with the benchmark and linear regression models. Full article
(This article belongs to the Special Issue Advances in Water Use Efficiency in a Changing Environment)
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15 pages, 2493 KiB  
Article
A Hybrid Model for Streamflow Forecasting in the Basin of Euphrates
by Huseyin Cagan Kilinc and Bulent Haznedar
Water 2022, 14(1), 80; https://doi.org/10.3390/w14010080 - 3 Jan 2022
Cited by 38 | Viewed by 3658
Abstract
River flow modeling plays a crucial role in water resource management and ensuring its sustainability. Therefore, in recent years, in addition to the prediction of hydrological processes through modeling, applicable and highly reliable methods have also been used to analyze the sustainability of [...] Read more.
River flow modeling plays a crucial role in water resource management and ensuring its sustainability. Therefore, in recent years, in addition to the prediction of hydrological processes through modeling, applicable and highly reliable methods have also been used to analyze the sustainability of water resources. Artificial neural networks and deep learning-based hybrid models have been used by scientists in river flow predictions. Therefore, in this study, we propose a hybrid approach, integrating long-short-term memory (LSTM) networks and a genetic algorithm (GA) for streamflow forecasting. The performance of the hybrid model and the benchmark model was taken into account using daily flow data. For this purpose, the daily river flow time series of the Beyderesi-Kılayak flow measurement station (FMS) from September 2000 to June 2019 and the data from Yazıköy from December 2000 to June 2018 were used for flow measurements on the Euphrates River in Turkey. To validate the performance of the model, the first 80% of the data were used for training, and the remaining 20% were used for the testing of the two FMSs. Statistical methods such as linear regression was used during the comparison process to assess the proposed method’s performance and to demonstrate its superior predictive ability. The estimation results of the models were evaluated with RMSE, MAE, MAPE, STD and R2 statistical metrics. The comparison of daily streamflow predictions results revealed that the LSTM-GA model provided promising accuracy results and mainly presented higher performance than the benchmark model and the linear regression model. Full article
(This article belongs to the Special Issue Advances in Water Use Efficiency in a Changing Environment)
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