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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
closed (31 December 2024)
Manuscript submission deadline
closed (31 March 2025)
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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 		3.4 6.7 2011 17.2 Days CHF 2600
Hydrology
hydrology 		3.2 5.9 2014 15.7 Days CHF 1800
Remote Sensing
remotesensing 		4.1 8.6 2009 24.9 Days CHF 2700
Sustainability
sustainability 		3.3 7.7 2009 19.3 Days CHF 2400
Water
water 		3.0 6.0 2009 19.1 Days CHF 2600

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Published Papers (3 papers)

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22 pages, 3097 KiB  
Article
Triple Collocation-Based Model Error Estimation of VIC-Simulated Soil Moisture at Spatial and Temporal Scales in the Continental United States in 2010–2020
by Yize Li, Jianzhong Lu, Pingping Huang, Xiaoling Chen, Heping Jin, Qiang Zhu and Huiheng Luo
Water 2024, 16(21), 3049; https://doi.org/10.3390/w16213049 - 24 Oct 2024
Viewed by 1251
Abstract
The model error is a direct reflection of the accuracy of the model simulation. However, it is challenging to estimate the model error due to the presence of numerous uncertainties inherent to the atmospheric and soil data, as well as the structure and [...] Read more.
The model error is a direct reflection of the accuracy of the model simulation. However, it is challenging to estimate the model error due to the presence of numerous uncertainties inherent to the atmospheric and soil data, as well as the structure and parameters of the model itself. This paper addresses the fundamental issue of error estimation in the simulation of soil moisture by the Variable Infiltration Capacity (VIC) model, with a particular focus on the continental United States from 2010 to 2020. The paper develops a model error estimation method based on the Triple Collocation (TC) error estimation and in situ data validation of the VIC model at different temporal and spatial scales. Furthermore, it addresses the issue of failing to consider the variability of temporal and spatial scales in model error estimations. Furthermore, it generates the standard product data on soil moisture simulation errors for the VIC model in the continental United States. The mean of the simulation error variance of the VIC model, estimated using the TC method for spatially scaled soil moisture in the continental United States, is found to be 0.0045 (m3/m3)2, with a median value of 0.0042 (m3/m3)2. The mean time-scale error variance of the VIC model, validated using ground station data, is 0.0096 (m3/m3)2, with a median value of 0.0078 (m3/m3)2. Concurrently, the paper employs Köppen climate classification and land cover data as supplementary data, conducting a comprehensive investigation and analysis of the characteristics and alterations of the VIC model error in the study area from both temporal and spatial perspectives. The findings indicate a proclivity for reduced error rates during the summer months and elevated rates during the winter, with lower rates observed in the western region and higher rates in the eastern region. The objective of this study is twofold: firstly, to conduct a quantitative assessment and analysis of the VIC model’s simulation capabilities; secondly, to validate the accuracy and quality of the soil moisture products simulated by the model. The accurate estimation of model errors is a fundamental prerequisite for the numerical simulation and data assimilation of models, which has a vast range of applications in numerical meteorological and hydrological forecasting, natural environment monitoring, and other fields. Full article
(This article belongs to the Topic Hydrology and Water Resources in Agriculture and Ecology—2nd Edition)
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19 pages, 6652 KiB  
Article
Response of the Normalized Difference Vegetation Index (NDVI) to Snow Cover Changes on the Qinghai–Tibet Plateau
by Yuantao Zhou, Fenggui Liu, Guoming Zhang and Jing’ai Wang
Remote Sens. 2024, 16(12), 2140; https://doi.org/10.3390/rs16122140 - 13 Jun 2024
Cited by 2 | Viewed by 1685
Abstract
The eco-hydrological process related to vegetation on the Qinghai–Tibet Plateau is special, and the impact of snow cover on the growth of vegetation is unique and important. In this study, we analyzed the multi-year variations in the normalized difference vegetation index (NDVI) and [...] Read more.
The eco-hydrological process related to vegetation on the Qinghai–Tibet Plateau is special, and the impact of snow cover on the growth of vegetation is unique and important. In this study, we analyzed the multi-year variations in the normalized difference vegetation index (NDVI) and snow cover on the Qinghai–Tibet Plateau from spatial and temporal perspectives and determined the relationship between the changes in the NDVI and snow cover. Results showed that in the last 40 years, the rate of change in the snow depth on the plateau was −0.016 mm/a, and the NDVI changed by 0.0005/a. The correlations (|R| values) between the different factors and the NDVI followed the order of precipitation (0.77) > snow depth (0.76) > temperature (0.67) > solar radiation (0.21). The responses of the NDVI to changes in meteorological elements were synchronous, whereas the opposite was found for the snow cover. The snow cover had more significant impacts on vegetation at higher elevations. The NDVI had a lag of about 2 months from the onset of the snow cover, and heavy snow events had negative impacts on the NDVI for more than 3 years. Our findings will facilitate studies of ecological vulnerability and the predictions of changes in vegetation on the plateau. Full article
(This article belongs to the Topic Hydrology and Water Resources in Agriculture and Ecology—2nd Edition)
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14 pages, 2789 KiB  
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
Cited by 1 | Viewed by 1765
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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