Hydrology and Water Resources in Agriculture and Ecology—2nd Edition

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

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 (5 papers)

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25 pages, 1882 KiB  

Open AccessArticle

An Assessment of Collector-Drainage Water and Groundwater—An Application of CCME WQI Model

by Nilufar Rajabova, Vafabay Sherimbetov, Rehan Sadiq and Alaa Farouk Aboukila

Water 2025, 17(15), 2191; https://doi.org/10.3390/w17152191 - 23 Jul 2025

Viewed by 285

Abstract

According to Victor Ernest Shelford’s ‘Law of Tolerance,’ organisms within ecosystems thrive optimally when environmental conditions are favorable. Applying this principle to ecosystems and agro-ecosystems facing water scarcity or environmental challenges can significantly enhance their productivity. In these ecosystems, phytocenosis adjusts its conditions [...] Read more.

According to Victor Ernest Shelford’s ‘Law of Tolerance,’ organisms within ecosystems thrive optimally when environmental conditions are favorable. Applying this principle to ecosystems and agro-ecosystems facing water scarcity or environmental challenges can significantly enhance their productivity. In these ecosystems, phytocenosis adjusts its conditions by utilizing water with varying salinity levels. Moreover, establishing optimal drinking water conditions for human populations within an ecosystem can help mitigate future negative succession processes. The purpose of this study is to evaluate the quality of two distinct water sources in the Amudarya district of the Republic of Karakalpakstan, Uzbekistan: collector-drainage water and groundwater at depths of 10 to 25 m. This research is highly relevant in the context of climate change, as improper management of water salinity, particularly in collector-drainage water, may exacerbate soil salinization and degrade drinking water quality. The primary methodology of this study is as follows: The Food and Agriculture Organization of the United Nations (FAO) standard for collector-drainage water is applied, and the water quality index is assessed using the CCME WQI model. The Canadian Council of Ministers of the Environment (CCME) model is adapted to assess groundwater quality using Uzbekistan’s national drinking water quality standards. The results of two years of collected data, i.e., 2021 and 2023, show that the water quality index of collector-drainage water indicates that it has limited potential for use as secondary water for the irrigation of sensitive crops and has been classified as ‘Poor’. As a result, salinity increased by 8.33% by 2023. In contrast, groundwater quality was rated as ‘Fair’ in 2021, showing a slight deterioration by 2023. Moreover, a comparative analysis of CCME WQI values for collector-drainage and groundwater in the region, in conjunction with findings from Ethiopia, India, Iraq, and Turkey, indicates a consistent decline in water quality, primarily due to agriculture and various other anthropogenic pollution sources, underscoring the critical need for sustainable water resource management. This study highlights the need to use organic fertilizers in agriculture to protect drinking water quality, improve crop yields, and promote soil health, while reducing reliance on chemical inputs. Furthermore, adopting WQI models under changing climatic conditions can improve agricultural productivity, enhance groundwater quality, and provide better environmental monitoring systems. Full article

(This article belongs to the Topic Hydrology and Water Resources in Agriculture and Ecology—2nd Edition)

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25 pages, 2721 KiB  

Open AccessArticle

GIS-Based Assessment of Stormwater Harvesting Potentials: A Sustainable Approach to Alleviate Water Scarcity in Rwanda’s Eastern Savanna Agroecological Zone

by Herve Christian Tuyishime and Kyung Sook Choi

Water 2025, 17(14), 2045; https://doi.org/10.3390/w17142045 - 8 Jul 2025

Viewed by 486

Abstract

Water scarcity remains critical in Rwanda’s Eastern Savanna Agroecological Zone due to erratic rainfall, prolonged dry seasons, and rising water demands. This challenge threatens agricultural productivity, food security, and livelihoods. Stormwater harvesting presents a sustainable solution that increases water availability and mitigates the [...] Read more.

Water scarcity remains critical in Rwanda’s Eastern Savanna Agroecological Zone due to erratic rainfall, prolonged dry seasons, and rising water demands. This challenge threatens agricultural productivity, food security, and livelihoods. Stormwater harvesting presents a sustainable solution that increases water availability and mitigates the impacts of climate variability. This study utilizes Geographic Information System (GIS) tools and SCS-CN to assess stormwater harvesting potential in the region. The methodology includes analyzing land use, soil type, rainfall data (30 years, from 1994 to 2023), and topography. Key research steps involve delineating catchment areas, estimating runoff volumes, and selecting optimal storage sites using multi-criteria decision analysis. Findings include eight main water reservoirs, each with a unique code (W_R1 to W_R8), geographic coordinates (X and Y), and 10 million cubic meters storage volumes. W_R1 has the smallest volume at 0.242 × 10⁶ m³, while W_R2 has the largest volume at 8.51 × 10⁶ m³. W_R3, W_R5, and W_R7 are additional noteworthy reservoirs with sizable capacities. The findings contribute to policy formulation and Sustainable Development Goals (SDGs) related to clean water, food security, and climate action. This research provides a replicable framework for addressing water scarcity and enhancing long-term resilience in water-stressed regions. Full article

(This article belongs to the Topic Hydrology and Water Resources in Agriculture and Ecology—2nd Edition)

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22 pages, 3097 KiB  

Open AccessArticle

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

Cited by 1 | Viewed by 1304

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  

Open AccessArticle

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 1776

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  

Open AccessArticle

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 1820

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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