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Hydrology
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Geographic Information Systems (GIS) Techniques and Applications for Sustainable Water...
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Geographic Information Systems (GIS) Techniques and Applications for Sustainable Water Resources Management in Agriculture

A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Water Resources and Risk Management".

Deadline for manuscript submissions: 22 November 2025 | Viewed by 5486

Special Issue Editors

Dr. Iolanda Borzì

E-Mail Website
Guest Editor
Department of Engineering, University of Messina, Villaggio Sant’Agata, 98166 Messina, Italy
Interests: hydrology; water resources management; groundwater; drought; floods; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Beatrice Monteleone

E-Mail Website
Guest Editor
Department of Sciences, Technologies and Society, University School for Advanced Studies of Pavia, 27100 Pavia, Italy
Interests: drought; crop models; crop vulnerability functions; crop water needs; spatial analysis;climate change
Prof. Dr. Hailong Yin

E-Mail Website
Guest Editor
College of Environmental Science and Engineering, Tongji University, Shanghai 200070, China
Interests: hydrologic and water quality modeling; watershed hydrology; machine learning model; environmental modeling with GIS; stormwater management; urban non-point source pollution control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue addresses the critical role of Geographic Information Systems (GIS) in tackling water resource challenges within agriculture. With the growing pressure on freshwater supplies in a climate change context, GIS emerges as a game-changer for hydrologists and water managers.

Articles within this Special Issue will showcase how GIS empowers experts to achieve the following:

  • Map Hydrologic Dynamics: GIS integrates diverse data layers, such as precipitation gauges, well records, soil moisture levels, and remote-sensed data, to create detailed maps of surface and groundwater resources. This spatial understanding empowers hydrologists to model water flow patterns and identify areas of potential scarcity or overexploitation.
  • Optimize Irrigation Networks: By analyzing soil types, crop water needs, and field boundaries within a GIS framework, water managers can design efficient irrigation systems that deliver water precisely where and when needed to minimize water losses and maximize crop yields, particularly in drought-prone regions.
  • Avoid Water Pollution: Integrating water quality data with land use information allows for the identification of potential contamination sources, such as fertilizer runoff. This empowers proactive management practices to safeguard water resources and prevent agricultural pollution.
  • Predict and Mitigate Water Extremes: Advanced GIS models, incorporating historical data and weather forecasts, can predict droughts or floods, thus allowing for the implementation of proactive water management strategies, such as reservoir storage optimization during wet seasons or early warning systems for drought preparedness.

This Special Issue highlights how GIS empowers hydrologists and water managers to address complex water resource issues in agriculture. By harnessing the power of spatial data analysis, stakeholders can ensure the sustainable use of water resources, promote crop productivity, and safeguard the future of agriculture in a water-scarce world. Papers proposing innovative GIS techniques used to address the aforementioned topics and articles dealing with the application of GIS techniques to specific case studies are welcome.

We look forward to receiving your original research articles and reviews.

Dr. Iolanda Borzì
Dr. Beatrice Monteleone
Prof. Dr. Hailong Yin
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. Hydrology is an international peer-reviewed open access monthly 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 1800 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

  • agricultural water mapping
  • sustainable water management
  • crop water needs
  • agricultural water allocation
  • mapping hydrological extremes in agriculture
  • irrigation planning
  • water pollution mapping

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Published Papers (4 papers)

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Research

27 pages, 9650 KiB  
Article
Impact of Spatio-Temporal Variability of Droughts on Streamflow: A Remote-Sensing Approach Integrating Combined Drought Index
by Anoma Srimali, Luminda Gunawardhana, Janaka Bamunawala, Jeewanthi Sirisena and Lalith Rajapakse
Hydrology 2025, 12(6), 142; https://doi.org/10.3390/hydrology12060142 - 7 Jun 2025
Viewed by 767
Abstract
Understanding how spatial drought variability influences streamflow is critical for sustainable water management under changing climate conditions. This study developed a novel Combined Drought Index (CDI) and a method to assess spatial drought impacts on different flow components by integrating remote sensing and [...] Read more.
Understanding how spatial drought variability influences streamflow is critical for sustainable water management under changing climate conditions. This study developed a novel Combined Drought Index (CDI) and a method to assess spatial drought impacts on different flow components by integrating remote sensing and hydrological modelling frameworks with generic applicability. The CDI was constructed using Principal Component Analysis to merge multiple standardized indicators: the Standardized Precipitation Evapotranspiration Index, Temperature Condition Index, Vegetation Condition Index, and Soil Moisture Condition Index. The developed framework was applied to the Giriulla sub-basin of the Maha Oya River Basin, Sri Lanka. The CDI strongly correlated with standardized streamflow with a Pearson correlation coefficient of 0.74 and successfully captured major drought and flood events between 2015 and 2023. A semi-distributed hydrological model was used to simulate streamflow variations across sub-catchments under varying drought conditions. Results show upstream sub-catchments were more sensitive to droughts, with sharper declines in specific discharge. Spatial drought variability had different impacts under high- and low-flow conditions: wetter sub-catchments contributed more during high flows, while resilience during low flows varied with catchment characteristics. This integrated approach provides a valuable framework that can be generically applicable for enhanced drought impact assessments. Full article
(This article belongs to the Special Issue Geographic Information Systems (GIS) Techniques and Applications for Sustainable Water Resources Management in Agriculture)
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17 pages, 6573 KiB  
Article
Balancing Hydrological Sustainability and Heritage Conservation: A Decadal Analysis of Water-Yield Dynamics in the Honghe Hani Rice Terraces
by Linlin Huang, Yunting Lyu, Linxuan Miao and Sen Li
Hydrology 2025, 12(6), 135; https://doi.org/10.3390/hydrology12060135 - 31 May 2025
Viewed by 1046
Abstract
The Honghe Hani Rice Terraces, a UNESCO World Heritage agroecosystem, embody a millennia-old synergy of cultural heritage and ecological resilience, yet face declining water yields amid land-use intensification and climate variability. This study employs the InVEST model and geographic detector analysis to quantify [...] Read more.
The Honghe Hani Rice Terraces, a UNESCO World Heritage agroecosystem, embody a millennia-old synergy of cultural heritage and ecological resilience, yet face declining water yields amid land-use intensification and climate variability. This study employs the InVEST model and geographic detector analysis to quantify water-yield dynamics from 2010 to 2020 and identify their spatial and mechanistic drivers. Annual water yield averaged 558 mm, with cultivated lands contributing 33% of total volume, while built-up areas reached 980 mm per unit in 2018. A 31% decline by 2020, driven by cropland fragmentation and tourism growth, revealed persistent-yield hotspots in forested central-eastern terraces and cold spots in southwestern dryland margins. Land-use pattern accounted for 80–95% of yield variability, exacerbated by temperature interactions. Forests, delivering 68.7 million m3 over the decade, highlight the hydrological significance of traditional landscape mosaics. These findings advocate reforestation in critical recharge zones, terrace restoration to preserve agroecological integrity, and regulated tourism integrating rainwater harvesting to sustain water security and cultural heritage. By blending hydrological modeling with socio-cultural insights, this study provides a scalable framework for safeguarding terraced agroecosystems worldwide, aligning heritage conservation with sustainable development. Full article
(This article belongs to the Special Issue Geographic Information Systems (GIS) Techniques and Applications for Sustainable Water Resources Management in Agriculture)
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22 pages, 3320 KiB  
Article
Modeling Estuarine Algal Bloom Dynamics with Satellite Data and Spectral Index-Based Classification
by Mayya Podsosonnaya, Maria J. Schreider and Sergei Schreider
Hydrology 2025, 12(6), 130; https://doi.org/10.3390/hydrology12060130 - 26 May 2025
Viewed by 1629
Abstract
Macroalgae are an integral part of estuarine primary production; however, their excessive growth may have severe negative impacts on the ecosystem. Although it is generally believed that algal blooms may be caused by a combination of excessive nutrients and temperature, their occurrences are [...] Read more.
Macroalgae are an integral part of estuarine primary production; however, their excessive growth may have severe negative impacts on the ecosystem. Although it is generally believed that algal blooms may be caused by a combination of excessive nutrients and temperature, their occurrences are hard to predict, and quantitative monitoring is a logistical challenge which requires the development of reliable and inexpensive techniques. This can be achieved by implementation of processing algorithms and indices on multi-spectral satellite images. Tuggerah Lakes estuary on the Central Coast of NSW was studied because of the regular occurrences of blooms, primarily of green filamentous algae. The detection of algal blooms based on the red-edge effect of the chlorophyll provided consistent results supported by direct observations. The Floating Algae Index (FAI) was identified as the most accurate index for detecting algal blooms in shallow areas, following a comparative analysis of six commonly used algae detection indices. Logistic regression was implemented where FAI was used as a predictor of two clusters, “bloom” and “non-bloom”. FAI was calculated for multi-spectral satellite images based on pixels of 20 × 20 m, covering the entire area of the Tuggerah Lakes. Seven sample points (pixels) were chosen, and the optimal threshold was found for each pixel to assign it to one of the two clusters. The logistic regression model was trained for each pixel; then the optimal parameters for its coefficients and the optimal classification threshold were obtained by cross-validation based on bootstrapping. Probabilities for classifying clusters as either “bloom” or “non-bloom” were predicted with respect to the optimal threshold. The resulting model can be used to estimate probability of macroalgal blooms in coastal estuaries, allowing quantitative monitoring through time and space. Full article
(This article belongs to the Special Issue Geographic Information Systems (GIS) Techniques and Applications for Sustainable Water Resources Management in Agriculture)
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29 pages, 8879 KiB  
Article
An Integrated Approach to the Regional Estimation of Soil Moisture
by Luis Pastor Sánchez-Fernández, Diego Alberto Flores-Carrillo and Luis Alejandro Sánchez-Pérez
Hydrology 2024, 11(10), 170; https://doi.org/10.3390/hydrology11100170 - 11 Oct 2024
Viewed by 1326
Abstract
Automatic or smart irrigation systems benefit irrigation water management. However, measurement sensor networks in automatic irrigation systems are complex, and maintenance is essential. Regional soil moisture estimation avoids the multiple measurements necessary when deploying an irrigation system. In this sense, a fuzzy estimation [...] Read more.
Automatic or smart irrigation systems benefit irrigation water management. However, measurement sensor networks in automatic irrigation systems are complex, and maintenance is essential. Regional soil moisture estimation avoids the multiple measurements necessary when deploying an irrigation system. In this sense, a fuzzy estimation approach based on decision-making (FEADM) has been used to obtain soil moisture point estimates. However, FEADM requires intelligent weather adjustment based on spatial features (IWeCASF) to perform regional soil moisture estimation. The IWeCASF-FEADM integrated approach for regional soil moisture estimation is developed in this work. IWeCASF provides the inputs for FEADM. FEADM is performed R times; R is the number of checkpoints at which a point estimate is obtained. In this way, regional estimation is achieved when the set of R soil moisture point estimates is completed. Additionally, IWeCASF-FEADM considers the irrigation water records, which are not included in either method individually. This method can detect when the soil moisture is deficient in a region, allowing actions to prevent water stress. This regional estimation reduces an irrigation system’s operational and maintenance complexity. This integrated approach has been tested over several years by comparing the results of regional soil moisture estimation with measurements obtained at many points in the study region. Full article
(This article belongs to the Special Issue Geographic Information Systems (GIS) Techniques and Applications for Sustainable Water Resources Management in Agriculture)
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