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Deep Learning of Climate Change and Extreme Events, Hydrological Processes...
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Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Systems and Global Change".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 5870

Special Issue Editors

Prof. Dr. Hanoch Lavee

E-Mail Website
Guest Editor
Department of Environment, Planning and Sustainability, Bar-Ilan University, Ramat Gan, Israel
Interests: soil erosion; overland flow generation and continuity; hillslope processes; arid & semi-arid geomorphology
Special Issues, Collections and Topics in MDPI journals
Dr. Jinping Liu

E-Mail Website
Guest Editor
1. College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
2. Hydraulics and Geotechnics Section, KU Leuven, Kasteelpark Arenberg 40, BE-3001 Leuven, Belgium
Interests: extreme climatic events; climate change and human health impacts; hydrology modeling; water resources; vegetation remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The impact of climate change, particularly the rise in severe extreme events, on hydrological processes, land use patterns, and ecosystem health changes is a critical area of research for understanding and managing the future of our globe. At the same time, changes in hydrological processes and land use, such as decreasing surface flow, deforestation, and urbanization, can contribute to climate change.

With its ability to process large datasets and identify hidden patterns, deep learning has provided new tools for analyzing complex environmental data and developing predictive models. These tools offer a promising avenue for advancing our potential response to environmental challenges.

This Special Issue aims to bring together researchers from diverse fields to apply deep learning methods in investigating climate change, extreme climate events, and their impact on surface flow response and land use changes to enhance our capacity for predicting their inter-relationships and adaptation and mitigating their adverse effects.

We seek to promote the development of new models and tools that can improve our ability to predict and manage complex interactions between the above-mentioned environmental components.

We welcome submissions that address topics including, but not limited to, the following:

  • Application of deep learning techniques for predicting extreme climate events and their impact on hydrological process response and land use change.
  • Use of deep learning to model the feedback loops between hydrological process response, land use change, and climate dynamics.
  • Development of deep learning-based tools for assessing vulnerability and resilience of surface flow processes and land systems to climate change.
  • Integration of remote sensing data with deep learning to monitor surface flow and land use changes under extreme climate conditions.
  • Deep learning approaches for optimizing hydrological structures, land use planning, and climate adaptation strategies.
  • Deep learning enhances the understanding of ecosystem services in the context of climate change, hydrological process response, and land use change.
  • Case studies demonstrating the effectiveness of deep learning in managing hydrological structures and land use in the face of extreme climate events.
  • Ethical considerations and challenges in applying deep learning to environmental research and decision making.

Prof. Dr. Hanoch Lavee
Dr. Jinping Liu
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. Land 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 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

  • deep learning techniques
  • climate change impact
  • extreme event forecasting
  • hydrological data analysis
  • land use change monitoring
  • predictive environmental modeling
  • remote sensing applications
  • disaster risk assessment
  • water resource optimization
  • ecosystem service evaluation
  • sustainable development strategies
  • environmental decision support

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (8 papers)

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Research

31 pages, 9739 KiB  
Article
Spatiotemporal Relationship Between Carbon Metabolism and Ecosystem Service Value in the Rural Production–Living–Ecological Space of Northeast China’s Black Soil Region: A Case Study of Bin County
by Yajie Shang, Yuanyuan Chen, Yalin Zhai and Lei Wang
Land 2025, 14(1), 199; https://doi.org/10.3390/land14010199 - 19 Jan 2025
Viewed by 669
Abstract
Amid global climate challenges and an urgent need for ecological protection, the northeastern black soil region—one of the world’s remaining “three major black soil regions”—confronts significant tensions between agricultural economic development and land ecological protection, threatening national food security. Based on the “production–ecology–life” [...] Read more.
Amid global climate challenges and an urgent need for ecological protection, the northeastern black soil region—one of the world’s remaining “three major black soil regions”—confronts significant tensions between agricultural economic development and land ecological protection, threatening national food security. Based on the “production–ecology–life” (PLE) classification system, this study established a dual-dimensional evaluation for carbon metabolism and ESV in horizontal and vertical dimensions. The horizontal flow of carbon and ESV was traced across different ecosystems, while the spatial and temporal dynamics of carbon metabolism and ESV were analyzed vertically. Spatial autocorrelation analyses were employed to examine the interaction patterns between carbon metabolism and ESV. The findings reveal that (1) cropland production space remains the dominant spatial type, exhibiting fluctuating patterns in the size of other spatial types, with a notable reduction in water ecological space. (2) From 2000 to 2020, high-value carbon metabolism density areas were primarily concentrated in the central region, while low-value areas gradually decreased in size. Cropland production space and urban living space served as key compartments and dominant pathways for carbon flow transfer in the two periods, respectively. (3) The total ecosystem service value (ESV) showed a downward trend, decreasing by CNY 1.432 billion from 2000 to 2020. The spatial distribution pattern indicates high values in the center and northwest, contrasting with lower values in the southeast. The flow of ecological value from forest ecological space to cropland production space represents the main loss pathway. (4) A significant negative correlation exists between carbon metabolism density and ESV, with areas of high correlation predominantly centered around cropland production space. This study provides a scientific foundation for addressing the challenges facing the black soil region, achieving synergistic resource use in pursuit of carbon neutrality, and constructing a more low-carbon and sustainable spatial pattern. Full article
(This article belongs to the Special Issue Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships)
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34 pages, 18805 KiB  
Article
Artificial-Intelligence-Based Investigation on Land Use and Land Cover (LULC) Changes in Response to Population Growth in South Punjab, Pakistan
by Tanweer Abbas, Muhammad Shoaib, Raffaele Albano, Muhammad Azhar Inam Baig, Irfan Ali, Hafiz Umar Farid and Muhammad Usman Ali
Land 2025, 14(1), 154; https://doi.org/10.3390/land14010154 - 13 Jan 2025
Viewed by 540
Abstract
Land use and land cover (LULC) changes are significantly impacting the natural environment. Human activities and population growth are negatively impacting the natural environment. This negative impact directly relates to climate change, sustainable agriculture, inflation, and food security at local and global levels. [...] Read more.
Land use and land cover (LULC) changes are significantly impacting the natural environment. Human activities and population growth are negatively impacting the natural environment. This negative impact directly relates to climate change, sustainable agriculture, inflation, and food security at local and global levels. Remote sensing and GIS tools can provide valuable information about change detection. This study examines the correlation between population growth rate and LULC dynamics in three districts of South Punjab, Pakistan—Multan, Bahawalpur, and Dera Ghazi Khan—over a 30-year period from 2003 to 2033. Landsat 7, Landsat 8, and Sentinel-2 satellite imagery within the Google Earth Engine (GEE) cloud platform was utilized to create 2003, 2013, and 2023 LULC maps via supervised classification with a random forest (RF) classifier, which is a subset of artificial intelligence (AI). This study achieved over 90% overall accuracy and a kappa value of 0.9 for the classified LULC maps. LULC was classified into built-up, vegetation, water, and barren classes in Multan and Bahawalpur, with an additional “rock” class included for Dera Ghazi Khan due to its unique topography. LULC maps (2003, 2013, and 2023) were prepared and validated using Google Earth Engine. Future predictions for 2033 were generated using the MOLUSCE model in QGIS. The results for Multan indicated substantial urban expansion as built-up areas increased from 8.36% in 2003 to 25.56% in 2033, with vegetation and barren areas displaying decreasing trends from 82.96% to 70% and 7.95% to 3.5%, respectively. Moreover, areas containing water fluctuated and ultimately changed from 0.73% in 2003 to 0.9% in 2033. In Bahawalpur, built-up areas grew from 1.33% in 2003 to 5.80% in 2033, while barren areas decreased from 79.13% to 74.31%. Dera Ghazi Khan expressed significant increases in built-up and vegetation areas from 2003 to 2033 as 2.29% to 12.21% and 22.53% to 44.72%, respectively, alongside reductions in barren and rock areas from 32.82% to 10.83% and 41.23% to 31.2%, respectively. Population projections using a compound growth model for each district emphasize the demographic impact on LULC changes. These results and findings focus on the need for policies to manage unplanned urban sprawl and focus on environmentally sustainable practices. This study provides critical awareness to policy makers and urban planners aiming to balance urban growth with environmental sustainability. Full article
(This article belongs to the Special Issue Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships)
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32 pages, 27272 KiB  
Article
Enhancing Drought Forecast Accuracy Through Informer Model Optimization
by Jieru Wei, Wensheng Tang, Pakorn Ditthakit, Jiandong Shang, Hengliang Guo, Bei Zhao, Gang Wu and Yang Guo
Land 2025, 14(1), 126; https://doi.org/10.3390/land14010126 - 9 Jan 2025
Viewed by 541
Abstract
As droughts become more frequent due to climate change and shifts in land use, enhancing the accuracy of drought prediction is becoming crucial for informed land and water resource management. This study employed the Informer model to forecast drought and conducted a comparative [...] Read more.
As droughts become more frequent due to climate change and shifts in land use, enhancing the accuracy of drought prediction is becoming crucial for informed land and water resource management. This study employed the Informer model to forecast drought and conducted a comparative analysis with Autoregressive Integrated Moving Average (ARIMA), long short-term memory (LSTM), and Convolutional Neural Network (CNN) models. The findings indicate that the Informer model outperforms the other three models in terms of drought forecasting accuracy across all time scales. Nevertheless, the predictive capacity of the Informer model remains suboptimal when it comes to short-term intervals. Aiming at the problem of drought forecasting accuracy in a short time scale, this study proposed a drought forecasting model named VMD-JAYA-Informer based on Variational Mode Decomposition (VMD) and the JAVA optimization algorithm to improve the Informer model. This study conducted a comparative analysis of VMD-JAYA-ARIMA, VMD-JAYA-LSTM, VMD-JAYA-CNN, and VMD-JAYA-Informer drought prediction models. The performance of these models was evaluated using the root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), and Mean Absolute Error (MAE). The VMD-JAYA-Informer model’s forecast for the 1-month SPEI significantly surpasses that of alternative models and demonstrates a robust agreement with the actual data. Simultaneously, the model exhibits equally optimal forecasting performance across different time scales. In order to validate the VMD-JAYA-Informer model, four meteorological stations in the Songliao River Basin were chosen at random. The validation results demonstrate that VMD-JAYA-Informer outperforms the Informer model in terms of prediction accuracy on the 1-month time scale (NSE values of 0.8663, 0.8765, 0.8822, and 0.8416, respectively). Additionally, the model outperforms Informer in terms of prediction performance on other time scales, further demonstrating its generalizability and excellence in drought prediction on shorter time scales. Full article
(This article belongs to the Special Issue Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships)
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19 pages, 2360 KiB  
Article
Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices
by Caixia Hu, Jie Li, Yaxu Pang, Lan Luo, Fang Liu, Wenhao Wu, Yan Xu, Houyu Li, Bingcang Tan and Guilong Zhang
Land 2025, 14(1), 69; https://doi.org/10.3390/land14010069 - 2 Jan 2025
Viewed by 459
Abstract
Nitrate leaching from soil presents a significant threat to soil health, as it can result in nutrient loss, soil acidification, and structural damage. It is crucial to quantify the spatial heterogeneity of nitrate leaching and its drivers. A total of 509 observational data [...] Read more.
Nitrate leaching from soil presents a significant threat to soil health, as it can result in nutrient loss, soil acidification, and structural damage. It is crucial to quantify the spatial heterogeneity of nitrate leaching and its drivers. A total of 509 observational data points regarding nitrate leaching in northern China were collected, capturing the spatial and temporal variations across crops such as winter wheat, maize, and greenhouse vegetables. A machine learning (ML) model for predicting nitrate leaching was then developed, with the random forest (RF) model outperforming the support vector machine (SVM), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) models, achieving an R2 of 0.75. However, the performance improved significantly after integrating the four models with Bayesian optimization (all models had R2 > 0.56), which realized quantitative prediction capabilities for nitrate leaching loss concentrations. Moreover, the XGBoost model exhibited the highest fitting accuracy and the smallest error in estimating nitrate leaching losses, with an R2 value of 0.79 and an average absolute error (MAE) of 3.87 kg/ha. Analyses of the feature importance and SHAP values in the optimal XGBoost model identified soil organic matter, chemical nitrogen fertilizer input, and water input (including rainfall and irrigation) as the main indicators of nitrate leaching loss. The ML-based modeling method developed overcomes the difficulty of the determination of the functional relationship between nitrate loss intensity and its influencing factors, providing a data-driven solution for estimating nitrate–nitrogen loss in farmlands in North China and strengthening sustainable agricultural practices. Full article
(This article belongs to the Special Issue Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships)
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20 pages, 1554 KiB  
Article
How Does Agricultural Land Lease Policy Affect Agricultural Carbon Emission? Evidence of Carbon Reduction Through Decreasing Transaction Costs in the Context of Heterogeneous Efficiency
by Shuokai Wang, Bo Zeng, Yong Feng and Fangping Cao
Land 2024, 13(12), 2192; https://doi.org/10.3390/land13122192 - 15 Dec 2024
Viewed by 679
Abstract
Given the increasing environmental pressures, it is essential that agriculture achieves the goal of sustainable and low-carbon development. In 2010, China, as the top carbon emitter, introduced a policy on agricultural land lease (ALL), which has been met with considerable approval from farmers [...] Read more.
Given the increasing environmental pressures, it is essential that agriculture achieves the goal of sustainable and low-carbon development. In 2010, China, as the top carbon emitter, introduced a policy on agricultural land lease (ALL), which has been met with considerable approval from farmers and has resulted in a notable surge in the rate of ALL within the country. Nevertheless, the question of how the ALL policy affects agricultural carbon emissions (ACEs) remains unanswered. What are the transmission mechanisms? To answer these questions, this paper presents an equilibrium model that accounts for the heterogeneous production efficiency among farmers. It offers a theoretical analysis of the impact of ALL policy on agricultural carbon emission reduction (ACER) and presents an empirical test of this impact using a difference-in-differences (DID) model. Our research shows that the ALL policy gives impetus to ACER. This conclusion persists even after conducting the robustness and endogeneity tests. The mechanism posits that the policy achieves ACER through reducing the proportion of rural agricultural employees. Heterogeneity analysis indicates that the policy effect is significant in both the northern and southern regions of China. Nonetheless, the effect is only observable in economically developed areas, regions with high chemical fertilizer application rates, and areas with restricted agricultural progress. This study elucidates the connection between land transfer and agricultural carbon emissions, offering empirical evidence to support the advancement of green and low-carbon agricultural development. Full article
(This article belongs to the Special Issue Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships)
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26 pages, 24865 KiB  
Article
Exploring the Impact of Climate Variables and Scenario Simulation on Ecosystem Service Value Profits and Losses in China
by Dahai Yu and Chang You
Land 2024, 13(11), 1924; https://doi.org/10.3390/land13111924 - 15 Nov 2024
Viewed by 523
Abstract
Ecosystem restoration can yield multiple benefits, and the quantitative accounting of ecosystem service value (ESV) profits and losses is of significant importance to the economic benefits of ecosystem restoration. This study reveals the dynamic impacts of climate change on ESVs by analyzing the [...] Read more.
Ecosystem restoration can yield multiple benefits, and the quantitative accounting of ecosystem service value (ESV) profits and losses is of significant importance to the economic benefits of ecosystem restoration. This study reveals the dynamic impacts of climate change on ESVs by analyzing the effects of climate variables on ESV profits and losses across different periods and scenarios. The research findings are as follows: (1) From 1990 to 2020, and extending to simulated projections for 2030, China’s ESV exhibits a high distribution pattern in the southern regions. In 2030, under the natural development scenario (NDS), the southwestern region shows a coexistence of high and low ESVs. Under the ecological protection scenario (EPS), ESV in the southwestern region increases, whereas under the urban development scenario (UDS), ESV in the southwest decreases. (2) In both the NDS and UDS, the trends in ESV profits and losses continue from 2010 to 2020. Under the EPS, there is a significant increase in ESV in the southwestern region. The largest contributors to ESV loss are the conversion of grassland to unused land and forest to farmland. The southwestern region shows the most significant spatial differences in ESV profits and losses, with an increase in ESV profits in the northeastern region. In contrast, other regions show no significant spatial differences in ESV profits and losses. (3) From 1990 to 2000, Bio13 (the precipitation of the wettest month) and Bio12 (annual precipitation) had a significant positive impact on ESV profits and losses, indicating that increased precipitation promotes the functioning of ESVs. This study indicates that fluctuations in precipitation and temperature are significant climate factors influencing the value of ESV. Due to climate change, precipitation patterns and temperature swings are now key determinants of ESV changes. By carefully studying ESV profits and losses and their driving factors, this research can serve as the scientific basis for ecosystem restoration and management strategies. Full article
(This article belongs to the Special Issue Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships)
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16 pages, 9480 KiB  
Article
Assessing Climate and Land-Use Change Scenarios on Future Desertification in Northeast Iran: A Data Mining and Google Earth Engine-Based Approach
by Weibo Yin, Qingfeng Hu, Jinping Liu, Peipei He, Dantong Zhu and Abdolhossein Boali
Land 2024, 13(11), 1802; https://doi.org/10.3390/land13111802 - 31 Oct 2024
Viewed by 748
Abstract
Desertification poses a significant threat to dry and semi-arid regions worldwide, including Northeast Iran. This study investigates the impact of future climate and land-use changes on desertification in this region. Six remote sensing indices were selected to model desertification using four machine learning [...] Read more.
Desertification poses a significant threat to dry and semi-arid regions worldwide, including Northeast Iran. This study investigates the impact of future climate and land-use changes on desertification in this region. Six remote sensing indices were selected to model desertification using four machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Generalized Linear Models (GLM). To enhance the model’s reliability, an ensemble model was employed. Future climate and land-use scenarios were projected using the CNRM-CM6 model and Markov chain analysis, respectively. Results indicate that the RF and SVM models performed best in mapping current desertification patterns. The ensemble model highlights a 2% increase in decertified areas by 2040, primarily in the northwestern regions. The study underscores the importance of land-use change and climate change in driving desertification and emphasizes the need for sustainable land management practices and climate change adaptation strategies to mitigate future impacts. Full article
(This article belongs to the Special Issue Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships)
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22 pages, 13791 KiB  
Article
A Coupled Model for Forecasting Spatiotemporal Variability of Regional Drought in the Mu Us Sandy Land Using a Meta-Heuristic Algorithm
by Changfu Tong, Hongfei Hou, Hexiang Zheng, Ying Wang and Jin Liu
Land 2024, 13(11), 1731; https://doi.org/10.3390/land13111731 - 22 Oct 2024
Viewed by 740
Abstract
Vegetation plays a vital role in terrestrial ecosystems, and droughts driven by rising temperatures pose significant threats to vegetation health. This study investigates the evolution of vegetation drought from 2010 to 2024 and introduces a deep-learning-based forecasting model for analyzing regional spatial and [...] Read more.
Vegetation plays a vital role in terrestrial ecosystems, and droughts driven by rising temperatures pose significant threats to vegetation health. This study investigates the evolution of vegetation drought from 2010 to 2024 and introduces a deep-learning-based forecasting model for analyzing regional spatial and temporal variations in drought. Extensive time-series remote-sensing data were utilized, and we integrated the Temperature–Vegetation Dryness Index (TVDI), Drought Severity Index (DSI), Evaporation Stress Index (ESI), and the Temperature–Vegetation–Precipitation Dryness Index (TVPDI) to develop a comprehensive methodology for extracting regional vegetation drought characteristics. To mitigate the effects of regional drought non-stationarity on predictive accuracy, we propose a coupling-enhancement strategy that combines the Whale Optimization Algorithm (WOA) with the Informer model, enabling more precise forecasting of long-term regional drought variations. Unlike conventional deep-learning models, this approach introduces rapid convergence and global search capabilities, utilizing a sparse self-attention mechanism that improves performance while reducing model complexity. The results demonstrate that: (1) compared to the traditional Transformer model, test accuracy is improved by 43%; (2) the WOA–Informer model efficiently handles multi-objective forecasting for extended time series, achieving MAE (Mean Absolute Error) ≤ 0.05, MSE (Mean Squared Error) ≤ 0.001, MSPE (Mean Squared Percentage Error) ≤ 0.01, and MAPE (Mean Absolute Percentage Error) ≤ 5%. This research provides advanced predictive tools and precise model support for long-term vegetation restoration efforts. Full article
(This article belongs to the Special Issue Deep Learning of Climate Change and Extreme Events, Hydrological Processes and Land Use Dynamics Relationships)
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