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Search Results (183)

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Keywords = Hydrological Predictions for the Environment

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19 pages, 654 KB  
Article
Optimizing Time Series Models for Forecasting Environmental Variables: A Rainfall Case Study
by Alexander D. Pulido-Rojano, Neyfe Sablón-Cossío, Jhoan Iglesias-Ortega, Sheila Ruiz-Berdugo, Silvia Torres-Cervantes and Josueth Durant-Daza
Water 2025, 17(19), 2863; https://doi.org/10.3390/w17192863 - 1 Oct 2025
Abstract
The application of time series models for forecasting environmental variables such as precipitation is essential for understanding climatic patterns and supporting sustainable urban planning in environments characterized by high or moderate levels of risk. This study aims to evaluate and optimize time series [...] Read more.
The application of time series models for forecasting environmental variables such as precipitation is essential for understanding climatic patterns and supporting sustainable urban planning in environments characterized by high or moderate levels of risk. This study aims to evaluate and optimize time series forecasting models for rainfall prediction in Barranquilla, Colombia. To this end, five models were applied, namely, Simple Moving Average (SMA), Weighted Moving Average (WMA), Exponential Smoothing (ES), and multiplicative and additive Holt–Winters models, using 139 monthly precipitation records from the IDEAM database covering the period 2013–2025. Model accuracy was evaluated using Mean Absolute Error (MAE) and Mean Squared Error (MSE), and nonlinear optimization techniques were applied to estimate smoothing and weighting parameters for improved accuracy. The results showed that optimization significantly enhances model performance, particularly in the multiplicative Holt–Winters model, which achieved the lowest errors, with a minimum MAE of 75.33 mm and an MSE of 9647.07. The comparative analysis with previous studies demonstrated that even simple models can yield substantial improvements when properly optimized. Furthermore, forecasts optimized using MAE were more stable and consistent, whereas those optimized with MSE were more sensitive to extreme variations. Overall, the findings confirm that seasonal models with optimized parameters offer superior predictive capacity, making them valuable tools for hydrological risk management. Full article
(This article belongs to the Section Hydrology)
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26 pages, 4609 KB  
Article
Coupling a Physically Based Hydrological Model with a Modified Transformer for Long-Sequence Runoff and Peak-Flow Prediction
by Yicheng Gu, Bing Yan, Siru Wang, Zhao Cai and Hongwei Liu
Sustainability 2025, 17(19), 8618; https://doi.org/10.3390/su17198618 - 25 Sep 2025
Abstract
Climate change and human activities are intensifying the hydrologic cycle and increasing extreme events, challenging accurate prediction. This study builds on the Transformer architecture by introducing a sliding time window and runoff classification mechanism, enabling high-precision long-term runoff forecasting and significantly improving the [...] Read more.
Climate change and human activities are intensifying the hydrologic cycle and increasing extreme events, challenging accurate prediction. This study builds on the Transformer architecture by introducing a sliding time window and runoff classification mechanism, enabling high-precision long-term runoff forecasting and significantly improving the simulation of extreme floods. However, the generalization ability of data-driven models remains limited in non-stationary environments. To address this issue, we further propose a hybrid framework that couples the process-based GBHM with the enhanced Transformer via bias correction. This fusion leverages the strengths of both models: the process-based model explicitly captures topographic heterogeneity, the spatial distribution of meteorological forcings, and their temporal variability, while the data-driven model excels at uncovering latent relationships among hydrological variables. The results demonstrate that the coupled model significantly outperforms traditional approaches in peak-flow prediction and exhibits superior robustness and generalizability under changing environmental conditions. Full article
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28 pages, 5626 KB  
Review
An Ecogeomorphological Approach to Land-Use Planning and Natural Hazard Risk Mitigation: A Literature Review
by Zhiyi Zhang, Jakub Tyc and Michael Hensel
Land 2025, 14(9), 1911; https://doi.org/10.3390/land14091911 - 19 Sep 2025
Viewed by 405
Abstract
The overarching topic of this article is land-use planning (LUP) for risk mitigation of natural hazards. In this context, landslides are one of the most destructive natural hazards, resulting in significant negative impacts on humans, ecosystems, and environments. This study presents a semi-systematic [...] Read more.
The overarching topic of this article is land-use planning (LUP) for risk mitigation of natural hazards. In this context, landslides are one of the most destructive natural hazards, resulting in significant negative impacts on humans, ecosystems, and environments. This study presents a semi-systematic review of emerging ecogeomorphological principles for LUP to advance the mitigation of landslide risks. By integrating ecological and geomorphological systems, an ecogeomorphological approach offers a novel perspective for tackling landslide risk mitigation. This includes accounting for factors such as water flow accumulation, fractional vegetation cover, and soil erosion, using computational methods, applying artificial intelligence (AI) to process and predict risk, and integrating the internet of things (IoT) to real-time environmental data. We primarily explore the role of ecogeomorphology in fostering sustainable and risk-aware LUP, as well as how landslide research can be applied within LUP to strengthen broader management frameworks. The study reveals much evidence of ecogeomorphological factors in LUP, emphasising the integration of ecology, geomorphology, and hydrology for effective landslide mitigation. With the ongoing shift from traditional to emerging methodologies in risk management, our review addresses the existing research gap by proposing an up-to-date ecogeomorphological framework for practice. Full article
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28 pages, 11202 KB  
Article
Enhancing Streamflow Modeling in Data-Scarce Catchments with Similarity-Guided Source Selection and Transfer Learning
by Yuxuan Gao, Rupal Mandania, Jun Ma, Jack Chen and Wuyi Zhuang
Water 2025, 17(18), 2762; https://doi.org/10.3390/w17182762 - 18 Sep 2025
Viewed by 292
Abstract
Accurate streamflow modeling in data-scarce catchments remains a significant challenge due to the limited availability of historical records. Transfer Learning (TL), increasingly applied in hydrology, leverages knowledge from data-rich catchments (sources) to enhance predictions in data-scarce catchments (targets), providing new possibilities of hydrological [...] Read more.
Accurate streamflow modeling in data-scarce catchments remains a significant challenge due to the limited availability of historical records. Transfer Learning (TL), increasingly applied in hydrology, leverages knowledge from data-rich catchments (sources) to enhance predictions in data-scarce catchments (targets), providing new possibilities of hydrological predictions. Most existing TL approaches pre-train models on large-scale meteoro-hydrological datasets and show good generalizability across multiple target catchments. However, for a specific target catchment, it remains unclear which source catchments contribute most effectively to the accurate prediction. Including many irrelevant sources may even degrade model performance. In this study, we investigated how source catchment selection affects TL performance by employing similarity-guided strategies based on three key factors, i.e., spatial distance, physical attributes, and flow regime characteristics. Using the CAMELS-GB dataset, we conducted comparative experiments by pre-training the networks with different ranked groups of the source catchments and fine-tuning them on three target catchments representing distinct hydrological environments. The results showed that carefully selected small subsets (fewer than 40, or even as few as 10) of highly similar catchments can achieve comparable or better TL performance than using all 668 available source catchments. All three target catchments yielded better NSE results from source catchments with closer spatial proximity and more consistent flow regimes. The TL performance of physical attribute similarity-based selection varied depending on the attribute combinations, with those related to land cover, climate, and soil properties leading to superior performance. These findings highlight the importance of similarity-guided source selection in hydrological TL. In addition, they demonstrate ways to reduce computational costs while improving modeling accuracy in data-scarce regions. Full article
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22 pages, 2331 KB  
Article
Cyanobacterial Bloom in Urban Rivers: Resource Use Efficiency Perspectives for Water Ecological Management
by Qingyu Chai, Yongxin Zhang, Yuxi Zhao and Hongxian Yu
Microorganisms 2025, 13(9), 1981; https://doi.org/10.3390/microorganisms13091981 - 25 Aug 2025
Viewed by 520
Abstract
Cyanobacterial blooms in urban rivers present critical ecological threats worldwide, yet their mechanisms in fluvial systems remain inadequately explored compared to lacustrine environments. This study addresses this gap by investigating bloom dynamics in the eutrophic Majiagou River (Harbin, China) through phytoplankton resource use [...] Read more.
Cyanobacterial blooms in urban rivers present critical ecological threats worldwide, yet their mechanisms in fluvial systems remain inadequately explored compared to lacustrine environments. This study addresses this gap by investigating bloom dynamics in the eutrophic Majiagou River (Harbin, China) through phytoplankton resource use efficiency (RUE), calculated as chlorophyll-a per unit TN/TP. Seasonal sampling (2022–2024) across 25 rural-to-urban sites revealed distinct spatiotemporal patterns: urban sections exhibited 1.9× higher cyanobacterial relative abundance (RAC, peaking at 40.65% in autumn) but 28–30% lower RUE than rural areas. Generalized additive models identified nonlinear RAC–RUE relationships with critical thresholds: in rural sections, RAC peaked at TN-RUE 40–45 and TP-RUE 25–30, whereas urban sections showed lower TN-RUE triggers (20–25) and suppressed dominance above TP-RUE 10. Seasonal extremes drove RUE maxima in summer and minima during freezing/thawing periods. These findings demonstrate that hydrological stagnation (e.g., river mouths) and pulsed nutrient inputs reduce nutrient conversion efficiency while lowering bloom-triggering thresholds under urban eutrophication. The study establishes RUE as a predictive indicator for bloom risk, advocating optimized N/P ratios coupled with flow restoration rather than mere nutrient reduction. This approach provides a science-based framework for sustainable management of urban river ecosystems facing climate and anthropogenic pressures. Full article
(This article belongs to the Section Environmental Microbiology)
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21 pages, 9989 KB  
Article
Machine Learning-Based Comparative Analysis on Direct and Indirect Mapping of Soil Texture Types Through Soil Particle Size Fractions Using Multi-Source Remote Sensing
by Jia Liu, Yingcong Ye, Cui Wang, Songchao Chen, Yameng Jiang, Xi Guo and Yefeng Jiang
Agriculture 2025, 15(13), 1395; https://doi.org/10.3390/agriculture15131395 - 28 Jun 2025
Cited by 2 | Viewed by 2074
Abstract
Soil texture, defined by the proportions of sand, silt, and clay particles in the soil, is one of the most essential physical properties of soil. High-resolution soil texture data can provide critical parameter support for soil hydrological modeling, agricultural production management, and ecosystem [...] Read more.
Soil texture, defined by the proportions of sand, silt, and clay particles in the soil, is one of the most essential physical properties of soil. High-resolution soil texture data can provide critical parameter support for soil hydrological modeling, agricultural production management, and ecosystem assessment. In digital soil mapping, previous studies often predicted the sand, silt, and clay contents in soil and then indirectly calculated soil texture. Currently, approaches that directly map soil texture by classification modeling are gaining increasing attention due to the decreased error from data conversion, but few studies have systematically compared these two methods yet. In this study, we comprehensively assessed the performance of direct and indirect predicting soil texture using four machine learning algorithms (e.g., extreme gradient boosting, random forest, gradient boosting decision tree, and extremely randomized tree) with 190 covariates from the Digital Elevation Model, Sentinel-1/2 satellite images, and classification maps and generated a 10 m resolution soil texture map based on 405 topsoil (0–20 cm) sample data collected in Suichuan County, China. The results showed that compared with indirect predictions, direct predictions improved overall accuracy (OA) by 20.57–44.19% and the Kappa coefficient (Kappa) by 0.220–0.402. Among the models used, the XGB model achieved the highest accuracy (OA: 0.948; Kappa: 0.931) and the lowest uncertainty (confusion index: 0.052). The direct prediction map (nine classes recorded) exhibited more detailed and diverse spatial distribution patterns than the indirect prediction map (six classes recorded), aligning better with the actual environment. Based on accuracy validation and spatial distribution, the performance of the XGB model was best during direct prediction. The Shapley additive explanation from the XGB model revealed that the normalized height and stream power indices were the most significant factors driving the soil texture in the study area. Our results provide a reference for future studies on soil texture mapping using machine learning models. Full article
(This article belongs to the Section Agricultural Soils)
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19 pages, 3735 KB  
Article
Hybrid Hydrological Forecasting Through a Physical Model and a Weather-Informed Transformer Model: A Case Study in Greek Watershed
by Haris Ampas, Ioannis Refanidis and Vasilios Ampas
Appl. Sci. 2025, 15(12), 6679; https://doi.org/10.3390/app15126679 - 13 Jun 2025
Cited by 2 | Viewed by 2191
Abstract
This study explores a hybrid AI framework for streamflow forecasting that integrates physically based hydrological modeling, bias correction, and deep learning. HEC-HMS simulations generate synthetic discharge, which a machine learning-based bias correction model adjusts for irrigation-induced discrepancies—improving the Nash–Sutcliffe Efficiency (NSE) from 0.55 [...] Read more.
This study explores a hybrid AI framework for streamflow forecasting that integrates physically based hydrological modeling, bias correction, and deep learning. HEC-HMS simulations generate synthetic discharge, which a machine learning-based bias correction model adjusts for irrigation-induced discrepancies—improving the Nash–Sutcliffe Efficiency (NSE) from 0.55 to 0.84, the Kling–Gupta Efficiency (KGE) from 0.67 to 0.89, and reducing the RMSE from 1.084 to 0.301 m3/s. The corrected discharge is used as input to a Temporal Fusion Transformer (TFT) trained on hourly meteorological data to predict streamflow at 24-, 48-, and 72-h horizons. In a semi-arid, irrigated basin in Northern Greece, the TFT achieves NSEs of 0.84, 0.78, and 0.71 and RMSEs of 0.301, 0.743, and 0.980 m3/s, respectively. Probabilistic forecasts deliver uncertainty bounds with coverage near nominal levels. In addition, the model’s built-in interpretability reveals temporal and meteorological influences—such as precipitation—that enhance predictive performance. This framework demonstrates the synergistic benefits of combining physically based modeling with state-of-the-art deep learning to support robust, multi-horizon forecasts in irrigation-influenced, data-scarce environments. Full article
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26 pages, 3807 KB  
Article
Evaluation of IMERG Precipitation Product Downscaling Using Nine Machine Learning Algorithms in the Qinghai Lake Basin
by Ke Lei, Lele Zhang and Liming Gao
Water 2025, 17(12), 1776; https://doi.org/10.3390/w17121776 - 13 Jun 2025
Viewed by 845
Abstract
High-quality precipitation data are vital for hydrological research. In regions with sparse observation stations, reliable gridded data cannot be obtained through interpolation, while the coarse resolution of satellite products fails to meet the demands of small watershed studies. Downscaling satellite-based precipitation products offers [...] Read more.
High-quality precipitation data are vital for hydrological research. In regions with sparse observation stations, reliable gridded data cannot be obtained through interpolation, while the coarse resolution of satellite products fails to meet the demands of small watershed studies. Downscaling satellite-based precipitation products offers an effective solution for generating high-resolution data in such areas. Among these techniques, machine learning plays a pivotal role, with performance varying according to surface conditions and algorithmic mechanisms. Using the Qinghai Lake Basin as a case study and rain gauge observations as reference data, this research conducted a systematic comparative evaluation of nine machine learning algorithms (ANN, CLSTM, GAN, KNN, MSRLapN, RF, SVM, Transformer, and XGBoost) for downscaling IMERG precipitation products from 0.1° to 0.01° resolution. The primary objective was to identify the optimal downscaling method for the Qinghai Lake Basin by assessing spatial accuracy, seasonal performance, and residual sensitivity. Seven metrics were employed for assessment: correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), standard deviation ratio (Sigma Ratio), Kling-Gupta Efficiency (KGE), and bias. On the annual scale, KNN delivered the best overall results (KGE = 0.70, RMSE = 17.09 mm, Bias = −3.31 mm), followed by Transformer (KGE = 0.69, RMSE = 17.20 mm, Bias = −3.24 mm). During the cold season, KNN and ANN both performed well (KGE = 0.63; RMSE = 5.97 mm and 6.09 mm; Bias = −1.76 mm and −1.75 mm), with SVM ranking next (KGE = 0.63, RMSE = 6.11 mm, Bias = −1.63 mm). In the warm season, Transformer yielded the best results (KGE = 0.74, RMSE = 23.35 mm, Bias = −1.03 mm), followed closely by ANN and KNN (KGE = 0.74; RMSE = 23.38 mm and 23.57 mm; Bias = −1.08 mm and −1.03 mm, respectively). GAN consistently underperformed across all temporal scales, with annual, cold-season, and warm-season KGE values of 0.61, 0.43, and 0.68, respectively—worse than the original 0.1° IMERG product. Considering the ability to represent spatial precipitation gradients, KNN emerged as the most suitable method for IMERG downscaling in the Qinghai Lake Basin. Residual analysis revealed error concentrations along the lakeshore, and model performance declined when residuals exceeded specific thresholds—highlighting the need to account for model-specific sensitivity during correction. SHAP analysis based on ANN, KNN, SVM, and Transformer identified NDVI (0.218), longitude (0.214), and latitude (0.208) as the three most influential predictors. While longitude and latitude affect vapor transport by representing land–sea positioning, NDVI is heavily influenced by anthropogenic activities and sandy surfaces in lakeshore regions, thus limiting prediction accuracy in these areas. This work delivers a high-resolution (0.01°) precipitation dataset for the Qinghai Lake Basin and provides a practical basis for selecting suitable downscaling methods in similar environments. Full article
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31 pages, 4590 KB  
Article
Impact of a Saline Soil Improvement Project on the Spatiotemporal Evolution of Groundwater Dynamic Field and Hydrodynamic Process Simulation in the Hetao Irrigation District
by Yule Sun, Liping Wang, Zuting Liu, Yonglin Jia and Zhongyi Qu
Agronomy 2025, 15(6), 1346; https://doi.org/10.3390/agronomy15061346 - 30 May 2025
Viewed by 588
Abstract
This study examined groundwater dynamics under saline–alkali improvement measures in a 3.66 × 107 m2 study area in Wuyuan County, Hetao Irrigation District, where agricultural sustainability is constrained by soil salinization. This work investigated the spatiotemporal evolution patterns and influencing factors [...] Read more.
This study examined groundwater dynamics under saline–alkali improvement measures in a 3.66 × 107 m2 study area in Wuyuan County, Hetao Irrigation District, where agricultural sustainability is constrained by soil salinization. This work investigated the spatiotemporal evolution patterns and influencing factors of the groundwater environment in the context of soil salinity–alkalinity improvement, as well as the impact of irrigation on the ionic characteristics of groundwater. Furthermore, based on this analysis, a groundwater numerical model and a prediction model for the study area were developed using Visual MODFLOW Flex 6.1 software to forecast the future groundwater levels in the study area and evaluate the effects of varying irrigation scenarios on these levels. The key findings are as follows: (1) The groundwater depth stabilized at 1.63 ± 0.15 m (0.4 m increase) post-improvement measures, maintaining equilibrium under current irrigation but increasing with reductions in water supply. The groundwater salinity increased by 0.59–1.2 g/L across the crop growth period. (2) Spring irrigation raised the groundwater total dissolved solids by 15.6%, as influenced by rock weathering (38.2%), evaporation (31.5%), and cation exchange (30.3%). (3) Maintaining current irrigation systems and planting structures could stabilize groundwater levels at 1.60–1.65 m over the next decade, confirming the sustainable hydrological effects of soil improvement measures. Reducing irrigation to 80% of the current water supply of the Yellow River enables groundwater level stabilization (2.05 ± 0.12 m burial depth) within 5–7 years. This approach decreases river water dependency by 20% while boosting crop water efficiency by 18.7% and reducing root zone salt stress by 32.4%. Full article
(This article belongs to the Section Water Use and Irrigation)
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17 pages, 3947 KB  
Article
A Novel Flood Probability Index Based on Radar Rainfall and Soil Moisture Estimates for a Small Vegetated Watershed in Southeast Brazil
by Thaísa Giovana Lopes, Helber Custódio de Freitas, Leonardo Moreno Domingues and Demerval Soares Moreira
Atmosphere 2025, 16(6), 633; https://doi.org/10.3390/atmos16060633 - 22 May 2025
Viewed by 759
Abstract
Floods result from intense and/or prolonged rainfall that exceeds the soil’s infiltration capacity, generating surface runoff and increasing river discharge. These events can cause substantial societal damage and may even lead to fatalities. In this study, we analyzed flood events in Lençóis Paulista, [...] Read more.
Floods result from intense and/or prolonged rainfall that exceeds the soil’s infiltration capacity, generating surface runoff and increasing river discharge. These events can cause substantial societal damage and may even lead to fatalities. In this study, we analyzed flood events in Lençóis Paulista, southeastern Brazil, between 2016 and 2024, by evaluating estimated precipitation and soil moisture conditions to develop a flood prediction index for the city. Precipitation estimates were derived from reflectivity data provided by the Bauru weather radar, while soil moisture estimates were obtained from the Joint UK Land Environment Simulator (JULES) land surface model, operated at IPMet-Unesp. Although the index was not developed based on formal hydrological modeling or physical process simulation, the analysis of these variables within the Lençóis River sub-basins revealed that elevated soil moisture in the days preceding flood events was a key contributing factor. This is consistent with the increased susceptibility of wetter soils to surface runoff generation. Based on the identification of relevant variables, we developed the Flood Probability Index (FPI) using data from only nine flood events and applied it to classify the likelihood of flooding in the city. The index produced satisfactory results, highlighting its potential as a tool for flood prediction and early warning for the local population. Full article
(This article belongs to the Section Meteorology)
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15 pages, 5838 KB  
Article
Design and Performance Verification of Deep Learning-Based River Flood Prediction System Design and Digital Twin-Based Its Application
by Heesang Eom, Younghun Kim and Jongho Paik
Mathematics 2025, 13(11), 1696; https://doi.org/10.3390/math13111696 - 22 May 2025
Viewed by 928
Abstract
This paper presents a digital twin-based river management and flood prediction system designed for hydrological environments, including volcanic geology. To address the problems of rapid runoff and complex terrain, a deep learning-based hybrid model is proposed that integrates a Convolutional Neural Network (CNN) [...] Read more.
This paper presents a digital twin-based river management and flood prediction system designed for hydrological environments, including volcanic geology. To address the problems of rapid runoff and complex terrain, a deep learning-based hybrid model is proposed that integrates a Convolutional Neural Network (CNN) for spatial feature extraction and a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) units for temporal sequence modeling. The performance evaluation results show that the proposed CNN-RNN hybrid model outperforms individual CNN and RNN baselines. The hybrid model achieves a macro-average precision of 0.97, a recall of 0.99, and an F1 score of 0.98, significantly outperforming existing methods. The system is also integrated with a 3D digital twin visualization platform to enable real-time monitoring and data-driven decision-making. Full article
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14 pages, 5063 KB  
Article
Can Forest Management Improve Water Retention Conservation Under Climate Change? A Case Study of the Republic of Korea
by Mina Hong, Youngjin Ko, Sujong Lee, Minkyung Song and Woo-Kyun Lee
Forests 2025, 16(5), 862; https://doi.org/10.3390/f16050862 - 21 May 2025
Viewed by 777
Abstract
This study aimed to analyze changes in water retention conservation in response to climate change and forest management strategies and to propose methods for securing sustainable water resources. The KO-G-Dynamic model, a Korean forest growth model, was utilized alongside aboveground and belowground water [...] Read more.
This study aimed to analyze changes in water retention conservation in response to climate change and forest management strategies and to propose methods for securing sustainable water resources. The KO-G-Dynamic model, a Korean forest growth model, was utilized alongside aboveground and belowground water resource prediction models to evaluate changes in water retention conservation under various climate change scenarios and forest management strategies. The analysis revealed that under climate change and current forest management levels, water retention conservation was projected to reach 37.553 billion tons per year in the 2030s, 38.274 billion tons per year in the 2050s, and 40.306 billion tons per year in the 2080s. Under optimal forest management policies, the water yield and storage were expected to increase to 37.863 billion tons per year in the 2030s, 38.877 billion tons per year in the 2050s, and 41.495 billion tons per year in the 2080s. Notably, watershed-based forest management offers a more practical management unit than conventional legal boundaries, as it reflects hydrological flow and the ecological characteristics of forest environments. Furthermore, the watershed-based forest management scenario demonstrated greater feasibility in securing water resources. This study provides foundational data for climate change adaptation and sustainable forest management and may aid national and local forest planning. The findings underscore the critical role of forest management in mitigating climate change impacts and ensuring long-term water sustainability. Full article
(This article belongs to the Section Forest Hydrology)
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22 pages, 31214 KB  
Article
A Comparative Study of a Two-Dimensional Slope Hydrodynamic Model (TDSHM), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) Models for Runoff Prediction
by Yuhao Zhou, Jing Pan and Guangcheng Shao
Water 2025, 17(9), 1380; https://doi.org/10.3390/w17091380 - 3 May 2025
Cited by 1 | Viewed by 874
Abstract
Accurate runoff prediction in complex slope catchments remains challenging due to terrain heterogeneity and dynamic rainfall interactions. This study conducts a systematic comparison between a physics-based Two-Dimensional Slope Hydrodynamic Model (TDSHM) and data-driven deep learning models (LSTM and CNN) for runoff forecasting under [...] Read more.
Accurate runoff prediction in complex slope catchments remains challenging due to terrain heterogeneity and dynamic rainfall interactions. This study conducts a systematic comparison between a physics-based Two-Dimensional Slope Hydrodynamic Model (TDSHM) and data-driven deep learning models (LSTM and CNN) for runoff forecasting under variable rainfall conditions. Using 214 rainfall–runoff events (2013–2023) from the Qiaotou watershed in Nanjing, China, the TDSHM integrates rainfall momentum, wind effects, and hydrodynamic principles to resolve spatiotemporal flow dynamics, while LSTM and CNN models leverage seven hydrological features for data-driven predictions. Results demonstrate that the TDSHM achieved superior accuracy, with a mean relative error of 10.77%, Nash–Sutcliffe Efficiency (NSE) of 0.801, and Mean Absolute Error (MAE) of 3.17 mm, outperforming LSTM (24.38% error, NSE = 0.751, MAE = 4.61 mm) and CNN (28.10% error, NSE = 0.506, MAE = 6.82 mm). The TDSHM’s explicit physical interpretability enabled precise simulation of vegetation-modulated runoff processes, validated against field observations (92% predictions within ±15% error). While LSTM captured temporal dependencies effectively, CNN exhibited limitations in sequential data processing. This study highlights the TDSHM’s robustness for scenarios requiring mechanistic insights and the complementary role of LSTM in data-rich environments. The findings provide critical guidance for flood risk management, soil conservation, and model selection trade-offs between physical fidelity and computational efficiency. Full article
(This article belongs to the Section Hydrology)
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22 pages, 4222 KB  
Article
Simulating Anomalous Migration of Radionuclides in Variably Saturation Zone Based on Fractional Derivative Model
by Mengke Zhang, Jingyu Liu, Yang Li, Hongguang Sun and Chengpeng Lu
Water 2025, 17(9), 1337; https://doi.org/10.3390/w17091337 - 29 Apr 2025
Viewed by 546
Abstract
The migration of radioactive waste in geological environments often exhibits anomalies, such as tailing and early arrival. Fractional derivative models (FADE) can provide a good description of these phenomena. However, developing models for solute transport in unsaturated media using fractional derivatives remains an [...] Read more.
The migration of radioactive waste in geological environments often exhibits anomalies, such as tailing and early arrival. Fractional derivative models (FADE) can provide a good description of these phenomena. However, developing models for solute transport in unsaturated media using fractional derivatives remains an unexplored area. This study developed a variably saturated fractional derivative model combined with different release scenarios, to capture the abnormal increase observed in monitoring wells at a field site. The model can comprehensively simulate the migration of nuclides in the unsaturated zone (impermeable layer)—saturated zone system. This study fully analyzed the penetration of pollutants through the unsaturated zone (retardation stage), and finally the rapid lateral and rapid diffusion of pollutants along the preferential flow channels in the saturated zone. Comparative simulations indicate that the spatial nonlocalities effect of fractured weathered rock affects solute transport much more than the temporal memory effect. Therefore, a spatial fractional derivative model was selected to simulate the super-diffusive behavior in the preferential flow pathways. The overall fitness of the proposed model is good (R2 ≈ 1), but the modeling accuracy will be lower with the increased distance from the waste source. The spatial differences between simulated and observed concentrations reflect the model’s limitations in long-distance simulations. Although the model reproduced the overall temporal variation of solute migration, it does not explain all the variability and uncertainty of the specific sites. Based on the sensitivity analysis, the fractional derivative parameters of the unsaturated zone show higher sensitivity than those of the saturated zone. Finally, the advantages and limitations of the fractional derivative model in radionuclide contamination prediction and remediation are discussed. In conclusion, the proposed FADE model coupled with unsaturated and saturated flow conditions, has significant application prospects in simulating nuclide migration in complex geological and hydrological environments. Full article
(This article belongs to the Special Issue Recent Advances in Subsurface Flow and Solute Transport Modelling)
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32 pages, 23634 KB  
Article
Predictive Archaeological Risk Assessment at Reservoirs with Multitemporal LiDAR and Machine Learning (XGBoost): The Case of Valdecañas Reservoir (Spain)
by Enrique Cerrillo-Cuenca and Primitiva Bueno-Ramírez
Remote Sens. 2025, 17(7), 1306; https://doi.org/10.3390/rs17071306 - 5 Apr 2025
Cited by 2 | Viewed by 1219
Abstract
The conservation and monitoring of archaeological sites submerged in water reservoirs have become increasingly necessary in a climatic context where water management policies are possibly accelerating erosion and sedimentation processes. This study assesses the potential of using multitemporal LiDAR data and Machine Learning [...] Read more.
The conservation and monitoring of archaeological sites submerged in water reservoirs have become increasingly necessary in a climatic context where water management policies are possibly accelerating erosion and sedimentation processes. This study assesses the potential of using multitemporal LiDAR data and Machine Learning (ML)—specifically the XGBoost algorithm—to predict erosional and sedimentary processes affecting archaeological sites in the Valdecañas Reservoir (Spain). Using data from 2010 to 2023, topographic variations were calculated through a robust workflow that included the co-registration of LiDAR point clouds and the generation of high-resolution DEMs. Hydrological variables, topographic descriptors, and water dynamics-related factors were extracted and used to train models based on the detected measurement errors and the temporal ranges of the DEMs. The model trained with 2018–2023 data exhibited the highest predictive performance (R2 = 0.685), suggesting that sedimentary and erosional patterns are partially predictable. Finally, a multicriteria approach was applied using a DEM generated from 1957 aerial photographs to estimate past variations based on historical terrain conditions. The results indicate that areas exposed to fluctuating water levels and different topographic orientations suffer greater damage. This study highlights the value of LiDAR and ML in assessing the vulnerability of archaeological sites in highly dynamic environments. Full article
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