Advances in Hydrological Remote Sensing

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Atmosphere atmosphere	2.3	4.9	2010	19.7 Days	CHF 2400	Submit
Climate climate	3.2	5.7	2013	20.8 Days	CHF 1800	Submit
Geomatics geomatics	2.8	5.1	2021	22.6 Days	CHF 1200	Submit
Hydrology hydrology	3.2	5.9	2014	17.9 Days	CHF 1800	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.3 Days	CHF 2700	Submit
Water water	3.0	6.0	2009	18.9 Days	CHF 2600	Submit

37 pages, 33258 KB

Open AccessArticle

An Intelligent Gated Fusion Network for Waterbody Recognition in Multispectral Remote Sensing Imagery

by Tong Zhao, Chuanxun Hou, Zhili Zhang and Zhaofa Zhou

Remote Sens. 2026, 18(7), 1088; https://doi.org/10.3390/rs18071088 - 4 Apr 2026

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Accurate water body segmentation from multispectral remote sensing imagery is critical for hydrological monitoring and environmental management. However, leveraging transfer learning with pre-trained models remains challenging due to the dimensional mismatch between three-channel RGB-based architectures and multi-band spectral data. To address this, this [...] Read more.

Accurate water body segmentation from multispectral remote sensing imagery is critical for hydrological monitoring and environmental management. However, leveraging transfer learning with pre-trained models remains challenging due to the dimensional mismatch between three-channel RGB-based architectures and multi-band spectral data. To address this, this study proposes a novel segmentation network, termed Intelligent Gated Fusion Network (IGF-Net), built upon a dual-branch feature encoder module and a core Intelligent Gated Fusion Module (IGFM). The IGFM achieves adaptive fusion of visual and spectral features through a cascaded mechanism integrating differences-and-commonalities parallel modeling, channel-context priors, and adaptive temperature control. We evaluate IGF-Net on the newly constructed Tiangong-2 remote sensing image water body semantic segmentation dataset, which comprises 3776 meticulously annotated multispectral image patches. Comprehensive experiments demonstrate that IGF-Net achieves strong and consistent performance on this dataset, with an Intersection over Union of 0.8742 and a Dice coefficient of 0.9239, consistently outperforming the evaluated baseline methods, such as FCN, U-Net, and DeepLabv3+. It also exhibits strong cross-dataset generalization capabilities on an independent Sentinel-2 water segmentation dataset. Ablation studies and visualization analyses confirm that the proposed fusion strategy significantly enhances segmentation accuracy and stability, particularly in complex scenarios. placeholder Full article

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24 pages, 3870 KB

Open AccessArticle

Hybrid Ensemble Learning for TWSA Prediction in Water-Stressed Regions: A Case Study from Casablanca–Settat Region, Morocco

by Youssef Laalaoui, Naïma El Assaoui, Oumaima Ouahine, Thanh Thi Nguyen and Ahmed M. Saqr

Hydrology 2026, 13(2), 53; https://doi.org/10.3390/hydrology13020053 - 1 Feb 2026

Viewed by 1294

Abstract

A hybrid machine learning framework has been developed in this study to estimate Terrestrial Water Storage Anomalies (TWSA) in Morocco’s Casablanca–Settat region, which faces serious groundwater stress due to rapid urbanization, intensive agriculture, and climate variability. In this study, TWSA is used as [...] Read more.

A hybrid machine learning framework has been developed in this study to estimate Terrestrial Water Storage Anomalies (TWSA) in Morocco’s Casablanca–Settat region, which faces serious groundwater stress due to rapid urbanization, intensive agriculture, and climate variability. In this study, TWSA is used as an integrated proxy for groundwater-related storage changes, while acknowledging that it also includes contributions from soil moisture and surface water. The approach combines satellite-based observations from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) with key environmental indicators such as rainfall, evapotranspiration, and land use data to track changes in groundwater availability with improved spatial detail. After preprocessing the data through feature selection, normalization, and outlier handling, the model applies six base learners, i.e., Huber regressor, automatic relevance determination regression, kernel ridge, long short-term memory, k-nearest neighbors, and gradient boosting. Their predictions are aggregated using a random forest meta-learner to improve accuracy and stability. The ensemble achieved strong results, with a root mean square error of 0.13, a mean absolute error of 0.108, and a determination coefficient of 0.97—far better than single-model baselines—based on a temporally independent train-test split. Spatial analysis highlighted clear patterns of groundwater depletion linked to land cover and usage. These results can guide targeted aquifer recharge efforts, drought response planning, and smarter irrigation management. The model also aligns with national goals under Morocco’s water sustainability initiatives and can be adapted for use in other regions with similar environmental challenges. Full article

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21 pages, 3354 KB

Open AccessArticle

Fusion and Evaluation of Multi-Source Satellite Remote Sensing Precipitation Products Based on Transformer Machine Learning

by Qingyuan Luo, Dongzhi Wang, Lina Liu, Caihong Hu and Chengshuai Liu

Water 2026, 18(3), 358; https://doi.org/10.3390/w18030358 - 30 Jan 2026

Viewed by 416

Abstract

Satellite precipitation products offer great potential for acquiring reliable precipitation data in data-sparse areas, yet they have inherent uncertainties and errors as indirect observations. This study evaluated the accuracy of multi-source satellite precipitation products from daily and precipitation magnitude perspectives and discussed the [...] Read more.

Satellite precipitation products offer great potential for acquiring reliable precipitation data in data-sparse areas, yet they have inherent uncertainties and errors as indirect observations. This study evaluated the accuracy of multi-source satellite precipitation products from daily and precipitation magnitude perspectives and discussed the spatiotemporal variation in their inversion errors. Based on ground rainfall observations, satellite products, and environmental factors, a Transformer-based multi-source precipitation fusion method was proposed, with its effectiveness preliminarily analyzed for daily precipitation in the Jingle River Basin. The main conclusions are as follows: (1) Compared with the observed precipitation data, the GSMaP_Gauge satellite remote sensing precipitation product showed the closest agreement with the observations, ranking first in all indicators except the Probability of Detection (POD). The MSWEP satellite remote sensing precipitation product followed in performance, while the CHIRPS satellite product performed the poorest. Satellite products showed distinct error characteristics across seasons and rainfall intensities, as well as general overestimation of light rain frequency and insufficient heavy rain capture; however, these products also showed better detection capability in flood seasons. Error spatial distribution was consistent with topography, vegetation coverage, and temperature. (2) Verification demonstrated that the Transformer fusion algorithm effectively reduced relative bias and improved correlation with ground data. The scheme which incorporated environmental factors outperformed the other, which only considered precipitation characteristics, achieving higher estimation accuracy and fusion stability. Full article

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20 pages, 5180 KB

Open AccessArticle

Multi-Source Data Fusion and Heuristic-Optimized Machine Learning for Large-Scale River Water Quality Parameters Monitoring

by Kehang Fang, Feng Wu, Xing Gao and Zhihui Li

Remote Sens. 2026, 18(2), 320; https://doi.org/10.3390/rs18020320 - 18 Jan 2026

Viewed by 606

Abstract

Accurate and efficient surface water quality monitoring is essential for ecological protection and sustainable development. However, conventional monitoring methods, such as fixed-site observations, often suffer from spatial limitations and overlook crucial auxiliary variables. This study proposes an innovative modeling framework for large-scale river [...] Read more.

Accurate and efficient surface water quality monitoring is essential for ecological protection and sustainable development. However, conventional monitoring methods, such as fixed-site observations, often suffer from spatial limitations and overlook crucial auxiliary variables. This study proposes an innovative modeling framework for large-scale river water quality inversion that integrates multi-source data—including Sentinel-2 imagery, meteorological conditions, land use classification, and landscape pattern indices. To improve predictive accuracy, three tree-based machine learning models (Random Forest, XGBoost, and LightGBM) were constructed and further optimized using the Whale Optimization Algorithm (WOA), a nature-inspired metaheuristic technique. Additionally, model interpretability was enhanced using SHAP (Shapley Additive Explanations), enabling a transparent understanding of each variable’s contribution. The framework was applied to the Red River Basin (RRB) to predict six key water quality parameters: dissolved oxygen (DO), ammonia nitrogen (NH₃-N), total phosphorus (TP), total nitrogen (TN), pH, and permanganate index (COD_Mn). Results demonstrate that integrating landscape and meteorological variables significantly improves model performance compared to remote sensing alone. The best-performing models achieved R² values exceeding 0.45 for all parameters (DO: 0.70, NH₃-N: 0.46, TP: 0.59, TN: 0.71, pH: 0.83, COD_Mn: 0.57). Among them, WOA-optimized LightGBM consistently delivered superior performance. The study also confirms the feasibility of applying the models across the entire basin, offering a transferable and interpretable approach to spatiotemporal water quality prediction in other large-scale or data-scarce regions. Full article

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21 pages, 3201 KB

Open AccessReview

Advances in Laser-Induced Acoustic Technology for Underwater Detection

by Jin Zhao, Kexin Yu, Shuaiqi Xu, Maorong Wang, Yiguang Yang, Degang Xu, Jianquan Yao and Xia Wang

Water 2025, 17(22), 3285; https://doi.org/10.3390/w17223285 - 17 Nov 2025

Cited by 1 | Viewed by 1585

Abstract

Laser-induced acoustic (LIA) underwater detection, as a next-generation sensing paradigm, combines high spatial resolution, rapid temporal response, and cross-medium detection capability, positioning it as a strategically significant technology in marine resource exploration, military security, and ocean environmental monitoring. The fundamental principles underlying LIA [...] Read more.

Laser-induced acoustic (LIA) underwater detection, as a next-generation sensing paradigm, combines high spatial resolution, rapid temporal response, and cross-medium detection capability, positioning it as a strategically significant technology in marine resource exploration, military security, and ocean environmental monitoring. The fundamental principles underlying LIA technology are systematically examined, together with recent advances in representative experimental systems and critical enabling techniques. The characteristics of the laser–acoustic transmission channel are comprehensively investigated, and the mechanisms through which laser parameters modulate the properties of acoustic signals are rigorously elucidated. Moreover, several challenges hindering practical applications are underscored, including laser energy attenuation, interference arising from complex underwater environments, and the comparatively high cost of equipment. Finally, future research directions are outlined, encompassing the development of high-efficiency laser sources, multimodal integrated sensing strategies, intelligent signal processing algorithms, and improved environmental adaptability. These efforts are intended to provide theoretical underpinnings for the continued advancement and broader application of LIA-based underwater detection technologies. Full article

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33 pages, 47566 KB

Open AccessArticle

Spatiotemporal Patterns of Climate-Vegetation Regulation of Soil Moisture with Phenological Feedback Effects Using Satellite Data

by Hanmin Yin, Xiaohan Liao, Huping Ye, Jie Bai, Wentao Yu, Yue Li, Junbo Wei, Jincheng Yuan and Qiang Liu

Remote Sens. 2025, 17(22), 3714; https://doi.org/10.3390/rs17223714 - 14 Nov 2025

Viewed by 1141

Abstract

Global soil moisture has undergone significant changes in recent decades due to climate change and vegetation greening. However, the seasonal and climate zonal variations in soil moisture dynamics at different depths, driven by both climate and vegetation, remain insufficiently explored. This study provides [...] Read more.

Global soil moisture has undergone significant changes in recent decades due to climate change and vegetation greening. However, the seasonal and climate zonal variations in soil moisture dynamics at different depths, driven by both climate and vegetation, remain insufficiently explored. This study provides a comprehensive analysis of the global patterns in rootzone and surface soil moisture and leaf area index (LAI) across different seasons and climate zones, utilizing satellite observations from 1982 to 2020. We investigate how climatic factors and LAI influence soil moisture variations and quantify their dominant contributions. Furthermore, by employing key vegetation phenological indicators, namely the peak of growing season (POS) and the corresponding maximum LAI (LAI_MAX), we assess the feedback effects of vegetation phenology on soil moisture dynamics. The results indicate that the greening trend (as reflected by LAI increases) from 2000 to 2020 was significantly stronger than that observed during 1982–1999 across all seasons and climate zones. Both rootzone and surface soil moisture shifted from a decreasing (drying) trend (1982–1999) to an increasing (wetting) trend (2000–2020). From 1982 to 2020, the LAI induced moistening trends in both surface and rootzone soil moisture. In arid and temperate zones, precipitation drove rootzone soil moisture increases only during the summer. Among all seasons and climate zones, solar radiation induced the strongest surface soil drying in tropical summers, with a rate of −0.04 × 10⁻³ m³m⁻³/Wm⁻². For rootzone soil moisture, LAI dominated over individual climatic factors in winter and spring globally. In contrast, solar radiation became the primary driver during summer and autumn, followed by precipitation. For surface soil moisture, precipitation exhibited the strongest control in winter, but solar radiation surpassed it as the dominant factor from spring through autumn. In the tropical autumn, the sensitivity of rootzone and surface soil moisture to POS (and LAI_MAX) was highest, at 0.059 m³m⁻³·d⁻¹ (0.256 m³m⁻³/m²m⁻²) and 0.052 m³m⁻³·d⁻¹ (0.232 m³m⁻³/m²m⁻²), respectively. This research deepens the understanding of how climate and vegetation regulate soil moisture across different climate zones and seasons. It also provides a scientific basis for improving global soil moisture prediction models and managing water resource risks in the context of climate change. Full article

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20 pages, 3920 KB

Open AccessArticle

Impact Analysis of Climate Change on Hydropower Resource Development in the Vakhsh River Basin of Tajikistan

by Hailong Liu, Aminjon Gulakhmadov and Firdavs Shaimuradov

Hydrology 2025, 12(11), 294; https://doi.org/10.3390/hydrology12110294 - 5 Nov 2025

Cited by 1 | Viewed by 1270

Abstract

With increasing energy demands and environmental pressures, hydropower, as a clean and renewable energy source, has attracted widespread attention for its development and utilization. However, hydropower systems are highly sensitive to climate change, significantly impacting generation, management, and safety. This study addresses the [...] Read more.

With increasing energy demands and environmental pressures, hydropower, as a clean and renewable energy source, has attracted widespread attention for its development and utilization. However, hydropower systems are highly sensitive to climate change, significantly impacting generation, management, and safety. This study addresses the stability of hydropower resources in the Vakhsh River Basin, Tajikistan, using digital analysis, snowmelt runoff simulation, and soil erosion assessment to estimate spatial distribution. Under three climate scenarios (RCP2.6, RCP4.5, and RCP8.5), hydropower trends were simulated, and soil erosion was quantified. Results show annual hydropower potentials: Garm (55.465 billion kWh/a), Rogun (112.737 billion kWh/a), Nurex (78.853 billion kWh/a). Across all scenarios, runoff and hydropower generation increase (162–328,108 kWh/a), with growth rates following RCP4.5 < RCP2.6 < RCP8.5. Soil erosion simulation results indicate that a one millimeter increase in precipitation could lead to sediment deposition of 1.57 × 10⁶ kWh/year in upstream reservoirs. These results demonstrate that climate change has a significant impact on hydropower development in the Vakhsh River Basin. The research provides technical support for hydropower development under climate change. Full article

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19 pages, 15366 KB

Open AccessArticle

Quantifying the Contribution of Global Precipitation Product Uncertainty to Ensemble Discharge Simulations and Projections: A Case Study in the Liujiang Catchment, Southwest China

by Yong Chang, Nan Mu, Yaoyong Qi and Ling Liu

Atmosphere 2025, 16(11), 1260; https://doi.org/10.3390/atmos16111260 - 3 Nov 2025

Viewed by 595

Abstract

Reliable precipitation inputs are essential for hydrological modeling, yet global precipitation products often exhibit substantial discrepancies that introduce significant uncertainties into streamflow simulations and projections. In this study, we assessed the relative contribution of precipitation dataset uncertainty to discharge simulations and projections, in [...] Read more.

Reliable precipitation inputs are essential for hydrological modeling, yet global precipitation products often exhibit substantial discrepancies that introduce significant uncertainties into streamflow simulations and projections. In this study, we assessed the relative contribution of precipitation dataset uncertainty to discharge simulations and projections, in comparison with uncertainties from model structure, model parameters, and climate projections, in the Liujiang catchment, southwest China. Three widely used satellite-based products (CHIRPS, PERSIANN, and IMERG) and one reanalysis dataset (ERA5) were combined with three hydrological models of varying structural complexity to simulate streamflow. Using an ANOVA-based variance decomposition framework, we quantified the contributions of different uncertainty sources under both historical and future climate conditions. Results showed that precipitation input uncertainty dominates discharge simulations during the calibration period, contributing over 60% of total variance particularly at high flows, while interactions among precipitation, model structure, and parameters govern low-flow simulations. Under future climate scenarios, climate projection uncertainty overwhelmingly dominates discharge predictions with 50–80% of uncertainty contribution, yet precipitation products still contribute significantly across time scales. The compensation of precipitation biases by hydrological models can cause parameter values to deviate from their true physical meaning. This deviation may further amplify the differences in discharge projections driven by different precipitation products under future climate conditions and increase the overall uncertainty of streamflow projections. Overall, this study introduced an integrated approach to simultaneously assess precipitation uncertainty across flow regimes and future climate scenarios. These results emphasized the necessity of using ensemble approaches that incorporate multiple precipitation products in hydrological forecasting and impact studies, particularly in data-scarce regions reliant on global datasets. Full article

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27 pages, 4619 KB

Open AccessArticle

Assessing the Impact of Assimilated Remote Sensing Retrievals of Precipitation on Nowcasting a Rainfall Event in Attica, Greece

by Aikaterini Pappa, John Kalogiros, Maria Tombrou, Christos Spyrou, Marios N. Anagnostou, George Varlas, Christine Kalogeri and Petros Katsafados

Hydrology 2025, 12(8), 198; https://doi.org/10.3390/hydrology12080198 - 28 Jul 2025

Cited by 1 | Viewed by 1547

Abstract

Accurate short-term rainfall forecasting, an essential component of the broader framework of nowcasting, is crucial for managing extreme weather events. Traditional forecasting approaches, whether radar-based or satellite-based, often struggle with limited spatial coverage or temporal accuracy, reducing their effectiveness. This study tackles these [...] Read more.

Accurate short-term rainfall forecasting, an essential component of the broader framework of nowcasting, is crucial for managing extreme weather events. Traditional forecasting approaches, whether radar-based or satellite-based, often struggle with limited spatial coverage or temporal accuracy, reducing their effectiveness. This study tackles these challenges by implementing the Local Analysis and Prediction System (LAPS) enhanced with a forward advection nowcasting module, integrating multiple remote sensing rainfall datasets. Specifically, we combine weather radar data with three different satellite-derived rainfall products (H-SAF, GPM, and TRMM) to assess their impact on nowcasting performance for a rainfall event in Attica, Greece (29–30 September 2018). The results demonstrate that combining high-resolution radar data with the broader coverage and high temporal frequency of satellite retrievals, particularly H-SAF, leads to more accurate predictions with lower uncertainty. The assimilation of H-SAF with radar rainfall retrievals (HX experiment) substantially improved forecast skill, reducing the unbiased Root Mean Square Error by almost 60% compared to the control experiment for the 60 min rainfall nowcast and 55% for the 90 min rainfall nowcast. This work validates the effectiveness of the specific LAPS/advection configuration and underscores the importance of multi-source data assimilation for weather prediction. Full article

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15 pages, 1236 KB

Open AccessReview

From Climate to Cloud: Advancing Fog Detection Through Satellite Imagery

by Andrés Gabriel Arguedas Chaverri, Rogério Hartung Toppa and Kelly Cristina Tonello

Climate 2025, 13(6), 110; https://doi.org/10.3390/cli13060110 - 27 May 2025

Cited by 1 | Viewed by 2730

Abstract

The broad spatiotemporal coverage provided by satellite remote sensing is fundamental for monitoring fog events, a phenomenon that impacts transportation, agriculture, and ecosystem functioning. Despite advances in remote sensing technology, significant knowledge gaps remain regarding the application of these techniques to fog detection, [...] Read more.

The broad spatiotemporal coverage provided by satellite remote sensing is fundamental for monitoring fog events, a phenomenon that impacts transportation, agriculture, and ecosystem functioning. Despite advances in remote sensing technology, significant knowledge gaps remain regarding the application of these techniques to fog detection, especially over terrestrial ecosystems. This scoping review synthesizes the trends in methods used for fog detection by analyzing 38 papers retrieved from Scopus and Web of Science. Only studies that utilized satellite imagery to analyze the spatiotemporal dynamics of fog were included. Articles that employed non-satellite methodologies or focused on processes other than the detection, formation, or identification of fog events were excluded. In addition to a term co-occurrence analysis of abstracts using VOSviewer, this study examines key parameters of the detection methods—including sensor type, spectral bands, temporal resolution, and algorithmic approaches (e.g., threshold methods and deep learning techniques)—to evaluate their evolution and current limitations. Our results reveal that while approximately 53% of studies rely on geostationary satellite data (95% CI: 36.7–68.5%), favored for their high temporal resolution, the remaining 47% employ polar-orbiting sensors (95% CI: 31.5–63.2%) that offer superior spatial resolution. Notably, most research has concentrated on maritime fog detection, with few studies extending these techniques to complex terrestrial environments. The review highlights critical gaps in current approaches and proposes an integrated framework that combines traditional brightness temperature difference methods with emerging machine learning techniques, which could advance fog detection in diverse settings. Full article

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28 pages, 38630 KB

Open AccessArticle

Vegetation Response to Flash Drought Events Considering Resilience in Southwestern China

by Liangliang Jiang, Guangming Wu, Qijin Li and Xiaoran Liu

Water 2025, 17(5), 653; https://doi.org/10.3390/w17050653 - 24 Feb 2025

Cited by 1 | Viewed by 2326

Abstract

Flash drought events occur frequently in Southwestern China, and a notable upward trend is predicted for the future. Attention should be given to how the severity of flash droughts and vegetation vulnerability hinder vegetation from recovering to their original state, leading to losses. [...] Read more.

Flash drought events occur frequently in Southwestern China, and a notable upward trend is predicted for the future. Attention should be given to how the severity of flash droughts and vegetation vulnerability hinder vegetation from recovering to their original state, leading to losses. Vegetation resilience and vulnerability to flash droughts was assessed in dry years by adopting a ‘resistance–resilience’ framework from a new perspective, and we measured the significance of various drought characteristics in affecting vegetation reduction by using the boosted regression tree (BRT) model. The results showed that croplands in the Sichuan Basin displayed low resistance to flash droughts, whereas grasslands and forests in mountainous areas had high resistance. Croplands in the Sichuan Basin demonstrated high vegetation resilience, while Guizhou province showed low vegetation resilience. Most regions experienced high vegetation vulnerability to flash droughts, especially in the Sichuan Basin and Yunnan province. We found that croplands and forests in 2006 exhibited a significant decrease in LAI during flash drought events. Croplands experienced a significant decrease in LAI in regions where the drought duration (DD) exceeded 60 days, and the drought interval (DIV) ranged from 30 to 40 days. Forest regions with a DD exceeding 60 days and a DIV below 20 days experienced a high reduction in LAI. Furthermore, croplands and shrubs could recover once their vulnerability fell below thresholds of 0.34 and 0.30, respectively. The impact of species richness on vegetation resilience can be explored in future research. This study reveals the spatial patterns of vegetation vulnerability and provides information on preventing and managing vegetation deterioration in Southwestern China. Full article

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16 pages, 3161 KB

Open AccessArticle

Eutrophication Conditions in Two High Mountain Lakes: The Influence of Climate Conditions and Environmental Pollution

by Fátima Goretti García-Miranda, Claudia Muro, Yolanda Alvarado, José Luis Expósito-Castillo and Héctor Víctor Cabadas-Báez

Hydrology 2025, 12(2), 32; https://doi.org/10.3390/hydrology12020032 - 13 Feb 2025

Cited by 3 | Viewed by 3044

Abstract

The lakes known as El Sol and La Luna are high mountain water deposits located in Mexico within an inactive volcanic system. These lakes are of ecological importance because they are unique in Mexico. However, currently, the lakes have experienced changes in their [...] Read more.

The lakes known as El Sol and La Luna are high mountain water deposits located in Mexico within an inactive volcanic system. These lakes are of ecological importance because they are unique in Mexico. However, currently, the lakes have experienced changes in their shape and an increase in algae blooms, coupled with the degradation of the basin, which has alerted government entities to the need to address the lakes’ problems. To address the environmental status of El Sol and La Luna, a trophic study was conducted during the period of 2021–2023, including an analysis of the influence of climatic variables, lake water quality, and eutrophication conditions. The trophic state was established based on the eutrophication index. The Pearson correlations defined the eutrophication interrelation between the distinct factors influencing the lakes’ status. El Sol registered higher eutrophication conditions than La Luna. El Sol was identified as seasonal eutrophic and La Luna as transitioning from oligotrophic to mesotrophic, showing high levels of chlorophyll, total phosphorus, and total nitrogen and low water transparency. The principal factors altering the eutrophic conditions were water pollution and climatic variables (precipitation and ambient temperature). Eutrophication was the prime factor impacting perimeter loss at El Sol, whereas at La Luna, it was due to a decline in precipitation. Full article

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20 pages, 10145 KB

Open AccessArticle

Monitoring and Disaster Assessment of Glacier Lake Outburst in High Mountains Asian Using Multi-Satellites and HEC-RAS: A Case of Kyagar in 2018

by Long Jiang, Zhiqiang Lin, Zhenbo Zhou, Hongxin Luo, Jiafeng Zheng, Dongsheng Su and Minhong Song

Remote Sens. 2024, 16(23), 4447; https://doi.org/10.3390/rs16234447 - 27 Nov 2024

Cited by 6 | Viewed by 2556

Abstract

The glaciers in the High Mountain Asia (HMA) region are highly vulnerable to global warming, posing significant threats to downstream populations and infrastructure through glacier lake outburst floods (GLOFs). The monitoring and early warnings of these events are challenging due to sparse observations [...] Read more.

The glaciers in the High Mountain Asia (HMA) region are highly vulnerable to global warming, posing significant threats to downstream populations and infrastructure through glacier lake outburst floods (GLOFs). The monitoring and early warnings of these events are challenging due to sparse observations in these remote regions. To explore reproducing the evolution of GLOFs with sparse observations in situ, this study focuses on the outburst event and corresponding GLOFs in August 2018 caused by the Kyagar Glacier lake, a typical glacier lake of the HMA in the Karakoram, which is known for its frequent outburst events, using a combination of multi-satellite remote sensing data (Sentinel-1 and Sentinel-2) and the HEC-RAS hydrodynamic model. The water depth of the glacier lake and downstream was extracted from satellite data adapted by the Floodwater Depth Elevation Tool (FwDET) as a baseline to compare them with simulations. The elevation-water volume curve was obtained by extrapolation and was applied to calculate the water surface elevation (WSE). The inundation of the downstream of the lake outburst was obtained through flood modeling by incorporating a load elevation-water volume curve and the Digital Elevation Model (DEM) into the hydrodynamic model HEC-RAS. The results showed that the Kyagar glacial lake outburst was rapid and destructive, accompanied by strong currents at the end of each downstream storage ladder. A series of meteorological evaluation indicators showed that HEC-RAS reproduced the medium and low streamflow rates well. This study demonstrated the value of integrating remote sensing and hydrodynamic modeling into GLOF assessments in data-scarce regions, providing insights for disaster risk management and mitigation. Full article

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17 pages, 5156 KB

Open AccessArticle

Identifying Alpine Lakes with Shoreline Features

by Zhimin Hu, Min Feng, Yijie Sui, Dezhao Yan, Kuo Zhang, Jinhao Xu, Rui Liu and Earina Sthapit

Water 2024, 16(22), 3287; https://doi.org/10.3390/w16223287 - 15 Nov 2024

Cited by 1 | Viewed by 2218

Abstract

Alpine lakes located in high-altitude mountainous regions act as vital sentinels of environmental change. Remote-sensing-based identification of these lakes is crucial for understanding their response to climate variations and for assessing associated disaster risks. However, the complex terrain and weather conditions in these [...] Read more.

Alpine lakes located in high-altitude mountainous regions act as vital sentinels of environmental change. Remote-sensing-based identification of these lakes is crucial for understanding their response to climate variations and for assessing associated disaster risks. However, the complex terrain and weather conditions in these areas pose significant challenges to accurate detection. This paper proposes a method that leverages the high precision of deep learning for small lake and lake boundary extraction combined with deep learning to eliminate noise and errors in the identification results. Using Sentinel-2 data, we accurately identified and delineated alpine lakes in the eastern Himalayas. A total of 2123 lakes were detected, with an average lake area of 0.035 km². Notably, 76% of these lakes had areas smaller than 0.01 km². The slope data is crucial for the lake classification model in eliminating shadow noise. The accuracy of the proposed lake classification model reached 97.7%. In the identification of small alpine lakes, the recognition rate of this method was 96.4%, significantly surpassing that of traditional deep learning approaches. Additionally, this method effectively eliminated most shadow noise present in water body detection results obtained through machine learning techniques. Full article

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