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Remote Sensing of Hydrological Processes: Modelling and Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 22952

Special Issue Editors

Department of Mining and Civil Engineering, Universidad Politecnica de Cartagena, Paseo Alfonso XIII, 52, 30203 Cartagena, Spain
Interests: hydrology; water resources; hydrometeorology; remote sensing; climate change
Special Issues, Collections and Topics in MDPI journals
Department of Geography, History and Humanities, Universidad de Almería, Ctra. Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain
Interests: remote sensing; GIScience; geography; hydrology; sociohydrology
Civil Engineering Department, Pontificia Universidad Javeriana, Carrera 7a No. 40-62, Bogotá, Colombia
Interests: hydrology; climate change; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, Earth Observation (EO) data support the assessments of natural and human-induced changes on the Earth, providing information for taking decisions in the water science field at different spatiotemporal scales. The remote sensing in water science is related to the observation, understanding, and prediction of the spatial and temporal distribution of hydrological processes.

In the case of highly dynamic processes, such as flooding, information retrieved at the sub-daily scale and near real-time by different sensors and platforms is required. The precise modeling of water cycle processes demands the development of new sensors and platforms, as well as new remote sensing methodologies.

Usually, hydrological models are calibrated in specific sites, such as stream gauge stations located at the river basin outlet. However, on one side, the assessment of runoff in a point of the basin provides an aggregated and limited response of the hydrological system without accounting for spatial variations in hydrological parameters. On the other side, in the case of ungauged basins, other alternatives to evaluate the hydrological model performance are needed. Remote sensing data become a true alternative for spatial calibration and validation of hydrological models, considering the spatiotemporal variations of parameters and state variables.

This Special Issue aims to disseminate state-of-the-art research articles and emerging ideas using remote sensing and geospatial technologies of water cycle processes, including:

  • New methods and techniques, particularly related with the development and application of satellite missions, radar, airborne and drone sensors, to monitor spatially distributed hydrological processes (such as precipitation, evapotranspiration, soil moisture, groundwater infiltration, and surface water runoff) as well as wetlands and water bodies, across a wide range of temporal scales;
  • New techniques to use spatially distributed remote sensing data for spatial calibration and validation of hydrological models, suitable for ungauged basins;
  • Use of remote sensing data for global and regional hydrological applications and water resource management, to support decision taking as a way to predict and resolve water conflicts in a changing climate and with increasing demands on limited water supplies; and
  • Application of remote sensing for the study of the impact of human activities on the hydrological cycle (especially infiltration and runoff generation); floods, droughts and water resource availability.

Dr. Sandra G. García Galiano
Dr. Fulgencio Cánovas García
Dr. Juan Diego Giraldo-Osorio
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • Hydrological processes
  • Hydrometeorology
  • Spatiotemporal variability
  • Quantitative precipitation estimation
  • Rainfall runoff modeling
  • Flood modeling
  • Droughts
  • Spatial calibration
  • Satellite remote sensing
  • Drones

Published Papers (9 papers)

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Editorial

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4 pages, 203 KiB  
Editorial
Editorial for Special Issue: “Remote Sensing of Hydrological Processes: Modelling and Applications”
by Sandra G. García-Galiano, Fulgencio Cánovas-García and Juan Diego Giraldo-Osorio
Remote Sens. 2023, 15(5), 1466; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15051466 - 06 Mar 2023
Cited by 1 | Viewed by 967
Abstract
Improvements in satellite remote sensing techniques have allowed the development of several platforms that are able to capture multitemporal data with a wide range of spatial and temporal resolutions [...] Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Processes: Modelling and Applications)

Research

Jump to: Editorial

21 pages, 21920 KiB  
Article
In Situ Experimental Study of Cloud-Precipitation Interference by Low-Frequency Acoustic Waves
by Yang Shi, Zhen Qiao, Guangqian Wang and Jiahua Wei
Remote Sens. 2023, 15(4), 993; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15040993 - 10 Feb 2023
Cited by 2 | Viewed by 1303
Abstract
Since acoustic agglomeration is an effective pre-treatment technique for removing fine particles, it can be considered as a potential technology for applications in aerosol pollution control, industrial dust and mist removal, and cloud and precipitation interference. In this study, the cloud-precipitation interference effect [...] Read more.
Since acoustic agglomeration is an effective pre-treatment technique for removing fine particles, it can be considered as a potential technology for applications in aerosol pollution control, industrial dust and mist removal, and cloud and precipitation interference. In this study, the cloud-precipitation interference effect was evaluated in situ based on a multi-dimensional multi-scale monitoring system. The variations in the spatial and temporal distribution of rainfall near the surface and the characteristics of precipitation droplets in the air were investigated. The results indicate that strong low-frequency acoustic waves had a significant impact on the macro-characteristics of rainfall clouds, the microphysical structure of rain droplets and near-surface precipitation, and various microwave parameters. In terms of physical structure, the precipitation cloud’s base height decreased significantly upon opening the acoustic device, while agglomeration and de-agglomeration of raindrops were in a dynamic equilibrium. When the sound generator was on, the particle concentration at a sampling attitude of 500−1700 m and the proportion of particles with diameters of 1–1.5 mm decreased significantly (by 1–5 ln [1/m3·mm]). In contrast, the particle concentration increased by 1–3 ln [1/m3·mm] at a sampling attitude below 400 m. Moreover, during acoustic interference, the reflectivity factor surged by 2.71 dBZ within 1200 m of the operation centre. Overall, the spatial and temporal distributions of rainfall rates and cumulative precipitation within 5 km of acoustic operation were uneven and influenced by local terrain and background winds. Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Processes: Modelling and Applications)
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20 pages, 12224 KiB  
Article
Assessment of Runoff Components of River Flow in the Karakoram Mountains, Pakistan, during 1995–2010
by Mateeul Haq, Muhammad Jawed Iqbal, Khan Alam, Zhongwei Huang, Thomas Blaschke, Salman Qureshi and Sher Muhammad
Remote Sens. 2023, 15(2), 399; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020399 - 09 Jan 2023
Cited by 3 | Viewed by 1484
Abstract
Glaciers are generally believed to be subjugating by global warming but the Karakoram glaciers are reportedly maintaining their balance. Earlier studies in the Karakoram and its sub-basins have mostly addressed a short span of time and used complex models to understand the phenomenon. [...] Read more.
Glaciers are generally believed to be subjugating by global warming but the Karakoram glaciers are reportedly maintaining their balance. Earlier studies in the Karakoram and its sub-basins have mostly addressed a short span of time and used complex models to understand the phenomenon. Thus, this study is based on a long-term trend analysis of the computed runoff components using satellite data with continuous spatial and temporal coverage incorporated into a simple degree day Snowmelt Runoff Model (SRM). The trends of melt runoff components can help us understanding the future scenarios of the glaciers in the study area. The SRM was calibrated against the recorded river flows in the Hunza River Basin (HRB). Our simulations showed that runoff contribution from rain, snow, and glaciers are 14.4%, 34.2%, and 51.4%, respectively during 1995–2010. The melting during the summer has slightly increased, suggesting overall but modest glacier mass loss which consistent with a few recent studies. The annual stream flows showed a rising trend during the 1995–2010 period, while, rainfall and temperatures showed contrasting increasing/decreasing behavior in the July, August, and September months during the same period. The average decreasing temperatures (0.08 °C per annum) in July, August, and September makes it challenging and unclear to explain the reason for this rising trend of runoff but a rise in precipitation in the same months affirms the rise in basin flows. At times, the warmer rainwater over the snow and glacier surfaces also contributed to excessive melting. Moreover, the uncertainties in the recorded hydrological, meteorological, and remote sensing data due to low temporal and spatial resolution also portrayed contrasting results. Gradual climate change in the HRB can affect river flows in the near future, requiring effective water resource management to mitigate any adverse impacts. This study shows that assessment of long-term runoff components can be a good alternative to detect changes in melting glaciers with minimal field observations. Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Processes: Modelling and Applications)
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16 pages, 12153 KiB  
Article
Monitoring Land Use/Cover Changes by Using Multi-Temporal Remote Sensing for Urban Hydrological Assessment: A Case Study in Beijing, China
by Crispin Kabeja, Rui Li, Digne Edmond Rwabuhungu Rwatangabo and Jiawei Duan
Remote Sens. 2022, 14(17), 4273; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174273 - 30 Aug 2022
Cited by 7 | Viewed by 1946
Abstract
Understanding the change in hydrological response due to urban dynamics is important for better flood preparedness and future sustainable urban planning. This study investigated the influence of urban land cover change on spatiotemporal changes in flood peak discharge and flood volume within a [...] Read more.
Understanding the change in hydrological response due to urban dynamics is important for better flood preparedness and future sustainable urban planning. This study investigated the influence of urban land cover change on spatiotemporal changes in flood peak discharge and flood volume within a rapidly urbanizing catchment located in Beijing, China. We used Landsat satellite data ranging from 1986 to 2017 to monitor and quantify urban growth. Moreover, the Hydrological Modeling System (HEC-HMS) coupled with meteorological data was utilized to examine the impact of urban growth on hydrological responses. The results revealed that major changes in land use/cover (LULC) were detected in the urban landscape, which increased from 25.22% to 65.48% of the total catchment area, while agricultural land decreased from 64.85% to 25.28% during 1986–2017. The flood peak discharge and flood volume average of the three rainstorms events reached 7.02% and 11.93%, respectively. Furthermore, the changes in flood peak discharge and flood volume were more obvious at the sub-catchment scale. These findings indicate that urban growth enhanced the possible flooding risk in the study catchment. This study improves the understanding of the isolated impacts of urbanization on flooding and provides essential information for sustainable urban planning. Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Processes: Modelling and Applications)
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28 pages, 36399 KiB  
Article
Spatiotemporal Dynamics of NDVI, Soil Moisture and ENSO in Tropical South America
by Diana M. Álvarez and Germán Poveda
Remote Sens. 2022, 14(11), 2521; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112521 - 24 May 2022
Cited by 2 | Viewed by 2365
Abstract
We evaluated the coupled dynamics of vegetation dynamics (NDVI) and soil moisture (SMOS) at monthly resolution over different regions of tropical South America and the effects of the Eastern Pacific (EP) and the Central Pacific (CP) El Niño–Southern Oscillation (ENSO) events. We used [...] Read more.
We evaluated the coupled dynamics of vegetation dynamics (NDVI) and soil moisture (SMOS) at monthly resolution over different regions of tropical South America and the effects of the Eastern Pacific (EP) and the Central Pacific (CP) El Niño–Southern Oscillation (ENSO) events. We used linear Pearson cross-correlation, wavelet and cross wavelet analysis (CWA) and three nonlinear causality methods: ParrCorr, GPDC and PCMCIplus. Results showed that NDVI peaks when SMOS is transitioning from maximum to minimum monthly values, which confirms the role of SMOS in the hydrological dynamics of the Amazonian greening up during the dry season. Linear correlations showed significant positive values when SMOS leads NDVI by 1–3 months. Wavelet analysis evidenced strong 12- and 64-month frequency bands throughout the entire record length, in particular for SMOS, whereas the CWA analyses indicated that both variables exhibit a strong coherency at a wide range of frequency bands from 2 to 32 months. Linear and nonlinear causality measures also showed that ENSO effects are greater on SMOS. Lagged cross-correlations displayed that western (eastern) regions are more associated with the CP (EP), and that the effects of ENSO manifest as a travelling wave over time, from northwest (earlier) to southeast (later) over tropical South America and the Amazon River basin. The ParrCorr and PCMCIplus methods produced the most coherent results, and allowed us to conclude that: (1) the nonlinear temporal persistence (memory) of soil moisture is stronger than that of NDVI; (2) the existence of two-way nonlinear causalities between NDVI and SMOS; (3) diverse causal links between both variables and the ENSO indices: CP (7/12 with ParrCorr; 6/12 with PCMCIplus), and less with EP (5/12 with ParrCorr; 3/12 with PCMCIplus). Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Processes: Modelling and Applications)
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20 pages, 63213 KiB  
Article
A Hybrid Triple Collocation-Deep Learning Approach for Improving Soil Moisture Estimation from Satellite and Model-Based Data
by Wenting Ming, Xuan Ji, Mingda Zhang, Yungang Li, Chang Liu, Yinfei Wang and Jiqiu Li
Remote Sens. 2022, 14(7), 1744; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071744 - 05 Apr 2022
Cited by 14 | Viewed by 2731
Abstract
Satellite retrieval and land surface models have become the mainstream methods for monitoring soil moisture (SM) over large regions; however, the uncertainty and coarse spatial resolution of these products limit their applications at the regional and local scales. We proposed a hybrid approach [...] Read more.
Satellite retrieval and land surface models have become the mainstream methods for monitoring soil moisture (SM) over large regions; however, the uncertainty and coarse spatial resolution of these products limit their applications at the regional and local scales. We proposed a hybrid approach combining the triple collocation (TC) and the long short-term memory (LSTM) network, which was designed to generate a high-quality SM dataset from satellite and modeled data. We applied the proposed approach to merge SM data from Soil Moisture Active Passive (SMAP), Global Land Data Assimilation System-Noah (GLDAS-Noah), and the land component of the fifth generation of European Reanalysis (ERA5-Land), and we then downscaled the merged SM data from 0.36° to 0.01° resolution based on the relationship between the SM data and auxiliary environmental variables (elevation, land surface temperature, vegetation index, surface albedo, and soil texture). The merged and downscaled SM results were validated against in situ observations. The results showed that: (1) the TC-based validation results were consistent with the in situ-based validation, indicating that the TC method was reasonable for the comparison and evaluation of satellite and modeled SM data. (2) TC-based merging was superior to simple arithmetic average merging when the parent products had large differences. (3) Downscaled SM of the TC-based merged product had better performance than that of the parent products in terms of ubRMSE and bias values, implying that the fusion of satellite and model-based SM data would result in better downscaling accuracy. (4) Downscaled SM of TC-based merged data not only improved the representation of the SM spatial variability but also had satisfactory accuracy with a median of R (0.7244), ubRMSE (0.0459 m3/m3), and bias (−0.0126 m3/m3). The proposed approach was effective for generating a SM dataset with fine resolution and reliable accuracy for wide hydrometeorological applications. Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Processes: Modelling and Applications)
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31 pages, 6054 KiB  
Article
Calibration and Validation of SWAT Model by Using Hydrological Remote Sensing Observables in the Lake Chad Basin
by Ali Bennour, Li Jia, Massimo Menenti, Chaolei Zheng, Yelong Zeng, Beatrice Asenso Barnieh and Min Jiang
Remote Sens. 2022, 14(6), 1511; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061511 - 21 Mar 2022
Cited by 21 | Viewed by 4515
Abstract
Model calibration and validation are challenging in poorly gauged basins. We developed and applied a new approach to calibrate hydrological models using distributed geospatial remote sensing data. The Soil and Water Assessment Tool (SWAT) model was calibrated using only twelve months of remote [...] Read more.
Model calibration and validation are challenging in poorly gauged basins. We developed and applied a new approach to calibrate hydrological models using distributed geospatial remote sensing data. The Soil and Water Assessment Tool (SWAT) model was calibrated using only twelve months of remote sensing data on actual evapotranspiration (ETa) geospatially distributed in the 37 sub-basins of the Lake Chad Basin in Africa. Global sensitivity analysis was conducted to identify influential model parameters by applying the Sequential Uncertainty Fitting Algorithm–version 2 (SUFI-2), included in the SWAT-Calibration and Uncertainty Program (SWAT-CUP). This procedure is designed to deal with spatially variable parameters and estimates either multiplicative or additive corrections applicable to the entire model domain, which limits the number of unknowns while preserving spatial variability. The sensitivity analysis led us to identify fifteen influential parameters, which were selected for calibration. The optimized parameters gave the best model performance on the basis of the high Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and determination coefficient (R2). Four sets of remote sensing ETa data products were applied in model calibration, i.e., ETMonitor, GLEAM, SSEBop, and WaPOR. Overall, the new approach of using remote sensing ETa for a limited period of time was robust and gave a very good performance, with R2 > 0.9, NSE > 0.8, and KGE > 0.75 applying to the SWAT ETa vs. the ETMonitor ETa and GLEAM ETa. The ETMonitor ETa was finally adopted for further model applications. The calibrated SWAT model was then validated during 2010–2015 against remote sensing data on total water storage change (TWSC) with acceptable performance, i.e., R2 = 0.57 and NSE = 0.55, and remote sensing soil moisture data with R2 and NSE greater than 0.85. Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Processes: Modelling and Applications)
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19 pages, 3359 KiB  
Article
Evaluating the Potential of GloFAS-ERA5 River Discharge Reanalysis Data for Calibrating the SWAT Model in the Grande San Miguel River Basin (El Salvador)
by Javier Senent-Aparicio, Pablo Blanco-Gómez, Adrián López-Ballesteros, Patricia Jimeno-Sáez and Julio Pérez-Sánchez
Remote Sens. 2021, 13(16), 3299; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163299 - 20 Aug 2021
Cited by 19 | Viewed by 2936
Abstract
Hydrological modelling requires accurate climate data with high spatial-temporal resolution, which is often unavailable in certain parts of the world—such as Central America. Numerous studies have previously demonstrated that in hydrological modelling, global weather reanalysis data provides a viable alternative to observed data. [...] Read more.
Hydrological modelling requires accurate climate data with high spatial-temporal resolution, which is often unavailable in certain parts of the world—such as Central America. Numerous studies have previously demonstrated that in hydrological modelling, global weather reanalysis data provides a viable alternative to observed data. However, calibrating and validating models requires the use of observed discharge data, which is also frequently unavailable. Recent, global-scale applications have been developed based on weather data from reanalysis; these applications allow streamflows with satisfactory resolution to be obtained. An example is the Global Flood Awareness System (GloFAS), which uses the fifth generation of reanalysis data produced by the European Centre for Medium-Range Weather Forecasts (ERA5) as input. It provides discharge data from 1979 to the present with a resolution of 0.1°. This study assesses the potential of GloFAS for calibrating hydrological models in ungauged basins. For this purpose, the quality of data from ERA5 and from the Climate Hazards Group InfraRed Precipitation and Temperature with Station as well as the Climate Forecast System Reanalysis (CFSR) was analysed. The focus was on flow simulation using the Soil and Water Assessment Tool (SWAT) model. The models were calibrated using GloFAS discharge data. Our results indicate that all the reanalysis datasets displayed an acceptable fit with the observed precipitation and temperature data. The correlation coefficient (CC) between the reanalysis data and the observed data indicates a strong relationship at the monthly level all of the analysed stations (CC > 0.80). The Kling–Gupta Efficiency (KGE) also showed the acceptable performance of the calibrated SWAT models (KGE > 0.74). We concluded that GloFAS data has substantial potential for calibrating hydrological models that estimate the monthly streamflow in ungauged watersheds. This approach can aid water resource management. Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Processes: Modelling and Applications)
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22 pages, 6799 KiB  
Article
Multiple Data Products Reveal Long-Term Variation Characteristics of Terrestrial Water Storage and Its Dominant Factors in Data-Scarce Alpine Regions
by Xuanxuan Wang, Liu Liu, Qiankun Niu, Hao Li and Zongxue Xu
Remote Sens. 2021, 13(12), 2356; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122356 - 16 Jun 2021
Cited by 6 | Viewed by 2184
Abstract
As the “Water Tower of Asia” and “The Third Pole” of the world, the Qinghai–Tibet Plateau (QTP) shows great sensitivity to global climate change, and the change in its terrestrial water storage has become a focus of attention globally. Differences in multi-source data [...] Read more.
As the “Water Tower of Asia” and “The Third Pole” of the world, the Qinghai–Tibet Plateau (QTP) shows great sensitivity to global climate change, and the change in its terrestrial water storage has become a focus of attention globally. Differences in multi-source data and different calculation methods have caused great uncertainty in the accurate estimation of terrestrial water storage. In this study, the Yarlung Zangbo River Basin (YZRB), located in the southeast of the QTP, was selected as the study area, with the aim of investigating the spatio-temporal variation characteristics of terrestrial water storage change (TWSC). Gravity Recovery and Climate Experiment (GRACE) data from 2003 to 2017, combined with the fifth-generation reanalysis product of the European Centre for Medium-Range Weather Forecasts (ERA5) data and Global Land Data Assimilation System (GLDAS) data, were adopted for the performance evaluation of TWSC estimation. Based on ERA5 and GLDAS, the terrestrial water balance method (PER) and the summation method (SS) were used to estimate terrestrial water storage, obtaining four sets of TWSC, which were compared with TWSC derived from GRACE. The results show that the TWSC estimated by the SS method based on GLDAS is most consistent with the results of GRACE. The time-lag effect was identified in the TWSC estimated by the PER method based on ERA5 and GLDAS, respectively, with 2-month and 3-month lags. Therefore, based on the GLDAS, the SS method was used to further explore the long-term temporal and spatial evolution of TWSC in the YZRB. During the period of 1948–2017, TWSC showed a significantly increasing trend; however, an abrupt change in TWSC was detected around 2002. That is, TWSC showed a significantly increasing trend before 2002 (slope = 0.0236 mm/month, p < 0.01) but a significantly decreasing trend (slope = −0.397 mm/month, p < 0.01) after 2002. Additional attribution analysis on the abrupt change in TWSC before and after 2002 was conducted, indicating that, compared with the snow water equivalent, the soil moisture dominated the long-term variation of TWSC. In terms of spatial distribution, TWSC showed a large spatial heterogeneity, mainly in the middle reaches with a high intensity of human activities and the Parlung Zangbo River Basin, distributed with great glaciers. The results obtained in this study can provide reliable data support and technical means for exploring the spatio-temporal evolution mechanism of terrestrial water storage in data-scarce alpine regions. Full article
(This article belongs to the Special Issue Remote Sensing of Hydrological Processes: Modelling and Applications)
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