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Remote Sensing of Water Cycle Components and Its Application in Hydrological Modeling

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 34747

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Special Issue Editors

Dr. Zheng Duan

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Department of Physical Geography and Ecosystem Science, Lund University, S-22362 Lund, Sweden
Interests: environmental remote sensing; water quality sensing; machine learning; ecohydrological modelling
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Junzhi Liu

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Guest Editor

School of Geography, Nanjing Normal University, Nanjing, China
Interests: land–surface modeling; ecohydrology

Prof. Dr. Hongkai Gao

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Guest Editor

School of Geographical Sciences, East China Normal University, Shanghai, China
Interests: rainfall–runoff modeling; glacier hydrology; ecohydrology; remote sensing hydrology

Prof. Dr. Shanhu Jiang

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Guest Editor

College of Hydrology and Water Resources, Hohai University, Nanjing, China
Interests: hydrological modeling; flood and drought disaster monitoring; remote sensing hydrology; climate change

Dr. Jian Peng

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Guest Editor

School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK
Interests: remote sensing; water cycle; climate extremes; land–atmosphere interaction; land surface modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Jianzhi Dong

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Guest Editor

USDA Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA
Interests: data assimilation; remote sensing; soil moisture; land-atmosphere coupling; land surface modeling; hydrological modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

The water cycle or hydrological cycle involves the continuous movement of water on, above, and below the surface of the Earth. In general, hydrological cycle components (e.g., precipitation, evaporation, water storage, and runoff) are characterized by large temporal and spatial variability. Accurate monitoring of various hydrological cycle components and developing hydrological models are important for improving our understanding of hydrological processes. With significant development of sensor technology and sharply growing platforms in past decades, remote sensing offers enhanced capability to monitor various hydrological cycle components at different temporal and spatial scales to complement conventional in situ measurements. Considerable efforts have been made to explore the potentials of remotely sensed data from a vast range of different platforms (e.g., satellite, airborne, drone, ground-based radar) and sensors (e.g., optical, infrared, microwave) in advancing hydrology research, particularly in poorly gauged and ungauged regions. The application of remote sensing in hydrology is expected to increase with enhanced recognition of its potentials and continuous development of advanced sensors (e.g., new satellite missions) and retrieval methods (e.g., innovative machine learning and data assimilation techniques).

The aim of this Special Issue is to present and discuss recent advances in the remote sensing of hydrological cycle components as well as the application of remote sensing in hydrological modeling. We encourage studies to investigate the performance of remotely sensed data in multi-variable calibration and spatial evaluation of hydrological models. Manuscripts related to all aspects of the aforementioned topics (e.g., methods development, evaluation, integration, application) are welcome.

Dr. Zheng Duan
Prof. Dr. Junzhi Liu
Prof. Dr. Hongkai Gao
Prof. Dr. Shanhu Jiang
Dr. Jian Peng
Dr. Jianzhi Dong
Guest Editors

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Keywords

remote sensing
hydrology
water cycle
land–atmosphere interaction
inland water bodies
hydrological modeling and applications
evaluation
integration
calibration
uncertainty

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Published Papers (11 papers)

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Research

22 pages, 15476 KiB

Open AccessArticle

Attributing the Impacts of Vegetation and Climate Changes on the Spatial Heterogeneity of Terrestrial Water Storage over the Tibetan Plateau

by Yuna Han, Depeng Zuo, Zongxue Xu, Guoqing Wang, Dingzhi Peng, Bo Pang and Hong Yang

Remote Sens. 2023, 15(1), 117; https://doi.org/10.3390/rs15010117 - 26 Dec 2022

Cited by 6 | Viewed by 3077

Abstract

Terrestrial water storage (TWS) is of great importance to the global water and energy budget, which modulates the hydrological cycle and then determines the spatiotemporal distributions of water resources availability. The Tibetan Plateau is the birthplace of the Yangtze, Yellow, and Lancang–Mekong River, where the water resources are directly related to the life of the Eastern and Southeastern Asian people. Based on multi-source datasets during the period 1981–2015, the long-term spatiotemporal variabilities of the TWS over the Tibetan Plateau were investigated by the Sen’s slope and Mann–Kendall test trend analysis methods; the changing mechanisms were explored from two perspectives of components analysis and the hydrological cycle. The water conservation capacity of vegetation in the alpine mountainous areas was also discussed by geostatistical methods such as correlation analysis, extracted by attributes and zonal statistics. The results show that the TWS of the Tibetan Plateau increased with the speed of 0.7 mm/yr as the precipitation accumulated and the glaciers melted during the period 1981–2015. The TWS values were low and generally present a trend of obvious accumulation over the northern Tibetan Plateau, while the high and decreasing values were distributed in the south of Tibetan Plateau. The results of the components analysis indicate that the TWS mainly consisted of soil moisture at one-fourth layers, which are 0–200 cm underground in most areas of the Tibetan Plateau. The precipitation is mainly lost through evapotranspiration over the northern Tibetan Plateau, while in the northwestern corner of the Tibetan Plateau, the Himalayas, and northeastern Yarlung Zangbo River basin, the runoff coefficients were larger than 1.0 due to the influence of snow melting. In the alpine mountains, different climate and vegetation conditions have complex effects on water resources. The results are helpful for understanding the changing mechanism of water storage over the Tibetan Plateau and have scientific meaning for the development, utilization, and protection of regional water resources. Full article

(This article belongs to the Special Issue Remote Sensing of Water Cycle Components and Its Application in Hydrological Modeling)

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28 pages, 31277 KiB

Open AccessArticle

Total Phosphorus and Nitrogen Dynamics and Influencing Factors in Dongting Lake Using Landsat Data

by Yuanyuan Zhang, Shuanggen Jin, Ning Wang, Jiarui Zhao, Hongwei Guo and Petri Pellikka

Remote Sens. 2022, 14(22), 5648; https://doi.org/10.3390/rs14225648 - 9 Nov 2022

Cited by 14 | Viewed by 3236

Abstract

Total phosphorus (TP) and total nitrogen (TN) reflect the state of eutrophication. However, traditional point-based water quality monitoring methods are time-consuming and labor-intensive, and insufficient to estimate and assess water quality at a large scale. In this paper, we constructed machine learning models for TP and TN inversion using measured data and satellite imagery band reflectance, and verified it by in situ data. Atmospheric correction was performed on the Landsat Top of Atmosphere (TOP) data by removing the effect of the adjacency effect and correcting differences between Landsat sensors. Then, using the established model, the TP and TN patterns in Dongting Lake with a spatial resolution of 30 m from 1996 to 2021 were derived for the first time. The annual and monthly spatio-temporal variation characteristics of TP and TN in Dongting Lake were investigated in details, and the influences of hydrometeorological elements on water quality variations were analyzed. The results show that the established empirical model can accurately estimate TP with coefficient (R²) ≥ 0.70, root mean square error (RMSE) ≤ 0.057 mg/L, mean relative error (MRE) ≤ 0.23 and TN with R² ≥ 0.73, RMSE ≤ 0.48 mg/L and MRE ≤ 0.20. From 1996 to 2021, TP in Dongting Lake showed a downward trend and TN showed an upward trend, while the summer value was much higher than the other seasons. Furthermore, the influencing factors on TP and TN variations were investigated and discussed. Between 1996 and 2003, the main contributors to the change of water quality in Dongting Lake were external inputs such as water level and flow. The significant changes in water quantity and sediment characteristics following the operation of the Three Gorges Dam (TGD) in 2003 also had an impact on the water quality in Dongting Lake. Full article

(This article belongs to the Special Issue Remote Sensing of Water Cycle Components and Its Application in Hydrological Modeling)

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22 pages, 30284 KiB

Open AccessArticle

Satellite Soil Moisture Data Reconstruction in the Temporal and Spatial Domains: Latent Error Assessments and Performances for Tracing Rainstorms and Droughts

by Yi Liu, Ruiqi Chen, Shanshui Yuan, Liliang Ren, Xiaoxiang Zhang, Changjun Liu and Qiang Ma

Remote Sens. 2022, 14(19), 4841; https://doi.org/10.3390/rs14194841 - 28 Sep 2022

Cited by 3 | Viewed by 2168

Abstract

Intermittent records of satellite soil moisture data are major obstacles that constrain their hydrometeorological applications. Based on the European Space Agency Climate Change Initiative (ESA CCI) soil moisture combined product, two machine learning models were employed to reconstruct soil moisture in China during 1979–2019 in both temporal and spatial domains, and latent errors for reconstructed series, as well as their performances for tracing climate extremes, were analyzed. The results showed that with the homogeneity of available data over space, the spatial approach performed well in reproducing the spatial heterogeneity of soil moisture (with medians of the correlation coefficient (CC) above 0.8 and root mean square errors (RMSEs) ranging from 0.02 to 0.03 m³∙m⁻³). The temporal approach (CC values of 0.7 and RMSEs ranging between 0.02 and 0.03 m³∙m⁻³) was superior in capturing the seasonality features and the timely and accurate mapping of short-term soil moisture dynamics impacted by rainstorms. However, both approaches failed to identify the location and severity of droughts accurately. The findings highlight the benefits of combining the strengths of both temporal and spatial gap-filling approaches for improving the estimation of missing values and hydrometeorological applications. Full article

(This article belongs to the Special Issue Remote Sensing of Water Cycle Components and Its Application in Hydrological Modeling)

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19 pages, 4807 KiB

Open AccessArticle

Assessing the Potential of IMERG and TMPA Satellite Precipitation Products for Flood Simulations and Frequency Analyses over a Typical Humid Basin in South China

by Shanhu Jiang, Yu Ding, Ruolan Liu, Linyong Wei, Yating Liu, Mingming Ren and Liliang Ren

Remote Sens. 2022, 14(17), 4406; https://doi.org/10.3390/rs14174406 - 4 Sep 2022

Cited by 12 | Viewed by 2173

Abstract

The availability of the new generation Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06 products facilitates the utility of long-term higher spatial and temporal resolution precipitation data (0.1° × 0.1° and half-hourly) for monitoring and modeling extreme hydrological events in data-sparse watersheds. This study aims to evaluate the utility of IMERG Final run (IMERG-F), Late run (IMERG-L) and Early run (IMERG-E) products, in flood simulations and frequency analyses over the Mishui basin in Southern China during 2000–2017, in comparison with their predecessors, the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) products (3B42RT and 3B42V7). First, the accuracy of the five satellite precipitation products (SPPs) for daily precipitation and extreme precipitation events estimation was systematically compared by using high-density gauge station observations. Once completed, the modeling capability of the SPPs in daily streamflow simulations and flood event simulations, using a grid-based Xinanjiang model, was assessed. Finally, the flood frequency analysis utility of the SPPs was evaluated. The assessment of the daily precipitation accuracy shows that IMERG-F has the optimum statistical performance, with the highest CC (0.71) and the lowest RMSE (8.7 mm), respectively. In evaluating extreme precipitation events, among the IMERG series, IMERG-E exhibits the most noticeable variation while IMERG-L and IMERG-F display a relatively low variation. The 3B42RT exhibits a severe inaccuracy and the improvement of 3B42V7 over 3B42RT is comparatively limited. Concerning the daily streamflow simulations, IMERG-F demonstrates a superior performance while 3B42V7 tends to seriously underestimate the streamflow. With regards to the simulations of flood events, IMERG-F has performed optimally, with an average DC of 0.83. Among the near-real-time SPPs, IMERG-L outperforms IMERG-E and 3B42RT over most floods, attaining a mean DC of 0.81. Furthermore, IMERG-L performs the best in the flood frequency analyses, where bias is within 15% for return periods ranging from 2–100 years. This study is expected to contribute practical guidance to the new generation of SPPs for extreme precipitation monitoring and flood simulations as well as promoting the hydro-meteorological applications. Full article

(This article belongs to the Special Issue Remote Sensing of Water Cycle Components and Its Application in Hydrological Modeling)

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20 pages, 6252 KiB

Open AccessArticle

Increasing Streamflow in Poor Vegetated Mountain Basins Induced by Greening of Underlying Surface

by Lilin Zheng, Jianhua Xu, Yaning Chen and Zhenhui Wu

Remote Sens. 2022, 14(13), 3223; https://doi.org/10.3390/rs14133223 - 4 Jul 2022

Cited by 4 | Viewed by 2439

Abstract

Arid ecosystems have exhibited greening trends in recent decades. There is no consensus on how underlying surface changes influence streamflow across vegetation gradients. We investigated this issue for the four typical arid mountain basins using a 30-year runoff database and the Budyko framework to quantify the contributions of climate and underlying surface changes to streamflow variations during summer periods. Results showed that in the poor vegetated basins, i.e., Heizi Basin and Kuche Basin, the underlying surface change has increased summer streamflow by 14.01 and 35.67 mm, respectively; climate contributed only −7.32 and 1.86 mm to summer streamflow changes, respectively. Comparatively, in the well-vegetated basins, i.e., Huangshui Basin and Kaidu Basin, climate change dominated summer streamflow variations by increasing 21.50 and 24.65 mm, respectively; the underlying surface change only increased summer streamflow by 3.72 and 1.56 mm, respectively. Additionally, the decomposition results were extended to monthly scale (from June to September) to reveal the effects of climate and underlying surface changes on monthly streamflow. This study deepens our knowledge of runoff responses, which can provide important references to support water resources management in other regions that receive water from mountains. Full article

(This article belongs to the Special Issue Remote Sensing of Water Cycle Components and Its Application in Hydrological Modeling)

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24 pages, 10297 KiB

Open AccessArticle

Simulating River/Lake–Groundwater Exchanges in Arid River Basins: An Improvement Constrained by Lake Surface Area Dynamics and Evapotranspiration

by Peter Vasilevskiy, Ping Wang, Sergey Pozdniakov, Tianye Wang, Yichi Zhang, Xuejing Zhang and Jingjie Yu

Remote Sens. 2022, 14(7), 1657; https://doi.org/10.3390/rs14071657 - 30 Mar 2022

Cited by 8 | Viewed by 2662

Abstract

Surface water–groundwater interactions in arid zones are characterized by water exchange processes in a complex system comprising intermittent streams/terminal lakes, shallow aquifers, riparian zone evapotranspiration, and groundwater withdrawal. Notable challenges arise when simulating such hydrological systems; for example, field observations are scarce, and hydrogeological parameters exhibit considerable spatial heterogeneity. To reduce the simulation uncertainties, in addition to groundwater head and river discharge measurements, we adopted remote sensing-based evapotranspiration data and lake area dynamics as known conditions to calibrate the model. We chose the Ejina Basin, located in the lower reaches of the Heihe River Basin in arid northwest China, as the study area to validate our modelling approach. The hydrological system of this basin is characterized by intensive, spatiotemporally variable surface water–groundwater interactions. The areas of the terminal lakes into which all river runoff flows vary significantly depending on the ratio between river runoff and lake evaporation. Simulation results with a monthly time step from 2000 to 2017 indicate that river leakage accounted for approximately 61% of the total river runoff. Our study shows that for areas where surface water and groundwater observations are sparse, combining remote sensing product data of surface water areas and evapotranspiration can effectively reduce the uncertainty in coupled surface water and groundwater simulations. Full article

(This article belongs to the Special Issue Remote Sensing of Water Cycle Components and Its Application in Hydrological Modeling)

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22 pages, 7358 KiB

Open AccessArticle

Towards Consistent Soil Moisture Records from China’s FengYun-3 Microwave Observations

by Guojie Wang, Xiaowen Ma, Daniel Fiifi Tawia Hagan, Robin van der Schalie, Giri Kattel, Waheed Ullah, Liangliang Tao, Lijuan Miao and Yi Liu

Remote Sens. 2022, 14(5), 1225; https://doi.org/10.3390/rs14051225 - 2 Mar 2022

Cited by 3 | Viewed by 3106

Abstract

Soil moisture plays an essential role in the land-atmosphere interface. It has become necessary to develop quality large-scale soil moisture data from satellite observations for relevant applications in climate, hydrology, agriculture, etc. Specifically, microwave-based observations provide more consistent land surface records because they are unhindered by cloud conditions. The recent microwave radiometers onboard FY-3B, FY-3C and FY-3D satellites launched by China’s Meteorological Administration (CMA) extend the number of available microwave observations, covering late 2011 up until the present. These microwave observations have the potential to provide consistent global soil moisture records to date, filling the data gaps where soil moisture estimates are missing in the existing records. Along these lines, we studied the FY-3C to understand its added value due to its unique time of observation in a day (ascending: 22:15, descending: 10:15) absent from the existing satellite soil moisture records. Here, we used the triple collocation technique to optimize a benchmark retrieval model of land surface temperature (LST) tailored to the observation time of FY3C, by evaluating various soil moisture scenarios obtained with different bias-imposed LSTs from 2014 to 2016. The globally optimized LST was used as an input for the land parameter retrieval model (LPRM) algorithm to obtain optimized global soil moisture estimates. The obtained FY-3C soil moisture observations were evaluated with global in situ and reanalysis datasets relative to FY3B soil moisture products to understand their differences and consistencies. We found that the RMSEs of their anomalies were mostly concentrated between 0.05 and 0.15 m³ m⁻³, and correlation coefficients were between 0.4 and 0.7. The results showed that the FY-3C ascending data could better capture soil moisture dynamics than the FY-3B estimates. Both products were found to consistently complement the skill of each other over space and time globally. Finally, a linear combination approach that maximizes temporal correlations merged the ascending and descending soil moisture observations separately. The results indicated that superior soil moisture estimates are obtained from the combined product, which provides more reliable global soil moisture records both day and night. Therefore, this study aims to show that there is merit to the combined usage of the two FY-3 products, which will be extended to the FY-3D, to fill the gap in existing long-term global satellite soil moisture records. Full article

(This article belongs to the Special Issue Remote Sensing of Water Cycle Components and Its Application in Hydrological Modeling)

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12 pages, 4789 KiB

Open AccessCommunication

Spatiotemporal Variations in Snow Cover and Hydrological Effects in the Upstream Region of the Shule River Catchment, Northwestern China

by Youyan Jiang, Wentao Du, Jizu Chen and Wenxuan Sun

Remote Sens. 2021, 13(16), 3212; https://doi.org/10.3390/rs13163212 - 13 Aug 2021

Cited by 7 | Viewed by 2100

Abstract

Precipitation and snow/ice melt water are the primary water sources in inland river basins in arid areas, and these are sensitive to global climate change. A dataset of snow cover in the upstream region of the Shule River catchment was established using MOD10A2 data from 2000 to 2019, and the spatiotemporal variations in the snow cover and its meteorological, runoff, and topographic impacts were analyzed. The results show that the spatial distribution of the snow cover is highly uneven owing to altitude differences. The snow cover in spring and autumn is mainly concentrated along the edges of the region, whereas that in winter and summer is mainly distributed in the south. Notable differences in snow accumulation and melting are observed at different altitudes, and the annual variation in the snow cover extent shows bimodal characteristics. The correlation between the snow cover extent and runoff is most significant in April. The snow cover effectively replenishes the runoff at higher altitudes (3300–4900 m), but this contribution weakens with increasing altitude (>4900 m). The regions with a high snow cover frequency are mostly concentrated at high altitudes. Regions with slopes of <30° show a strong correlation with the snow cover frequency, which decreases for slopes of >45°. The snow cover frequency and slope aspect show symmetrical changes. Full article

(This article belongs to the Special Issue Remote Sensing of Water Cycle Components and Its Application in Hydrological Modeling)

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20 pages, 10151 KiB

Open AccessArticle

Water Quality Variability and Related Factors along the Yangtze River Using Landsat-8

by Yang He, Shuanggen Jin and Wei Shang

Remote Sens. 2021, 13(12), 2241; https://doi.org/10.3390/rs13122241 - 8 Jun 2021

Cited by 30 | Viewed by 4305

Abstract

Chlorophyll-a (Chl-a), total nitrogen (TN), and total phosphorus (TP) are important indicators to evaluate water environmental quality. Monitoring water quality and its variability can help control water pollution. However, traditional monitoring techniques of water quality are time-consuming and laborious, and can mostly conduct with sample point-to-point at the edge of lakes and rivers. In this study, an empirical (regression-based) model is proposed to retrieve Chl-a, TN, and TP concentrations in the Yangtze River by Landsat-8 images from 2014 to 2020. The spatial-temporal distribution and variability of water quality in the whole Yangtze River are analyzed in detail. Furthermore, the driving forces of water quality variations are explored. The results show that the mean absolute percentage error (MAPE) of the water quality parameters are 25.88%, 4.3%, and 8.37% for Chl-a, TN, and TP concentrations, respectively, and the root mean square errors (RMSE) are 0.475 μg/L, 0.110 mg/L, and 0.01 mg/L, respectively. The concentrations of Chl-a, TN, and TP in the upstream of the Yangtze River are lower than those in the midstream and downstream. These water quality parameters have a seasonal cycle with a maximum in summer and minimum in winter. The hydrological and meteorological factors such as water level, flow, temperature, and precipitation are positively correlated with Chl-a, TN, and TP concentrations. The larger the impervious surface and cropland area, the greater the cargo handling capacity (CHC), higher ratio of TP to TN will further pollute the water. The methods and results provide essential information to evaluate and control water pollution in the Yangtze River. Full article

(This article belongs to the Special Issue Remote Sensing of Water Cycle Components and Its Application in Hydrological Modeling)

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21 pages, 6374 KiB

Open AccessArticle

Comparing Groundwater Storage Changes in Two Main Grain Producing Areas in China: Implications for Sustainable Agricultural Water Resources Management

by Longqun Zheng, Yun Pan, Huili Gong, Zhiyong Huang and Chong Zhang

Remote Sens. 2020, 12(13), 2151; https://doi.org/10.3390/rs12132151 - 4 Jul 2020

Cited by 31 | Viewed by 3887

Abstract

Balancing groundwater supply and food production is challenging, especially in large regions where there is often insufficient information on the groundwater budget, such as in the North China Plain (NCP) and the Northeast China Plain (NECP), which are major food producing areas in China. This study aimed to understand this process in a simple but efficient way by using Gravity Recovery and Climate Experiment (GRACE) data, and it focused on historical and projected groundwater storage (GWS) changes in response to changes in grain-sown areas. The results showed that during 2003–2016, the GWS was depleted in the NCP at a rate of −17.2 ± 0.8 mm/yr despite a decrease in groundwater abstraction along with an increase in food production and a stable sown area, while in the NECP, the GWS increased by 2.3 ± 0.7 mm/yr and the groundwater abstraction, food production and the sown area also increased. The scenario simulation using GRACE-derived GWS anomalies during 2003–2016 as the baseline showed that the GWS changes in the NCP can be balanced (i.e., no decreasing trend in storage) by reducing the area of winter wheat and maize by 1.31 × 10⁶ ha and 3.21 × 10⁶ ha, respectively, or by reducing both by 0.93 × 10⁶ ha. In the NECP, the groundwater can sustain an additional area of 0.62 × 10⁶ ha of maize without a decrease in storage. The results also revealed that the current groundwater management policies cannot facilitate the recovery of the GWS in the NCP unless the sown ratio of drought-resistance wheat is increased from 90% to 95%. This study highlights the effectiveness of using GRACE to understanding the nexus between groundwater supply and food production at large scales. Full article

(This article belongs to the Special Issue Remote Sensing of Water Cycle Components and Its Application in Hydrological Modeling)

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24 pages, 6048 KiB

Open AccessArticle

Precipitation Dominates Long-Term Water Storage Changes in Nam Co Lake (Tibetan Plateau) Accompanied by Intensified Cryosphere Melts Revealed by a Basin-Wide Hydrological Modelling

by Xiaoyang Zhong, Lei Wang, Jing Zhou, Xiuping Li, Jia Qi, Lei Song and Yuanwei Wang

Remote Sens. 2020, 12(12), 1926; https://doi.org/10.3390/rs12121926 - 14 Jun 2020

Cited by 15 | Viewed by 3574

Abstract

Lakes on the Tibetan Plateau (TP) have changed dramatically as a result of climate change during recent decades. Studying the changes in long-term lake water storage (LWS) is of great importance for regional water security and ecosystems. Nam Co Lake is the second largest lake in the central TP. To investigate the long-term changes in LWS, a distributed cryosphere-hydrology model (WEB-DHM) driven by multi-source data was evaluated and then applied to simulate hydrological processes across the whole Nam Co Lake basin from 1980 to 2016. Firstly, a comparison of runoff (lake inflow), land surface temperature, and snow depth between the model simulations and observations or remote sensing products showed that WEB-DHM could accurately simulate hydrological processes in the basin. Meanwhile, the simulated daily LWS was in good agreement with satellite-derived data during 2000–2016. Secondly, long-term simulations showed that LWS increased by 9.26 km³ during 1980–2016, reaching a maximum in 2010 that was 10.25 km³ greater than that in 1980. During this period, LWS firstly decreased (1980–1987), then increased (1988–2008), and decreased again (2009–2016). Thirdly, the contributions of precipitation runoff, melt-water runoff, lake surface precipitation, and lake evaporation to Nam Co LWS were 71%, 33%, 24%, and -28%, respectively. Snow and glacier melting have significantly intensified during recent decades (2.96 m³ s⁻¹/decade on average), contributing a mean proportion of 22% of lake inflows. These findings are consistent with the significant increasing trends of annual precipitation and temperature in the lake basin (25 mm/decade and 0.4 K/decade, respectively). We conclude that long-term variations in Nam Co LWS during 1980–2016 were largely controlled by precipitation; however, the contribution of precipitation runoff to total lake inflow has decreased while the contribution from warming-induced snow and glacier melting has significantly increased. Full article

(This article belongs to the Special Issue Remote Sensing of Water Cycle Components and Its Application in Hydrological Modeling)

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