Accurate, large-scale, and temporally explicit lake mapping is critical for water resource management, hazard risk assessment, and understanding lake responses to climate change. The Tibetan Plateau (TP) hosts a high density of lakes, many of which are small and difficult to detect due to highly heterogeneous environments, including mountain shadows, glacier and snow cover, clouds, and turbid waters. Existing studies often produce multi-year composite datasets, which enhance the detectability of lakes by emphasizing their long-term occurrence. However, these datasets ultimately remain static, reflecting the long-term aggregated distribution of lakes without capturing their spatial extent in any specific year. In this study, we processed 17,942 Sentinel-2 images acquired from July to October 2020 using an automated, deep learning-based water extraction framework to extract lakes across the TP. The method demonstrated high accuracy under challenging conditions, with Intersection over Union (IoU) values exceeding 92% in cloudy, glacial, and mountainous test areas. Applying this framework, we generated a comprehensive 2020 lake inventory, identifying 57,841 lakes larger than 0.01 km(2). Approximately 65% of the total lake coverage was concentrated in the Inner Plateau Basin, with the 4,500-5,000 m elevation exhibiting the highest density (29,200 lakes covering 32,892.51 km(2), representing 50.5% of all lakes and 59.3% of total lake area). Compared with previous studies, this dataset improves the detection of small lakes and provides a temporally explicit, year-specific map of lake distributions, distinguishing multiple lake types (large natural lakes, glacial lakes, thermokarst lakes, and reservoirs). This high-resolution, comprehensive dataset constitutes a valuable resource for hydrological, climatic, and ecohydrological research across TP. The lake dataset generated in this study has been archived and made publicly available through Zenodo (https://doi.org/10.5281/zenodo.15639602).

In this work, a comprehensive three-dimensional optical-thermal-luminescent coupled model was established for GAGG:Ce ceramic phosphors under blue laser excitation. By simultaneously solving the fluorescence radiative transfer equations (FRTEs) and the heat diffusion equation (HDE), alongside the incorporation of Mie scattering theory and the Maxwell-Eucken thermal conductivity model, the impact of chemical doping concentration and micro-structural features, such as porosity and anisotropy factor on the luminescent performance and spatial temperature distribution in phosphors was systematically investigated. Studies have shown that the specific phosphor material properties have a significant impact on the minimum photoluminescence (PL) spot size and phosphor temperature distribution.

In this study, long-term lake surface water temperature (LSWT) data were used to investigate the impact of climate change on thermal conditions in 25 Polish lowland lakes. The results show that the warming rate of the annual mean LSWT ranges from 0.14 °C per decade to 0.69 °C per decade with an average value of 0.44 °C per decade. The annual maximum LSWT presented the strongest warming trend, with the warming rate varying between 0.26 °C per decade and 1.06 °C per decade (average value of 0.65 °C per decade). Warming rates were observed in all seasons but with different intensities, with warming rates increasing from spring to autumn and then to summer. The warming rate of the summer LSWT varied between 0.29 °C per decade and 0.87 °C per decade with an average value of 0.56 °C per decade. Conversely, winter and annual minimum LSWTs did not present clear increasing trends. The increase in the annual maximum, annual average, and seasonal LSWTs correlated well with the inter-annual variability in air temperature. To understand the relationship between LSWT and air temperature, the non-linear regression model (S-curve) was used in this study. The results indicate that the non-linear regression model can help to present the relationship between LSWT and air temperature in the studied lakes (the average values of the root mean squared error (RMSE), the mean absolute error (MAE), and the Nash–Sutcliffe efficiency coefficient (NSE) are 1.68 °C, 1.28 °C, and 0.95, respectively). The warming trends of LSWTs observed for the studied lakes in Poland are coherent and in some cases larger than the data from other lakes worldwide, and should be seriously considered by policy makers.

As global climate change intensifies, extreme climate events are becoming more frequent, presenting significant challenges to socioeconomic systems and ecosystems. Northeast China, a region highly sensitive to climate change, has been profoundly impacted by compound drought and heat extremes (CDHEs), affecting agriculture, society, and the economy. To evaluate the characteristics and evolution of summer CDHEs in this region, this study analyzed observational data from 81 meteorological stations (1961–2020) and developed a Standardized Temperature–Precipitation Index (STPI) using the Copula joint probability method. The STPI’s effectiveness in characterizing compound drought and heat conditions was validated against historical records. Using the constructed STPI, this study conducted a comprehensive analysis of the spatiotemporal distribution of CDHEs. The Theil–Sen median trend analysis, Mann–Kendall trend tests, and the frequency of CDHEs were employed to examine drought and heatwave patterns and their influence on compound events. The findings demonstrated an increase in the severity of compound drought and heat events over time. Although the STPI exhibited a slight interannual decline, its values remained above −2.0, indicating the continued intensification of these events in the study area. Most of the stations showed a non-significant decline in the Standardized Precipitation Index and a significant rise in the Standardized Temperature Index, indicating that rising temperatures primarily drive the increasing severity of compound drought and heat events. The 1990s marked a turning point with a significant increase in the frequency, severity, and spatial extent of these events.

Accurately identifying the spatial differences in the response of regional runoff to climate and land use changes can clarify the mechanism of regional runoff changes and provide a scientific basis for adopting the ap-propriate water resource protection policies.In this study,based on the Budyko theory,we quantitatively evaluated the spatial differences in the response of runoff to climate and land use changes in the Yiluo River Basin after 2000;calculated the sensitivity of runoff changes to precipitation(P),potential evapotranspiration(E0)and land use changes;and quantified the contributions of those three factors to runoff changes.The findings revealed that with decreasing elevation,precipitation gradually decreases,potential evapotranspiration gradually increases,and run-off gradually decreases in the Yiluo River basin.Influenced by the population density,both cultivated land and construction land are widely distributed with the middle and lower reaches of the basin,while the upper reaches are dominated by forest land.Compared with the base period(1985-1989),precipitation and potential evapotranspira-tion in the watershed during the change period(2000-2017)basically showed decreasing and increasing trends,respectively,with obvious spatial differentiation.P increased significantly in the upper reaches of the Yi River,with an average of 35.2 mm(‒83.8-84.7 mm),while P increased and decreased in the other five subbasins,but the decreasing trend was more prominent.Among the subbasins,the upper and middle reaches of the Luo River showed the largest reductions in P,with an average of ‒34.2 mm(‒145.9-20.6 mm),whereas the middle reaches of the Yi River showed the smallest reduction in P,with an average of ‒10.9 mm(‒84.2-59.5 mm).The E0 in the different regions during the change period showed an increasing trend,and the increase in E0 gradually decreased from the upper reaches to the lower reaches.The E0 in the upper reaches of the Luo River showed the largest change,with an average of 45.3 mm(38.2-48.3 mm),while the lower reaches of the Yiluo River showed the smallest change,with an average of 7.3 mm(‒3.2-17.1 mm).Land use changes were primarily from cultivated to construction land in the middle and lower reaches.Runoff changes were positively correlated with precipitation changes and negatively correlated with potential evapotranspiration and land use changes.The absolute values of the sensitivity coefficients of runoff to these environmental factors decreased with lower altitude,indicating a re-duced responsiveness of the basin runoff under a warming and drying climate trend.Reductions in precipitation and changes in potential evapotranspiration have led to reductions in runoff ranging from 4.7 to 17.4 mm and from 0.7 to 9.1 mm,respectively,while land use changes led to corresponding runoff reductions of 23.0 to 46.5 mm,suggesting that land use changes are more likely to trigger runoff changes in the basin than climatic fluctuations.Given the dominance of cultivated land,especially in the middle and lower reaches,and the region's high susceptibility to human activities,there has been a significant reduction in runoff in recent years.The contribution of land use change to the runoff reduction in the Yiluo River Basin was greater at lower elevations,up to 86.1%,while climatic effects were more significant at higher elevations,up to 27.8% .Therefore,promoting the implementation of projects such as water ecological restoration and returning farmland to forests are of great significance to curb the over-exploitation of groundwater,to formulate scientific management and scheduling policies in order to realize the transformation of the water balance in the river basin from a non-steady state to a steady state,and to promote the integrity of the ecosystem of the lower reaches of the Yellow River and ensure its sustainable development.

This study focused on the chemical composition of the rivers in the middle and upper reaches of the Yarlung Tsangpo River system. Samples were collected in April 2015 to analyze spatiotemporal variation characteristics and determine weathering processes and CO2 consumption using principal component analysis (PCA) and a modified forward model. The TDS on the southern bank of the upper and middle reaches of the Yarlung Tsangpo basin was found to be higher than that on the northern bank because of the difference in stratigraphic structure. The results show that the chemical facies of the rivers all belonged to Ca-HCO3, and the rate of sulfuric acid-dominated chemical weathering was extremely high in the sub-watershed by TZ+⁎/HCO3−⁎. Four major reservoirs (precipitation, silicates, carbonates, and evaporites) produce ions. The results of the chemical budget show that their contribution rates were 7.80% vs. 5.09% (PCA vs. modified forward model, the same below): 21.8% vs. 24.7%, 42.80% vs. 50.22%, and 10.30% vs. 21.59%, respectively. The ionic components from carbonate weathering in the study area were dominant, which is the main reason why the calculated results of the carbonate weathering rate (CWR) were higher than the silicate weathering rate (SWR). The CWR reached its maximum value during the monsoon period, whereas the SWR showed the opposite trend. Moreover, CO2 sequestration by chemical weathering of rivers might be the main carbon sink in Tibet, which contributes to the realization of carbon neutrality in Tibet.

The 14th Five-Year Plan states the following:"Accelerate the construction of digital countryside,build a comprehensive information service system for agriculture and rural areas,establish an inclusive service mechanism for agriculture-related information,and promote the digitalization of rural management services."As an important combination of"Digital China"and"rural revitalization"strategies,the so-called digital countryside not only helps promote the development of digital agriculture but also is an important aspect of rural digital governance,which is significant in the modernization of agriculture and rural areas.The spatial collaborative development of a county digital countryside needs to re-examine the spatial and temporal patterns related to the development of a county digital countryside.Accordingly,this study adopts an exploratory spatial data analysis method to study the spatial autocorrelation characteristics related to the development of a county digital countryside.The three-stage nested Theil index method is used to measure the spatial difference characteristics regarding the development of a county digital countryside.The driving factors behind the development of the digital countryside are determined via the geographic detector analysis method.Subsequently,this study presents that the development level of China's county-level digital villages shows obvious"gradient"characteristics,and the development level of digital villages gradually decreases from east to west.Moreover,the digital villages show"flake distribution"in the Yangtze River Delta region,and"dot distribution"in the western and northeast regions.China's county-level digital villages exhibit the spatial characteristics of"large agglomeration and small dispersion."The regions with high development level of digital villages are mainly distributed in the Yangtze River Delta urban agglomeration and spread around Jiangsu and Zhejiang provinces as the center.The spatial autocorrelation index between county digital countryside,rural financial infrastructure,rural digital infrastructure,rural economy digitization,rural governance digitization,and rural life digitization is significantly positive,indicating that the development of digital countryside has significant spatial autocorrelation characteristics and is affected by geographical proximity.For example,a county unit with high digital rural development level is commonly adjacent to another county unit with the same high digital rural development level.The development of China's county-level digital villages exhibits obvious spatial agglomeration characteristics,forming"high-high"agglomeration in the Yangtze River Delta region with higher economic development level and relatively perfect network infrastructure.Digital rural development presents a"center-periphery"structure with sub-hot spots and hot spots clustered around high hot spots.A T-shaped strip is formed between the hot and cold spot areas to separate them.The overall Gini coefficient of rural digital development at the county level in China is 0.035 9,and the regional gap in rural digital development is the main source of this gap.The industrial economy,population,education,finance,and infrastructure strongly influence the spatial distribution of the digital countryside.The contribution of this study is mainly reflected in revealing the spatial pattern,spatial difference,spatial agglomeration,and driving factors behind the development of the digital countryside from the perspective of a county level,which enriches the relevant research on the spatial analysis of the digital countryside and provides empirical evidence for promoting the collaborative development of the digital countryside.

The flow regime change of rivers, especially transboundary rivers, affected by reservoir regulations is evident worldwide and has received much attention. Investigating dam-induced flow regime alterations is essential for understanding potential adverse downstream effects and facilitating dialogue around coordinated water use in transboundary basins, such as the Lancang River Basin (LRB). This study explored the value of combining several types of satellite Earth observation (EO) datasets that monitor different water balance components to constrain the parameter space of lumped conceptual hydrological models. Thus, we aimed to reconstruct the natural flow regimes upstream and downstream of the cascade reservoirs. Specifically, reservoir water storage changes were first estimated using satellite imagery and altimetry datasets. Then, storage changes were combined with hydrological model simulations of reservoir inflow to estimate the regulated flow regime downstream. Our results showed that integrated hydrological modeling combined with EO datasets exhibited better overall performance. Continuous warming and drying of the LRB resulted in a decrease in discharge of approximately 47 %. By comparing the simulated natural and regulated flow regimes, we revealed the pivotal role of the Xiaowan and Nuozhadu reservoirs in regulating natural flows. The wet season shortens (approximately 45 days), the flood peak flattens, and the low flow in the dry season has primarily increases. The two reservoirs attenuated 50 % of the flood peaks in the wet seasons and mitigated droughts by releasing up to 100 % of the natural flows in the dry seasons at the China-Laos border. Overall, these results enhance the understanding of upper reservoir operation, and the approaches can be applied to studies of dammed basins under climate change scenarios when knowledge of the upstream area is limited.

Differences in model application effectiveness, insufficient numbers of disaster samples, and unreasonable selection of non-hazard samples are common problems in landslide susceptibility studies. Therefore, in this paper, we propose a semi-integrated supervised approach to improve the prediction performance of machine learning (ML) models in landslide susceptibility studies. First, taking the lower reaches of the Jinsha River as the study area, a geospatial dataset consisting of 349 landslides, an equal number of randomly selected non-landslide points, and 12 environmental factors were randomly divided into training (70%) and testing (30%) datasets. Then, K-nearest neighbors (KNN), random forest (RF), and Bayesian-regularized neural network (BRNN) models were built. Second, the three models were combined to form an integrated weighted model. Very high- and low-prone areas were selected and, combined with the prediction results and remote sensing images, landslide and non-landslide samples were identified. The identified samples were then combined with the original samples to form new samples, which were used to sequentially construct the ensemble-supervised K-nearest neighbors (ESKNN), ensemble-supervised random forest (ESRF), and ensemble-supervised Bayesian-regularized neural network (ESBRNN) models. Finally, the area under the curve (AUC), true skill statistic (TSS), and frequency ratio (FR) values were used to test the accuracy of each model. The traditional ML model results and accuracy were improved by the semi-integrated supervised method. The ESRF model had the best prediction effect (AUC = 0.939, TSS = 0.440, and FR = 95.8%). The proposed semi-integrated supervised ML model solved the problems observed in traditional landslide susceptibility studies and provided insights for reducing variations in model applications, expanding landslide data sources, and improving non-landslide sample selection.

Climate change and anthropogenic factors have resulted in intense and frequent droughts and floods in the Hengduan Mountain Region (HMR). Exploring the source supply of evaporation and transportation processes for the precipitation in the HMR can help us understand regional precipitation characteristics and provide valuable insights regarding effective water resource management, disaster prevention, and mitigation. Based on the ERA5 reanalysis data, we identified the annual and seasonal precipitationsheds of the HMR, using the Water Accounting Model-2layer, to investigate the potential mechanisms that drive the changes in the precipitation in the HMR, in terms of external moisture contribution and local recycling. The identified precipitationshed in the HMR varied in response to different types of precipitation during the different seasons. The decrease in the annual precipitation was mainly attributed to the reduced moisture supply from the extended southwest (SW) and northwest (NW) regions, portraying a decreasing trend of -2.08 mm/yr/yr and -0.92 mm/yr/yr, respectively. The detected difference in the moisture sources and transport pathways, in terms of the response of wet and dry precipitation years, can improve our understanding of the moisture patterns of wet and dry years. We could conclude that the increase in moisture from the SW regions, due to El Nin similar to o, may have triggered the heavy precipitation in the HMR in 1998. Additionally, the reduction in moisture from the SW regions, due to La Nin similar to a, may have triggered an extreme drought in the HMR in 2011. Notably, woodland and water surface expansion tend to weaken local recycling (-0.07 %/yr) after 2000. In the future, the decreasing trend of local recycling will likely be intensified owing to large-scale artificial reservoir construction and the surface changes caused by underlying impoundment.

Although Taihu watershed is an “acid-insensitive” region, anthropogenic acidification has greatly changed the water chemistry in Taihu Lake. However, how soil carbonates responded to the long-term human-induced acidification received less attention. In this work, we investigated soil carbonate concentrations from different land uses in the upstream of the lake and sediment carbonate profiles in the lake, to explore the linkage of carbonates dissolution in the land and sedimentation in the lake. The result showed that the wheat-rice surface soil, the most acidification-impacted by fertilization and acid deposition, had significantly lower pH than vegetable and wetland soils ( p < 0.05). Meanwhile, the carbonate concentration in wetland soils, only impacted by acid deposition, was significantly higher than that in wheat-rice and vegetable soils ( p < 0.05). The pH profile of fertilized soils, with an increasing trend from the surface to bottom, further indicated the acidifying effect of fertilization. Although the average soil pH across all land uses was 6.6 in the upstream of the lake, remaining carbonate buffering system, the significant carbonate decrease especially in surface soils evidenced the definite carbonate dissolution by acidification, which is cumulative and irreversible. Contrary to the topsoils, the sediment carbonate concentration presented an increasing trend from the depth of 15 cm (denoting around the early 1980s) to the surface, indicating that lake sediment is a major sink of carbonate Ca and Mg from the watershed, particular under an alkaline lake environment caused by frequent algae blooms in the past decades. In addition, Ca/Mg ratio in the sediment, having higher values in a higher pH environment, was quite different from the watershed soil pattern, suggesting different biogeochemical processes Ca and Mg underwent during their transportation and sedimentation. The effects of acidification-altered re-distribution of carbonate Ca and Mg and Ca/Mg ratio in the terrestrial and aquatic environments deserve wider considerations of ecosystem consequence.

Rainfall is the main factor that induces debris flow. Satellite rainfall products provide a new source of data in terms of debris flow-triggering conditions to overcome the lack of rainfall data coverage from ground-based rainfall gauges in large-scale mountainous regions. In this study, the applicability of four satellite rainfall products (CMORPH, GPM, MSWEP, and PERSIANN) in the Hengduan Mountain region (HMR) was evaluated with reference to ground observation data from 2000 to 2020. The critical rainfall and rainfall thresholds under different rainfall patterns and warning levels that trigger debris flows were analyzed according to the empirical cumulative distribution function (ECDF) and cumulative probability. The results showed that CMORPH (comprehensive indicator score (CI = 0.72) and GPM (CI = 0.70) performed better in the simulation of daily rainfall sequence consistency and extreme rainfall conditions in the study area. CMORPH also had the highest reconstruction rate for correctly capturing rainfall events that triggered debris flows, with a value of 89%. Approximately half of the rainfall patterns that cause debris flows are antecedent-effective-rainfall-dominated. Both intraday-rainfall-dominated and intraday-antecedent-rainfall-balanced patterns were below 30%. There were evident differences in the critical rainfall for different rainfall patterns under the same warning level. By comparing the results of previous studies on rainfall thresholds, it is believed that the results of this study confirm the application of satellite rainfall products; in addition, the calculated rainfall thresholds can provide a reference for the early warning of debris flows in the HMR. In general, this work is of great significance to the prediction and early warning of debris flow hazards.

Monitoring and analysis of irrigated area is of great significance to evaluate irrigation benefit, agricultural drought situation and regional water resources utilization. This article selects MODIS surface temperature products from 2016 to 2020. Using the method of daily difference in surface temperature, eliminating the influence of precipitation, realizing remote sensing monitoring of irrigation intensity and spatial distribution of irrigation area. The results showed that the average number of irrigation times in five years was in the range of 6-8. Due to factors such as rainfall during the irrigation period, the overall irrigation frequency from 2016 to 2018 is less than the overall irrigation frequency in 2019 and 2020. On June 11, 2018, remote sensing images detected irrigation areas of Buzi Town, Longhe Town, Yangbei Town and Chenji Town, which were consistent with the data of irrigation areas.

Groundwater recharge supports sustainable development for drinking water and irrigated agriculture in vast areas worldwide. Human activities and climate change (e.g., uneven precipitation) have significantly impacted groundwater dynamics. A long term monitoring and sampling campaign was conducted for the major water bodies to investigate the evolution of groundwater and its capacity to support sustainable development in a subtropical agroforestry catchment in the middle of the Sichuan Basin with horizontal sedimentary bedrock (red beds), where seasonal droughts are notable. Major hydrochemical indicators, including dD and delta O-18 were measured to trace the water movement. The results showed that the chemical type of shallow groundwater was Ca center dot Mg-HCO3 or Ca-HCO3, which was mainly controlled by the weathering and hydrolysis of rocks. The interaction processes in the rainy season were stronger than those in the dry season. The isotopic signature in rainwater showed distinct seasonal pattern was strengthened by the local basin climate. The isotopic signals of groundwater responding to rainfall showed that recharge cycles of shallow groundwater in the rainy season lasted approximately 45-75 days. The optimized estimation based on mixing model and chloride ion balance (CMB) showed that the rainfall recharge ratio to shallow groundwater averaged about 27.83% (4.3%-58.0%) at event scale and the annual rainfall recharge ratios averaged about 19.36% (12.2%-45.7%). Groundwater in this region was renewed rapidly, as its groundwater storage capacity could be low with horizontal sedimentary bedrock. By revealing the water chemistry dynamics of shallow groundwater in the study area, this work preliminarily identified the groundwater recharge sources and estimated the recharge ratios by rainfall in a typical hilly area with red beds, thus providing a scientific basis for further regional assessment of groundwater resources.

Abstract Based on observed precipitation and runoff data, monthly actual evapotranspiration (ETa) was calculated by the hydrological budget balance method in the Nu River Basin (NRB) and Lancang River Basin (LCRB). The performance of three developed complementary relationship methods, the nonlinear advection-aridity (nonlinear AA) method, generalized complementary relationship method (B2015), and sigmoid generalized complementary function (H2018), on simulating (ETa) were evaluated. The evaluation results showed that three methods were able to accurately simulate monthly (ETa) series. The NSE between the monthly (ETa) simulated by the nonlinear AA, B2015, and H2018 methods and the water-balance-derived (ETa) were 0.89, 0.83, and 0.91, respectively. The R-square were 0.90, 0.84, and 0.93, respectively. Overall, the H2018 method showed the best performance. The parameter α had a negative correlation with regional aridity index. Annual (ETa) and precipitation showed significant increasing trends during 1956–2018 in the basins at all temporal scales (dry and wet seasons and annual series). Runoff also exhibited an increasing trend in each sub-basin, except for the downstream region of the LCRB. The increasing magnitudes of wet reason precipitation and runoff in the mid-stream region was the highest, with the value of 73.7 mm/10a and 44.9 mm/10a, respectively. The (ETa) increased dramatically in the downstream region, the magnitude reached 25.9 mm/10a. Precipitation was the main factor leasing to (ETa) change. The increasing magnitude of (ETa) accounted for 42.4% of the precipitation increment. Research on the influence mechanism between meteorological factors and (ETa) showed that the contribution rate of air temperature to (ETa) was the highest, reaching 23.5%, which showed a significant positive correlation. The second was wind speed, whose contribution rate was − 10.2% on average, and even reached − 14.1% in the upstream region of the NRB. The correlation coefficient between (ETa) and wind speed was highest in mid-stream region of the NRB, which was greater than 0.80. The contribution rates of increasing humidity to (ETa) were − 12.5% and − 9.2% in the NRB and LCRB, respectively. (ETa) was negatively correlated with humidity. The negative correlation was especially strong in the mid-stream region, with coefficients were greater than − 0.65. The sunshine hours had the least effect on (ETa), and the contribution rates were − 6.5% and − 4.1%, respectively.

Glaciers and snow cover are important constituents of the surface of the Tibetan Plateau. The responses of these phenomena to global environmental changes are sensitive, rapid and intensive due to the high altitudes and arid cold climate of the Tibetan Plateau. Based on multisource remote sensing data, including Landsat images, MOD10A2 snow product, ICESat, Cryosat‐2 altimetry data and long‐term ground climate observations, we analysed the dynamic changes of glaciers, snow melting and lake in the Paiku Co basin using extraction methods for glaciers and lake, the degree‐day model and the ice and lake volume method. The interaction among the climate, ice‐snow and the hydrological elements in Paiku Co is revealed. From 2000 to 2018, the basin tended to be drier, and rainfall decreased at a rate of −3.07 mm/a. The seasonal temperature difference in the basin increased, the maximum temperature increased at a rate of 0.02°C/a and the minimum temperature decreased at a rate of −0.06°C/a, which accelerated the melting from glaciers and snow at rates of 0.55 × 107 m3/a and 0.29 × 107 m3/a, respectively. The rate of contribution to the lake from rainfall, snow and glacier melted water was 55.6, 27.7 and 16.7%, respectively. In the past 18 years, the warmer and drier climate has caused the lake to shrink. The water level of the lake continued to decline at a rate of −0.02 m/a, and the lake water volume decreased by 4.85 × 108 m3 at a rate of −0.27 × 108 m3/a from 2000 to 2018. This evaluation is important for understanding how the snow and ice melting in the central Himalayas affect the regional water cycle.