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Thermodynamic and liquid water profiles can be retrieved by a ground-based microwave radiometer (MWR) in nearly all weather conditions, which is useful for detecting mesoscale phenomena. This paper reviews the advances in remote sensing of atmospheric profiles and cloud properties by MWR in central China. Comparative studies indicate that MWR retrieval accuracy is different under various skies, especially those that decay under precipitation. The off-zenith method is proven to be capable of reducing the impact of precipitation and snow on MWR retrieval accuracy. Application studies demonstrate that MWR retrievals are helpful for early warning of rainstorms, hailstorms, and thunderstorms. Moreover, MWR retrievals provide a way to study cloud properties. The temporal variations of cloud occurrence frequency (COF) and liquid water path (LWP) are different for low, middle, and high clouds, and the vertical distribution of COF is also different in autumn and other seasons. Note that MWR can infer valid retrievals over the eastern Tibetan Plateau due to the weak precipitation over there. Also, cloud properties over the eastern Tibetan Plateau present differences from those over central China, and this is related to the different characteristics of atmospheric water vapor between these two regions. To bring more benefits for mechanism study and early warning of severe weather and numerical weather prediction, the decayed accuracy of MWR zenith retrievals under precipitation should be resolved. And combining MWR with other instruments is necessary for MWR application in detecting multi-layer clouds and ice clouds. [ABSTRACT FROM AUTHOR]

Lakes are an important component of global water resources. In order to achieve accurate lake extractions on a large scale, this study takes the Tibetan Plateau as the study area and proposes an Automated Lake Extraction Workflow (ALEW) based on the Google Earth Engine (GEE) and deep learning in response to the problems of a low lake identification accuracy and low efficiency in complex situations. It involves pre-processing massive images and creating a database of examples of lake extraction on the Tibetan Plateau. A lightweight convolutional neural network named LiteConvNet is constructed that makes it possible to obtain spatial–spectral features for accurate extractions while using less computational resources. We execute model training and online predictions using the Google Cloud platform, which leads to the rapid extraction of lakes over the whole Tibetan Plateau. We assess LiteConvNet, along with thresholding, traditional machine learning, and various open-source classification products, through both visual interpretation and quantitative analysis. The results demonstrate that the LiteConvNet model may greatly enhance the precision of lake extraction in intricate settings, achieving an overall accuracy of 97.44%. The method presented in this paper demonstrates promising capabilities in extracting lake information on a large scale, offering practical benefits for the remote sensing monitoring and management of water resources in cloudy and climate-differentiated regions. [ABSTRACT FROM AUTHOR]

The current limited approaches to calculating hillslope erosion rate hamper the study of the relationships among the rates of hillslope erosion, river incision, and tectonic uplift and hence the discussion of steady-state landscape evolution. In this paper, we use remote sensing and geochronological methods to calculate the upper and lower bounding hillslope erosion rates in the Qilian Shan range, Tibet. Our analysis focuses on five convex landslide sediment units derived from the weathered hillslopes at Qingyang Mountain on the tectonically active northeastern Tibetan Plateau. These sediment units range in thickness from 5.5 to 12.8 m and in volume from 119 × 103 to 260 × 103 m3. Based on field observations, measurements extracted from high-resolution DEMs, and optical stimulated luminescence (OSL) ages on fluvial terraces, we obtain lower and upper bounding rates of 0.13 ± 0.03 and 0.21 ± 0.04 mm/yr, respectively. Finally, we calculate incision rates, ranging from 0.21 ± 0.02 to 0.39 ± 0.01 mm/yr, from heights of a dated fluvial terrace above the present river and the time of abandonment of the associated bedrock strath estimated from OSL ages. The rates of hillslope erosion and river incision at Qingyang Mountain and the tectonic uplift of the Qilian Mountains are estimated to be within a factor of two over the past 117 ka, suggesting that a state of dynamic equilibrium has likely existed on this timescale. [ABSTRACT FROM AUTHOR]

The Wudaoliang–Changshagongma fault is one of the NW-trending faults located within the southern Bayan Har Block of the Tibetan Plateau in China. In this paper, we used high-resolution imagery and digital elevation model data to study the geomorphological and geological characteristics of the fault. Furthermore, the result also determined the fault trace and estimated the average horizontal slip rate of the fault since the late Quaternary to have been 2.6 ± 0.6 mm/a. This slip rate is approximately equivalent to that of the Awancang, Madoi–Garde, and Dari faults, which are also located within the block. Furthermore, the slip rates of these faults obtained by remote sensing and geological methods are consistent with GPS observations. It indicates that tectonic deformation within the block is continuous and diffuse. Using trenching study results and sedimentary radiocarbon dating, we identified four paleoearthquake events that occurred at 42,378–32,975, 33,935–20,663, 5052–4862, and after 673–628 cal BP, respectively. The recurrence intervals of large earthquakes on the faults within the block are much longer than those of the boundary faults, and the slip rates are also smaller, indicating that faults within the block play a regulatory role in the tectonic deformation of the Bayan Har Block. [ABSTRACT FROM AUTHOR]

As one of the critical indicators of the lake ecosystem, the lake surface water temperature is an important indicator for measuring lake ecological environment. However, there is a complex nonlinear relationship between lake surface water temperature and climate variables, making it difficult to accurately predict. Fortunately, satellite remote sensing provides a wealth of data to support further improvements in prediction accuracy. In this paper, we construct a new deep learning model for mining the nonlinear dynamics from climate variables to obtain more accurate prediction of lake surface water temperature. The proposed model consists of the variable correlation information module and the temporal correlation information module. The variable correlation information module based on the Self-Attention mechanism extracts key variable features that affect lake surface water temperature. Then, the features are input into the temporal correlation information module based on the Gated Recurrent Unit (GRU) model to learn the temporal variation patterns. The proposed model, called Attention-GRU, is then applied to lake surface water temperature prediction in Qinghai Lake, the largest inland lake located in the Tibetan Plateau region in China. Compared with the seven baseline models, the Attention-GRU model achieved the most accurate prediction results; notably, it significantly outperformed the Air2water model which is the classic model for lake surface water temperature prediction based on the volume-integrated heat balance equation. Finally, we analyzed the factors influencing the surface water temperature of Qinghai Lake. There are different degrees of direct and indirect effects of climatic variables, among which air temperature is the dominant factor. [ABSTRACT FROM AUTHOR]

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole. [ABSTRACT FROM AUTHOR]

The decrease in lake area has garnered significant attention within the global ecological community, prompting extensive research in remote sensing and computer vision to accurately segment lake areas from satellite images. However, existing image segmentation models suffer from poor generalization performance, the imprecise depiction of water body edges, and the inadequate inclusion of water body segmentation information. To address these limitations and improve the accuracy of water body segmentation in remote sensing images, we propose a novel GEA-MSNet segmentation model. Our model incorporates a global efficient attention module (GEA) and multi-scale feature fusion to enhance the precision of water body delineation. By emphasizing global semantic information, our GEA-MSNet effectively learns image features from remote sensing data, enabling the accurate detection and segmentation of water bodies. This study makes three key contributions: firstly, we introduce the GEA module within the encode framework to aggregate shallow feature semantics for the improved classification accuracy of lake pixels; secondly, we employ a multi-scale feature fusion structure during decoding to expand the acceptance domain for feature extraction while prioritizing water body features in images; thirdly, extensive experiments are conducted on both scene classification datasets and Tibetan Plateau lake datasets with ablation experiments validating the effectiveness of our proposed GEA module and multi-scale feature fusion structure. Ultimately, our GEA-MSNet model demonstrates exceptional performance across multiple datasets with an average intersection ratio union (mIoU) improved to 75.49%, recall enhanced to 83.79%, pixel accuracy (PA) reaching 90.21%, and the f1-score significantly elevated to 83.25%. [ABSTRACT FROM AUTHOR]

The quantitative characterization of the thermal conditions in the Tibetan Plateau has long been a focal point of global research. Downward shortwave radiation, as a crucial component, plays an important role in numerous land surface processes while also serving as a significant indicator of the plateau's thermal state. In order to gain a more comprehensive understanding of the Earth's radiation budget in the Tibetan Plateau region, this study undertook an evaluation of six radiation products (ISCCP-FH, CERES-SYN, GLASS DSR, Himawari-8, MCD18A1, and ERA5). Two sets of ground measurements (downward shortwave radiation values from 10 CMA sites and 6 sites provided by the National Tibetan Plateau Data Center) in 2015 and 2016 were used as validation data to verify the accuracy of the remote sensing products. The results show that in the Tibetan Plateau region, CERESC products show the highest accuracy among the six data products with a bias (relative bias) of −7.57 W/m2 (3.46%), RMSE (relative RMSE) of 32.77 W/m2 (14.99%), and coefficient of determination of 0.80. Among all products, only the ERA5 products overestimated the value of downward shortwave radiation in the Tibetan Plateau region with a bias (relative bias) of 15.62 W/m2 (7.14%). By employing a spatial resolution upscaling approach, we assessed the influence of varying spatial resolutions on the validation accuracy, with the results indicating minimal impact. Through an analysis of the impact of cloud factors and aerosol factors on the validation accuracy, it is deduced that ERA5, Himawari-8, and MCD18A1 products are significantly influenced by cloud factors, whereas the CERES-SYN product is notably affected by aerosol factors. [ABSTRACT FROM AUTHOR]

Bare permafrost refers to permafrost with almost no vegetation on the surface, which is an essential part of the ecosystem of the Tibetan Plateau. An accurate extraction of the boundaries of bare permafrost is vital for studying how it is being impacted by climate change. The accuracy of permafrost and bare land distribution maps is inadequate, and the spatial and temporal resolution is low. This is due to the challenges associated with obtaining significant amounts of data in high-altitude and alpine regions and the limitations of current mapping techniques in effectively integrating multiple factors. This study introduces a novel approach to extracting information about the distribution of bare permafrost. The approach introduced here involves amalgamating a sample extraction method, the fusion of multi-source remote sensing information, and a hierarchical classification strategy. Initially, the available multi-source permafrost data, expert knowledge, and refinement rules for training samples are integrated to produce extensive and consistent permafrost training samples. Using the random forest method, these samples are then utilized to create features and classify permafrost. Subsequently, a methodology utilizing a hierarchical classification approach in conjunction with machine learning techniques is implemented to identify an appropriate threshold for fractional vegetation cover, thereby facilitating the extraction of bare land. The bare permafrost boundary is ultimately derived through layer overlay analysis. The permafrost classification exhibits an overall accuracy of 90.79% and a Kappa coefficient of 0.806. The overall accuracies of the two stratified extractions in bare land were 97.47% and 96.99%, with Kappa coefficients of 0.954 and 0.911. The proposed approach exhibits superiority over the extant bare land and permafrost distribution maps. It is well-suited for retrieving vast bare permafrost regions and is valuable for acquiring bare permafrost distribution data across a vast expanse. It offers technical assistance in acquiring extended-term data on the distribution of exposed permafrost on the Tibetan Plateau. Furthermore, it facilitates the elucidation of the impact of climate change on exposed permafrost. [ABSTRACT FROM AUTHOR]

Potential rockfall source areas are widely distributed in the high mountain areas of the Tibetan Plateau, posing significant hazards to human lives, infrastructures, and lifeline facilities. In a combination of field investigation, high-precision aerial photogrammetry, and numerical simulation, we took the Maoyaba basin as an example to explore a rapid identification method for high-altitude rockfall sources. An automatic potential rockfall source identification (PRSI) procedure was introduced to simplify the process of rockfall source identification. The study revealed that rockfall sources are concentrated in areas with intense frost weathering. Our identification results were validated using rockfall inventory data detection from remote sensing images and field investigation. Of the rockfall source areas identified by the PRSI procedure, 80.85% overlapped with the remote sensing images result. The accuracy assessment using precision, recall, and F1 score was 0.91, 0.81, and 0.85, respectively, which validates the reliability and effectiveness of the PRSI procedure. Meanwhile, we compared the rockfall source distribution of five DEMs with different resolutions and four neighborhood areas. We discovered that, in addition to high-resolution DEMs (i.e., 1 m and 2 m), medium-resolution DEMs (i.e., 5 m, 12.5 m) also perform well in identifying rockfall sources. Finally, we conducted a hazard assessment based on Culmann's two-dimensional slope stability model and rockfall hazard vector method. Appropriate protective measures should be taken at high-hazard sections to safeguard pedestrians, vehicles, and related infrastructure from rockfalls. [ABSTRACT FROM AUTHOR]

The Qinghai-Tibet Plateau (QTP) is known as the Third Polar of the earth and theWater Tower of Asia, with more than 70% of the area on the QTP is covered by permafrost possibly. An accurate permafrost distribution map based on valid and available methods is indispensable for the local environment evaluation and engineering constructions planning. Most of the previous permafrost maps have employed traditional mapping method based on field surveys and borehole investigation data. However their accuracy is limited because it is extremely difficulties in obtaining mass data in the high-altitude and cold regions as the QTP; moreover, the mapping method, which would effectively integrate many factors, is still facing great challenges. With the rapid development of remote sensing technology in permafrost mapping, spatial data derived from the satellite sensors can recognize the permafrost environment features and quantitatively estimate permafrost distribution. Until now there is no map indicated permafrost presence/absence on the QTP that has been generated only by remote sensing data as yet. Therefore, this paper used permafrost-influencing factors and examined distribution features of each factor in permafrost regions and seasonally frozen ground regions. Then, using the Decision Tree method with the environmental factors, the 1 km resolution permafrost map over the QTP was obtained. The result shows higher accuracy compared to the previous published map of permafrost on the QTP and the map of the glaciers, frozen ground and deserts in China, which also demonstrates that making comprehensive use of remote sensing technology in permafrost mapping research is fast, macro and feasible. Furthermore, this result provides a simple and valid method for further permafrost research. [ABSTRACT FROM AUTHOR]

Clouds are the major source of clutter in optical remote sensing (RS) images. Approximately 60% of the Earth's surface is covered by clouds, with the equatorial and Tibetan Plateau regions being the most affected. Although the implementation of techniques for cloud removal can significantly improve the efficiency of remote sensing imagery, its use is severely restricted due to the poor timeliness of time-series cloud removal techniques and the distortion-prone nature of single-frame cloud removal techniques. To thoroughly remove thin clouds from remote sensing imagery, we propose the Saliency Cloud Matting Convolutional Neural Network (SCM-CNN) from an image fusion perspective. This network can automatically balance multiple loss functions, extract the cloud opacity and cloud top reflectance intensity from cloudy remote sensing images, and recover ground surface information under thin cloud cover through inverse operations. The SCM-CNN was trained on simulated samples and validated on both simulated samples and Sentinel-2 images, achieving average peak signal-to-noise ratios (PSNRs) of 30.04 and 25.32, respectively. Comparative studies demonstrate that the SCM-CNN model is more effective in performing cloud removal on individual remote sensing images, is robust, and can recover ground surface information under thin cloud cover without compromising the original image. The method proposed in this article can be widely promoted in regions with year-round cloud cover, providing data support for geological hazard, vegetation, and frozen area studies, among others. [ABSTRACT FROM AUTHOR]

The Tian Shan is one of the most active intracontinental orogenic belts in the world. It has undergone complex deformation that has resulted in the formation of several fold-and-thrust belts (FTBs) in the piedmonts and intermontane basins. Investigating the deformation histories of these FTBs is important for understanding the tectonic propagation processes of the Tian Shan. Here, we gain insight into these crustal shortening processes by deciphering the Houyanshan structure, a typical fold-thrust belt in the eastern Chinese Tian Shan. We first describe a curved thrust ramp and related fold pairs of the structure using high-resolution remote sensing photography, deformation of fluvial terraces, and field-based geological cross-section. Combined with deformed terrace records and optically stimulated luminescence (OSL) dating results, the kinematic style allows us to yield a geologic shortening rate of 1.6 ± 0.2 mm/a since ~52 ka. Second, to reduce uncertainty in the seismic interpretation and quantify the amount and time of crustal shortening, we interpret three seismic reflection profiles by using the theory of quantitative fault-related fold, area-depth-strain (ADS), and reverse modeling analyses. These profiles provide direct evidence that this structure connects by means of a listric thrust ramp to a shallow detachment level. ADS analysis reveals that the maximum shortening of the Huoyanshan structure is ~4.5 km, which is consistent with the result of quantitative inverse modeling. Each of the structural analysis methods gives similar parameters, and the high consistency of results greatly improves the soundness of a given geologic interpretation. Finally, the shortening rate and total shortening amount suggest that the structure may have formed at 1.8–3.7 Ma, which is nearly synchronous around the Tibetan Plateau. Together, these results indicate that this combined geological and geomorphological analysis provides greater insight into deformation information than can be achieved by any individual technique in studying fold-and-thrust belts worldwide. [ABSTRACT FROM AUTHOR]

High-resolution meteorological datasets are urgently needed for understanding the hydrological cycle of the Tibetan Plateau (TP), where ground-based meteorological stations are sparse. Rapid advances in remote sensing create possibilities to represent spatiotemporal properties of precipitation at a high resolution. In this study, the hourly precipitation characteristics over the TP from two gridded precipitation products, one from global reanalysis (the fifth generation of the European Center for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate; ERA5) and the other is simulated by Global-to-Regional Integrated forecast SysTem (GRIST) global nonhydrostatic model, are compared against satellite-gauge merged precipitation analysis (China Merged Precipitation Analysis; CMPA) from 27 July to 31 August 2014, and a satellite-retrieved precipitation estimate from the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) is also evolved. Two aspects are mainly focused on: the spatial distribution and the elevation dependence of hourly precipitation characteristics (including precipitation amount, frequency, intensity, diurnal variations, and frequency–intensity structure). Results indicate that: (1) The precipitation amount, frequency, and intensity of CMPA and IMERG decrease with altitude in the Yarlung Tsangpo river valley (YTRV), but increase at first and then decrease with altitude (except for intensity) in the eastern periphery of TP (EPTP). ERA5 performed well on the variation of precipitation amount with altitude (especially in EPTP), but poorly on the frequency and intensity. GRIST is the antithesis of ERA5, but they all overestimate (underestimate) the frequency (intensity) at all heights; (2) With increasing altitude, the diurnal phase of precipitation of CMPA and IMERG shifted from night to evening in the two sub-regions. IMERG's diurnal phase is 1 to 3 h earlier than CMPA's, and the discrepancy decreases (increases) as the altitude increases in YTRV (EPTP). The diurnal phase of precipitation amount and frequency in ERA5 and GRIST is significantly earlier than CMPA, and the frequency peaks around midday except in the basin. GRIST's simulation of the diurnal variation in intensity at various altitudes is consistent with CMPA; (3) ERA5 and GRIST overestimate (underestimate) the frequency of weak (intense) precipitation, with ERA5's deviance being the most severe. The deviations increased with altitude. These findings provide intensive metrics to evaluate precipitation in complex terrain and are helpful for deepening the understanding of simulated biases for further improving performance in high-resolution simulation. [ABSTRACT FROM AUTHOR]

The Jiaju ancient landslide is a giant landslide located upstream of the Dadu River, eastern Tibetan Plateau, with a volume of approx. 7.04 × 108 m3. The Jiaju ancient landslide is complex and comprises five secondary sliding bodies, e.g., the Jiaju landslide (H01), Niexiaping landslide (H02), Xiaobawang landslide (H03), Niela landslide (H04), and Mt.-peak landslide (H05). Affected by regional neotectonic movement, heavy rainfall, river erosion, and lithology, the secondary sliding bodies of the Jiaju ancient landslide are undergoing significantly different creep-sliding deformation, which will cause great damage to villages, roads, and rivers around the sliding bodies. Combined with the SBAS-InSAR method, Sentinel-1A data from June 2018 to August 2021, remote sensing and field surveys, this study obtained the Jiaju ancient landslide deformation characteristics and deformation rate in the line-of-sight direction (VLOS), slope (VSlope), and vertical (VVertical). It is concluded that the maximum deformation rate of the Jiaju ancient landslide is significant. The maximum of VLOS, VSlope, and VVertical are −179 mm/a, −211 mm/a, and −67 mm/a, respectively. The Niela landslide (H04), Jiaju landslide (H01), and Mt.-peak landslide (H05) are very large and suffer strong deformation. Among these, the Niela landslide (H04) is in the accelerative deformation stage and at the Warn warning level, and the Jiaju landslide (H01) is in the creep deformation and attention warning level, especially heavy rainfall, which will accelerate landslide deformation and trigger reactivation. Because the geological structure is very complex for the Jiaju ancient landslide and strong neotectonic movement, under heavy rainfall, the secondary landslide creep-sliding rate of the Jiaju ancient landslide is easily accelerated and finally slides in part or as a whole, resulting in river blocking. It is suggested to strengthen the landslide deformation monitoring of the Niela landslide and Jiaju landslide and provide disaster mitigation and prevention support to the government and residents along the Dadu River watershed. [ABSTRACT FROM AUTHOR]

Changes in lake water volume can reflect variations in regional hydrometeorology and are a sensitive indicator of regional environmental change. The Tibetan Plateau, referred to as the "Asian Water Tower", has a large number of lakes. These lakes are in a natural state and are relatively unaffected by human activities. Understanding the changes to lake water volume is a key issue for the study of lake-atmosphere interactions and the effects of lake expansion and contraction on regional climate. By using multisource remote sensing and water level observations, this study systematically analyzed inter-annual changes from 1970 to 2021 of three typical inland lakes basin (Bamu Co-Peng Co basin, Langa Co-Mapum Yumco basin andLongmu Co-Songmuxi Co basin), which are located in different climatic regions of the Tibetan Plateau and monthly changes from 2019 to 2021 of Bamu Co, Langa Co and Longmu Co in the lake area, water level, and water volume. In addition, the study analyzed the response of lakes in different climate regions to climate change from 1979 to 2018. The main conclusions are as follows. (1) From 1970 to 2021, there were similar trends in lake changes between the primary and twin lakes. (2) The changes to lakes in different climatic regions are different: lakes in the monsoon-dominated region showed a significant trend of expansion from 2000 to 2014, but the trend slowed down and stabilized after 2014; lakes in the westerlies-dominated region showed a small expansion trend; lakes in the region affected by both westerlies and the monsoon showed an overall shrinking trend. (3) The monthly variation of lake water volume showed a trend of first increasing and then decreasing, with the largest relative change of lake water volume in August and September. (4) Precipitation is a dominant factor controlling lake variation during the year. (5) Temperature and precipitation are dominant meteorological elements affecting the decadal variation of the lake, and with the warming of the TP, temperature plays an increasingly important role. [ABSTRACT FROM AUTHOR]

The Qinghai–Tibet Plateau (QTP), also known as the Third Pole of the Earth, is sensitive to climate change, and it has become a hotspot area for research. As a typical natural ecosystem on the QTP, alpine wetlands are particularly sensitive to climate change. The identification of different types of alpine wetland and analysis of changes in their distributions and areas are the most direct indicators for characterizing the impact of climate change on wetlands. To understand the dynamic change process of the alpine wetlands in the QTP and their responses to climate change, the Maqu wetlands, located at the source of the Three Rivers in the eastern part of the QTP, was taken as an example; the Google Earth Engine (GEE) remote sensing cloud platform and long-term dense Landsat time series data from 1990 to 2020 were used to map the annual wetland classification and to analyze the evolution characteristics of the wetlands and their driving forces. The results revealed that (1) based on dense Landsat time series data, different alpine wetland types can be effectively distinguished, including swamp, swamp meadow, and wet meadow. (2) From 1990 to 2020, the area of the Maqu wetlands exhibited an overall fluctuating decrease, with the total area decreasing by about 23.35%, among which the swamp area decreased the most (by 27.15%). The overall type of change was from wet to dry. All of the types of wetlands were concentrated between 3400 and 3600 m above sea level, and the reduction in the wetland area was concentrated on slopes < 3°, with the greatest loss of wetland area occurring on shady slopes. (3) The driving forces of the changes in the wetlands were predominantly temperature and precipitation, and the greatest correlation was between the total wetland area and the growing season temperature. The results of this study provide valuable information for the conservation of alpine wetlands. [ABSTRACT FROM AUTHOR]

Glaciers and glacial lakes in the Bosula Mountain Range need special attention, because their instability may cause disastrous consequences to the downstream settlements and the Sichuan-Tibet Road. The latter is a pivotal traffic line in the Southeast Tibetan Plateau. In order to investigate the state of glaciers and glacial lakes in the Bosula Mountain Range, we estimated the changes in glacier/glacial lake boundaries, glacier surface elevation, and glacier flow velocity between 2000 and 2021 based on multisource remote sensing data. Our results showed that, from the period 2000–2013 to the period 2013–2021, the average shrinking rate of glacier area increased from 0.99 km2/a to 1.74 km2/a, and the average expanding rate of glacial lake area increased from 0.04 km2/a to 0.06 km2/a. From the period 1990–2011 to the period 2015–2019, the average thinning rate of glaciers increased from 0.83 m/a to 1.58 m/a. These results indicate the Bosula Mountain Range is one of the fastest melting glacierized regions in the High Mountain Asia, and the factors that account for this may include quick temperature rise, abundant summer rainfall, and thin debris cover. In spite of strong ice melting, the observed changes in glacier boundaries, surface elevation, and flow velocity show no sign of surge activity, and the frequency of glacier lake outburst has not increased since 1989. Currently, three proglacial lakes that expanded quickly during 2000–2021 are now prominent hazards. They are directly threatened by accidental ice calving and ice avalanche, and their outburst could cause considerable damage to the downstream settlements and the Sichuan-Tibet Road. [ABSTRACT FROM AUTHOR]

Mastering the fluctuation of water levels and the water storage capacity of plateau lakes is greatly important for monitoring the water balance of the Tibetan Plateau and predicting regional and global climate change. The water level of plateau lakes is difficult to measure, and the ground measured data of long-time series are difficult to obtain. Ngoring Lake is considered in this study, using spaceborne single-photon lidar ICESat-2/ATL13 inland lake standard data products, the water level values provided by Hydroweb laboratory, and the image data of an optical remote sensing satellite. A new method is proposed in the absence of measured data. The method uses multisource remote sensing data to estimate the long-term changes in the water levels, surface area, and water storage capacity of Ngoring Lake in the past three decades. The results show that the water level values of ICESat-2 and Hydroweb on overlapping observation days are highly correlated, with R2 = 0.9776, MAE = 0.420 m, RMSE = 0.077 m, and the average absolute height difference is 0.049 m. The fusion of multiple altimetry data can obtain more continuous long-time series water-level observation results. From 1992 to 2021, the water body information of Ngoring Lake basin fluctuated greatly and showed different variation characteristics in different time periods. The lowest water level in January 1997 was approximately 4268.49 m, and it rose to its highest in October 2009, approximately 4272.44 m. The change in the water level in the basin was mainly affected by natural factors, such as precipitation, air temperature, and human activities. The analysis shows that ICESat-2 can be combined with other remote sensing data to realize the long-time series dynamic monitoring of plateau lakes, showing great advantages in the comprehensive observation of plateau lakes in no man's land. [ABSTRACT FROM AUTHOR]

Changes in vegetation dynamics play a critical role in terrestrial ecosystems and environments. Remote sensing products and dynamic global vegetation models (DGVMs) are useful for studying vegetation dynamics. In this study, we revised the Community Land Surface Biogeochemical Dynamic Vegetation Model (referred to as the BGCDV_CTL experiment) and validated it for the Tibetan Plateau (TP) by comparing vegetation distribution and carbon flux simulations against observations. Then, seasonal–deciduous phenology parameterization was adopted according to the observed parameters (referred to as the BGCDV_NEW experiment). Compared to the observed parameters, monthly variations in gross primary productivity (GPP) showed that the BGCDV_NEW experiment had the best performance against the in situ observations on the TP. The climatology from the remote sensing and simulated GPPs showed similar patterns, with GPP increasing from northwest to southeast, although the BGCDV_NEW experiment overestimated GPP in the semi-arid and arid regions of the TP. The results show that temperature warming was the dominant factor resulting in the increase in GPP based on the remote sensing products, while precipitation enhancement was the reason for the GPP increase in the model simulation. [ABSTRACT FROM AUTHOR]

A critical evaluation of the newly released precipitation data set is very important for both the end users and data developers. Meanwhile, the evaluation may provide a benchmark for the product's continued development and future improvement. To these ends, the four precipitation estimates including IMERG (the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement) V04A, IMERG V03D, CMORPH (the Climate Prediction Center Morphing technique)-CRT and TRMM (the Tropical Rainfall Measuring Mission) 3B42 are systematically evaluated against the gauge precipitation estimates at multiple spatiotemporal scales from 1 June 2014 to 30 November 2015 over three different topographic and climatic watersheds in China. Meanwhile, the statistical methods are utilized to quantize the performance of the four satellite-based precipitation estimates. The results show that: (1) over the Tibetan Plateau cold region, among all products, IMERG V04A underestimates precipitation with the largest RB (-46.98%) during the study period and the similar results are seen at the seasonal scale. However, IMERG V03D demonstrates the best performance according to RB (7.46%), RMSE (0.44 mm/day) and RRMSE (28.37%). Except for in summer, TRMM 3B42 perform better than CMORPH according to RMSEs, RRMSEs and Rs; (2) within the semi-humid Huaihe River Basin, IMERG V04A has a slight advantage over the other three satellite-based precipitation products with the lowest RMSE (0.32 mm/day) during the evaluation period and followed by IMERG V03D, TRMM 3B42 and CMORPH orderly; (3) over the arid/semi-arid Weihe River Basin, in comparison with the other three products, TRMM 3B42 demonstrates the best performance with the lowest RMSE (0.1 mm/day), RRMSE (8.44%) and highest R (0.92) during the study period. Meanwhile, IMERG V03D perform better than IMERG V04A according all the statistical indicators; (4) in winter, IMERG V04A and IMERG V03D tend to underestimate the total precipitation with RBs (-70.62% vs. -6.47% over the Tibetan Plateau, -46.92% vs. -0.66% over the Weihe River Basin, respectively); and (5) overall, except for IMERG V04A in Tibetan Plateau, all satellite-based precipitation captured the gauge-based precipitation well over the three regions according to RRMSEs, Rs and Rbs during the study period. IMERG V03D performs better than its predecessors-TRMM 3B42 and CMORPH over the Tibetan Plateau region and the Huaihe River Basin, while IMERG V04A only does so over the latter. Between the two IMERG products, IMERG V04A does not show an advantage over IMERG V03D over the Tibetan Plateau region and theWeihe River Basin. In particular, over the former, IMERG V04A performs far worse than IMERG V03D. These findings provide valuable feedback for both IMERG algorithm developers and data users. [ABSTRACT FROM AUTHOR]

Abstract Satellite remote sensing provides a powerful tool for assessing lake water surface temperature (LWST) variations, particularly for large water bodies that reside in remote areas. In this study, the MODIS land surface temperature (LST) product level 3 (MOD11A2) was used to investigate the spatiotemporal variation of LWST for 56 large lakes across the Tibetan Plateau and examine the factors affecting the LWST variations during 2000–2015. The results show that the annual cycles of LWST across the Tibetan Plateau ranged from −19.5 °C in early February to 25.1 °C in late July. Obvious diurnal temperature differences (DTDs) were observed for various lakes, ranging from 1.3 to 8.9 °C in summer, and large and deep lakes show less DTDs variations. Overall, a LWST trend cannot be detected for the 56 lakes in the plateau over the past 15 years. However, 38 (68%) lakes show a temperature decrease trend with a mean rate of −0.06 °C/year, and 18 (32%) lakes show a warming rate of (0.04 °C/year) based on daytime MODIS measurements. With respect to nighttime measurements, 27 (48%) lakes demonstrate a temperature increase with a mean rate of 0.051 °C/year, and 29 (52%) lakes exhibit a temperature decrease trend with a mean rate of −0.062 °C/year. The rate of LWST change was statistically significant for 19 (21) lakes, including three (eight) warming and 17 (13) cooling lakes for daytime (nighttime) measurements, respectively. This investigation indicates that lake depth and area (volume), attitude, geographical location and water supply sources affect the spatiotemporal variations of LWST across the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Precipitation is an important controlling parameter for land surface processes, and is crucial to ecological, environmental, and hydrological modeling. In this study, we propose a spatial downscaling approach based on precipitation-land surface characteristics. Land surface temperature features were introduced as new variables in addition to the Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM) to improve the spatial downscaling algorithm. Two machine learning algorithms, Random Forests (RF) and support vector machine (SVM), were implemented to downscale the yearly Tropical Rainfall Measuring Mission 3B43 V7 (TRMM 3B43 V7) precipitation data from 25 km to 1 km over the Tibetan Plateau area, and the downscaled results were validated on the basis of observations from meteorological stations and comparisons with previous downscaling algorithms. According to the validation results, the RF and SVM-based models produced higher accuracy than the exponential regression (ER) model and multiple linear regression (MLR) model. The downscaled results also had higher accuracy than the original TRMM 3B43 V7 dataset. Moreover, models including land surface temperature variables (LSTs) performed better than those without LSTs, indicating the significance of considering precipitation-land surface temperature when downscaling TRMM 3B43 V7 precipitation data. The RF model with only NDVI and DEM produced much worse accuracy than the SVM model with the same variables. This indicates that the Random Forests algorithm is more sensitive to LSTs than the SVM when downscaling yearly TRMM 3B43 V7 precipitation data over Tibetan Plateau. Moreover, the precipitation-LSTs relationship is more instantaneous, making it more likely to downscale precipitation at a monthly or weekly temporal scale. [ABSTRACT FROM AUTHOR]

Considering large and complex areas like the Tibetan Plateau, an analysis of the spatial distribution of the solar radiative budget over time not only requires the use of satellite remote sensing data, but also of an algorithm that accounts for strong variations of topography. Therefore, this research aims at developing a method to produce time series of solar radiative fluxes at high temporal and spatial resolution based on observed surface and atmosphere properties and topography. The objective is to account for the heterogeneity of the land surface using multiple land surface and atmospheric MODIS data products combined with a digital elevation model to produce estimations daily at the kilometric level. The developed approach led to the production of a three-year time series (2008-2010) of daily solar radiation budget at one kilometer spatial resolution across the Tibetan Plateau. The validation showed that the main improvement from the proposed method is a higher spatial and temporal resolution as compared to existing products. However, even if the solar radiation estimates are satisfying on clear sky conditions, the algorithm is less reliable under cloudy sky condition and the albedo product used here has a too coarse temporal resolution and is not accurate enough over rugged terrain. [ABSTRACT FROM AUTHOR]

Effective soil temperature Teff is one of the basic parameters in passive microwave remote sensing of soil moisture. At present, dedicated satellite soil moisture monitoring missions use the L-band as the operating frequency. However, Teff at the L-band is strongly affected by soil moisture and temperature profiles. Recently, a two-layer scheme and a corresponding multilayer form have been developed to accommodate such influences. In this study, the soil moisture/temperature data collected and simulated by the Noah land surface model across the Maqu Network are used to verify the newly developed schemes. There are two key findings. Firstly, the new two-layer scheme is able to assess which site provides relatively higher accuracy when estimating Teff. It is found that, on average, nearly 20% of the Teff signal cannot be captured by the Maqu Network, in the currently assumed common installation configuration. This knowledge is important, since the spatial averaged brightness temperature (a function of Teff) is used to determine soil moisture. Secondly, the developed method has made it possible to identify that the optimal mounting depths for the observation pair are 5 cm and 20 cm for calculating Teff at the center station in the Maqu Network. It has been suggested that the newly developed method can provide an objective way to configure an optimal soil moisture/temperature network and improve the representativeness of the existing networks regarding the calculation of Teff. [ABSTRACT FROM AUTHOR]

The alpine lakes on the Tibetan Plateau (TP) are indicators of climate change. The assessment of lake dynamics on the TP is an important component of global climate change research. With a focus on lakes in the 33° N zone of the central TP, this study investigates the temporal evolution patterns of the lake areas of different types of lakes, i.e., non-glacier-fed endorheic lakes and non-glacier-fed exorheic lakes, during 1988–2017, and examines their relationship with changes in climatic factors. From 1988 to 2017, two endorheic lakes (Lake Yagenco and Lake Zhamcomaqiong) in the study area expanded significantly, i.e., by more than 50%. Over the same period, two exorheic lakes within the study area also exhibited spatio-temporal variability: Lake Gaeencuonama increased by 5.48%, and the change in Lake Zhamuco was not significant. The 2000s was a period of rapid expansion of both the closed lakes (endorheic lakes) and open lakes (exorheic lakes) in the study area. However, the endorheic lakes maintained the increase in lake area after the period of rapid expansion, while the exorheic lakes decreased after significant expansion. During 1988–2017, the annual mean temperature significantly increased at a rate of 0.04 °C/a, while the annual precipitation slightly increased at a rate of 2.23 mm/a. Furthermore, the annual precipitation significantly increased at a rate of 14.28 mm/a during 1995–2008. The results of this study demonstrate that the change in precipitation was responsible for the observed changes in the lake areas of the two exorheic lakes within the study area, while the changes in the lake areas of the two endorheic lakes were more sensitive to the annual mean temperature between 1988 and 2017. Given the importance of lakes to the TP, these are not trivial issues, and we now need accelerated research based on long-term and continuous remote sensing data. [ABSTRACT FROM AUTHOR]

A glacier surge, which is quasi-periodic and involves rapid flow, is an abnormal glacier motion. Although some glaciers have been found to be surging, little is known about surging glaciers on the Tibetan Plateau (TP), especially the Central and Northern TP. Here, we found a surging glacier (GLIMS ID: G085885E34389N) on the Zangser Kangri ice field (ZK), Central TP, by means of the digital elevation models (DEMs) from the Shuttle Radar Topography Mission (SRTM), TanDEM-X 90 m, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEMs, and High Mountain Asia 8-m DEM (HMA), combined with Landsat images and the Global Land Ice Velocity Extraction from Landsat 8 (GoLIVE) dataset. This surge event was confirmed by the crevasses, shear margin, and visible advancing snout shown in the Landsat images produced since 2014 and the HMA. The inter-comparison of these DEMs and the surface velocity changes showed that the surge event started between October 2012 and January 2014. The glacier may have also surged in the 1970s, based on a comparison between the topographical map and Landsat images. The glacier mass balance here has been slightly positive from 1999 onward (+0.03 ± 0.06 m w.e.a−1 from 1999 to 2015, +0.02 ± 0.07 m w.e.a−1 from 1999 to December 2011), which may indicate that the ZK is located on the southern edge of the mass balance anomaly on the TP. Combining with other surging glaciers on the Central and Northern TP, the relatively balanced mass condition, large size, and shallow slope can be associated with glacier surges on the Central and Northern TP. [ABSTRACT FROM AUTHOR]

Turbidity is an important indicator of riverine conditions, especially in a fragile environment such as the Tibetan Plateau. Remote sensing, with the advantages of large-scale observations, has been widely applied to monitor turbidity change in lakes and rivers; however, few studies have focused on turbidity change of rivers on the Tibetan Plateau. We investigated the pattern of turbidity change in the middle reaches of the Yarlung Zangbo River, southern Tibetan Plateau, based on multispectral satellite imagery and in situ measurements. We developed empirical models from in situ measured water leaving reflectance and turbidity, and applied the best performed s-curve models on satellite imagery from Sentinel-2, Landsat 8, and Landsat 5 to derive turbidity change in 2007–2017. Our results revealed an overall decreasing spatial trend from the upper to lower streams. Seasonal variations were observed with high turbidity from July to September and low turbidity from October to May. Annual turbidity showed a temporally slightly declining trend from 2007 to 2017. The pattern of turbidity change is affected by the confluence of tributaries and the changes in precipitation and vegetation along the river. These findings provide important insights into the responses of riverine turbidity to climate and environmental changes on the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Permafrost is degrading on the Qinghai-Tibet Plateau (QTP) due to climate change. Permafrost degradation can result in ecosystem changes and damage to infrastructure. However, we lack baseline data related to permafrost thermal dynamics at a local scale. Here, we model climate change impacts on permafrost from 1986 to 2075 at a high resolution using a numerical model for the Beiluhe basin, which includes representative permafrost environments of the QTP. Ground surface temperatures are derived from air temperature using an n-factor vs Normalized Differential Vegetation Index (NDVI) relationship. Soil properties are defined by field measurements and ecosystem types. The climate projections are based on long-term observations. The modelled ground temperature (MAGT) and active-layer thickness (ALT) are close to in situ observations. The results show a discontinuous permafrost distribution (61.4%) in the Beiluhe basin at present. For the past 30 years, the permafrost area has decreased rapidly, by a total of 26%. The mean ALT has increased by 0.46 m. For the next 60 years, 8.5–35% of the permafrost area is likely to degrade under different trends of climate warming. The ALT will probably increase by 0.38–0.86 m. The results of this study are useful for developing a deeper understanding of ecosystem change, permafrost development, and infrastructure development on the QTP. [ABSTRACT FROM AUTHOR]

In this study, contour lines from the topographic maps at a 1:100,000 scale (mapped in 1968), Landsat MSS/TM/OLI images, ASTER images and SPOT 6-7 stereo image pairs were used to study changes in glacier length, area and surface elevation. We summarized the results using the following three conclusions: (1) During the period from 1973 to 2013, glaciers retreated by 412 ± 32 m at a mean retraction rate of 10.3 ± 0.8 m·year−1 and the relative retreat was 5.6 ± 0.4%. The glacier area shrank by 7.5 ± 3.4%, which was larger than the glacier length. In the periods of 1968–2000, 2000–2005 and 2000–2013, the glacier surface elevation change rates were −7.7 ± 1.4 m (−0.24 ± 0.04 m·year−1), −1.9 ± 1.5 m (−0.38 ± 0.25 m·year−1) and −5.0 ± 1.4 m (−0.38 ± 0.11 m·year−1), respectively. The changes in the glacier area and thickness exhibited similar trends, both showing a significant increasing reduction after 2000. (2) Eleven glaciers were identified as surging glaciers. Changes of the mass balance in surging glaciers were stronger than in non-surging glaciers between 1968 and 2013. Changes of area in surging glaciers were weaker than in non-surging glaciers. (3) Increasing temperature was the major cause of glacier thickness reduction and area shrinkage. The increase in precipitation, to a certain extent, inhibited glacial ablation but it did not change the status of the shrinkage in the glacial area and the reduction in the glacier thickness. [ABSTRACT FROM AUTHOR]

The sparse rain gauge networks over the Tibetan Plateau (TP) cause challenges for hydrological studies and applications. Satellite-based precipitation datasets have the potential to overcome the issues of data scarcity caused by sparse rain gauges. However, large uncertainties usually exist in these precipitation datasets, particularly in complex orographic areas, such as the TP. The accuracy of these precipitation products needs to be evaluated before being practically applied. In this study, five (quasi-)global satellite precipitation products were evaluated in two gauge-sparse river basins on the TP during the period 1998–2012; the evaluated products are CHIRPS, CMORPH, PERSIANN-CDR, TMPA 3B42, and MSWEP. The five precipitation products were first intercompared with each other to identify their consistency in depicting the spatial–temporal distribution of precipitation. Then, the accuracy of these products was validated against precipitation observations from 21 rain gauges using a point-to-pixel method. We also investigated the streamflow simulation capacity of these products via a distributed hydrological model. The results indicated that these precipitation products have similar spatial patterns but significantly different precipitation estimates. A point-to-pixel validation indicated that all products cannot efficiently reproduce the daily precipitation observations, with the median Kling–Gupta efficiency (KGE) in the range of 0.10–0.26. Among the five products, MSWEP has the best consistency with the gauge observations (with a median KGE = 0.26), which is thus recommended as the preferred choice for applications among the five satellite precipitation products. However, as model forcing data, all the precipitation products showed a comparable capacity of streamflow simulations and were all able to accurately reproduce the observed streamflow records. The values of the KGE obtained from these precipitation products exceed 0.83 in the upper Yangtze River (UYA) basin and 0.84 in the upper Yellow River (UYE) basin. Thus, evaluation of precipitation products only focusing on the accuracy of streamflow simulations is less meaningful, which will mask the differences between these products. A further attribution analysis indicated that the influences of the different precipitation inputs on the streamflow simulations were largely offset by the parameter calibration, leading to significantly different evaporation and water storage estimates. Therefore, an efficient hydrological evaluation for precipitation products should focus on both streamflow simulations and the simulations of other hydrological variables, such as evaporation and soil moisture. [ABSTRACT FROM AUTHOR]

The characteristics of aerosol optical depth (AOD) over the Tibetan Plateau (TP) were analyzed using 8-year (from January 2007 to December 2014) Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) level 2 aerosol layer products. Firstly, the overall feature of AOD over the Tibetan Plateau was investigated, including the seasonal diversities of AODS (the sum of AODs from all aerosol layers), and A (the amounts of aerosol layers). Then we deeply studied the characteristics of AOD within the lowest aerosol layer over TP, including the seasonal variations of AOD1 (The AOD of the first aerosol layer), HB1 (the height of the first aerosol layer base), TL1 (the thickness of the first aerosol layer) and PAOD1 (The AOD proportion of the first aerosol layer). The AODS was generally low (<0.2) in the main body of TP in each season. The value of A was lower (~1–1.5) than other areas around the TP, indicating that the main body of TP generally had only one aerosol layer. The HTT (height of the highest aerosol layer top) was higher in spring (~8 km) and summer (~9 km), and lower in fall (~6.5 km) and winter (~6.5 km). The PAOD1 was high in each season except spring. The high PAOD1 values (>0.9) indicated that the aerosols were mainly concentrated in the lowest layer in summer, fall, and winter in the main body of TP. In spring, the PAOD1 value was relatively low (~0.7–0.85) and the distribution exhibited obvious differences between the southern (~0.85) and the northern (~0.75) TP, which appeared to be consistent with A. Most of the aerosol loads in summer were concentrated in the lowest aerosol layer with high aerosol loads. Most of the aerosol loads in fall and winter were also concentrated in the lowest aerosol layer, but with low aerosol loads. [ABSTRACT FROM AUTHOR]