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  • Wang, Yingzheng  (3)
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  • 1
    In: Remote Sensing, MDPI AG, Vol. 14, No. 16 ( 2022-08-20), p. 4078-
    Abstract: Due to climate warming, the glaciers of the Tibetan Plateau have experienced rapid mass loss and the patterns of glacier changes have exhibited high spatiotemporal heterogeneity, especially in interior areas. As the largest ice field within the Tibetan Plateau, the Puruogangri Ice Field has attracted a lot of attention from the scientific community. However, relevant studies that are based on satellite data have mainly focused on a few periods from 2000–2016. Long-term and multiperiod observations remain to be conducted. To this end, we estimated the changes in the glacier area and mass balance of the Puruogangri Ice Field over five subperiods between 1975 and 2021, based on multisource remote sensing data. Specifically, we employed KH-9 and Landsat images to estimate the area change from 1975 to 2021 using the band ratio method. Subsequently, based on KH-9 DEM, SRTM DEM, Copernicus DEM and ZY-3 DEM data, we evaluated the glacier elevation changes and mass balance over five subperiods during 1975–2021. The results showed that the total glacier area decreased from 427.44 ± 12.43 km2 to 387.87 ± 11.02 km2 from 1975 to 2021, with a decrease rate of 0.86 km2 a−1. The rate of area change at a decade timescale was −0.74 km2 a−1 (2000–2012) and −1.00 km2 a−1 (2012–2021). Furthermore, the rates at a multiyear timescale were −1.23 km2 a−1, −1.83 km2 a−1 and −0.42 km2 a−1 for 2012–2015, 2015–2017 and 2017–2021, respectively. In terms of the glacier mass balance, the region-wide results at a two-decade timescale were −0.23 ± 0.02 m w.e. a−1 for 1975–2000 and −0.29 ± 0.02 m w.e. a−1 for 2000–2021, indicating a sustained and relatively stable mass loss over the past nearly five decades. After 2000, the loss rate at a decade timescale was −0.04 ± 0.01 m w.e. a−1 for 2000–2012 and −0.17 ± 0.01 m w.e. a−1 for 2012–2021, indicating an increasing loss rate over recent decades. It was further found that the mass loss rate was −0.12 ± 0.02 m w.e. a−1 for 2012–2015, −0.03 ± 0.01 m w.e. a−1 for 2015–2017 and −0.40 ± 0.03 m w.e. a−1 for 2017–2021. These results indicated that a significant portion of the glacier mass loss mainly occurred after 2017. According to our analysis of the meteorological measurements in nearby regions, the trends of precipitation and the average annual air temperature both increased. Combining these findings with the results of the glacier changes implied that the glacier changes seemed to be more sensitive to temperature increase in this region. Overall, our results improved our understanding of the status of glacier changes and their reaction to climate change in the central Tibetan Plateau.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
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  • 2
    In: Remote Sensing, MDPI AG, Vol. 14, No. 3 ( 2022-01-21), p. 506-
    Abstract: The response of lake-terminating glaciers to climate change is complex, and their rapid changes are often closely linked to glacial-lake outburst floods. However, the eastern Tanggula Mountains, which are the only area where lake-terminating glaciers are found within the Tibetan Plateau, have received little attention to date. In this study, to address this gap, we generated updated glacier boundaries and estimated the interdecadal area changes for 2000–2020 based on the interpretation of Landsat-5/8 and Sentinel-2 images. In addition, based on the method of digital elevation model (DEM) differencing, we quantified the changes in glacier thickness and mass balance using TanDEM-X radar data and SRTM DEM over almost the same periods. The final results show that the glaciers in the eastern Tanggula Mountains, as a whole, have experienced accelerated area shrinkage (with a rate of area loss increasing from −0.34 ± 0.83 km2 a−1 to −0.93 ± 0.81 km2 a−1 for 2000–2013 and 2013–2020, respectively) and accelerated ice thinning (changing from −0.19 ± 0.05 m a−1 and −0.53 ± 0.08 m a−1 for 2000−2012 and 2012–2020, respectively). Furthermore, the region-wide glacier mass balance was −0.16 ± 0.04 m w.e. a−1 and −0.45 ± 0.07 m w.e. a−1 for these two sub-periods, corresponding to a 1.8 times acceleration of mass loss rate. The average mass balance during 2000–2020 was −0.23 ± 0.04 m w.e. a−1, which is equivalent to a rate of mass loss of −0.04 Gt a−1. More specifically, within the region, the lake-terminating glaciers have exhibited more significant acceleration of area loss and mass loss, compared to the land-terminating glaciers. However, interestingly, the average thinning rate of the lake-terminating glaciers is always lower than that of the land-terminating glaciers over all study periods, which is in contrast with previous findings in other high mountain areas (e.g., the Himalaya Mountains). Field study and proglacial lakes monitoring suggest that the local topography plays a vital role in the evolution of the glacial lakes in this region, which further affects the glacier changes. Furthermore, the present status of the glacier changes in this region can be attributed to the long-term increase in air temperature. Our findings provide a comprehensive overview of the current state of glacier changes across the eastern Tanggula Mountains and will help to improve the understanding of the heterogeneous response of glaciers to climate change.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
    Location Call Number Limitation Availability
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  • 3
    In: Remote Sensing, MDPI AG, Vol. 15, No. 18 ( 2023-09-14), p. 4527-
    Abstract: Due to the scarcity of observational data and the intricate precipitation–runoff relationship, individually applying physically based hydrological models and machine learning (ML) techniques presents challenges in accurately predicting floods within data-scarce glacial river basins. To address this challenge, this study introduces an innovative hybrid model that synergistically harnesses the strengths of multi-source remote sensing data, a physically based hydrological model (i.e., Spatial Processes in Hydrology (SPHY)), and ML techniques. This novel approach employs MODIS snow cover data and remote sensing-derived glacier mass balance data to calibrate the SPHY model. The SPHY model primarily generates baseflow, rain runoff, snowmelt runoff, and glacier melt runoff. These outputs are then utilized as extra inputs for the ML models, which consist of Random Forest (RF), Gradient Boosting (GDBT), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), Support Vector Machine (SVM) and Transformer (TF). These ML models reconstruct the intricate relationship between inputs and streamflow. The performance of these six hybrid models and SPHY model is comprehensively explored in the Manas River basin in Central Asia. The findings underscore that the SPHY-RF model performs better in simulating and predicting daily streamflow and flood events than the SPHY model and the other five hybrid models. Compared to the SPHY model, SPHY-RF significantly reduces RMSE (55.6%) and PBIAS (62.5%) for streamflow, as well as reduces RMSE (65.8%) and PBIAS (73.51%) for floods. By utilizing bootstrap sampling, the 95% uncertainty interval for SPHY-RF is established, effectively covering 87.65% of flood events. Significantly, the SPHY-RF model substantially improves the simulation of streamflow and flood events that the SPHY model struggles to capture, indicating its potential to enhance the accuracy of flood prediction within data-scarce glacial river basins. This study offers a framework for robust flood simulation and forecasting within glacial river basins, offering opportunities to explore extreme hydrological events in a warming climate.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2513863-7
    Location Call Number Limitation Availability
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