融合<strong>OGGM</strong>模型与探地雷达数据的羌塘<strong>2</strong>号冰川厚度分布模拟及冰储量变化预估

doi:10.11867/j.issn.1001-8166.2026.020

地球科学进展 ›› 2026, Vol. 41 ›› Issue (3): 301 -312. doi: 10.11867/j.issn.1001-8166.2026.020

融合OGGM模型与探地雷达数据的羌塘2号冰川厚度分布模拟及冰储量变化预估

崔天瑞¹^,²(

), 梁鹏斌²(

), 张乐乐¹, 田立德³, 高永鹏³, 牟建新⁴

^1.青海师范大学地理科学学院，青海西宁 810008
^2.青海理工学院生态与环境科学学院，青海西宁 810016
^3.云南大学国际河流与生态安全研究院，云南昆明 650500
^4.中国科学院西北生态环境资源研究院冰冻圈科学与冻土工程重点实验室，甘肃兰州 730000

收稿日期:2025-10-28 修回日期:2026-02-28 出版日期:2026-03-10
通讯作者: 梁鹏斌 E-mail:Trevglacier@163.com;pbliang@qhit.edu.cn
基金资助:
青海省“昆仑英才”人才引进科研项目(2023-QLGKLYCZX-001)

Simulation of Ice Thickness Distribution and Volume Change Estimation for Qiangtang No. 2 Glacier by Integrating the OGGM Model with Ground-Penetrating Radar Data

Tianrui Cui¹^,²(), Pengbin Liang²(), Lele Zhang¹, Lide Tian³, Yongpeng Gao³, Jianxin Mu⁴

^1.College of Geographical Science, Qinghai Normal University, Xining 810008, China
^2.School of Ecology and Environmental Science, Qinghai Institute of Technology, Xining 810016, China
^3.Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China
^4.Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

Received:2025-10-28 Revised:2026-02-28 Online:2026-03-10 Published:2026-03-01
Contact: Pengbin Liang E-mail:Trevglacier@163.com;pbliang@qhit.edu.cn
About author:Cui Tianrui, research areas include cryospheric dynamics and climate effects. E-mail: Trevglacier@163.com
Supported by:
the Top-notch Talent of the Qinghai Province “Kunlun Talent. High-end Innovation and Entrepreneurship Talent” Program(2023-QLGKLYCZX-001)

全文 ( 21 ) 下载PDF ( 116 )

冰川厚度是评估冰川动态和预测其未来演变对气候强迫响应所需的一个基础参数，亦是决定区域水资源供给量和维系生态系统稳定性的关键指标。羌塘高原作为青藏高原核心区域及“亚洲水塔”的重要组成部分，其冰川储量变化对区域水资源安全与生态环境稳定具有重要影响。集成探地雷达实测数据与全球开放冰川模型（OGGM），对羌塘2号冰川厚度空间分布进行模拟并估算冰储量，进一步结合CMIP6的3种共享社会经济路径气候情景数据，对其未来变化趋势进行预估。结果表明，羌塘2号冰川平均厚度为87.7 m，冰储量约为0.331 km³。未来情景预估显示，在SSP1-2.6情景下，2020—2100年冰川面积预计减少73.3%；在SSP3-7.0和SSP5-8.5情景下，羌塘2号冰川预计将在2080年前后基本消融殆尽。研究结果可为羌塘高原地区冰川水资源变化评估提供科学依据，并有助于深化对极大陆型冰川演变过程及气候响应机制的认识。

关键词: 羌塘2号冰川, 全球开放冰川模型（OGGM）, 探地雷达, 冰储量变化预估, 气候变化情景

Glacier thickness is a fundamental parameter for evaluating glacier dynamics and predicting their future evolution under climate forcing. It is also a key indicator for determining regional water resource supply and maintaining ecosystem stability. As a core region of the Qinghai-Xizang Plateau and an important component of the “Asian Water Tower,” the Qiangtang Plateau plays a crucial role in regional hydrology and ecological security. Changes in glacier storage in this region have significant implications for water resource availability and environmental stability. In this study, we integrated Ground Penetrating Radar (GPR) observations with the Open Global Glacier Model (OGGM) to reconstruct the spatial distribution of ice thickness for Qiangtang Glacier No. 2 and to estimate its total ice volume. Furthermore, future glacier evolution from 2020 to 2100 was projected using climate forcing data from three Shared Socioeconomic Pathway (SSP) scenarios of CMIP6. The GPR measurements revealed an average ice thickness of 107.1 m at the survey points, with a maximum thickness of 161.1 m at an elevation of 5 621.9 m. Building upon these observations, the interpolation simulation yielded an average glacier thickness of 87.7 m and an ice volume of approximately 0.331 km³. Comparative analysis showed a high correlation (0.970 2) between the modeled and GPR-measured thicknesses, with a mean error of 3.2 m and a root mean square error of 18.52 m, indicating that OGGM performed with the highest accuracy among the models evaluated for this glacier. Future projections under the SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios consistently indicate a sustained and pronounced retreat of the glacier. Mass thinning predominates through 2050, followed by significant area loss toward 2100. The SSP5-8.5 scenario shows the fastest decline, with the glacier effectively melting away by 2070. Under SSP3-7.0, retreat starts slowly but accelerates, resulting in losses of 97.9% in area and 98.7% in volume by 2100. Notably, even the low-forcing SSP1-2.6 scenario predicts a 73.3% area loss and a 96.7% volume reduction by 2100. The findings of this study provide a scientific basis for evaluating glacier water resource changes on the Qiangtang Plateau and contribute to improving the understanding of the evolution processes and climate responses of extreme-glaciers.

Key words: Qiangtang No.2 Glacier, Open Global Glacier Model (OGGM), Ground penetrating radar, Estimation of ice volume, Climate change scenarios

中图分类号:

P343.6

图1 羌塘2号冰川位置与探地雷达（GPR）测线分布

Fig. 1 The location of the Qiangtang No. 2 Glacier and the distribution of the Ground Penetrating Radar （GPR） lines

表1 第六次耦合模式比较计划（CMIP6）中3种共享社会经济路径信息

Table 1 Three types of shared socio-economic path information in Coupled Model Intercomparison Project Phase 6 （CMIP6）

共享社会经济路径情景	说明
SSP1-2.6	低辐射强迫路径，2100年辐射强迫稳定在2.6 W/m²
SSP3-7.0	中辐射强迫路径，2100年辐射强迫稳定在7.0 W/m²
SSP5-8.5	高辐射强迫路径，2100年辐射强迫稳定在8.5 W/m²

表1 第六次耦合模式比较计划（CMIP6）中3种共享社会经济路径信息

Table 1 Three types of shared socio-economic path information in Coupled Model Intercomparison Project Phase 6 （CMIP6）

共享社会经济路径情景	说明
SSP1-2.6	低辐射强迫路径，2100年辐射强迫稳定在2.6 W/m²
SSP3-7.0	中辐射强迫路径，2100年辐射强迫稳定在7.0 W/m²
SSP5-8.5	高辐射强迫路径，2100年辐射强迫稳定在8.5 W/m²

图2 羌塘2号冰川探地雷达（GPR）测线横纵剖面
AA'、DD'、EE '和FF '为与冰川中流线垂直相交的横向测线，BB'和CC'为沿中流线方向布设的纵向测线，GG'、HH '及II'为靠近冰川底部及冰舌末端斜向测线。

Fig. 2 Transverse and longitudinal profiles of the Ground Penetrating Radar （GPR） lines on the Qiangtang No. 2 Glacier
AA'， DD'， EE '， and FF ' are transverse profiles perpendicular to the glacier centerline， BB' and CC' are longitudinal profiles aligned with the centerline， GG'， HH'， and II' are oblique profiles near the glacier base and terminus.

图3 羌塘2号冰川厚度空间分布特征

Fig. 3 Ice thickness spatial distribution of Qiangtang No. 2 Glacier

图4 OGGM模型结果与Farinotti等^［20］的模拟结果综合对比

Fig. 4 Comprehensive comparison of OGGM model results with the simulation results of Farinotti et al. ^［20］

表2 不同模型的平均冰川厚度与体积估算对比

Table 2 Comparison of different models at ice thickness and volume estimation

方法	平均厚度/m	体积/km³	偏差率/%
GPR插值 OGGM	87.6 79.4	0.331 0.313	— -2.9/-5.4
HF	97.2	0.377	+10.9/+13.9
Fürst	64.6	0.251	-26.3/-24.1
GlabTop2	59.0	0.229	-32.7/-30.8

表2 不同模型的平均冰川厚度与体积估算对比

Table 2 Comparison of different models at ice thickness and volume estimation

方法	平均厚度/m	体积/km³	偏差率/%
GPR插值 OGGM	87.6 79.4	0.331 0.313	— -2.9/-5.4
HF	97.2	0.377	+10.9/+13.9
Fürst	64.6	0.251	-26.3/-24.1
GlabTop2	59.0	0.229	-32.7/-30.8

图5 SSP1-2.6、SSP3-7.0和SSP5-8.5情景下2020—2100年羌塘2号冰川区域气温和降水变化

Fig. 5 Changes of temperature and precipitation in Qiangtang No. 2 Glacier during 2020-2100 under three scenarios （SSP1-2.6， SSP3-7.0， SSP5-8.5）

图6 SSP1-2.6、SSP3-7.0、SSP5-8.5情景下冰川未来变化预估

Fig. 6 Prediction of glacier change under three scenarios （SSP1-2.6， SSP3-7.0， SSP5-8.5）

图7 2020—2100年羌塘2号冰川储量变化

Fig. 7 Change of Qiangtang No. 2 Glacier reserves from 2020 to 2100

表3 与其他变化预估研究结果损失率的对比 (%)

Table 3 Comparison with other change estimation research results about the loss ratio

情景	本文	Kraaijenbrink等^［28］	Zekollari等^［29］	Rounce等^［4］
SSP1-2.6	96.7±1.4	81.6	78.9±12.3	89.0±8.6
SSP3-7.0	98.7±0.6	—	97.2±2.8	98.7±0.6
SSP5-8.5	99.2±0.9	97.2	98.9±1.6	99.8±0.5

表3 与其他变化预估研究结果损失率的对比 (%)

Table 3 Comparison with other change estimation research results about the loss ratio

情景	本文	Kraaijenbrink等^［28］	Zekollari等^［29］	Rounce等^［4］
SSP1-2.6	96.7±1.4	81.6	78.9±12.3	89.0±8.6
SSP3-7.0	98.7±0.6	—	97.2±2.8	98.7±0.6
SSP5-8.5	99.2±0.9	97.2	98.9±1.6	99.8±0.5

[1]	Immerzeel W W， Lutz A F， Andrade M， et al. Importance and vulnerability of the world’s water towers［J］. Nature， 2020， 577（7 790）： 364-369.
[2]	Zemp M， Jakob L， Dussaillant I， et al. Community estimate of global glacier mass changes from 2000 to 2023［J］. Nature， 2025， 639（8 054）： 382-388.
[3]	Huss M， Hock R. Global-scale hydrological response to future glacier mass loss［J］. Nature Climate Change， 2018， 8（2）： 135-140.
[4]	Rounce D， Hock R， Maussion F， et al. Global glacier change in the 21st century： every increase in temperature matters［J］. Science， 2023， 379（6 627）： 78-83.
[5]	Rabatel A， Sanchez O， Vincent C， et al. Estimation of glacier thickness from surface mass balance and ice flow velocities： a case study on Argentière Glacier， France［J］. Frontiers in Earth Science， 2018， 6： 112.
[6]	Welty E， Zemp M， Navarro F， et al. Worldwide version-controlled database of glacier thickness observations［J］. Earth System Science Data， 2020， 12（4）： 3 039-3 055.
[7]	Huss M， Farinotti D. Distributed ice thickness and volume of all glaciers around the globe［J］. Journal of Geophysical Research： Earth Surface， 2012， 117（F4）： 2012JF002523.
[8]	Millan R， Mouginot J， Rabatel A， et al. Ice velocity and thickness of the world’s glaciers［J］. Nature Geoscience， 2022， 15（2）： 124-129.
[9]	Frey H， Machguth H， Huss M， et al. Estimating the volume of glaciers in the Himalayan-Karakoram region using different methods［J］. The Cryosphere， 2014， 8（6）： 2 313-2 333.
[10]	Van Pelt W J J， Oerlemans J， Reijmer C H， et al. An iterative inverse method to estimate basal topography and initialize ice flow models［J］. The Cryosphere， 2013， 7（3）： 987-1 006.
[11]	Werder M A， Huss M， Paul F， et al. A Bayesian ice thickness estimation model for large-scale applications［J］. Journal of Glaciology， 2020， 66（255）： 137-152.
[12]	Clarke G K C， Berthier E， Schoof C G， et al. Neural networks applied to estimating subglacial topography and glacier volume［J］. Journal of Climate， 2009， 22（8）： 2 146-2 160.
[13]	Lu Y J， Zhang Z， Shangguan D H， et al. Novel machine learning method integrating ensemble learning and deep learning for mapping debris-covered glaciers［J］. Remote Sensing， 2021， 13（13）： 2595.
[14]	Lopez Uroz L， Yan Y J， Benoit A， et al. Using deep learning for glacier thickness estimation at a regional scale［J］. IEEE Geoscience and Remote Sensing Letters， 2024， 21： 1-5.
[15]	Li Z Q， Li J， Ma X Y， et al. A glacier ice thickness estimation method based on deep convolutional neural networks［J］. Geosciences， 2025， 15（7）： 242.
[16]	Liang Pengbin， Mou Jianxin， Gao Yongpeng， et al. Ice thickness simulation and storage estimation of the extreme glaciers on the Qiangtang Plateau ［J］. Advances in Earth Science， 2024， 39（7）： 726-736.
	梁鹏斌，牟建新，高永鹏，等. 羌塘高原极大陆型冰川厚度分布模拟与冰储量估算［J］. 地球科学进展， 2024， 39（7）： 726-736.
[17]	Wang Liping， Xie Zichu， Liu Shiyin， et al. Glacierized area variation and its response to climate change in Qiangtang Plateau during 1970-2000 ［J］. Journal of Glaciology and Geocryology， 2011， 33（5）： 979-990.
	王利平，谢自楚，刘时银，等. 1970—2000年羌塘高原冰川变化及其预测研究［J］. 冰川冻土， 2011， 33（5）： 979-990.
[18]	Shi Yafeng. Concise glacier inventory of China［M］. Shanghai： Shanghai Popular Science Press， 2005.
	施雅风. 简明中国冰川目录［M］. 上海：上海科学普及出版社， 2005.
[19]	Li S H， Yao T D， Yang W， et al. Glacier energy and mass balance in the inland Tibetan Plateau： seasonal and interannual variability in relation to atmospheric changes［J］. Journal of Geophysical Research： Atmospheres， 2018， 123（12）： 6 390-6 409.
[20]	Farinotti D， Huss M， Fürst J J， et al. A consensus estimate for the ice thickness distribution of all glaciers on Earth［J］. Nature Geoscience， 2019， 12（3）： 168-173.
[21]	Zhu Dayun， Tian Lide， Wang Jianli， et al. The Qiangtang Glacier No.1 in the middle of the Tibetan Plateau： depth sounded by using GPR and volume estimated ［J］. Journal of Glaciology and Geocryology， 2014， 36（2）： 278-285.
	朱大运，田立德，王建力，等. 青藏高原中部双湖羌塘1号冰川厚度特征及冰储量估算［J］. 冰川冻土， 2014， 36（2）： 278-285.
[22]	Jin S， Li Z Q， Wang Z M， et al. Ice thickness distribution and volume estimation of Burqin Glacier No. 18 in the Chinese Altay Mountains［J］. Journal of Arid Land， 2020， 12（6）： 905-916.
[23]	Farinotti D， Brinkerhoff D J， Clarke G K C， et al. How accurate are estimates of glacier ice thickness？Results from ITMIX， the Ice Thickness Models Intercomparison eXperiment［J］. The Cryosphere， 2017， 11（2）： 949-970.
[24]	Li H L， Ng F， Li Z Q， et al. An extended “perfect-plasticity” method for estimating ice thickness along the flow line of mountain glaciers［J］. Journal of Geophysical Research： Earth Surface， 2012， 117（F1）： 2011JF002104.
[25]	Driedger C L， Kennard P M. Glacier volume estimation on cascade volcanoes： an analysis and comparison with other methods［J］. Annals of Glaciology， 1986， 8： 59-64.
[26]	Zhang Yong， Liu Shiyin， Wang Xin. A dataset of spatial distribution of degree-day factors for glaciers in High Mountain Asia ［J］. China Scientific Data， 2019， 4（3）： 141-151.
	张勇，刘时银，王欣. 高亚洲冰川区度日因子空间分布数据集［J］. 中国科学数据， 2019， 4（3）： 141-151.
[27]	Feng Bo， Meng Xianhong， Yang Xianyu， et al. Temperature and precipitation assessment and extreme climate events prediction based on the Coupled Model Intercomparison Project Phase 6 over the Qinghai-Xizang Plateau［J］. Plateau Meteorology， 2025， 44（2）： 265-278.
	冯波，孟宪红，杨显玉，等. 第六次国际耦合模式比较计划（CMIP6）中青藏高原气温和降水的适用性评估及极端气候事件变化预估［J］. 高原气象， 2025， 44（2）： 265-278.
[28]	Kraaijenbrink P D A， Bierkens M F P， Lutz A F， et al. Impact of a global temperature rise of 1.5 degrees Celsius on Asia’s glaciers［J］. Nature， 2017， 549（7 671）： 257-260.
[29]	Zekollari H， Huss M， Schuster L， et al. Twenty-first century global glacier evolution under CMIP6 scenarios and the role of glacier-specific observations［J］. The Cryosphere， 2024， 18（11）： 5 045-5 066.

[1]	梁鹏斌, 牟建新, 高永鹏, 田立德, 李林涛. 羌塘高原极大陆型冰川厚度分布模拟与冰储量估算[J]. 地球科学进展, 2024, 39(7): 726-736.
[2]	武震,张世强,刘时银,杜文涛. 祁连山老虎沟12号冰川冰内结构特征分析[J]. 地球科学进展, 2011, 26(6): 631-641.
[3]	崔喜红,陈晋,关琳琳. 探地雷达技术在植物根系探测研究中的应用[J]. 地球科学进展, 2009, 24(6): 606-611.
[4]	武震,刘时银,张世强. 祁连山老虎沟12号冰川冰下形态特征分析[J]. 地球科学进展, 2009, 24(10): 1149-1158.
[5]	熊伟,杨婕,林而达,许吟隆. 未来不同气候变化情景下我国玉米产量的初步预测[J]. 地球科学进展, 2008, 23(10): 1092-1101.
[6]	范丽军;符淙斌;陈德亮;. 统计降尺度法对未来区域气候变化情景预估的研究进展[J]. 地球科学进展, 2005, 20(3): 320-329.

阅读次数

全文

摘要

选择文件类型/文献管理软件名称

选择包含的内容

Simulation of Ice Thickness Distribution and Volume Change Estimation for Qiangtang No. 2 Glacier by Integrating the OGGM Model with Ground-Penetrating Radar Data

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

Simulation of Ice Thickness Distribution and Volume Change Estimation for Qiangtang No. 2 Glacier by Integrating the OGGM Model with Ground-Penetrating Radar Data