Remote Sensing and Modeling of the Cryosphere in High Mountain Asia: A Multidisciplinary Review DOI Creative Commons
Qinghua Ye, Yuzhe Wang, Lin Liu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(10), P. 1709 - 1709

Published: May 11, 2024

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

Language: Английский

Global water resources and the role of groundwater in a resilient water future DOI
Bridget R. Scanlon, Sarah Fakhreddine, Ashraf Rateb

et al.

Nature Reviews Earth & Environment, Journal Year: 2023, Volume and Issue: 4(2), P. 87 - 101

Published: Jan. 31, 2023

Language: Английский

Citations

579
Climate change threatens terrestrial water storage over the Tibetan Plateau DOI
Xueying Li, Di Long, Bridget R. Scanlon

et al.

Nature Climate Change, Journal Year: 2022, Volume and Issue: 12(9), P. 801 - 807

Published: Aug. 15, 2022

Language: Английский

Citations

243
Over 60% precipitation transformed into terrestrial water storage in global river basins from 2002 to 2021 DOI Creative Commons
Yulong Zhong, Baoming Tian, Hyunglok Kim

et al.

Communications Earth & Environment, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 27, 2025

Language: Английский

Citations

8
Long-range transport of atmospheric microplastics deposited onto glacier in southeast Tibetan Plateau DOI
Zhaoqing Wang, Yulan Zhang, Shichang Kang

et al.

Environmental Pollution, Journal Year: 2022, Volume and Issue: 306, P. 119415 - 119415

Published: May 5, 2022

Language: Английский

Citations

58
Spatio-Temporal Evolution of Glacial Lakes in the Tibetan Plateau over the Past 30 Years DOI Creative Commons
Xiangyang Dou, Xuanmei Fan, Xin Wang

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(2), P. 416 - 416

Published: Jan. 10, 2023

As the Third Pole of Earth and Water Tower Asia, Tibetan Plateau (TP) nurtures large numbers glacial lakes, which are sensitive to global climate change. These lakes modulate freshwater ecosystem in region but concurrently pose severe threats valley population by means sudden lake outbursts consequent floods (GLOFs). The lack high-resolution multi-temporal inventory TP hampers a better understanding prediction future trend risk lakes. Here, we created using 30-year record 42,833 satellite images (1990–2019), discussed their characteristics spatio-temporal evolution over years. Results showed that number area had increased 3285 258.82 km2 last 3 decades, respectively. We noticed different regions exhibited varying change rates size; most show expansion increase while some decreasing such as western Pamir eastern Hindu Kush. mapping uncertainty is about 17.5%, lower than other available datasets, thus making our reliable for analysis TP. Our data publicly published, it can help study change–glacier–glacial lake–GLOF interactions serve input various hydro-climatic studies.

Language: Английский

Citations

24
Downscaled‐GRACE Data Reveal Anthropogenic and Climate‐Induced Water Storage Decline Across the Indus Basin DOI Creative Commons
Arfan Arshad, Ali Mirchi, Saleh Taghvaeian

et al.

Water Resources Research, Journal Year: 2024, Volume and Issue: 60(7)

Published: July 1, 2024

Abstract GRACE (Gravity Recovery and Climate Experiment) has been widely used to evaluate terrestrial water storage (TWS) groundwater (GWS). However, the coarse‐resolution of data limited ability identify local vulnerabilities in changes associated with climatic anthropogenic stressors. This study employs high‐resolution (1 km 2 ) generated through machine learning (ML) based statistical downscaling illuminate TWS GWS dynamics across twenty sub‐regions Indus Basin. Monthly anomalies obtained from a geographically weighted random forest (RF gw model maintained good consistency original at 25 grid scale. The downscaled 1 resolution illustrate spatial heterogeneity depletion within each sub‐region. Comparison in‐situ 2,200 monitoring wells shows that significantly improves agreement data, evidenced by higher Kling‐Gupta Efficiency (0.50–0.85) correlation coefficients (0.60–0.95). Hotspots highest decline rate between 2002 2023 were Dehli Doab (−442, −585 mm/year), BIST (−367, −556 Rajasthan (−242, −381 BARI (−188, −333 mm/year). Based on general additive model, 47%–83% was stressors mainly due increasing trends crop sown area, consumption, human settlements. lower (i.e., −25 −75 mm/year) upstream (e.g., Yogo, Gilgit, Khurmong, Kabul) where factors (downward shortwave radiations, air temperature, sea surface temperature) explained 72%–91% TWS/GWS changes. relative influences varied sub‐regions, underscoring complex interplay natural‐human activities basin. These findings inform place‐based resource management Basin advancing understanding vulnerabilities.

Language: Английский

Citations

14
Changes in glacier surface temperature across the Third Pole from 2000 to 2021 DOI

Shaoting Ren,

Tandong Yao, Wei Yang

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 305, P. 114076 - 114076

Published: March 6, 2024

Language: Английский

Citations

10
Glacier mass-balance estimates over High Mountain Asia from 2000 to 2021 based on ICESat-2 and NASADEM DOI Creative Commons
Yubin Fan, Chang‐Qing Ke, Xiaobing Zhou

et al.

Journal of Glaciology, Journal Year: 2022, Volume and Issue: 69(275), P. 500 - 512

Published: Sept. 16, 2022

Abstract High Mountain Asia (HMA) glaciers are critical water reserves for montane regions, which readily influenced by climate change. The glacier mass balance during 2000–2021 over HMA was estimated comparing the elevations from ICESat-2 and NASADEM. Radar penetration depth could be one of intrinsic error sources in estimating using Therefore, we doubled elevation differences between X-band Shuttle Topography Missions (SRTMs) NASADEM to estimate potential error. spatial characteristics altitude-dependent can detected most sub-regions HMA. Relatively deep penetrations Himalaya (2.3–3.7 m) Hissar Alay (4.3 regions small south-eastern (1.0 were observed. region experienced a significant loss at rate −0.18 ± 0.12 m w.e. −1 , Hengduan Shan exhibited highest −0.62 0.10 West Kun Lun substantial gain 0.23 0.13 Karakoram showed more or less balance. Our results agreement with previous studies that assessed different methods.

Language: Английский

Citations

31
Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia DOI Creative Commons
Weiwei Ren, Zhongzheng Zhu,

Yingzheng Wang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(6), P. 956 - 956

Published: March 8, 2024

Accurately simulating glacier mass balance (GMB) data is crucial for assessing the impacts of climate change on dynamics. Since physical models often face challenges in comprehensively accounting factors influencing glacial melt and uncertainties inputs, machine learning (ML) offers a viable alternative due to its robust flexibility nonlinear fitting capability. However, effectiveness ML modeling GMB across diverse types within High Mountain Asia has not yet been thoroughly explored. This study addresses this research gap by evaluating used simulation annual glacier-wide data, with specific focus comparing maritime glaciers Niyang River basin continental Manas basin. For purpose, meteorological predictive derived from monthly ERA5-Land datasets, topographical obtained Randolph Glacier Inventory, along target rooted geodetic observations, were employed drive four selective models: random forest model, gradient boosting decision tree (GBDT) deep neural network ordinary least-square linear regression model. The results highlighted that generally exhibit superior performance compared ones. Moreover, among models, GBDT model was found consistently coefficient determination (R2) values 0.72 0.67 root mean squared error (RMSE) 0.21 m w.e. 0.30 river basins, respectively. Furthermore, reveals climatic differentially influence simulations glaciers, providing key insights into dynamics response change. In summary, ML, particularly demonstrates significant potential simulation. application can enhance accuracy modeling, promising approach assess

Language: Английский

Citations

8
Hydrological Projections in the Third Pole Using Artificial Intelligence and an Observation‐Constrained Cryosphere‐Hydrology Model DOI Creative Commons
Junshui Long, Lei Wang, Deliang Chen

et al.

Earth s Future, Journal Year: 2024, Volume and Issue: 12(4)

Published: April 1, 2024

Abstract The water resources of the Third Pole (TP), highly sensitive to climate change and glacier melting, significantly impact food security millions in Asia. However, projecting future spatial‐temporal runoff changes for TP's mountainous basins remains a formidable challenge. Here, we've leveraged long short‐term memory model (LSTM) craft grid‐scale artificial intelligence (AI) named LSTM‐grid. This has enabled production hydrological projections seven major river TP. LSTM‐grid integrates monthly precipitation, air temperature, total mass (total_GMC) data at 0.25‐degree grid. Training employed gridded historical evapotranspiration sets generated by an observation‐constrained cryosphere‐hydrology headwaters TP during 2000–2017. Our results demonstrate LSTM grid's effectiveness usefulness, exhibiting Nash‐Sutcliffe Efficiency coefficient exceeding 0.92 verification periods (2013–2017). Moreover, monsoon region exhibited higher rate increase compared those westerlies region. Intra‐annual indicated notable increases spring runoff, especially where meltwater contributes runoff. Additionally, aptly captures before after turning points highlighting growing influence precipitation on reaching maximum total_GMC. Therefore, offers fresh perspective understanding spatiotemporal distribution high‐mountain glacial regions tapping into AI's potential drive scientific discovery provide reliable data.

Language: Английский

Citations

8