The Formation of a Dry‐Belt in the North Side of Central Himalaya Mountains

Wang, Yan; Yang, Kun; Zhou, Xu; Wang, Binbin; Chen, Deliang; Lu, Hui; Lin, Changgui; Zhang, Fuqing

doi:10.1029/2018gl081061

Cited by 15 publications

(5 citation statements)

References 40 publications

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“…S2 in the Supplement). Strong lake surface heating and a reduction in wind speed together contributed to the development of thermal stratification (Wetzel, 2001). During the summer stratification period, the surface water warmed rapidly from 7 to ∼ 13 • C between July and August, while the bottom water warmed much more slowly.…”

Section: Thermal Regimementioning

confidence: 99%

Contrasting hydrological and thermal intensities determine seasonal lake-level variations – a case study at Paiku Co on the southern Tibetan Plateau

Lei

Yang

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Evaporation from hydrologically closed lakes is one of the largest components of the lake water budget; however, its effects on seasonal lake-level variations remain unclear on the Tibetan Plateau (TP) due to a lack of comprehensive observations. In this study, weekly lake evaporation and its effects on seasonal lake-level variations are investigated at Paiku Co on the southern TP using in situ observations of thermal structure and hydrometeorology (2015–2018). Lake evaporation from Paiku Co was estimated to be 975±142 mm during the ice-free period (May to December), characterized by low values of 1.7 ± 0.6 mm d−1 during the pre-monsoon season (May to June), high values of 5.5±0.6 mm d−1 during the post-monsoon season (October to December), and intermediate values of 4.0±0.6 mm d−1 during the monsoon season (July to September). There was a ∼ 5-month lag between the maximum net radiation (June) and maximum lake evaporation (November). These results indicate that the seasonal pattern of lake evaporation from Paiku Co was significantly affected by the large lake heat storage. Contrasting hydrological and thermal intensities may play an important role in the large amplitude of seasonal lake-level variations at deep lakes like Paiku Co. High inflow from monsoon precipitation and glacier melting and moderate lake evaporation, for instance, drove rapid lake-level increase during the monsoon season. In contrast, high lake evaporation and reduced inflow caused lake level to decrease significantly during the post-monsoon season. This study implies that lake evaporation may play an important role in the different amplitudes of seasonal lake-level variations on the TP.

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Section: Thermal Regimementioning

confidence: 99%

Contrasting hydrological and thermal intensities determine seasonal lake-level variations – a case study at Paiku Co on the southern Tibetan Plateau

Lei

Yang

et al. 2021

Hydrol. Earth Syst. Sci.

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“…which is closer to that of TPHiPr. A narrow rain shadow zone is present on the north side of the HLM [50], and the TPHiPr results show higher R values on the windward slopes of the HLM as well as on the south side of the NQM and GDM, as well as lower R values on the north side in the rain shadow. However, the R values of the IMERG-F results are not heterogeneous in these areas.…”

Section: Discussionmentioning

confidence: 84%

Rainfall Erosivity Mapping for Tibetan Plateau Using High-Resolution Temporal and Spatial Precipitation Datasets for the Third Pole

Yin,

Xie,

Liu

et al. 2023

Remote Sensing

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Low-density weather station and high topographic variance limited rainfall erosivity (RE) calculation for Tibetan Plateau (TP). The accuracy of RE prediction from three data sources (a High-resolution Precipitation dataset for the Third Pole (TPHiPr), IMERG Final Run (IMERG-F) and weather station daily precipitation data) were evaluated for the TP, and the variations were analyzed from 2001 to 2020. The results showed that TPHiPr can more accurately characterize spatial and temporal variations of the RE on the TP. TPHiPr can better represent the impact of topography on precipitation, effectively compensating the deficiencies in precipitation data from low-density stations. The R2 and NSE between the mean annual/monthly RE of TPHiPr and the station data were around 0.9. TPHiPr effectively revealed rain shadow areas on the northern slopes of the Himalayas and calculated RE more accurately in the broad-leaved evergreen forest zone on the southern flank of the Himalayas and the arid regions to the northwest. RE from 2001 to 2020 showed an overall increasing trend. However, TPHiPr produced underestimates in the southern valleys and the eastern Hengduan Mountains, while overestimates in the southeastern area at lower elevations. This research provided a new and more accurate RE data for the TP.

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“…of Wang et al (2019), is more evident in TPHiPr than in ERA5. Besides, TPHiPr shows greater spatial variability of precipitation than ERA5 in the Hengduan Mountains where the topography is much complex with many large mountain ranges and valleys.…”

Section: Comparison With Other Datasetsmentioning

confidence: 85%

TPHiPr: A long-term high-accuracy precipitation dataset for the Third Pole region based on high-resolution atmospheric modeling and dense observations

Jiang

Yang

et al. 2022

Preprint

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Reliable precipitation data are highly necessary for geoscience research in the Third Pole (TP) region but still lacking, due to the complex terrain and high spatial variability of precipitation here.Accordingly, this study produces a long-term (1979-2020) high-resolution (1/30°) precipitation dataset (TPHiPr) for the TP by merging the atmospheric simulation-based ERA5_CNN with gauge observations from more than 9000 rain gauges, using the Climatology Aided Interpolation and Random Forest methods. Validation shows that the TPHiPr is generally unbiased and has a root mean square error of 4.5 mm day -1 , a correlation of 0.84 and a critical success index of 0.67 with respect to all independent rain gauges in the TP, demonstrating that this dataset is remarkably better than the widely-used global/quasiglobal datasets, including the fifth-generation atmospheric reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA5), the final run version 6 of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and the Multi-Source Weighted-Ensemble Precipitation version 2 (MSWEP V2). Moreover, the TPHiPr can better detect precipitation extremes compared with the three widely-used datasets. Overall, this study provides a new precipitation dataset with high accuracy for the TP, which may have broad applications in meteorological, hydrological and ecological studies. The produced dataset can be accessed via https://doi.org/10.11888/Atmos.tpdc.272763 (Yang and Jiang, 2022).

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The Formation of a Dry‐Belt in the North Side of Central Himalaya Mountains

Cited by 15 publications

References 40 publications

Contrasting hydrological and thermal intensities determine seasonal lake-level variations – a case study at Paiku Co on the southern Tibetan Plateau

Contrasting hydrological and thermal intensities determine seasonal lake-level variations – a case study at Paiku Co on the southern Tibetan Plateau

Rainfall Erosivity Mapping for Tibetan Plateau Using High-Resolution Temporal and Spatial Precipitation Datasets for the Third Pole

TPHiPr: A long-term high-accuracy precipitation dataset for the Third Pole region based on high-resolution atmospheric modeling and dense observations

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