Cross-sectional rainfall observation on the central-western Tibetan Plateau in the warm season: System design and preliminary results

Yang, Kun; Chen, Yingying; La, Zhu; Zhan, Changhui; Ling, Xiaoyan; Zhou, Xu; Jiang, Yaozhi; Yao, Xiangnan; Lu, Hui; Ma, Xiaogang; Ouyang, Lin; Pan, Weihao; Ren, Yifei; Shao, Chong; Tian, Jiaxin; Wang, Yan; Yang, Hua; Yue, Siyu; Zhang, Ke; Zhao, Dingchi; Zhao, Long; Zhou, Jianhong; Zou, Minmin

doi:10.1007/s11430-022-1081-4

Cited by 12 publications

(8 citation statements)

References 54 publications

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“…The precipitation records from 23 independent rain gauges were also used for our evaluation. These rain gauges are completely independent of those gauges in CMPA (Yang et al., 2023). All these gauges are located above 4,000 m in the center of the TP where the ground gauges are sparsely distributed (31.40°N–32.51°N, 79.69°E–90.86°E).…”

Section: Study Area and Datasetsmentioning

confidence: 99%

A Novel Double Machine Learning Strategy for Producing High‐Precision Multi‐Source Merging Precipitation Estimates Over the Tibetan Plateau

Lyu,

Yong

2024

Water Resources Research

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Precipitation estimation over the Tibetan Plateau is a critical but challenging task due to sparse gauges and high altitudes. Traditional statistic methods are often insufficient to characterize the nonlinear relationship between different precipitation information, while machine learning techniques, particularly deep learning algorithms, offer a novel and powerful approach to improve the merging accuracy of multi‐source precipitation data by efficiently capturing their spatiotemporal dynamics features. This study introduced a novel strategy called Double Machine Learning (DML), which integrates meteorological information, satellite retrievals, and reanalysis data to produce a high‐precision multi‐source merging precipitation product at 0.1° × 0.1°, daily resolution for the Tibetan Plateau. The quantitative evaluation of DML was accomplished using both auto‐meteorological gauges and independent observations. Statistical scores indicate that the new DML‐based merging product apparently outperforms three widely‐used precipitation datasets (IMERG‐Final, GSMaP‐Gauge and ERA5) over the Tibetan Plateau. The proposed DML strategy effectively integrates the advantages of traditional machine learning and deep learning, significantly enhancing the algorithmic robustness and merging accuracy, particularly at medium‐high rain rates in summer. Furthermore, the contributions of multi‐source inputs to the final merging effect was systematically analyzed. It is found that meteorological information, as an auxiliary variable in DML, plays a crucial role in identifying rainy events and adjusting the bias of precipitation estimates, especially over those ungauged regions. This study affirms the call for improving the multi‐source precipitation estimates by combining different machine learning approaches. The new merging precipitation product reported here is recommended for hydrometeorological users of the Tibetan Plateau science community.

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Section: Study Area and Datasetsmentioning

confidence: 99%

A Novel Double Machine Learning Strategy for Producing High‐Precision Multi‐Source Merging Precipitation Estimates Over the Tibetan Plateau

Lyu,

Yong

2024

Water Resources Research

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“…They include 1) a New Integrated Observational System over the TP (NIOST) (Xu et al 2008), 2) the Third TP Atmospheric Scientific Experiment (TIPEX-III) (Zhao et al 2018), 3) the Third Pole Environment Integrated Three-dimensional Observation and Research Platform (TPEITORP) (Ma et al 2008(Ma et al , 2023a, 4) the TP-River (Wang et al 2021), and 5) the cross-sectional rainfall observation on the central-western TP (Yang et al 2023). Parallelly, various networks concentrate on local or basin-specific scales, including the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) by Li et al (2013), the multiscale Soil Moisture and Temperature Monitoring Network on the central TP (CTP-SMTMN) developed by Yang et al (2013), and the Cryospheric Hydrometeorology Observation in the Hulu Catchment (CHOICE) initiated by Han et al (2018). Additional efforts encompass the Qinghai Lake Basin Critical Zone Observatory (Li et al 2018), meteorology-cryosphere-hydrology observations in the upper Brahmaputra basin (Wang et al 2022), the Qomolangma Special Atmospheric Processes and Environmental Changes (QOMS) observation network (Ma et al 2023b), and the Soil Moisture Monitoring Network and in situ observations of soil hydraulic properties on the northeastern TP (upstream regions of Heihe River basin) (Tian et al 2023).…”

Section: E443mentioning

confidence: 99%

Cryosphere–Hydrometeorology Observations for a Water Tower Unit on the Tibetan Plateau Using the BeiDou-3 Navigation Satellite System

Liu,

Wang,

Wang

et al. 2024

Bulletin of the American Meteorological Society

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Life and civilization in arid regions depend on the availability of freshwater. Arid alpine river basins, where hydrological processes are highly sensitive to rapid warming, act as vital water towers for lowland oases. However, scientific understanding of precipitation variability and related cryosphere-hydrology processes is extremely limited because of the scarcity of in situ observations. The upper Danghe River Basin (UDB, ∼14,000 km2) is an arid and westerly- dominated basin on the northeastern Tibetan Plateau and is the water source for the Dunhuang Oasis in China. We have established a comprehensive cryosphere-hydrometeorology observation network in the basin since 2014. At present, the network consists of 21 automatic rain gauges, 22 soil freeze-thaw monitoring stations, 4 automatic weather stations (AWS), and a 50-m gradient meteorological tower with an eddy covariance system. In particular, the 18 sites, located in remote areas without public networks, are equipped with new-generation BeiDou-3 communication terminals that enable the observations to be easily, safely, and reliably read and quality-controlled in near real-time from offices in the city or at home. This integrated observation network over the UDB that facilitates the monitoring of cryosphere- hydrology processes, land-atmosphere interactions, and local weather processes. In addition, the observations are helpful for the objective evaluation, and continual improvement, of hydrological models, satellite-retrieval products, and reanalysis datasets. Finally, the network is expected to promote a better understanding of the status and role of water towers in arid zones and to provide basic data support for the sustainable development of the Dunhuang Oasis and the Belt and Road.

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“…Two observation transects running south-north and west-east across this region were used to obtain hourly rainfall data during the warm season (May-September). The south-north transect extends from Yadong Valley to Shuanghu County; the west-east transect extends from Shiquanhe to Naqu in the central TP [11]. We downloaded the hourly rain gauge data for the warm season (May-September) in the central and western Tibetan Plateau in 2018 from the National Tibetan Plateau Data Center DOI: 10.11888/Atmos.tpdc.272983 (accessed on 21 December 2023) and calculated total precipitation during the warm season for 19 stations along the south-north transect and for 20 stations along the west-east transect.…”

Section: High-resolution Precipitation Product Materials 241 Gauge Ra...mentioning

confidence: 99%

“…PR3(SMAI, EDI) = R(SMAI i,j , EDI i,j ) + R(NDV I A i,j , EDI i,j ) 2 (11) where PR1, PR2, PR3 is the correlation predictor, and i and j are the pixel column and line serial number, respectively.…”

Section: Pr1(smai Edi) =mentioning

confidence: 99%

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Can the Accuracy of Fine-Resolution Precipitation Products Be Assessed from the Surrounding Water Balance and Drought Chain (WBDC) in the Qinghai–Tibetan Plateau?

Li,

Shi,

Pan

et al. 2023

Remote Sensing

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The Qinghai–Tibetan Plateau (QTP), which has a unique and severe environment, suffers from the absence of rainfall gauges in western arid land. Using different precipitation products in this region would easily lead to contradictory results. To evaluate nine fine-resolution precipitation products in the QTP, we propose a “down to top” methodology, based on water balance and drought chain, by forecasting two accuracy assessment indices—multi-year precipitation bias and precipitation correlation. We assessed the biases of all products in the Jinsha–Yalong, Yellow, Heihe, Yangtze, Yarlung Zangbo catchments and interior drainage areas. And we assessed gauge-based correlation of precipitation products, based on the correlations between precipitation product-based effective drought index (EDI), Soil Moisture Active Passive (SMAP)-based soil moisture anomaly, and the moderate-resolution imaging spectroradiometer (MODIS)-based normalized difference vegetation index (NDVI) anomaly (R = 0.712, R = 0.36, and R = 0.785, respectively) for cross-sectional rainfall observations on the Tibetan Plateau in 2018. The results showed that ERA5-Land and IMERG merged precipitation dataset (EIMD) can efficiently close the water budget at the catchment scale. Moreover, the EIMD-based EDI exhibited the best performance in correlation with both the SMAP-based soil moisture anomaly and MODIS-based NDVI anomaly for the three main herbaceous species areas—Kobresia pygmaea meadow, Stipa purpurea steppe, and Carex moorcroftii steppe. Overall, we find that EIMD is the most accurate among the nine products. The annual average precipitation (2001–2018) was determined to be 568.16 mm in the QTP. Our assessment methodology has a remote sensing basis with low cost and can be used for other arid lands in the future.

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Cross-sectional rainfall observation on the central-western Tibetan Plateau in the warm season: System design and preliminary results

Cited by 12 publications

References 54 publications

A Novel Double Machine Learning Strategy for Producing High‐Precision Multi‐Source Merging Precipitation Estimates Over the Tibetan Plateau

A Novel Double Machine Learning Strategy for Producing High‐Precision Multi‐Source Merging Precipitation Estimates Over the Tibetan Plateau

Cryosphere–Hydrometeorology Observations for a Water Tower Unit on the Tibetan Plateau Using the BeiDou-3 Navigation Satellite System

Can the Accuracy of Fine-Resolution Precipitation Products Be Assessed from the Surrounding Water Balance and Drought Chain (WBDC) in the Qinghai–Tibetan Plateau?

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