Publication

@article{YANG2024101612,

title = {Assessing satellite and reanalysis-based precipitation products in cold and arid mountainous regions},

author = {Yaru Yang and Wenzheng Ji and Liting Niu and Zhaojun Zheng and Weidong Huang and Changshun Zhang and Xiaohua Hao and Jianshe Xiao and Hongyi* Li},

url = {https://www.sciencedirect.com/science/article/pii/S2214581823002999

http://www.coldregion.com/wp-content/uploads/2024/01/Assessing-satellite-and-reanalysis-based-precipitation-products.pdf},

doi = {https://doi.org/10.1016/j.ejrh.2023.101612},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Journal of Hydrology: Regional Studies},

volume = {51},

pages = {101612},

abstract = {Study region 

The Xiying and Heihe River basins, cold and arid mountainous regions of northwestern China. 

Study focus 

In data-limited areas of Northwestern China, satellite- and reanalysis-based precipitation products provide crucial assistance. However, a comprehensive evaluation of their advantages, limitations, and hydrological performance has yet to be ascertained. This knowledge gap impedes a deeper understanding of hydrological processes within the region. This study evaluates the accuracy of ERA5L, IMERG-F, GSMaP-G, and FY4A-QPE from three aspects: time, space, and extreme precipitation. Comprehensive validation was conducted on four products using discharge, flood events, and peak flood simulated by Geomorphology-Ecology-Hydrology Model. 

New hydrological insights for the region 

ERA5L data can be used to build long-term studies on regional meteorological and hydrological trends. IMERG-F is valuable for analyzing extreme precipitation and hydrological events. GSMaP-G is well-suited for identifying precipitation events in the region. It is essential to be cautious of precipitation underestimation when using satellite precipitation products at high elevations and during the winter. This study highlights the differences in accuracy among various precipitation products and provides recommendations for policymakers in selecting data for high-altitude research. It also offers critical information on hydrometeorology and directions for producing high-accuracy precipitation products.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chen2023,

title = {The benefits of the water diversion on a densely populated fragmented lake area — A remote sensing view},

author = {Tengfei Chen and Hongyi* Li and Chunlei Zhao and Yufei Chang and Yaru Yang and Jian Wang},

doi = {10.1088/2515-7620/acdfae},

issn = {2515-7620},

year  = {2023},

date = {2023-06-01},

urldate = {2023-06-01},

journal = {Environ. Res. Commun.},

volume = {5},

number = {6},

publisher = {IOP Publishing},

abstract = {<jats:title>Abstract</jats:title>

               <jats:p>Water diversion is crucial for maintaining sustainable development in populated lake areas. However, there has been no clear evaluation of how water diversion affects lake health in these areas, which are often limited by scarce <jats:italic>in situ</jats:italic> observations. To address this issue, we selected Baiyangdian, a fragmented freshwater shallow lake in Xiongan New Area, as our study area. We conducted a time series analysis of Baiyangdian’s water level and area changes, and identified the impact of water diversion on Baiyangdian. We used laser altimetry data (ICESat, ICESat-2), remotely-sensed lake extent, water diversion data, precipitation data, and <jats:italic>in situ</jats:italic> lake level data. Our results show that water diversion, rather than natural precipitation, has maintained healthy water levels in Baiyangdian in recent years. Baiyangdian’s water level has risen significantly and stabilized, moving from a drying-up level to a healthy level. The intensity of water diversion has increased from 0.91 million m<jats:sup>3</jats:sup> in the 2000s to 7.17 million m<jats:sup>3</jats:sup> in the 2020 s, with a corresponding water level rise from 4.93 m to 7.14 m, respectively. Moreover, a positive feedback loop between economic development, policy-making, and the lake’s ecological health can promote the sustainable development of both people and nature.</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Li2023,

title = {Machine Learning-Based Bias Correction of Precipitation Measurements at High Altitude},

author = {Hongyi* Li and Yang Zhang and Huajin Lei and Xiaohua Hao},

doi = {10.3390/rs15082180},

issn = {2072-4292},

year  = {2023},

date = {2023-04-00},

urldate = {2023-04-00},

journal = {Remote Sensing},

volume = {15},

number = {8},

publisher = {MDPI AG},

abstract = {<jats:p>Accurate precipitation measurements are essential for understanding hydrological processes in high-altitude regions. Conventional gauge measurements often yield large underestimations of actual precipitation, prompting the development of statistical methods to correct the measurement bias. However, the complex conditions at high altitudes pose additional challenges to the statistical methods. To improve the correction of precipitation measurements in high-altitude areas, we selected the Yakou station, situated at an altitude of 4147 m on the Tibetan plateau, as the study site. In this study, we employed the machine learning method XGBoost regression to correct precipitation measurements using meteorological variables and remote sensing data, including Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Additionally, we examined the transferability of this method between different stations in our study site, Norway, and the United States. Our results show that the Yakou station experiences a large underestimation of precipitation, with a magnitude of 51.4%. This is significantly higher than similar measurements taken in the Arctic or lower altitudes. Furthermore, the remote sensing precipitation datasets underestimated precipitation when compared to the Double Fence Intercomparison Reference (DFIR) precipitation observation. Our findings suggest that the machine learning method outperformed the traditional statistical method in accuracy metrics and frequency distribution. Introducing remote sensing data, especially the GSMaP precipitation, could potentially replace the role of in situ wind speed in precipitation correction, highlighting the potential of remote sensing data for correcting precipitation rather than in situ meteorological observation. Moreover, our results indicate that the machine learning method with remote sensing data demonstrated better transferability than the traditional statistical method when we cross-validated the method with sites located in different countries. This study offers a promising strategy for obtaining more accurate precipitation measurements in high-altitude regions.</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Li2023b,

title = {Revealing the river ice phenology on the Tibetan Plateau using Sentinel-2 and Landsat 8 overlapping orbit imagery},

author = {Haojie Li and Hongyi* Li and Jian Wang and Xiaohua Hao},

doi = {10.1016/j.jhydrol.2023.129285},

issn = {0022-1694},

year  = {2023},

date = {2023-04-00},

urldate = {2023-04-00},

journal = {Journal of Hydrology},

volume = {619},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shao2023,

title = {Adaptability analysis of snow in the Zhangjiakou competition zone of the Beijing Olympic Winter Games for the next 30 years},

author = {Donghang Shao and Hongyi* Li and Jian Wang and Xiaohua Hao and Liting Niu},

doi = {10.1016/j.ejrh.2023.101358},

issn = {2214-5818},

year  = {2023},

date = {2023-04-00},

urldate = {2023-04-00},

journal = {Journal of Hydrology: Regional Studies},

volume = {46},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ma2022,

title = {改进频率分布和风速纠正的青藏高原格点降水数据集（1980~2009年）},

author = {李弘毅* 马佳培 },

year  = {2022},

date = {2022-11-01},

urldate = {2022-11-01},

journal = {遥感技术与应用},

volume = {37},

issue = {2},

pages = {507-514},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{NIU2022,

title = {黑河流域上游2000–2015年积雪水资源数据集},

author = {Liting NIU and Hongyi* LI and Jian WANG},

doi = {10.11922/11-6035.ncdc.2021.0035.zh},

issn = {2096-2223},

year  = {2022},

date = {2022-09-30},

urldate = {2022-09-30},

journal = {中国科学数据},

volume = {7},

number = {3},

publisher = {Computer Network Information Center, Chinese Academy of Sciences},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{YANG2022,

title = {A dataset of snow cover phenology for the next 30 years in Zhangjiakou Venue Cluster  of the Olympics Winter Games Beijing 2022},

author = {Yaru YANG and Chunlei ZHAO and Hongyi LI and Donghang SHAO and Wenzheng JI},

doi = {10.11922/11-6035.ncdc.2021.0023.zh},

issn = {2096-2223},

year  = {2022},

date = {2022-09-30},

journal = {CSD},

volume = {7},

number = {3},

publisher = {Computer Network Information Center, Chinese Academy of Sciences},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wang2022,

title = {Continental Water Vapor Dominantly Impacts Precipitation during the Snow Season on the Northeastern Tibetan Plateau},

author = {Weiguo Wang and Hongyi* Li and Zeming Xie and Xiaofan Zhu and Linhong Xiao and Xiaohua Hao and Jian* Wang},

doi = {10.1175/jcli-d-21-0241.1},

issn = {1520-0442},

year  = {2022},

date = {2022-06-15},

urldate = {2022-06-15},

journal = {Journal of Climate},

volume = {35},

number = {12},

pages = {3819--3831},

publisher = {American Meteorological Society},

abstract = {<jats:title>Abstract</jats:title><jats:p>Atmospheric water vapor plays a key role in the water cycle, especially on the Tibetan Plateau, where precipitation is an invaluable contributor to water resources. To better understand which water vapor source areas influence precipitation on the northeastern Tibetan Plateau (NETP), we applied the flexible particle dispersion method (FLEXPART) to simulate water vapor trajectories and water vapor source contribution related to precipitation events during the snow season from 1979 to 2017 on the NETP. The results show that continental water vapor source areas contributed 92.33% of the water vapor to precipitation events on the NETP, which was obviously greater than the water vapor contribution from oceanic areas. One key continental water vapor source area, the Tibetan Plateau without the study area, contributed 66.13% of the water vapor to the precipitation, and central Asia supplied 8.69%, ranking second. Comparing the distributions of the water vapor contributions to extensive and regional precipitation events revealed that the only difference between extensive and regional precipitation events is in the magnitudes of the water vapor contributions, and the spatial distributions of the water vapor contributions are extremely similar. Central and southern China obviously supplied more water vapor to extensive precipitation events than to regional precipitation events. These results help us better understand the recent drastic precipitation changes on the NETP.</jats:p><jats:sec><jats:title>Significance Statement</jats:title><jats:p>We sought to understand how water vapor influences precipitation over the northeastern Tibetan Plateau and which water vapor source areas play a key role in the water vapor supply. Therefore, we applied a numerical model to investigate the relationship between water vapor and precipitation from 1979 to 2017 during the snow season. Continental water vapor source areas contributed considerably more water vapor than oceanic water vapor source areas. The most important continental water vapor contributor was the Tibetan Plateau without the northeastern Tibetan Plateau area, and the second highest contributor was central Asia. Future work should focus on how water vapor impacts the precipitation changes in this wetter and warming area.</jats:p></jats:sec>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhang2022,

title = {Monitoring the Ice Thickness in High-Order Rivers on the Tibetan Plateau with Dual-Polarized C-Band Synthetic Aperture Radar},

author = {Huan Zhang and Hongyi* Li and Haojie Li},

doi = {10.3390/rs14112591},

issn = {2072-4292},

year  = {2022},

date = {2022-06-00},

urldate = {2022-06-00},

journal = {Remote Sensing},

volume = {14},

number = {11},

publisher = {MDPI AG},

abstract = {<jats:p> River ice on the Tibetan Plateau has important impacts on the ecosystem and hydrology. High-resolution Synthetic Aperture Radar (SAR) is an important tool for monitoring the thickness of river ice in high-altitude areas without ground data. However, due to the complex topography and narrow width, it remains challenging to monitor the ice thickness of high-order rivers (high-level branches in the plateau river system) on the Tibetan Plateau using SAR. Therefore, this paper focuses on inverting the ice thickness by utilizing dual-polarized C-band radar data. We select a typical watershed in the northeastern Tibetan Plateau, namely, the Babao River basin (including the Babao River and Binggou River), as the study area. The results show the following: (1) Dual-polarized C-band radar data have the potential to monitor the ice thickness of high-order rivers. The RMSEs of the Babao and Binggou Rivers are 0.109 m and 0.258 m, respectively. (2) Ascending and descending orbit radar images perform differently in retrieving the ice thicknesses of rivers with different directions. (3) The thickness of river ice affects the inversion accuracy. (4) Polarization parameters have varying explanatory capacities depending on the river characteristics. Our findings can provide a reference for the subsequent development of highly generalizable river ice inversion equations using dual-polarized radar data.</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shao2022,

title = {Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach},

author = {Donghang Shao and Hongyi* Li and Jian Wang and Xiaohua Hao and Tao Che and Wenzheng Ji},

doi = {10.5194/essd-14-795-2022},

issn = {1866-3516},

year  = {2022},

date = {2022-00-00},

urldate = {2022-00-00},

journal = {Earth Syst. Sci. Data},

volume = {14},

number = {2},

pages = {795--809},

publisher = {Copernicus GmbH},

abstract = {<jats:p>Abstract. The snow water equivalent (SWE) is an important parameter of

surface hydrological and climate systems, and it has a profound impact on

Arctic amplification and climate change. However, there are great

differences among existing SWE products. In the land region above

45∘ N, the existing SWE products are associated with a limited

time span and limited spatial coverage, and the spatial resolution is

coarse, which greatly limits the application of SWE data in cryosphere

change and climate change studies. In this study, utilizing the ridge

regression model (RRM) of a machine learning algorithm, we integrated

various existing SWE products to generate a spatiotemporally seamless and

high-precision RRM SWE product. The results show that it is feasible to

utilize a ridge regression model based on a machine learning algorithm to

prepare SWE products on a global scale. We evaluated the accuracy of the RRM

SWE product using hemispheric-scale snow course (HSSC) observational data

and Russian snow survey data. The mean absolute error (MAE), RMSE, R, and R2

between the RRM SWE products and observed SWEs are 0.21, 25.37 mm, 0.89, and

0.79, respectively. The accuracy of the RRM SWE dataset is improved by

28 %, 22 %, 37 %, 11 %, and 11 % compared with the original

AMSR-E/AMSR2 (SWE), ERA-Interim SWE, Global Land Data Assimilation System

(GLDAS) SWE, GlobSnow SWE, and ERA5-Land SWE datasets, respectively, and it

has a higher spatial resolution. The RRM SWE product production method does

not rely heavily on an independent SWE product; it takes full advantage of

each SWE dataset, and it takes into consideration the altitude factor. The

MAE ranges from 0.16 for areas within <100 m elevation to 0.29

within the 800–900 m elevation range. The MAE is best in the Russian region

and worst in the Canadian region. The RMSE ranges from 4.71 mm for areas

within <100 m elevation to 31.14 mm within the >1000 m

elevation range. The RMSE is best in the Finland region and worst in the

Canadian region. This method has good stability, is extremely suitable for

the production of snow datasets with large spatial scales, and can be easily

extended to the preparation of other snow datasets. The RRM SWE product is

expected to provide more accurate SWE data for the hydrological model and

climate model and provide data support for cryosphere change and climate

change studies. The RRM SWE product is available from “A Big Earth Data

Platform for Three Poles” (https://doi.org/10.11888/Snow.tpdc.271556)

(Li et al., 2021).

                    </jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {LiDAR 的高寒山区遥感高程数据精度评估},

author = {张欢 and 李弘毅 and 李浩杰 and 车涛},

url = {



},

doi = {10.11873/j.issn.1004-0323.2021.6.1311},

year  = {2021},

date = {2021-12-00},

journal = {遥感技术与应用},

volume = {36},

number = {6},

pages = {1311-1320},

abstract = {遥感高程数据是获取缺资料地区DEM（Digital elevation models）数据的重要手段。然而，由于高寒山区实地高程测量稀少，难以对多源遥感DEM数据进行统一验证。ICESat-2等新的遥感高程数据在高寒山区也缺乏相应的精度评估。针对此问题，以青藏高原东北缘的冰沟流域作为研究区，采用机载航空遥感获取的大范围LiDAR（Light Detection And Ranging）DEM数据对新产品ICESat-2 ATL06（Ice， Cloud， and Land Elevation Satellite-2， Land Ice Height）、ALOS DEM（12.5 m分辨率）以及新版本SRTM V3（SRTM Arc-Second Global 1 V003）、ASTER GDEM V3（ASTER Global DEM）进行验证，并分析地形因子与均方根误差RMSE的关系。研究结果表明：ICESat-2 ATL06数据在高寒山区的RMSE为0.747 m。由于其较高的精度，可用于验证缺资料地区的其他遥感高程数据。其他遥感高程数据的精度都相对较低，ALOS 12.5 m数据的RMSE为5.284 m；ASTER GDEM V3版本的RMSE为9.903 m。实验所采用的4种遥感高程数据与机载LiDAR DEM均具有较高的相关性，相关系数在0.998与1.000之间。实验还揭示了坡度是影响遥感DEM精度的主要因素。除ICESat-2 ATL06外，其他高程数据的RMSE均随坡度的增大先减小再增大，且都存在一个最佳坡度值。鉴于地形复杂多样的冰沟流域具有青藏高原高寒山区的典型特征，多源遥感DEM数据在该区域的验证结论具有较好的代表性，可为相似地区DEM数据的使用和评估提供重要的知识补充。},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Li2021,

title = {Identifying river ice on the Tibetan Plateau based on the relative difference in spectral bands},

author = {Haojie Li and Hongyi* Li and Jian Wang and Xiaohua Hao},

doi = {10.1016/j.jhydrol.2021.126613},

issn = {0022-1694},

year  = {2021},

date = {2021-10-00},

urldate = {2021-10-00},

journal = {Journal of Hydrology},

volume = {601},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Lei2021,

title = {Comprehensive evaluation of satellite and reanalysis precipitation products over the eastern Tibetan plateau characterized by a high diversity of topographies},

author = {Huajin Lei and Hongyi Li and Hongyu Zhao and Tianqi Ao and Xiaodong Li},

doi = {10.1016/j.atmosres.2021.105661},

issn = {0169-8095},

year  = {2021},

date = {2021-09-00},

urldate = {2021-09-00},

journal = {Atmospheric Research},

volume = {259},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ma2020,

title = {Reducing the Statistical Distribution Error in Gridded Precipitation Data for the Tibetan Plateau},

author = {Jiapei Ma and Hongyi Li and Jian Wang and Xiaohua Hao and Donghang Shao and Huajin Lei},

doi = {10.1175/jhm-d-20-0096.1},

issn = {1525-7541},

year  = {2020},

date = {2020-11-00},

volume = {21},

number = {11},

pages = {2641--2654},

publisher = {American Meteorological Society},

abstract = {AbstractGridded precipitation data are very important for hydrological and meteorological studies. However, gridded precipitation can exhibit significant statistical bias that needs to be corrected before application, especially in regions where high wind speeds, frequent snowfall, and sparse observation networks can induce significant uncertainties in the final gridded datasets. In this paper, we present a method for the production of gridded precipitation on the Tibetan Plateau (TP). This method reduces the statistical distribution error by correcting for wind-induced undercatch and optimizing the interpolation method. A gridded precipitation product constructed by this method was compared with previous products on the TP. The results show that undercatch correction is necessary for station data, which can reduce the distributional error by 30% at most. A thin-plate splines interpolation algorithm considering altitude as a covariate is helpful to reduce the statistical distributional error in general. Our method effectively inhibits the smoothing effect in gridded precipitation, and compared to previous products, results in a higher mean value, larger 98th percentile, and greater temporal variance. This study can help to improve the quality of gridded precipitation over the TP.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shao2020,

title = {Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations},

author = {Donghang Shao and Wenbo Xu and Hongyi Li and Jian Wang and Xiaohua Hao},

doi = {10.3390/rs12183101},

issn = {2072-4292},

year  = {2020},

date = {2020-09-00},

journal = {Remote Sensing},

volume = {12},

number = {18},

publisher = {MDPI AG},

abstract = {Snow surface spectral reflectance is very important in the Earth’s climate system. Traditional land surface models with parameterized schemes can simulate broadband snow surface albedo but cannot accurately simulate snow surface spectral reflectance with continuous and fine spectral wavebands, which constitute the major observations of current satellite sensors; consequently, there is an obvious gap between land surface model simulations and remote sensing observations. Here, we suggest a new integrated scheme that couples a radiative transfer model with a land surface model to simulate high spectral resolution snow surface reflectance information specifically targeting multisource satellite remote sensing observations. Our results indicate that the new integrated model can accurately simulate snow surface reflectance information over a large spatial scale and continuous time series. The integrated model extends the range of snow spectral reflectance simulation to the whole shortwave band and can predict snow spectral reflectance changes in the solar spectrum region based on meteorological element data. The kappa coefficients (K) of both the narrowband snow albedo targeting Moderate Resolution Imaging Spectroradiometer (MODIS) data simulated by the new integrated model and the retrieved snow albedo based on MODIS reflectance data are 0.5, and both exhibit good spatial consistency. Our proposed narrowband snow albedo simulation scheme targeting satellite remote sensing observations is consistent with remote sensing satellite observations in time series and can predict narrowband snow albedo even during periods of missing remote sensing observations. This new integrated model is a significant improvement over traditional land surface models for the direct spectral observations of satellite remote sensing. The proposed model could contribute to the effective combination of snow surface reflectance information from multisource remote sensing observations with land surface models.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Li2020,

title = {Monitoring high-altitude river ice distribution at the basin scale in the northeastern Tibetan Plateau from a Landsat time-series spanning 1999–2018},

author = {Haojie Li and Hongyi Li and Jian Wang and Xiaohua Hao},

doi = {10.1016/j.rse.2020.111915},

issn = {0034-4257},

year  = {2020},

date = {2020-09-00},

journal = {Remote Sensing of Environment},

volume = {247},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wang2020,

title = {Water Vapor from Western Eurasia Promotes Precipitation during the Snow Season in Northern Xinjiang, a Typical Arid Region in Central Asia},

author = {Weiguo Wang and Hongyi Li and Jian Wang and Xiaohua Hao},

doi = {10.3390/w12010141},

issn = {2073-4441},

year  = {2020},

date = {2020-01-00},

journal = {Water},

volume = {12},

number = {1},

publisher = {MDPI AG},

abstract = {Atmospheric water vapor plays an important role in the water cycle, especially in arid Central Asia, where precipitation is invaluable to water resources. Understanding and quantifying the relationship between water vapor source regions and precipitation is a key problem in water resource research in typical arid Central Asia, Northern Xinjiang. However, the relationship between precipitation and water vapor sources is still unclear of snow season. This paper aimed at studying the role of water vapor source supply in the Northern Xinjiang precipitation trend, which was investigated using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The results showed that the total water vapor contributed from Western Eurasia and the North Polar area presented upward trends similar to the precipitation change trend, which indicated that the water vapor contribution from the two previous water vapor source regions supplied abundant water vapor and maintained the upward precipitation trend from 1980 to 2017 in Northern Xinjiang. From the climatology of water vapor transport, the region was controlled by midlatitude westerlies and major water vapor input from the western boundary, and the net water vapor flux of this region also showed an annual increasing trend. Western Eurasia had the largest moisture percentage contribution to Northern Xinjiang (48.11%) over the past 38 years. Northern Xinjiang precipitation was correlated with water vapor from Western Eurasia, the North Polar area, and Siberia, and the correlation coefficients were 0.66, 0.45, and 0.57, respectively. These results could aid in better understanding the water cycle process and climate change in this typical arid region of Central Asia.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {河冰遥感监测研究进展},

author = {李浩杰 and 李弘毅 and 王建 and 郝晓华},

doi = {10.11867/j.issn.1001-8166.2020.081},

year  = {2020},

date = {2020-00-00},

urldate = {2020-00-00},

journal = {地球科学进展},

volume = {35},

number = {10},

pages = {1041-1051},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {基于分位数映射法的黑河上游气候模式降水误差订正},

author = {雷华锦 and 马佳培 and 李弘毅 and 王建 and 邵东航 and 赵宏宇},

doi = {10.7522/j.issn.1000-0534.2019.00104},

year  = {2020},

date = {2020-00-00},

journal = {高原气象},

volume = {39},

number = {2},

pages = {14},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {基于环境信息和回归模型的青藏高原MODIS积雪面积比例产品制备},

doi = {10.11873/j.issn.1004-0323.2020.6.1303.},

year  = {2020},

date = {2020-00-00},

urldate = {2020-00-00},

journal = {遥感技术与应用},

volume = {35},

number = {6},

pages = {9},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Li2019b,

title = {Extending the Ability of Near‐Infrared Images to Monitor Small River Discharge on the Northeastern Tibetan Plateau},

author = {Haojie Li and Hongyi Li and Jian Wang and Xiaohua Hao},

doi = {10.1029/2018wr023808},

issn = {1944-7973},

year  = {2019},

date = {2019-11-00},

journal = {Water Resources Research},

volume = {55},

number = {11},

pages = {8404--8421},

publisher = {American Geophysical Union (AGU)},

abstract = {AbstractRiver discharge gauging is scarce in high mountainous regions, especially on the Tibetan Plateau, where rivers are widely distributed. Although remote sensing is an important mean of monitoring river discharge, previous methods are most suitable for large rivers, and the ability to monitor small rivers (with widths less than 100 m) is limited. To resolve this issue, a multiple pixel ratio (MPR) method is presented for monitoring the discharge of small rivers based on the relationship between river discharge and the near‐infrared reflectivity. Utilizing 281 Landsat images (1990–2015), we monitored river discharges in two subbasins in the upstream region of the Heihe River located on the northeastern Tibetan Plateau. Our results indicate that the performance of the MPR method is more stable than the previous calibration/measurement method. The monitoring accuracy was correlated with the length and location of the selected inundated river channel. Using selected inundated river channel lengths between 300 and 600 m can provide better monitoring accuracy. The Nash‐Sutcliffe efficiency of monitoring results (2013–2014) of Qilian station was 0.82; the Zhamashike station (2015) was 0.45; the two stations multiyears (1990–2013) monitoring results were 0.32 and 0.41; the ungauged basin was 0.45. Our results suggest that the MPR method can expand the ability of remote sensing to monitor discharge in small rivers with widths greater than 30 m on the Tibetan Plateau. In addition, the new method also has the potential to monitor the discharge of ungauged small rivers (with widths greater than 30 m).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Li2019,

title = {Tracing Snowmelt Paths in an Integrated Hydrological Model for Understanding Seasonal Snowmelt Contribution at Basin Scale},

author = {Hongyi Li and Xin Li and Dawen Yang and Jian Wang and Bing Gao and Xiaoduo Pan and Yanlin Zhang and Xiaohua Hao},

doi = {10.1029/2019jd030760},

issn = {2169-8996},

year  = {2019},

date = {2019-08-27},

journal = {JGR Atmospheres},

volume = {124},

number = {16},

pages = {8874--8895},

publisher = {American Geophysical Union (AGU)},

abstract = {AbstractSnowmelt contributions to water resources in cold regions are receiving increasing attention. However, there are clear challenges to accurately distinguish the snowmelt contributions to different hydrological processes in seasonal snow regions. Here, we present an improved method to evaluate snowmelt contributions by tracing the snowmelt flow paths in different media based on a distributed geomorphology‐based ecohydrological model coupled with a physically based snow module. The calculated snowmelt contribution is not limited to river discharge but is also applicable to evaporation and soil storage. The study region, the upstream Heihe River (UHR) basin, is located in the northeastern Tibetan Plateau. Our results indicate that snowmelt can continuously contribute to hydrological processes throughout the year because most of the snowmelt remains in soil voids, even on snow‐free days. In UHR, the snowmelt contribution to total runoff on snow days accounts for a minor part of the total snowmelt contribution, and the snowmelt contribution on snow‐free days accounts for a major part of the total. There is notable snowmelt retention in the soil in each year, which continues to supply discharge into next year. Our results also indicate that the role of snowmelt contribution to annual discharge could be gradually weakened if rainfall more greatly increases the summer discharge in the UHR basin. The improved evaluation method will contribute to a more comprehensive assessment of snowmelt contributions to hydrological processes in seasonal snow regions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wang2019,

title = {Estimation of surface latent heat fluxes in an oasis utilizing a two-source energy balance model based on land surface temperature decomposition},

author = {Runke Wang and Jian Wang and Gaofeng Zhu and Hongyi Li and Donghang Shao and Xiaohua Hao},

doi = {10.1117/1.jrs.13.034504},

issn = {1931-3195},

year  = {2019},

date = {2019-07-01},

journal = {J. Appl. Rem. Sens.},

volume = {13},

number = {03},

publisher = {SPIE-Intl Soc Optical Eng},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Li2019d,

title = {Area Change of Snow and Ice in the Babao River Basin, Tibetan Plateau},

author = {Haojie Li and Hongyi Li and Jian Wang},

doi = {10.1109/igarss.2019.8898368},

year  = {2019},

date = {2019-07-00},

urldate = {2019-07-00},

journal = {GARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium},

pages = {4084-4087},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Li2019e,

title = {Area Change of Snow and Ice in the Babao River Basin, Tibetan Plateau},

author = {Haojie Li and Hongyi Li and Jian Wang},

doi = {10.1109/igarss.2019.8898368},

year  = {2019},

date = {2019-07-00},

urldate = {2019-07-00},

journal = {GARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium},

pages = {4084-4087},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Wang2019b,

title = {Precipitation Change During the Snow Period in The Northern Xinjiang, a Typical Arid Region},

author = {Weiguo Wang and Hongyi Li and Jian Wang},

doi = {10.1109/igarss.2019.8898767},

year  = {2019},

date = {2019-07-00},

urldate = {2019-07-00},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ma2019,

title = {Retrieval of Snow Water Equivalent by Gamma},

author = {Yuan Ma and Hongyi Li and Jian Wang and Xiaohua Hao},

doi = {10.1109/igarss.2019.8900393},

year  = {2019},

date = {2019-07-00},

urldate = {2019-07-00},

journal = {GARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium},

pages = {4121-4124},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {面向寒区水文模拟的区域气候模式降水订正方法对比},

author = {马佳培 and 李弘毅 and 王建 and 邵东航},

year  = {2019},

date = {2019-00-00},

urldate = {2019-00-00},

journal = {遥感技术与应用},

volume = {34},

number = {3},

pages = {655-666},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shao2018,

title = {Reconstruction of Remotely Sensed Snow Albedo for Quality Improvements Based on a Combination of Forward and Retrieval Models},

author = {Donghang Shao and Wenbo Xu and Hongyi Li and Jian Wang and Xiaohua Hao},

doi = {10.1109/tgrs.2018.2846681},

issn = {1558-0644},

year  = {2018},

date = {2018-12-00},

journal = {IEEE Trans. Geosci. Remote Sensing},

volume = {56},

number = {12},

pages = {6969--6985},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shao2017,

title = {Distinguishing the Role of Wind in Snow Distribution by Utilizing Remote Sensing and Modeling Data: Case Study in the Northeastern Tibetan Plateau},

author = {Donghang Shao and Hongyi Li and Jian Wang and Xiaoduo Pan and Xiaohua Hao},

doi = {10.1109/jstars.2017.2716388},

issn = {2151-1535},

year  = {2017},

date = {2017-10-00},

journal = {IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing},

volume = {10},

number = {10},

pages = {4445--4456},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {利用伽马射线探测雪水当量方法的改进},

author = {马媛 and 李弘毅 and 张璞 and 刘大锋 and 黄晓东 and 郝晓华 and 邵东航 and 王建},

year  = {2017},

date = {2017-00-00},

journal = {遥感技术与应用},

volume = {32},

number = {1},

pages = {57-63},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {基于多源遥感数据的积雪反照率反演研究},

author = {邵东航 and 李弘毅 and 王建 and 郝晓华 and 王润科 and 马媛},

year  = {2017},

date = {2017-00-00},

urldate = {2017-00-00},

journal = {遥感技术与应用},

volume = {32},

number = {1},

pages = {71-77},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Li2015,

title = {Downscaling Snow Cover Fraction Data in Mountainous Regions Based on Simulated Inhomogeneous Snow Ablation},

author = {Hong Li and Yong He and Xiao Hao and Tao Che and Jian Wang and Xiao Huang},

doi = {10.3390/rs70708995},

issn = {2072-4292},

year  = {2015},

date = {2015-07-00},

journal = {Remote Sensing},

volume = {7},

number = {7},

pages = {8995--9019},

publisher = {MDPI AG},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Li2014,

title = {Synthesis method for simulating snow distribution utilizing remotely sensed data for the Tibetan Plateau},

author = {Hongyi Li and Zhiguang Tang and Jian Wang and Tao Che and Xiaoduo Pan and Chunlin Huang and Xufeng Wang and Xiaohua Hao and Shaobo Sun},

doi = {10.1117/1.jrs.8.084696},

issn = {1931-3195},

year  = {2014},

date = {2014-01-01},

journal = {J. Appl. Remote Sens},

volume = {8},

number = {1},

publisher = {SPIE-Intl Soc Optical Eng},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Role of blowing snow in snow processes in Qilian Mountainous region},

author = {Li, Hongyi and Wang, Jian and Hao, Xiaohua},

doi = {10.3724/SP.J.1226.2014.00124},

year  = {2014},

date = {2014-00-00},

urldate = {2014-00-00},

journal = {Sciences in Cold and Arid Regions},

volume = {6},

pages = {124-130},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {积雪水文模拟中的关键问题及其研究进展},

author = {李弘毅 and 王建},

year  = {2013},

date = {2013-00-00},

urldate = {2013-00-00},

journal = {冰川冻土},

volume = {35},

number = {2},

pages = {430-437},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Li2011,

title = {Simulation of snow distribution and melt under cloudy conditions in an Alpine watershed},

author = {H.-Y. Li and J. Wang},

doi = {10.5194/hess-15-2195-2011},

issn = {1607-7938},

year  = {2011},

date = {2011-00-00},

journal = {Hydrol. Earth Syst. Sci.},

volume = {15},

number = {7},

pages = {2195--2203},

publisher = {Copernicus GmbH},

abstract = {Abstract. An energy balance method and remote-sensing data were used to simulate snow distribution and melt in an alpine watershed in northwestern China within a complete snow accumulation-melt period. The spatial energy budgets were simulated using meteorological observations and a digital elevation model of the watershed. A linear interpolation method was used to estimate the daily snow cover area under cloudy conditions, using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Hourly snow distribution and melt, snow cover extent and daily discharge were included in the simulated results. The root mean square error between the measured snow-water equivalent samplings and the simulated results is 3.2 cm. The Nash and Sutcliffe efficiency statistic (NSE) between the measured and simulated discharges is 0.673, and the volume difference (Dv) is 3.9 %. Using the method introduced in this article, modelling spatial snow distribution and melt runoff will become relatively convenient.

                    },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Estimation of snow sublimation under a high-speed wind condition in alpine region watershed. },

author = {Li HY, Wang J, Li Z. },

year  = {2009},

date = {2009-00-00},

journal = {Journal of Sichuan University (Engineering Science Edition)},

volume = {46},

pages = {129-136},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = { 黑河上游冰沟流域典型积雪期水文情势},

author = {李弘毅 and 王建* and 白云洁 and 李哲},

year  = {2009},

date = {2009-00-00},

urldate = {2009-00-00},

journal = {冰川冻土},

volume = {31},

number = {2},

pages = {293-300},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{li2008,

title = { SRM融雪径流模型在黑河流域上游的模拟研究},

author = {李弘毅 and 王建*},

year  = {2008},

date = {2008-00-00},

urldate = {2008-00-00},

journal = {冰川冻土},

volume = {30},

number = {5},

pages = {769-775},

keywords = {},

pubstate = {published},

tppubtype = {article}

}