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龍笛

（清華大學長聘正教授）

鎖定
龍笛，教授，博士生導師，2001/09-2005/07 清華大學 水利水電工程系 學士。 [1] 
長期從事遙感水文學基礎理論及應用研究，提出了高強度人類活動影響下地下水儲量變化的重力衞星和水文模型協同反演預測理論和方法；突破了複雜氣候和下墊面水文要素遙感定量反演和多源多尺度數據時空融合技術；創建了無測站流域徑流及其雨雪冰成分劃分模型 [4]  。研究成果在外交部、水利部、多個流域機構等部門中得到應用。主持和完成國家傑出青年基金、國家青年人才計劃、自然基金重點、優青、面上和科技部十三五、十四五課題等項目，在Nature Climate Change、Nature Communications、Science Bulletin、Remote Sensing of Environment、Water Resources Research、Journal of Hydrology等國內外權威期刊發表論文110餘篇，獲國內外發明專利20項授權。曾獲2019年美國地球物理聯合會水文青年科學獎、李小文遙感科學獎、2020年高等學校水利類專業教學成果一等獎(排名第1)、2021年劉光文青年科技獎(全國4人，每2年評選一次)、2022年第十七屆中國青年科技獎(全國100人，每2年評選一次)、2022年教育部自然科學二等獎(排名1)等 [2-4]  。
中文名
龍笛 [1] 
國    籍
中國 [1] 
畢業院校
清華大學 [1] 
學位/學歷
博士研究生 [1] 
發表論文數目
110篇 [4] 
職    稱
教授 [1] 

目錄

  1. 1 教育經歷
  2. 2 工作經歷
  3. 3 開設課程
  1. 4 研究方向
  2. 5 科研項目
  3. 6 學術兼職
  1. 7 獎勵與榮譽
  2. 8 學術成果
  3. 9 發明專利

龍笛教育經歷

2001/09-2005/07 清華大學 水利水電工程系 學士 [1] 
2005/09-2008/07 中國科學院 遙感水文 碩士 [1] 
2008/08-2011/08 美國德克薩斯農業和工程大學 遙感水文 博士 [1] 

龍笛工作經歷

2011/09-2014/11美國德克薩斯大學奧斯汀分校地球科學學院，博士後 [1] 
2014/12-2017/6 清華大學水利水電工程系，助理教授，特別研究員 [1] 
2017/7-2020/1 清華大學水利水電工程系，副教授，特別研究員 [1] 
2020/2-2022/12 清華大學水利水電工程系，長聘副教授，特別研究員 [1] 
2023/1-至今 清華大學水利水電工程系，長聘正教授

龍笛開設課程

(1) 現代遙感水文（英文，研究生） [1] 
(2) 遙感基本原理與方法（英文，本科生） [1] 

龍笛研究方向

(1) 區域地下水儲量變化的重力衞星(GRACE)反演理論、方法和應用 [1] 
(2) 地表水儲量(湖、庫、冰、雪等)的多源遙感反演理論、方法和應用 [1] 
(3) 地表温度、蒸散發和土壤水分的熱紅外遙感反演理論、方法和應用 [1] 
(4) 冰凍圈水文學及無資料流域徑流預測和成分劃分 [1] 
(5) 社會水文學及強人類活動區水文過程模擬和預測 [1] 

龍笛科研項目

龍笛 龍笛 [3]
(1) 國家自然科學基金，傑出青年基金項目，遙感水文學(52325901)(2024-2028)，在研，主持； [1] 
(2) 國家科技部，十四五重點研發計劃課題，地下水超採區蓄變量反演和引調水生態效益評估(2021YFB3900604)(2021-2025)，在研，主持； [1] 
(3) 國家自然科學基金，面上項目，氣候變化和南水北調影響下華北平原及主要城市地下水儲量演變的評估和預測(52079065)(2021-2024)，在研，主持； [1] 
(4) 內蒙古自治區科技廳，十三五重大科技專項課題，黃河流域內蒙古段嵌套式多尺度生態水文一體化綜合觀測試驗及其時空格局和適宜性評估(2020SZD0031)(2020-2023)，在研，主持； [1] 
(5) 國家科技部，十三五重點研發計劃子課題，氣候變化下西北地區水儲量演變規律與預測及對能源開發的影響(2018YFE0196000)(2019-2021)，結題，主持； [1] 
(6) 國家科技部，第二次青藏高原綜合科學考察任務一專題五，青藏高原水儲量變化分析及各組分水儲量貢獻解析(2019QZKK0100)(2019-2022)，結題，主持； [1] 
(7) 國家自然科學基金，優秀青年科學基金項目，遙感水文學（51722903）(2018-2020)，結題，主持； [1] 
(8) 國家科技部，十三五重點研發計劃課題，水資源立體監測協同機理與國家水資源立體監測體系研究(2017YFC0405801)(2017-2020)，結題，主持； [1] 
(9) 國家自然科學基金，“西南河流源區徑流變化和適應性利用”重大研究計劃重點項目，西南河流源區關鍵水文氣象變量的多源遙感觀測與數據集成(91547210)(2016-2019)，結題，主持； [1] 
(10) 國家自然科學基金，面上項目，重力衞星總儲水量變化信號校正與回推重建方法研究與應用(51579128)(2016-2019)，結題，主持。 [1] 

龍笛學術兼職

Water Resources Research (IF = 5.4, 一區)，副主編 [1] 
Journal of Hydrology (IF =6.4, 一區)，副主編 [1] 
Remote Sensing of Environment (IF = 13.5, 一區)，副主編 [1] 
《中國科學：技術科學(英文版)》，青年編委 [1] 

龍笛獎勵與榮譽

2022年 第十七屆中國青年科技獎（全國100人，每2年評選一次） [1] 
2022年 教育部自然科學二等獎（排名1） [1] 
2022年 劉光文青年科技獎（全國4人，每2年評選一次） [1] 
2022年 科睿唯安全球高被引學者 [1] 
2021年 科睿唯安全球高被引學者 [1] 
2020年 高等學校水利類專業教學成果一等獎（排名第1） [1] 
2020年 清華大學優秀博士學位論文指導教師（博士生黃琦獲2020清華大學優秀博士論文） [1] 
2019年 美國地球物理聯合會水文青年科學家獎（首位獲此獎的華人學者） [1] 
2019年 李小文遙感科學獎（全國2名，每2年評選一次） [1] 
2019年 清華大學先進工作者 [1] 
2018年 清華大學年度教學優秀獎 [1] 
2017年 基金委“優秀青年基金”獲得者；清華大學年度教學優秀獎 [1] 
2014年 Geophysical Research Letters 優秀審稿人 [1] 
2014年 美國德克薩斯大學奧斯汀分校作者成就獎 [1] 
2013年 美國德克薩斯大學奧斯汀分校作者成就獎 [1] 
2009-2011年 美國德克薩斯農業和工程大學研究助理一等獎學金 [1] 
2009-2010年 美國德克薩斯水資源研究所Mills Scholarship Award [1] 
2008-2009年 美國德克薩斯農業和工程大學教學助理一等獎學金 [1] 
2008-2009年 美國德克薩斯農業和工程大學Graduate Enhancement Funds [1] 
2008-2009年 美國德克薩斯農業和工程大學激勵獎學金(Incentive Scholarship) [1] 
2022年，榮獲第十七屆中國青年科技獎。 [2] 

龍笛學術成果

第一作者論文 [1] 
[1] *Long, D., Yang W.T., Scanlon, B.R., Zhao, J.S., Liu, D.G., Burek, P., Pan, Y., You, L. Z., & Wada, Y (2020). South-to-North Water Diversion stabilizing Beijing’s groundwater levels. Nature Communications, 2020, 11(2020), 1863‒1880.
[2] *Long, D., Yan, L., Bai, L.L., Zhang, C.J., Li, X.Y., Lei, H.M., Yang, H.B., Tian, F.Q., Zeng, C., Meng, X.Y., & Shi, C.X(2020). Generation of MODIS-like land surface temperatures under all-weather conditions based on a data fusion approach. Remote Sensing of Environment, 246, 111863
[3] *Long, D., Bai, L., Yan, L., Zhang, C., Yang, W., Lei, H., *Quan, J., Meng, X., & Shi, C. (2019). Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution. Remote Sensing of Environment, 233, 111364.
[4] *Long, D., Pan, Y., Zhou, J., Chen, Y., Hou, X.Y., Hong, Y., Scanlon, B.R., & Longuevergne, L. (2017). Global analysis of spatiotemporal variability in merged total water storage changes using multiple GRACE products and global hydrological models. Remote Sensing of Environment, 192, 198-216.
[5] *Long, D., Chen, X., Scanlon, B.R., Wada, Y., Hong, Y., Singh, V.P., Chen, Y., Wang, C., Han, Z., & Yang, W. (2016). Have GRACE satellites overestimated groundwater depletion in the Northwest India Aquifer? Scientific Reports, 6, 24398.
[6] *Long, D., Longuevergne, L., & Scanlon, B.R. (2015a). Global analysis of approaches for deriving total water storage changes from GRACE satellites. Water Resources Research, 51, 2574-2594.
[7] *Long, D., Yang, Y.T., Wada, Y., Hong, Y., Liang, W., Chen, Y.N., Yong, B., Hou, A.Z., Wei, J.F., & Chen, L. (2015b). Deriving scaling factors using a global hydrological model to restore GRACE total water storage changes for China's Yangtze River basin. Remote Sensing of Environment, 168, 177-193.
[8] *Long, D., Longuevergne, L., & Scanlon, B.R. (2014a). Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites. Water Resources Research, 50, 1131–1151.
[9] *Long, D., Shen, Y.J., Sun, A.Y., Hong, Y., Longuevergne, L., Yang, Y.T., Li, B., & Chen, L. (2014b). Drought and flood monitoring for a large karst plateau in Southwest China using extended GRACE data. Remote Sensing of Environment, 145–160.
[10] *Long, D., Scanlon, B.R., Longuevergne, L., Sun, A.-Y., Fernando, D.N., & Save, H. (2013). GRACE satellite monitoring of large depletion in water storage in response to the 2011 drought in Texas. Geophysical Research Letters, 40, 3395–3401.
[11] *Long, D., & Singh, V.P. (2013a). Assessing the impact of end-member selection on the accuracy of satellite-based spatial variability models for actual evapotranspiration estimation. Water Resources Research, 49, 2601–2618.
[12] *Long, D., & Singh, V.P. (2013b). An entropy-based multispectral image classification algorithm. IEEE Transactions on Geoscience and Remote Sensing, 51, 5225–5238.
[13] *Long, D., Scanlon, B.R., Fernando, D.N., Meng, L., & Quiring, S.M. (2012a). Are Temperature and Precipitation Extremes Increasing over the U.S. High Plains? Earth Interactions, 16, 1–20.
[14] *Long, D., Singh, V.P., & Scanlon, B.R. (2012b). Deriving theoretical boundaries to address scale dependencies of triangle models for evapotranspiration estimation. Journal of Geophysical Research-Atmospheres, 117.
[15] *Long, D., & Singh, V.P. (2012a). A modified surface energy balance algorithm for land (M-SEBAL) based on a trapezoidal framework. Water Resources Research, 48.
[16] *Long, D., & Singh, V.P. (2012b). A Two-source Trapezoid Model for Evapotranspiration (TTME) from satellite imagery. Remote Sensing of Environment, 121, 370–388.
[17] *Long, D., Singh, V.P., & Li, Z.L. (2011). How sensitive is SEBAL to changes in input variables, domain size and satellite sensor? Journal of Geophysical Research-Atmospheres, 116.
[18] *Long, D., Gao, Y.C., & Singh, V.P. (2010). Estimation of daily average net radiation from MODIS data and DEM over the Baiyangdian watershed in North China for clear sky days. Journal of Hydrology, 388, 217–233.
[19] *Long, D., & Singh, V.P. (2010). Integration of the GG model with SEBAL to produce time series of evapotranspiration of high spatial resolution at watershed scales. Journal of Geophysical Research-Atmospheres, 115.
通訊作者論文 [1] 
[1] Li. X.Y., & *Long, D. (2020). An improvement in accuracy and spatiotemporal continuity of the MODIS precipitable water vapor product based on a data fusion approach. Remote Sensing of Environment, 248, 111966.
[2] Sun, Z.L., *Long. D., Yang W.T., Li. X.Y., & Pan, Y. (2020). Reconstruction of GRACE data on changes in total water storage over the global land surface and 60 basins. Water Resources Research, 55, e2019WR026250.
[3] Han, Z.Y., *Long. D., Huang, Q., Li, X.D., Zhao, F.Y., & Wang, J.H. (2020). Improving reservoir outflow estimation for ungauged basins using satellite observations and a hydrological model. Water Resources Research, 56, e2020WR027590.
[4] Huang, Q., *Long. D., Du, M.D., Han, Z.Y., & Han, P.F. (2020). Daily continuous river discharge estimation for ungauged basins using a hydrologic model calibrated by satellite altimetry: Implications for the SWOT mission. Water Resources Research, 56, e2020WR027309.
[5] Bai, L., *Long, D., Yan, L., 2019. Estimation of surface soil moisture with downscaled land surface temperatures using a data fusion approach for heterogeneous agricultural land. Water Resources Research, 55, 1105‒1128.
[6] Li, X., *Long, D., Huang, Q., Han, P., Zhao, F., & Wada, Y. (2019). High-temporal-resolution water level and storage change data sets for lakes on the Tibetan Plateau during 2000–2017 using multiple altimetric missions and Landsat-derived lake shoreline positions. Earth System Science Data, 11, 1603–1627.
[7] Yang, W., *Long, D., & Bai, P. (2019). Impacts of future land cover and climate changes on runoff in the mostly afforested river basin in North China. Journal of Hydrology, 570, 201‒219.
[8] Han, Z., *Long, D., Fang, Y., Hou, A., & Hong, Y. (2019). Impacts of climate change and human activities on the flow regime of the dammed Lancang River in Southwest China. Journal of Hydrology, 570, 96‒105.
[9] Li, X.Y., *Long, D., Han, Z.Y., Scanlon, B.R., Sun, Z.L., Han, P.F., & Hou, A.Z. (2019). Evapotranspiration estimation for Tibetan Plateau headwaters using conjoint terrestrial and atmospheric water balances and multisource remote sensing. Water Resources Research, 55, 8608‒8630.
[10] Han, P., *Long, D., Han, Z., Du, M., Dai, L., & Hao, X. (2019). Improved understanding of snowmelt runoff from the headwaters of China's Yangtze River using remotely sensed snow products and hydrological modeling. Remote Sensing of Environment, 224, 44‒59.
[11] Chen, X.N., *Long, D., Liang, S.L., He, L., Zeng, C., Hao, X.H., & Hong, Y. (2018). Developing a composite daily snow cover extent record over the Tibetan Plateau from 1981 to 2016 using multisource data. Remote Sensing of Environment, 215, 284‒299.
[12] Huang, Q., *Long, D., Du, M.D., Zeng, C., Li, X.D., Hou, A.Z., & Hong, Y. (2018). An improved approach to monitoring Brahmaputra River water levels using retracked altimetry data. Remote Sensing of Environment, 211, 112‒128.
[13] Huang, Q., *Long, D., Du, M.D., Zeng, C., Qiao, G., Li, X.D., Hou, A.Z., & Hong, Y. (2018). Discharge estimation in high-mountain regions with improved methods using multisource remote sensing: A case study of the Upper Brahmaputra River. Remote Sensing of Environment, 219, 115‒134.
[14] Tang, G.Q., *Long, D., *Hong, Y., Gao, J.Y., & Wan, W. (2018). Documentation of multifactorial relationships between precipitation and topography of the Tibetan Plateau using spaceborne precipitation radars. Remote Sensing of Environment, 208, 82‒96.
[15] Zeng, C., *Long, D., Shen, H., Wu, P., Cui, Y., & *Hong, Y. (2018). A two-step framework for reconstructing remotely sensed land surface temperatures contaminated by cloud. ISPRS Journal of Photogrammetry and Remote Sensing, 141, 30‒45.
[16] Tang, G., *Long, D., Behrangi, A., Wang, C., & *Hong, Y (2018). Exploring deep neural networks to retrieve rain and snow in high latitudes using multisensor and reanalysis data. Water Resources Research, 54 (10), 8253‒8278.
[17] Tang, G., Behrangi, A., Long, D.*, Li, C., and Hong, Y.*, 2018. Accounting for spatiotemporal errors of gauges: A critical step to evaluate gridded precipitation products. Journal of Hydrology, 294‒306.
[18] Chen, X.N., *Long, D., Hong, Y., Hao, X.H., & Hou, A.Z. (2018). Climatology of snow phenology over the Tibetan plateau for the period 2001-2014 using multisource data. International Journal of Climatology, 38, 2718‒2729.
[19] Chen, X., *Long, D., Hong, Y., Zeng, C., & Yan, D.H. (2017a). Improved modeling of snow and glacier melting by a progressive two-stage calibration strategy with GRACE and multisource data: How snow and glacier meltwater contributes to the runoff of the Upper Brahmaputra River basin? Water Resources Research, 53, 2431‒2466.
[20] Chen, X.N., *Long, D., Hong, Y., Liang, S.L., & Hou, A.Z. (2017b). Observed radiative cooling over the Tibetan Plateau for the past three decades driven by snow cover-induced surface albedo anomaly. Journal of Geophysical Research-Atmospheres, 122, 6170‒6185.
[21] Gao, Z., *Long, D., Tang, G.Q., Zeng, C., Huang, J.S., & Hong, Y. (2017). Assessing the potential of satellite-based precipitation estimates for flood frequency analysis in ungauged or poorly gauged tributaries of China's Yangtze River basin. Journal of Hydrology, 550, 478‒496.
[22] Cui, Y.K., *Long, D., *Hong, Y., Zeng, C., Zhou, J., Han, Z.Y., Liu, R.H., & Wan, W. (2016). Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau. Journal of Hydrology, 543, 242‒254.
[23] Hou, X.Y., *Long, D., *Hong, Y., & Xie, H.J. (2016). Seasonal to interannual variability of satellite-based precipitation estimates in the Pacific Ocean associated with ENSO from 1998 to 2014. Remote Sensing, 8, 18.
[24] Tang, G.Q., *Long, D., & *Hong, Y. (2016b). Systematic anomalies over inland water bodies of High Mountain Asia in TRMM precipitation estimates: No longer a problem for the GPM Era? IEEE Geoscience and Remote Sensing Letters, 13, 1762‒1766.
[25] Tang, G., Ma, Y., *Long, D., Zhong, L., & *Hong, Y. (2016a). Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Chinese mainland at multiple spatiotemporal scales. Journal of Hydrology, 533, 152‒167.
[26] Ma, Y.Z., Tang, G.Q., *Long, D., Yong, B., Zhong, L.Z., Wan, W., & *Hong, Y. (2016). Similarity and error intercomparison of the GPM and its predecessor-TRMM multisatellite precipitation analysis using the best available hourly gauge network over the Tibetan Plateau. Remote Sensing, 8, 17.
[27] Zeng, Z.Y., Tang, G.Q., *Long, D., Zeng, C., Ma, M.H., *Hong, Y., Xu, H., & Xu, J. (2016). A cascading flash flood guidance system: development and application in Yunnan Province, China. Natural Hazards, 84, 2071‒2093.
[28] Tang, G.Q., Zeng, Z.Y., *Long, D., Guo, X.L., Yong, B., Zhang, W.H., & Hong, Y. (2015). Statistical and hydrological comparisons between TRMM and GPM Level-3 products over a mid-latitude basin: Is Day-1 IMERG a good successor for the TMPA 3B42 Version-7 legacy? Journal of Hydrometeorology, 17, 121‒137.
[29] Huang, Q., Li, X., Han, P., *Long, D., Zhao, F., & Hou, A. (2019). Validation and application of water levels derived from Sentinel-3A for the Brahmaputra River. Science China Technological Sciences, 62, 1760‒1772.
[30] Tang, G.Q., Wen, Y.X., Gao, J.Y., *Long, D., Ma, Y.Z., Wan, W., & *Hong, Y. (2017a). Similarities and differences between three coexisting spaceborne radars in global rainfall and snowfall estimation. Water Resources Research, 53, 3835‒3853.
非第一作者非通訊作者論文 [1] 
[1] Li, D.N., Long, D., *Zhao, J.S., Lu, H., & Hong, Y. (2017). Observed changes in flow regimes in the Mekong River basin. Journal of Hydrology, 551, 217‒232.
[2] Wan, W., Long, D., *Hong, Y., Ma, Y.Z., Yuan, Y., Xiao, P.F., Duan, H.T., Han, Z.Y., & *Gu, X.F. (2016). A lake data set for the Tibetan Plateau from the 1960s, 2005, and 2014. Scientific Data, 3, 13.
[3] *Yang, Y.T., Long, D., Guan, H.D., Liang, W., Simmons, C.T., & Batelaan, O. (2015b). Comparison of three dual-source remote sensing evapotranspiration models during the MUSOEXE-12 campaign: Revisit of model physics. Water Resources Research, 51, 3145‒3165.
[4] Meng, L., Long, D., Quiring, S.M., & *Shen, Y. (2014). Statistical analysis of the relationship between spring soil moisture and summer precipitation in East China. International Journal of Climatology, 34, 1511‒1523.
[5] *Yang, Y., Long, D., Guan, H., Scanlon, B.R., Simmons, C.T., Jiang, L., & Xu, X. (2014a). GRACE satellite observed hydrological controls on interannual and seasonal variability in surface greenness over mainland Australia. Journal of Geophysical Research: Biogeosciences, 119, 2014JG002670.
[6] *Yang, Y.T., Long, D., & Shang, S.H. (2013). Remote estimation of terrestrial evapotranspiration without using meteorological data. Geophysical Research Letters, 40, 3026‒3030.
[7] *Gao, Y.C., & Long, D. (2008). Intercomparison of remote sensing-based models for estimation of evapotranspiration and accuracy assessment based on SWAT. Hydrological Processes, 22, 4850‒4869.
[8] *Gao, Y.C., Long, D., & Li, Z.L. (2008). Estimation of daily actual evapotranspiration from remotely sensed data under complex terrain over the upper Chao river basin in North China. International Journal of Remote Sensing, 29, 3295‒3315.
[9] *Zheng, H., Hong, Y., Long, D., & Jing, H. (2017). Monitoring surface water quality using social media in the context of citizen science. Hydrology and Earth System Sciences, 21, 949‒961.
[10] *Yang, Y., Guan, H., Long, D., Liu, B., Qin, G., Qin, J., & Batelaan, O. (2015a). Estimation of surface soil moisture from thermal infrared remote sensing using an improved trapezoid method. Remote Sensing, 7, 8250.
[11] Scanlon, B.R., Longuevergne, L., & Long, D. (2012). Ground referencing GRACE satellite estimates of groundwater storage changes in the California Central Valley, USA. Water Resources Research, 48.
[12] Tang, G., *Behrangi, A., Ma, Z., Long, D., and *Hong, Y., 2018. Downscaling of ERA-Interim temperature in the contiguous United States and its implications for rain-snow partitioning. Journal of Hydrometeorology, 19: 1215‒1233.
[13] Wan, W., Bai, W.H., Zhao, L.M., Long, D., Sun, Y.Q., Meng, X.G., Chen, H., Cui, X.A., & Hong, Y. (2015). Initial results of China's GNSS-R airborne campaign: soil moisture retrievals. Science Bulletin, 60, 964‒971.
[14] *Yang, Y.T., Guan, H.D., Shang, S.H., Long, D., & Simmons, C.T. (2014b). Toward the use of the MODIS ET product to estimate terrestrial GPP for nonforest ecosystems. IEEE Geoscience and Remote Sensing Letters, 11, 1624‒1628.
[15] Yang, X.*, Yong B.*, Ren, L., Zhang, Y., and Long, D. (2017). Multi-scale validation of GLEAM evapotranspiration products over China via ChinaFLUX ET measurements. International Journal of Remote Sensing, 38 (20), 5688‒5709.
[16] Wan, W., Li, H., *Xie, H.J., *Hong, Y., Long, D., Zhao, L.M., Han, Z.Y., Cui, Y.K., Liu, B.J., Wang, C.G., & Yang, W.T. (2017). A comprehensive data set of lake surface water temperature over the Tibetan Plateau derived from MODIS LST products 2001-2015. Scientific Data, 4, 10.
[17] *Yang, Y.T., Guan, H., Batelaan, O., McVicar, T.R., Long, D., Piao, S.L., Liang, W., Liu, B., Jin, Z., & Simmons, C.T. (2016). Contrasting responses of water use efficiency to drought across global terrestrial ecosystems. Scientific Reports, 6, 8.
[18] Tong, X., *Liu, T.X., Singh, V.P., Duan, L.M., & Long, D. (2016). Development of in situ experiments for evaluation of anisotropic reflectance effect on spectral mixture analysis for vegetation cover. IEEE Geoscience and Remote Sensing Letters, 13, 636‒640.
[19] Pei, H.W., Scanlon, B.R., *Shen, Y.J., Reedy, R.C., Long, D., & Liu, C.M. (2015). Impacts of varying agricultural intensification on crop yield and groundwater resources: comparison of the North China Plain and US High Plains. Environmental Research Letters, 10, 14.
[20] Tang, Y., Hooshyar, M., Zhu, T.J., Ringler, C., Sun, A.Y., Long, D., & *Wang, D.B. (2017b). Reconstructing annual groundwater storage changes in a large-scale irrigation region using GRACE data and Budyko model. Journal of Hydrology, 551, 397‒406.
[21] *Scanlon, B.R., Zhang, Z.Z., Save, H., Wiese, D.N., Landerer, F.W., Long, D., Longuevergne, L., & Chen, J. (2016). Global evaluation of new GRACE mascon products for hydrologic applications. Water Resources Research, 52, 9412‒9429.
[22] *Scanlon, B.R., Zhang, Z.Z., Reedy, R.C., Pool, D.R., Save, H., Long, D., Chen, J., Wolock, D.M., Conway, B.D., & Winester, D. (2015). Hydrologic implications of GRACE satellite data in the Colorado River Basin. Water Resources Research, 51, 9891‒9903.
[23] Liang, W., Yang, Y., Fan, D., Guan, H., Zhang, T., Long, D., Zhou, Y., & Bai, D. (2015b). Analysis of spatial and temporal patterns of net primary production and their climate controls in China from 1982 to 2010. Agricultural and Forest Meteorology, 204, 22‒36.
[24] Yang, L., Song, X., Zhang, Y., Han, D., Zhang, B., & Long, D. (2012). Characterizing interactions between surface water and groundwater in the Jialu River basin using major ion chemistry and stable isotopes. Hydrology and Earth System Sciences, 16, 4265‒4277.
[25] Ma, Y., *Hong, Y., Chen, Y., Yang, Y., Tang, G., Yao, Y., Long, D., Li, C., Han, Z., and *Liu, R. (2018). Performance of optimally merged multisatellite precipitation products using the dynamic Bayesian model averaging scheme over the Tibetan Plateau. Journal of Geophysical Research-Atmospheres, 123: 814‒834.
[26] Liu, X., *Song, X.F., Zhang, Y.H., Xia, J., Zhang, X.C., Yu, J.J., Long, D., Li, F.D., & Zhang, B. (2011). Spatio-temporal variations of delta H-2 and delta O-18 in precipitation and shallow groundwater in the Hilly Loess Region of the Loess Plateau, China. Environmental Earth Sciences, 63, 1105‒1118.
[27] Liang, W., *Bai, D., Wang, F., Fu, B., Yan, J., Wang, S., Yang, Y., Long, D., & Feng, M. (2015a). Quantifying the impacts of climate change and ecological restoration on streamflow changes based on a Budyko hydrological model in China's Loess Plateau. Water Resources Research, 51, 6500‒6519.
[28] Scanlon, B.R.*, Zhang, Z., Save, H., Sun, A.Y., Mueller Schmied, H., van Beek, L.P.H., Wiese, D.N., Wada, Y., Long D., Reedy, R. C., Longuevergne, L., Doll, P., and Bierkens, M.F.P. (2018). Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data. Proceedings of the National Academy of Sciences of the United States of America, 115 (6): E1080‒1089.
[29] *Li, B.L., Rodell, M., Kumar, S., Beaudoing, H.K., Getirana, A., Zaitchik, B.F., de Goncalves, L.G., Cossetin, C., Bhanja, S., Mukherjee, A., Tian, S.Y., Tangdamrongsub, N., Long, D., Nanteza, J., Lee, J., Policelli, F., Goni, I.B., Daira, D., Bila, M., de Lannoy, G., Mocko, D., Steele-Dunne, S.C., Save, H., and Bettadpur, S. (2019). Global GRACE data assimilation for groundwater and drought monitoring: Advances and challenges. Water Resources Research, 55, 7564‒7586.

龍笛發明專利

(1) 龍笛，洪陽. 聯合重力衞星獲取地下水儲量變化值的方法及系統 (已授權，專利號:201610972549.6) [1] 
(2) 龍笛，杜明達. 流域流量的獲取方法、裝置、設備及可讀存儲介質 (已授權，專利號:201810095189.5) [1] 
(3) 龍笛，黃琦. 河流水位的確定方法、裝置、計算機設備及可讀存儲介質 (已授權，專利號:201810174277.4) [1] 
(4) 龍笛，李興東，黃琦. 水量變化監測方法、裝置、計算機設備和存儲介質 (已授權，專利號:201811327253.4) [1] 
(5) 龍笛, 白亮亮. 土壤水分信息獲取方法、裝置、計算機設備和存儲介質 (已授權，專利號:201811003247.3) [1] 
(6) 龍笛，白亮亮，巖臘, 曾超. 一種全天候高分辨率地表温度監測方法 (已授權，專利號:201811002215.1) [1] 
(7) 龍笛，黃琦. 徑流模擬方法、裝置以及計算機設備 (已授權，專利號:201910753987.7); [1] 
(8) 龍笛，李雪瑩. 大氣水汽含量監測方法、裝置、計算機設備和存儲介質 (已授權，專利號:201910768506.X) [1] 
(9) 趙凡玉，龍笛. 冰川物質平衡量獲取方法、裝置、計算機設備及存儲介質 (已授權，專利號:201911070350.4) [1] 
(10) 韓忠穎, 龍笛. 水庫調節徑流的計算方法、裝置、計算機設備和存儲介質 (已授權，專利號:201911071637.9) [1] 
(11) 張才金, 龍笛，巖臘. 地表用水量計算方法、裝置、計算機設備和存儲介質 (已授權，專利號:202010288333.4) [1] 
(12) 李興東, 龍笛. 冰層厚度計算方法、裝置、計算機設備和存儲介質 (已授權，專利號:202010222282.5) [1] 
(13) 洪仲坤，龍笛，韓忠穎. 山區降水量的計算方法、裝置、計算機設備和存儲介質 (已授權，專利號：201911104700.4) [1] 
(14) 韓忠穎，龍笛. 徑流重建方法、裝置、計算機設備和存儲介質 (已授權，專利號：202110020755.8) [1] 
參考資料