“*”为通讯作者，统计截至2021年5月
[1] *Long, D., Yang, W., Scanlon, B.R., Zhao, J., Liu, D., Burek, P., Pan, Y., You, L., & Wada, Y. (2020). South-to-North Water Diversion stabilizing Beijing’s groundwater levels. Nature Communications, 11, 3665.
[2] *Long, D., Yan, L., Bai, L., Zhang, C., Li, X., Lei, H., Yang, H., Tian, F., Zeng, C., Meng, X., & Shi, C. (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.
[20] Han, P., *Long, D., Li, X., Huang, Q., Dai, L., & Sun, Z. (2021). A dual state-parameter updating scheme using the particle filter and high-spatial-resolution remotely sensed snow depths to improve snow simulation. Journal of Hydrology, 594, 125979.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[26] 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.
[27] 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.
[28] 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.
[29] 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.
[30] 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.
[31] 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.
[32] 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.
[33] 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.
[34] 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.
[35] 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.
[36] 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.
[37] 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.
[38] 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.
[39] 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.
[40] 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.
[41] 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.
[42] 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.
[43] 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.
[44] 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.
[45] 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.
[46] *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.
[47] 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.
[48] *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.
[49] *Yang, Y.T., Long, D., & Shang, S.H. (2013). Remote estimation of terrestrial evapotranspiration without using meteorological data. Geophysical Research Letters, 40, 3026‒3030.
[50] *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.
[51] *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.
[52] 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.
[53] *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.
[54] Tang, G., Ma, Y., *Long, D., Zhong, L., & *Hong, Y. (2016a). Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales. Journal of Hydrology, 533, 152‒167.
[55] 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.
[56] 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.
[57] 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.
[58] *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.
[59] 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.
[60] Abowarda, A.S., *Bai, L., Zhang, C., *Long, D., Li, X., Huang, Q., & Sun, Z. (2021). Generating surface soil moisture at 30 m spatial resolution using both data fusion and machine learning toward better water resources management at the field scale. Remote Sensing of Environment, 255, 112301.
[61] Hong, Z., Han, Z., Li, X., *Long, D., Tang, G., & Wang, J. (2021). Generation of an improved precipitation dataset from multisource information over the Tibetan Plateau. Journal of Hydrometeorology, 22, 1275–1295.
[62] 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.
[63] 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.
[64] 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.
[65] 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.
[66] *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.
[67] 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.
[68] 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.
[69] *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.
[70] 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.
[71] 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.
[72] 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.
[73] *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.
[74] *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.
[75] 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.
[76] 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.
[77] 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.
[78] 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.
[79] 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.
[80] 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.
[81] *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.
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