Coerver, B. and Michailovsky C. I.: Python Code: Investigating sources of variability in closing the terrestrial water balance with remote sensing, Zenodo [code], https://doi.org/10.5281/zenodo.8318720, 2023.
Dutt Vishwakarma, B., Devaraju, B., and Sneeuw, N.: Minimizing the effects of filtering on catchment scale GRACE solutions, Water Resour. Res., 52, 5868–5890, https://doi.org/10.1002/2016WR018960, 2016.
ESA and UCLouvain: GlobCover2009, ESA [data set]
http://due.esrin.esa.int/page_globcover.php (last access: 29 November 2023), 2010.
Fan, Y. and Schaller, M. F.: River basins as groundwater exporters and importers: Implications for water cycle and climate modeling, J. Geophys. Res.-Atmos., 114, 4103, https://doi.org/10.1029/2008JD010636, 2009.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared precipitation with stations – A new environmental record for monitoring extremes, Sci. Data, 2, 1–21, https://doi.org/10.1038/sdata.2015.66, 2015.
GRDC: Watershed Boundaries of GRDC Stations/Global Runoff Data Centre, Koblenz, Germany, Federal Institute of Hydrology (BfG) [data set],
https://www.bafg.de/GRDC/EN/02_srvcs/22_gslrs/222_WSB/watershedBoundaries_node.html (last access: 30 August 2019), 2011.
GRDC: GRDC Reference Dataset, The global runoff data centre, 56068 Koblenz, Germany, GRDC [data set],
https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html (last access: 30 August 2019), 2019.
Guerschman, J. P., Van Dijk, A. I. J. M., Mattersdorf, G., Beringer, J., Hutley, L. B., Leuning, R., Pipunic, R. C., and Sherman, B. S.: Scaling of potential evapotranspiration with MODIS data reproduces flux observations and catchment water balance observations across Australia, J. Hydrol., 369, 107–119, https://doi.org/10.1016/J.JHYDROL.2009.02.013, 2009.
Huffman, G. J., Adler, R. F., Bolvin, D. T., Gu, G., Nelkin, E. J., Bowman, K. P., Hong, Y., Stocker, E. F., and Wolff, D. B.: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales, J. Hydrometeorol., 8, 38–55, https://doi.org/10.1175/JHM560.1, 2007.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Jackson, T.: GPM IMERG Final Precipitation L3 1 month 0.1 degree
× 0.1 degree V06, Greenbelt, MD, Goddard Earth Sci. Data Inf. Serv. Cent. [data set], https://doi.org/10.5067/GPM/IMERG/3B-MONTH/06, 2019.
Kouraev, A. V., Zakharova, E. A., Samain, O., Mognard, N. M., and Cazenave, A.: Ob’ river discharge from TOPEX/Poseidon satellite altimetry (1992–2002), Remote Sens. Environ., 93, 238–245, https://doi.org/10.1016/J.RSE.2004.07.007, 2004.
Landerer, F. W.: CSR TELLUS GRACE Level-3 Monthly LAND Water-Equivalent-Thickness Surface-Mass Anomaly Release 6.0 in netCDF/ASCII/Geotiff Formats, PODAAC [data set], https://doi.org/10.5067/TELND-3AC06, 2019a.
Landerer, F. W.: GFZ TELLUS GRACE Level-3 Monthly LAND Water-Equivalent-Thickness Surface-Mass Anomaly Release 6.0 in netCDF/ASCII/Geotiff Formats, PODAAC [data set], https://doi.org/10.5067/TELND-3AG06, 2019b.
Landerer, F. W.: JPL TELLUS GRACE Level-3 Monthly LAND Water-Equivalent-Thickness Surface-Mass Anomaly Release 6.0 in netCDF/ASCII/Geotiff Formats, PODAAC [data set], https://doi.org/10.5067/TELND-3AJ06, 2019c.
Landerer, F. W. and Swenson, S. C.: Accuracy of scaled GRACE terrestrial water storage estimates, Water Resour. Res., 48, 4531, https://doi.org/10.1029/2011WR011453, 2012.
Lehmann, F., Vishwakarma, B. D., and Bamber, J.: How well are we able to close the water budget at the global scale?, Hydrol. Earth Syst. Sci., 26, 35–54, https://doi.org/10.5194/hess-26-35-2022, 2022.
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N., and Wisser, D.: High-resolution mapping of the world's reservoirs and dams for sustainable river-flow management, Front. Ecol. Environ., 9, 494–502, https://doi.org/10.1890/100125, 2011.
Lorenz, C., Kunstmann, H., Devaraju, B., Tourian, M. J., Sneeuw, N., and Riegger, J.: Large-scale runoff from landmasses: A global assessment of the closure of the hydrological and atmospheric water balances, J. Hydrometeorol., 15, 2111–2139, https://doi.org/10.1175/JHM-D-13-0157.1, 2014.
Michailovsky, C. I., McEnnis, S., Berry, P. A. M., Smith, R., and Bauer-Gottwein, P.: River monitoring from satellite radar altimetry in the Zambezi River basin, Hydrol. Earth Syst. Sci., 16, 2181–2192, https://doi.org/10.5194/hess-16-2181-2012, 2012.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
Moreira, A. A., Ruhoff, A. L., Roberti, D. R., de Arruda Souza, V., da Rocha, H. R., and de Paiva, R. C. D.: Assessment of terrestrial water balance using remote sensing data in South America, J. Hydrol., 575, 131–147, https://doi.org/10.1016/j.jhydrol.2019.05.021, 2019.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., and Veith, T. L.: Mode
l Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations, T. ASABE, 50, 885–900, https://doi.org/10.13031/2013.23153, 2007.
Mu, Q., Zhao, M., and Running, S. W.: Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sens. Environ., 115, 1781–1800, https://doi.org/10.1016/j.rse.2011.02.019, 2011.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Sahoo, A. K., Pan, M., Troy, T. J., Vinukollu, R. K., Sheffield, J., and Wood, E. F.: Reconciling the global terrestrial water budget using satellite remote sensing, Remote Sens. Environ., 115, 1850–1865, https://doi.org/10.1016/j.rse.2011.03.009, 2011.
Senay, G. B., Bohms, S., Singh, R. K., Gowda, P. H., Velpuri, N. M., Alemu, H., and Verdin, J. P.: Operational Evapotranspiration Mapping Using Remote Sensing and Weather Datasets: A New Parameterization for the SSEB Approach, J. Am. Water Resour. As., 49, 577–591, https://doi.org/10.1111/jawr.12057, 2013.
Sheffield, J., Ferguson, C. R., Troy, T. J., Wood, E. F., and McCabe, M. F.: Closing the terrestrial water budget from satellite remote sensing, Geophys. Res. Lett., 36, L07403, https://doi.org/10.1029/2009GL037338, 2009.
Sheffield, J., Wood, E. F., Pan, M., Beck, H., Coccia, G., Serrat-Capdevila, A., and Verbist, K.: Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data-Poor Regions, Water Resour. Res., 54, 9724–9758, https://doi.org/10.1029/2017WR022437, 2018.
Syed, T. H., Famiglietti, J. S., Chen, J., Rodell, M., Seneviratne, S. I., Viterbo, P., and Wilson, C. R.: Total basin discharge for the Amazon and Mississippi River basins from GRACE and a land-atmosphere water balance, Geophys. Res. Lett., 32, L24404, https://doi.org/10.1029/2005GL024851, 2005.
Stisen, S. and Sandholt, I.: Evaluation of remote-sensing-based rainfall products through predictive capability in hydrological runoff modelling, Hydrol. Process., 24, 879–891, https://doi.org/10.1002/hyp.7529, 2010.
Tang, G., Clark, M. P., Papalexiou, S. M., Ma, Z., and Hong, Y.: Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets, Remote Sens. Environ., 240, 111697, https://doi.org/10.1016/J.RSE.2020.111697, 2020.
Tian, Y. and Peters-Lidard, C. D.: A global map of uncertainties in satellite-based precipitation measurements, Geophys. Res. Lett., 37, L24407, https://doi.org/10.1029/2010GL046008, 2010.
Tian, Y., Liu, Y., Arsenault, K. R., and Behrangi, A.: A new approach to satellite-based estimation of precipitation over snow cover, Int. J. Remote Sens., 35, 4940–4951, https://doi.org/10.1080/01431161.2014.930208, 2014.
Vorosmarty, C., Askew, A., Grabs, W., Barry, R. G., Birkett, C., Doll, P., Goodison, B., Hall, A., Jenne, R., Kitaev, L., Landwehr, J., Keeler, M., Leavesley, G., Schaake, J., Strzepek, K., Sundarvel, S. S., Takeuchi, K., and Webster, F.: Global water data: A newly endangered species, EOS T. Am. Geophys. Un., 82, 54–54, https://doi.org/10.1029/01EO00031, 2001.
Wahr, J., Molenaar, M., and Bryan, F.: Time variability of the Earth's gravity field: Hydrological and oceanic effects and their possible detection using GRACE, J. Geophys. Res.-Sol. Ea., 103, 30205–30229, https://doi.org/10.1029/98JB02844, 1998.
Wahr, J., Swenson, S., Zlotnicki, V., and Velicogna, I.: Time-variable gravity from GRACE: First results, Geophys. Res. Lett., 31, L11501, https://doi.org/10.1029/2004GL019779, 2004.
Wang, L., Kaban, M. K., Thomas, M., Chen, C., and Ma, X.: The Challenge of Spatial Resolutions for GRACE-Based Estimates Volume Changes of Larger Man-Made Lake: The Case of China's Three Gorges Reservoir in the Yangtze River, Remote Sens., 11, 99, https://doi.org/10.3390/rs11010099, 2019.
Xu, T., Guo, Z., Xia, Y., Ferreira, V. G., Liu, S., Wang, K., Yao, Y., Zhang, X., and Zhao, C.: Evaluation of twelve evapotranspiration products from machine learning, remote sensing and land surface models over conterminous United States, J. Hydrol., 578, 124105, https://doi.org/10.1016/j.jhydrol.2019.124105, 2019.
Zhang, K., Kimball, J. S., and Running, S. W.: A review of remote sensing based actual evapotranspiration estimation, Wiley Interdiscip. Rev. Water, 3, 834–853, https://doi.org/10.1002/wat2.1168, 2016.
