Global Drought and Flood. Группа авторов

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      80 Huffman, G.J., Bolvin, D.T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P., & Yoo, S.‐H. (2015). NASA global precipitation measurement (GPM) integrated multi‐satellite retrievals for GPM (IMERG). Algorithm Theoretical Basis Document, Version, 4, 30. Greenbelt. MD: NASA.

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      89 Kalma, J.D., McVicar, T.R., & McCabe, M.F. (2008). Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data. Surveys in Geophysics, 29(4–5), 421–469.

      90 Kao, S., & Govindaraju, R.S. (2010). A copula‐based joint deficit index for droughts. Journal of Hydrology, 380(1–2), 121–134. https://doi.org/10.1016/j.jhydrol.2009.10.029

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      92 Kerr, Y.H., Waldteufel, P., Wigneron, J.P., Delwart, S., Cabot, F., Boutin, J., et al. (2010). The SMOS mission: New tool for monitoring key elements ofthe global water cycle. Proceedings of the IEEE, 98(5), 666–687.

      93 Khajehei, S., Ahmadalipour, A., & Moradkhani, H. (2018). An effective post‐processing of the North American multi‐model ensemble (NMME) precipitation forecasts over the continental US. Climate Dynamics, 51(1–2), 457–472.

      94 Khalili, D., Farnoud, T., Jamshidi, H., Kamgar‐Haghighi, A. A., & Zand‐Parsa, S. (2011). Comparability analyses of the SPI and RDI meteorological drought indices in different climatic zones. Water Resources Management, 25(6), 1737–1757.

      95 Khorshidi, M.S., Nikoo, M.R., Sadegh, M., & Nematollahi, B., (2019). A multi‐objective risk‐based game theoretic approach to reservoir operation policy in potential future drought condition. Water Resources Management, 33(6), 1999–2014.

      96 Kidd, C., Bauer, P., Turk, J., Huffman, G.J., Joyce, R., Hsu, K.‐L., & Braithwaite, D. (2012). Intercomparison of high‐resolution precipitation products over northwest Europe. Journal of Hydrometeorology, 13(1), 67–83.

      97 Knowles, J.F., Lestak, L.R., & Molotch, N.P. (2017). On the use of a snow aridity index to predict remotely sensed forest productivity in the presence of bark beetle disturbance. Water Resources Research, 53(6), 4891–4906.

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      99 Kongoli, C., Romanov, P., & Ferraro, R. (2012). Snow cover monitoring from remote satellites: Possibilities for drought assessment. In B.D. Wardlow, M.C. Anderson, J.P. Verdin (Eds), Remote Sensing of Drought (pp. 359–384). Taylor & Francis.

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      101 Kumar, S.V., Dirmeyer, P.A., Peters‐Lidard, C.D., Bindlish, R., & Bolten, J. (2018). Information theoretic evaluation of satellite soil moisture retrievals. Remote Sensing of Environment, 204, 392–400.

      102 Kumar, S.V., Peters‐Lidard, C.D., Mocko, D.M., Reichle, R., Liu, Y., Arsenault, K., et al. (2014). Assimilation of remotely sensed soil moisture and snow depth retrievals for drought estimation. Journal of Hydrometeorology, 15, 2446–2469. doi:10.1175/JHM‐D‐13‐0132.1

      103 Lambert, A., Read, W.G., Livesey, N.J., Santee, M.L., Manney, G.L., Froidevaux, L., et al. (2007). Validation of the Aura Microwave Limb Sounder middle atmosphere water vapor and nitrous oxide measurements. Journal of Geophysical Research: Atmospheres, 112(D24). https://doi.org/10.1029/2007JD008724

      104 Leonard, M., Westra, S., Phatak, A., Lambert, M., van den Hurk, B., McInnes, K., et al. (2014). A compound event framework for understanding extreme impacts. Wiley Interdisciplinary Reviews: Climate Change, 5(1), 113–128.

      105 Lettenmaier, D.P., Alsdorf, D., Dozier, J., Huffman, G.J., Pan, M., & Wood, E.F. (2015). Inroads of remote sensing into hydrologic science during the WRR era. Water Resources Research, 51(9), 7309–7342.

      106 Li, B., & Rodell, M. (2015). Evaluation of a model‐based groundwater drought indicator in the conterminous U.S. Journal of Hydrology, 526, 78–88. https://doi.org/10.1016/j.jhydrol.2014.09.027

      107 Littell, J.S., Peterson, D.L., Riley, K.L., Liu, Y., & Luce, C.H. (2016). A review of the relationships between drought and forest fire in the United States. Global Change Biology, 22(7), 2353–2369.

      108 Lu, X., Wei, M., Tang, G., & Zhang, Y. (2018). Evaluation and correction of the TRMM 3B43V7 and GPM 3IMERGM satellite precipitation products by use of ground‐based