A.R., Marks, D., Havens, S., Robertson, M., Johnson, M., Sandusky, M., et al. (2018). Direct insertion of NASA Airborne Snow Observatory‐derived snow depth time series into the iSnobal energy balance snow model. Water Resources Research, 54(10), 8045–8063.
75 Hess, M., Koepke, P., & Schult, I. (1998). Optical properties of aerosols and clouds: The software package OPAC. Bulletin of the American Meteorological Society, 79(5), 831–844.
76 Hirschi, M., Seneviratne, S.I., Alexandrov, V., Boberg, F., Boroneant, C., Christensen, O.B., et al. (2011). Observational evidence for soil‐moisture impact on hot extremes in southeastern Europe. Nature Geoscience, 4(1), 17–21. https://doi.org/10.1038/ngeo1032
77 Hou, A.Y., Kakar, R.K., Neeck, S., Azarbarzin, A.A., Kummerow, C.D., Kojima, M., et al. (2014). The global precipitation measurement mission. Bulletin of the American Meteorological Society, 95(5), 701–722.
78 Howitt, R., Medellín‐Azuara, J., MacEwan, D., Lund, J.R., & Sumner, D. (2014). Economic analysis of the 2014 drought for California agriculture. Davis, CA: Center for Watershed Sciences University of California.
79 Hsu, K., Gao, X., Sorooshian, S., & Gupta, H.V. (1997). Precipitation estimation from remotely sensed information using artificial neural networks. Journal of Applied Meteorology, 36(9), 1176–1190.
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.
81 Huffman, G.J., Bolvin, D.T., Nelkin, E.J., Wolff, D.B., Adler, R.F., Gu, G., et al. (2007). The TRMM multisatellite precipitation analysis (TMPA): Quasi‐global, multiyear, combined‐sensor precipitation estimates at fine scales. Journal of Hydrometeorology, 8(1), 38–55.
82 Hutchinson, C.F., & Herrmann, S.M. (2016). The scientific basis: Links between land degradation, drought and desertification. In P.M. Johnson, K. Mayrand (Eds.), Governing Global Desertification: Linking Environmental Degradation, Poverty and Participation (pp. 31–46). New York, Routledge.
83 Im, J., Park, S., Rhee, J., Baik, J., & Choi, M. (2016). Downscaling of AMSR‐E soil moisture with MODIS products using machine learning approaches. Environmental Earth Sciences, 75(15), 1120.
84 IPCC, 2007. Climate change 2007: the physical science basis. Agenda 6 (07), 333, Intergovernmental Panel on Climate Change, Geneva.
85 Jackson, R.D., Idso, S.B., Reginato, R.J., & Pinter Jr, P.J. (1981). Canopy temperature as a crop water stress indicator. Water Resources Research, 17(4), 1133–1138.
86 John, V.O., Holl, G., Allan, R.P., Buehler, S.A., Parker, D.E., & Soden, B.J. (2011). Clear‐sky biases in satellite infrared estimates of upper tropospheric humidity and its trends. Journal of Geophysical Research: Atmospheres, 116, D14108.
87 Joyce, R., & Arkin, P.A. (1997). Improved estimates of tropical and subtropical precipitation using the GOES precipitation index. Journal of Atmospheric and Oceanic Technology, 14(5), 997–1011.
88 Joyce, R.J., Janowiak, J.E., Arkin, P.A., & Xie, P. (2004). CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology, 5(3), 487–503.
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
91 Keetch, J.J., & Byram, G.M. (1968). A drought index for forest fire control (Research Paper SE‐38, 35 pp.). Asheville, NC: U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station.
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.
98 Kogan, F.N. (1995). Droughts of the late 1980s in the United States as derived from NOAA polar‐orbiting satellite data. Bulletin of the American Meteorological Society, 76(5), 655–668.
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.
100 Koster, R.D., Suarez, M.J., Ducharne, A., Stieglitz, M., & Kumar, P. (2000). A catchment‐based approach to modeling land surface processes in a general circulation model: 1. Model structure. Journal of Geophysical Research: Atmospheres, 105(D20), 24809–24822.
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