Читать книгу Global Drought and Flood - Группа авторов - Страница 27

1 Adler, R.F., Huffman, G.J., Chang, A., Ferraro, R., Xie, P.P., Janowiak, J., et al. (2003). The version‐2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). Journal of Hydrometeorology, 4(6), 1147–1167.

2 AghaKouchak, A., Cheng, L., Mazdiyasni, O., & Farahmand, A. (2014). Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought. Geophysical Research Letters, 41(24), 8847–8852. https://doi.org/10.1002/2014GL062308

3 Aghakouchak, A., Farahmand, A., Melton, F.S., Teixeira, J., Anderson, M.C., Wardlow, B.D., & Hain, C.R. (2015). Remote sensing of drought: Progress, challenges, and opportunities. Reviews of Geophysics, 53, 452–480. https://doi.org/10.1002/2014RG000456

4 AghaKouchak, A., Feldman, D., Hoerling, M., Huxman, T., & Lund, J. (2015). Water and climate: Recognize anthropogenic drought. Nature News, 524(7566), 409.

5 AghaKouchak, A., & Mehran, A. (2013). Extended contingency table: Performance metrics for satellite observations and climate model simulations. Water Resources Research, 49(10), 7144–7149.

6 AghaKouchak, A., Mehran, A., Norouzi, H., & Behrangi, A. (2012). Systematic and random error components in satellite precipitation data sets. Geophysical Research Letters, 39(9). https://doi.org/10.1029/2012GL051592

7 AghaKouchak, A., & Nakhjiri, N. (2012). A near real‐time satellite‐based global drought climate data record. Environmental Research Letters, 7(4), 44037.

8 Alborzi, A., Mirchi, A., Moftakhari, H., Mallakpour, I., Alian, S., Nazemi, A., et al. (2018). Climate‐informed environmental inflows to revive a drying lake facing meteorological and anthropogenic droughts. Environmental Research Letters, 13(8), p.084010.

9 Allen, C.D., Macalady, A.K., Chenchouni, H., Bachelet, D., McDowell, N., Vennetier, M., et al. (2010). A global overview of drought and heat‐induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management, 259(4), 660–684.

10 Allen, R.G., Pereira, L.S., Raes, D., & Smith, M. (1998). Crop evapotranspiration—Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. Food and Agriculture Organization, Rome.

11 Allen, R.G., Tasumi, M., & Trezza, R. (2007). Satellite‐based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. Journal of Irrigation and Drainage Engineering, 133(4), 380–394.

12 Ambaw, G.M. (2013). Satellite based remote sensing of soil moisture for drought detection and monitoring in the Horn of Africa. PhD thesis, Politecnico di Torino, Turin, Italy.

13 Andela, N., Liu, Y.Y., Van Dijk, A., De Jeu, R.A.M., & McVicar, T.R. (2013). Global changes in dryland vegetation dynamics (1988–2008) assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data. Biogeosciences, 10(10), 6657–6676. https://doi.org/10.5194/bg‐10‐6657‐2013

14 Anderson, M., & Kustas, W. (2008). Thermal remote sensing of drought and evapotranspiration. Eos, Transactions American Geophysical Union, 89(26), 233–234.

15 Anderson, M.C., Norman, J.M., Mecikalski, J.R., Otkin, J.A., & Kustas, W.P. (2007). A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. Journal of Geophysical Research: Atmospheres, 112(D11).

16 Anderson, M.C., Cammalleri, C., Hain, C.R., Otkin, J., Zhan, X., & Kustas, W. (2013). Using a diagnostic soil‐plant‐atmosphere model for monitoring drought at field to continental scales. Procedia Environmental Sciences, 19, 47–56.

17 Anderson, M.C., Zolin, C.A., Sentelhas, P.C., Hain, C.R., Semmens, K., Yilmaz, M.T., et al. (2016). The Evaporative Stress Index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts. Remote Sensing of Environment, 174, 82–99.

18 Anderson, W.B., Zaitchik, B.F., Hain, C.R., Anderson, M.C., Yilmaz, M.T., Mecikalski, J., & Schultz, L. (2012). Towards an integrated soil moisture drought monitor for East Africa. Hydrology and Earth System Sciences, 16(8), 2893–913.

19 Ashouri, H., Hsu, K.‐L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R., Cecil, L. D., et al. (2015). PERSIANN‐CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bulletin of the American Meteorological Society, 96(1), 69–83.

20 Ashraf, S., AghaKouchak, A., Nazemi, A., Mirchi, A., Sadegh, M., Moftakhari, H.R., et al. (2019). Compounding effects of human activities and climatic changes on surface water availability in Iran. Climatic Change, 52, 379–391.

21 Ault, T.R., Cole, J.E., Overpeck, J.T., Pederson, G.T., & Meko, D.M. (2014). Assessing the risk of persistent drought using climate model simulations and paleoclimate data. Journal of Climate, 27(20), 7529–7549. https://doi.org/10.1175/JCLI‐D‐12‐00282.1

22 Beck, H.E., McVicar, T.R., van Dijk, A.I.J.M., Schellekens, J., de Jeu, R.A.M., & Bruijnzeel, L.A. (2011). Global evaluation of four AVHRR–NDVI data sets: Intercomparison and assessment against Landsat imagery. Remote Sensing of Environment, 115(10), 2547–2563.

23 Behrangi, A., Tian, Y., Lambrigtsen, B.H., & Stephens, G.L. (2014). What does CloudSat reveal about global land precipitation detection by other spaceborne sensors? Water Resources Research, 50(6), 4893–4905.

24 Behrangi, A., Fetzer, E.J., Granger, S.L., Behrangi, A., Fetzer, E.J., Early, S.L.G., et al. (2016). Early detection of drought onset using near surface temperature and humidity observed from space. International Journal of Remote Sensing, 1161,3911–3923. https://doi.org/10.1080/01431161.2016.1204478

25 Bhalme, H.N., & Mooley, D.A. (1980). Large‐scale droughts/floods and monsoon circulation. Monthly Weather Review, 108(8), 1197–1211.

26 Bloomfield, J.P., & Marchant, B.P. (2013). Analysis of groundwater drought building on the standardised precipitation index approach. Hydrology and Earth System Sciences, 17, 4769–4787.

27 Bowman, D.M.J.S., & Johnston, F.H. (2005). Wildfire smoke, fire management, and human health. EcoHealth, 2(1), 76–80.

28 Boyle, J., & Klein, S.A. (2010). Impact of horizontal resolution on climate model forecasts of tropical precipitation and diabatic heating for the TWP‐ICE period. Journal of Geophysical Research: Atmospheres, 115(D23).

29 Brodzik, M.J., Long, D.G., Hardman, M.A., Paget, A. & Armstrong, R.(2016). MEaSUREs Calibrated Enhanced‐Resolution Passive Microwave Daily EASE‐Grid 2.0 Brightness Temperature ESDR, Version 1 (updated 2018). Boulder, CO: NASA NSIDC DAAC. 10.5067/MEASURES/CRYOSPHERE/NSIDC‐0630.001.

30 Brogniez, H., Fallourd, R., Mallet, C., Sivira, R., & Dufour, C. (2016). Estimating confidence intervals around relative humidity profiles from satellite observations: application to the SAPHIR sounder. Journal of Atmospheric and Oceanic Technology, 33(5), 1005–1022. https://doi.org/10.1175/JTECH‐D‐15‐0237.1

31 Brown, J.F., Wardlow, B.D., Tadesse, T., Hayes, M.J., & Reed, B.C. (2008). The Vegetation Drought Response Index (VegDRI): A new integrated approach for monitoring drought stress in vegetation. GIScience and Remote Sensing, 45(1), 16–46.

32 Byun, H.‐R., & Wilhite, D.A. (1999). Objective quantification of drought severity and duration. Journal of Climate, 12(9), 2747–2756.

33 Cassou, C., Terray, L., & Phillips, A.S. (2005). Tropical Atlantic influence on European heat waves. Journal of Climate, 18(15), 2805–2811.

34 Chang, K.‐Y., Xu, L., & Starr, G. (2018). A drought indicator reflecting ecosystem responses to water availability: The Normalized Ecosystem Drought Index. Agricultural and Forest Meteorology, 250, 102–117.

35 Chiang, F., Mazdiyasni, O., & AghaKouchak, A. (2018). Amplified warming of droughts in southern United States in observations and model simulations. Science Advances, 4(8), eaat2380.

36 Chikamoto, Y., Timmermann, A., Widlansky, M.J., Balmaseda, M.A., & Stott, L. (2017). Multi‐year predictability of climate, drought, and wildfire in southwestern North America. Nature Scientific Reports, 7(1), 1–12. https://doi.org/10.1038/s41598‐017‐06869‐7

37 Cunha, A.P.M., Alvalá, R.C., Nobre, C.A., & Carvalho, M.A. (2015). Monitoring vegetative drought dynamics in the Brazilian semiarid region. Agricultural and Forest Meteorology, 214–215, 494–505. https://doi.org/10.1016/j.agrformet.2015.09.010

38 D’Odorico, P., Laio, F., & Ridolfi, L. (2010). Does globalization of water reduce societal resilience to drought? Geophysical Research Letters, 37(13).

39 Dalezios, N. R., Blanta, A., & Spyropoulos, N. V. (2012). Assessment of remotely sensed drought features in vulnerable agriculture. Natural Hazards and Earth System Sciences, 12(10), 3139–3150.

40 De Jeu, R.A.M., Wagner, W., Holmes, T.R.H., Dolman, A.J., Van De Giesen, N.C., & Friesen, J. (2008). Global soil moisture patterns observed by space borne microwave radiometers and scatterometers. Surveys in Geophysics, 29(4–5), 399–420.

41 Dong, J., & Crow, W.T. (2017). An improved triple collocation analysis algorithm for decomposing autocorrelated and white soil moisture retrieval errors. Journal of Geophysical Research: Atmospheres, 122(24), 13,081–13,094. https://doi.org/10.1002/2017JD027387

42 Donohue, R.J., McVicar, T.R., & Roderick, M.L. (2010). Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate. Journal of Hydrology, 386(1–4), 186–197.

43 Dracup, J.A., Lee, K.S., & Paulson Jr, E.G. (1980). On the definition of droughts. Water Resources Research, 16(2), 297–302.

44 Durand, M., Molotch, N.P., & Margulis, S.A. (2008). A Bayesian approach to snow water equivalent reconstruction. Journal of Geophysical Research: Atmospheres, 113(D20). https://doi.org/10.1029/2008JD009894

45 Entekhabi, D., Njoku, E.G., O’Neill, P.E., Kellogg, K.H., Crow, W.T., Edelstein, W.N., et al. (2010). The soil moisture active passive (SMAP) mission. Proceedings of the IEEE, 98(5), 704–716.

46 Famiglietti, J.S., Lo, M., Ho, S.L., Bethune, J., Anderson, K.J., Syed, T.H., et al. (2011). Satellites measure recent rates of groundwater depletion in California’s Central Valley. Geophysical Research Letters, 38(3).

47 Farahmand, A., AghaKouchak, A., & Teixeira, J. (2015). A vantage from space can detect earlier drought onset: An approach using relative humidity. Nature Scientific Reports, 5, 8553.

48 Faunt, C.C., Stamos, C.L., Flint, L.E., Wright, M.T., Burgess, M.K., Sneed, M., et al. (2015). Hydrogeology, hydrologic effects of development, and simulation of groundwater flow in the Borrego Valley, San Diego County, California. Scientific Investigations Report 2015‐5150. Sacramento, CA: U.S. Geological Survey.

49 Ferranti, L., & Viterbo, P. (2006). The European summer of 2003: Sensitivity to soil water initial conditions. Journal of Climate, 19(15), 3659–3680.

50 Ferraro, R.R. (1997). Special sensor microwave imager derived global rainfall estimates for climatological applications. Journal of Geophysical Research: Atmospheres, 102(D14), 16715–16735.

51 Fetzer, E.J., Lambrigtsen, B.H., Eldering, A., Aumann, H.H., & Chahine, M.T. (2006). Biases in total precipitable water vapor climatologies from Atmospheric Infrared Sounder and Advanced Microwave Scanning Radiometer. Journal of Geophysical Research: Atmospheres, 111(D9). https://doi.org/10.1029/2005JD006598

52 Feudale, L., & Shukla, J. (2007). Role of Mediterranean SST in enhancing the European heat wave of summer 2003. Geophysical Research Letters, 34(3).

53 Ford, T.W., McRoberts, D.B., Quiring, S.M., & Hall, R.E. (2015). On the utility of in situ soil moisture observations for flash drought early warning in Oklahoma, USA. Geophysical Research Letters, 42(22), 9790–9798.

54 Foster, J.L., Chang, A.T.C., & Hall, D.K. (1997). Comparison of snow mass estimates from a prototype passive microwave snow algorithm, a revised algorithm and a snow depth climatology. Remote Sensing of Environment, 62(2), 132–142.

55 Foster, J.L., Hall, D.K., Eylander, J.B., Riggs, G.A., Nghiem, S.V, Tedesco, M., et al. (2011). A blended global snow product using visible, passive microwave and scatterometer satellite data. International Journal of Remote Sensing, 32(5), 1371–1395.

56 Gebremichael, M. (2010). Framework for satellite rainfall product evaluation. Geophysical Monographs Series, 191, 265–275. https://doi.org/10.1029/2010GM000974

57 Glenn, E.P., Neale, C.M.U., Hunsaker, D.J., & Nagler, P.L. (2011). Vegetation index‐based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems. Hydrological Processes, 25(26), 4050–4062.

58 Gober, P., Sampson, D.A., Quay, R., White, D.D., & Chow, W.T.L. (2016). Urban adaptation to mega‐drought: Anticipatory water modeling, policy, and planning for the urban Southwest. Sustainable Cities and Society, 27, 497–504.

59 Goulden, M. (2018). AmeriFlux US‐SCf Southern California Climate Gradient‐Oak/Pine Forest. AmeriFlux; University of California‐Irvine.

60 Griffin, D., & Anchukaitis, K.J. (2014). How unusual is the 2012–2014 California drought? Geophysical Research Letters, 41(24), 9017–9023.

61 Gruber, A., Scanlon, T., Schalie, R.V.D., Wagner, W., & Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology. Earth System Science Data, 11(2), 717–739.

62 Guan, B., Waliser, D.E., Molotch, N.P., Fetzer, E.J., & Neiman, P.J. (2012). Does the Madden–Julian oscillation influence wintertime atmospheric rivers and snowpack in the Sierra Nevada? Monthly Weather Review, 140(2), 325–342.

63 Gupta, P., Christopher, S. A., Wang, J., Gehrig, R., Lee, Y., & Kumar, N. (2006). Satellite remote sensing of particulate matter and air quality assessment over global cities, 40, 5880–5892. https://doi.org/10.1016/j.atmosenv.2006.03.016

64 Hall, D.K., Riggs, G.A., Salomonson, V.V, DiGirolamo, N.E., & Bayr, K.J. (2002). MODIS snow‐cover products. Remote Sensing of Environment, 83(1–2), 181–194.

65 Han, K.S., Viau, A.A., Kim, Y.S., & Roujean, J.L. (2005). Statistical estimate of the hourly near‐surface air humidity in eastern Canada in merging NOAA/AVHRR and GOES/IMAGER observations. International Journal of Remote Sensing, 26(21), 4763–4784. doi:10.1080/01431160500177711.

66 Hao, Z., & AghaKouchak, A. (2013). Multivariate standardized drought index: a parametric multi‐index model. Advances in Water Resources, 57, 12–18.

67 Hao, Z., & AghaKouchak, A. (2014). A nonparametric multivariate multi‐index drought monitoring framework, Journal of Hydrometeorology, 15(1), 89–101. https://doi.org/10.1175/JHM‐D‐12‐0160.1

68 Hao, Z., AghaKouchak, A., Nakhjiri, N., & Farahmand, A. (2014). Global integrated drought monitoring and prediction system. Nature Scientific Data, 1, 140001.

69 Harpold, A.A., (2016). Diverging sensitivity of soil water stress to changing snowmelt timing in the western US. Advances in Water Resources, 92, 116–129.

70 Harpold, A.A., Dettinger, M., & Rajagopal S. (2017). Defining snow drought and why it matters, Eos, Transactions American Geophysical Union, 98, https://doi.org/10.1029/2017EO068775

71 Harpold, A.A., Molotch, N.P., Musselman, K.N., Bales, R.C., Kirchner, P.B., Litvak, M., & Brooks, P.D. (2014). Soil moisture response to snowmelt timing in mixed‐conifer subalpine forests. Hydrological Processes, 29(12), 2782–2798.

72 Hatchett, B.J., & McEvoy, D.J. (2018). Exploring the origins of snow drought in the northern Sierra Nevada, California. Earth Interactions, 22(2), 1–3. https://doi.org/10.1175/EI‐D‐17‐0027.1

73 He, M., Hogue, T.S., Franz, K.J., Margulis, S.A., & Vrugt, J.A. (2011). Corruption of parameter behavior and regionalization by model and forcing data errors: A Bayesian example using the SNOW17 model. Water Resources Research, 47(7). https://doi.org/10.1029/2010WR009753

74 Hedrick, 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 data over Xinjiang, China. Environmental Earth Sciences, 77(5), 209.

109 Luthcke, S. B., Sabaka, T. J., Loomis, B. D., Arendt, A. A., McCarthy, J. J., & Camp, J. (2013). Antarctica, Greenland and Gulf of Alaska land‐ice evolution from an iterated GRACE global mascon solution. Journal of Glaciology, 59(216), 613–631.

110 Ma, M., Ren, L., Yuan, F., Jiang, S., Liu, Y., Kong, H., & Gong, L. (2014). A new standardized Palmer drought index for hydro‐meteorological use. Hydrological Processes, 28(23), 5645–5661. https://doi.org/10.1002/hyp.10063

111 Madadgar, S., & Moradkhani, H. (2014). Spatio‐temporal drought forecasting within Bayesian networks. Journal of Hydrology, 512, 134–146. https://doi.org/10.1016/j.jhydrol.2014.02.039

112 Madani, K., AghaKouchak, A., & Mirchi, A. (2016). Iran’s socio‐economic drought: challenges of a water‐bankrupt nation. Iranian Studies, 49(6), 997–1016.

113 Mallakpour, I., Sadegh, M., & AghaKouchak, A. (2018). A new normal for streamflow in California in a warming climate: Wetter wet seasons and drier dry seasons. Journal of Hydrology, 567, 203–211.

114 Margulis, S.A., Cortés, G., Girotto, M., & Durand, M. (2016). A Landsat‐era Sierra Nevada snow reanalysis (1985–2015). Journal of Hydrometeorology, 17, 1203–1221. https://doi.org/10.1175/JHM‐D‐15‐0177

115 Margulis, S.A., Wood, E.F., & Troch, P.A. (2006). The terrestrial water cycle: Modeling and data assimilation across catchment scales. Journal of Hydrometeorology, 7(3), 309–311.

116 Martínez‐Fernández, J., González‐Zamora, A., Sánchez, N., Gumuzzio, A., & Herrero‐Jiménez, C.M. (2016). Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil Water Deficit Index. Remote Sensing of Environment, 177, 277–286.

117 Massari, C., Brocca, L., Tarpanelli, A., & Moramarco, T. (2015) Data assimilation of satellite soil moisture into rainfall‐runoff modelling: A complex recipe?. Remote Sensing, 7(9), 11403–11433.

118 Mateus, P., Borma, L. S., da Silva, R. D., Nico, G., & Catalão, J. (2016). Assessment of two techniques to merge ground‐based and TRMM rainfall measurements: a case study about Brazilian Amazon Rainforest. GIScience and Remote Sensing, 53(6), 689–706.

119 Mazdiyasni, O., & AghaKouchak, A. (2015). Substantial increase in concurrent droughts and heatwaves in the United States. Proceedings of the National Academy of Sciences, 112(37), 11484–11489.

120 Mckee, T.B., Doesken, N.J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. American Meteorological Society 8th Conference on Applied Climatology (January, pp. 179–184). https://doi.org/citeulike‐article‐id:10490403

121 McQuigg, J. (1954). A simple index of drought conditions. Weatherwise, 7(3), 64–67.

122 McVicar, T.R., Roderick, M.L., Donohue, R.J., Li, L.T., Van Niel, T.G., Thomas, A., et al. (2012). Global review and synthesis of trends in observed terrestrial near‐surface wind speeds: Implications for evaporation. Journal of Hydrology, 416, 182–205.

123 Mehran, A., AghaKouchak, A., & Phillips, T.J. (2014). Evaluation of CMIP5 continental precipitation simulations relative to satellite‐based gauge‐adjusted observations. Journal of Geophysical Research: Atmospheres, 119(4), 1695–1707.

124 Meng, J., Li, L., Hao, Z., Wang, J., & Shao, Q. (2014). Suitability of TRMM satellite rainfall in driving a distributed hydrological model in the source region of Yellow River. Journal of Hydrology. 509, 320–332. https://doi.org/10.1016/j.jhydrol.2013.11.049

125 Merlin, O., Malbéteau, Y., Notfi, Y., Bacon, S., Khabba, S.E.‐R. S., & Jarlan, L. (2015). Performance metrics for soil moisture downscaling methods: Application to DISPATCH data in central Morocco. Remote Sensing, 7(4), 3783–3807.

126 Miralles, D.G., Teuling, A.J., Van Heerwaarden, C.C., & De Arellano, J.V.G. (2014). Mega‐heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nature Geoscience, 7(5), 345–349. https://doi.org/10.1038/ngeo2141

127 Mishra, A.K., & Singh, V.P. (2010). A review of drought concepts. Journal of Hydrology, 391(1–2), 202–216. https://doi.org/10.1016/j.jhydrol.2010.07.012

128 Mishra, A., Vu, T., Veettil, A. V., & Entekhabi, D. (2017). Drought monitoring with soil moisture active passive (SMAP) measurements. Journal of Hydrology, 552, 620–632.

129 Mladenova, I.E., Bolten, J.D., Crow, W.T., Sazib, N., Cosh, M.H., Tucker, C.J., & Reynolds, C. (2019). Evaluating the operational application of SMAP for global agricultural drought monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9), 3387–3397.

130 Mo, K. C., & Lettenmaier, D. P. (2015). Heat wave flash droughts in decline. Geophysical Research Letters, 42(8), 2823–2829.

131 Modaresi Rad, A., & Khalili, D. (2015). Appropriateness of clustered raingauge stations for spatio‐temporal meteorological drought applications. Water Resources Management, 29(11). https://doi.org/10.1007/s11269‐015‐1051‐6

132 Modaresi Rad, A., Ghahraman, B., Khalili, D., Ghahremani, Z., & Ardakani, S.A. (2017). Integrated meteorological and hydrological drought model: A management tool for proactive water resources planning of semi‐arid regions. Advances in Water Resources, 107, 336–353. https://doi.org/10.1016/j.advwatres.2017.07.007

133 Modaresi Rad, A., Khalili, D., Kamgar‐Haghighi, A.A., Zand‐Parsa, S., & Banimahd, S.A. (2016). Assessment of seasonal characteristics of streamflow droughts under semiarid conditions. Natural Hazards, 82(3). https://doi.org/10.1007/s11069‐016‐2256‐6

134 Moftakhari, H.R., AghaKouchak, A., Sanders, B.F., & Matthew, R.A. (2017). Cumulative hazard: The case of nuisance flooding. Earth’s Future, 5(2), 214–223.

135 Mohanty, S., Jha, M.K., Raul, S.K., Panda, R.K., & Sudheer, K.P. (2015). Using artificial neural network approach for simultaneous forecasting of weekly groundwater levels at multiple sites. Water Resources Management, 29(15), 5521–5532.

136 Moradi, I., Arkin, P., Ferraro, R., Eriksson, P., & Fetzer, E. (2016). Diurnal variation of tropospheric relative humidity in tropical regions. Atmospheric Chemistry and Physics, 16, 6913–6929. https://doi.org/10.5194/acp‐16‐6913‐2016

137 Moradkhani, H. (2008). Hydrologic remote sensing and land surface data assimilation. Sensors, 8(5), 2986–3004.

138 Moran, M.S., Clarke, T.R., Inoue, Y., & Vidal, A. (1994). Estimating crop water deficit using the relation between surface‐air temperature and spectral vegetation index. Remote Sensing of Environment, 49(3), 246–263.

139 Mote, P.W., Rupp, D.E., Li, S., Sharp, D.J., Otto, F., Uhe, P.F., et al. (2016). Perspectives on the causes of exceptionally low 2015 snowpack in the western United States. Geophysical Research Letters, 43(20), 10–980. https://doi.org/10.1002/2016GL069965

140 Mu, Q., Zhao, M., Kimball, J.S., McDowell, N.G., & Running, S.W. (2013). A remotely sensed global terrestrial drought severity index. Bulletin of the American Meteorological Society, 94(1), 83–98.

141 Mueller, B., & Seneviratne, S.I. (2012). Hot days induced by precipitation deficits at the global scale. Proceedings of the National Academy of Sciences, 109(31), 12398–12403. https://doi.org/10.1073/pnas.1204330109

142 Narasimhan, B., & Srinivasan, R. (2005). Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring. Agricultural and Forest Meteorology, 133(1–4), 69–88.

143 Nasrollahi, N., Hsu, K., & Sorooshian, S. (2013). An artificial neural network model to reduce false alarms in satellite precipitation products using MODIS and CloudSat observations. Journal of Hydrometeorology, 14(6), 1872–1883.

144 Njoku, E.G., Jackson, T.J., Lakshmi, V., Chan, T.K., & Nghiem, S.V. (2003). Soil moisture retrieval from AMSR‐E. IEEE Transactions on Geoscience and Remote Sensing, 41(2), 215–229.

145 Núñez, M., Pfister, S., Roux, P., & Antón, A. (2013). Estimating water consumption of potential natural vegetation on global dry lands: Building an LCA framework for green water flows. Environmental Science and Technology, 47(21), 12258–12265.

146 Oguntunde, P.G., Abiodun, B.J., & Lischeid, G. (2011). Rainfall trends in Nigeria, 1901–2000. Journal of Hydrology, 411(3–4), 207–218. https://doi.org/10.1016/j.jhydrol.2011.09.037

147 Olagunju, T.E. (2015). Drought, desertification and the Nigerian environment: A review. Journal of Ecology and the Natural Environment, 7(7), 196–209.

148 Otkin, J.A., Anderson, M.C., Hain, C., Svoboda, M., Johnson, D., Mueller, R., et al. (2016). Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought. Agricultural and Forest Meteorology, 218, 230–242.

149 Owe, M., deJeu, R., Walker, J., & Zukor, D.J. (2001). A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. IEEE Transactions on Geoscience and Remote Sensing, 39(8), 1643–1654.

150 Paneque, P. (2015). Drought management strategies in Spain. Water, 7(12), 6689–6701. https://doi.org/10.3390/w7126655

151 Park, S., Im, J., Park, S., & Rhee, J. (2017). Drought monitoring using high resolution soil moisture through multi‐sensor satellite data fusion over the Korean peninsula. Agricultural and Forest Meteorology, 237–238, 257–269. https://doi.org/10.1016/j.agrformet.2017.02.022

152 Pinzon, J.E., & Tucker, C.J. (2014). A non‐stationary 1981–2012 AVHRR NDVI3g time series. Remote Sensing, 6(8), 6929–6960.

153 Poumadere, M., Mays, C., Le Mer, S., & Blong, R. (2005). The 2003 heat wave in France: dangerous climate change here and now. Risk Analysis: An International Journal, 25(6), 1483–1494.

154 Raei, E., Nikoo, M.R., AghaKouchak, A., Mazdiyasni, O., & Sadegh, M., (2018). GHWR, a multi‐method global heatwave and warm‐spell record and toolbox. Nature Scientific Data, 5, 180206.

155 Rajsekhar, D., Singh, V. P., & Mishra, A. K. (2015). Multivariate drought index: An information theory based approach for integrated drought assessment. Journal of Hydrology, 526, 164–182. https://doi.org/10.1016/j.jhydrol.2014.11.031

156 Ramírez‐Beltrán, N. D., Salazar, C. M., Castro Sánchez, J. M., & González, J. E. (2019) A satellite algorithm for estimating relative humidity, based on GOES and MODIS satellite data. International Journal of Remote Sensing, 21, 1–23. https://doi.org/10.1080/01431161.2019.1629715

157 Rhee, J., Im, J., & Carbone, G.J. (2010b). Monitoring agricultural drought for arid and humid regions using multi‐sensor remote sensing data. Remote Sensing of Environment, 114(12), 2875–2887. https://doi.org/10.1016/j.rse.2010.07.005

158 Rind, D., Goldberg, R., Hansen, J., Rosenzweig, C., & Ruedy, R. (1990). Potential evapotranspiration and the likelihood of future drought. Journal of Geophysical Research: Atmospheres, 95(D7), 9983–10004.

159 Rodell, M., & Famiglietti, J.S. (2002). The potential for satellite‐based monitoring of groundwater storage changes using GRACE: the High Plains aquifer, Central US. Journal of Hydrology, 263(1–4), 245–256.

160 Rott, H., Yueh, S.H., Cline, D.W., Duguay, C., Essery, R., Haas, C., et al. (2010). Cold regions hydrology high‐resolution observatory for snow and cold land processes. Proceedings of the IEEE, 98(5), 752–765.

161 Rouse Jr, J., Haas, R.H., Schell, J.A., & Deering, D.W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite‐1 Symposium, Vol. I: Technical Presentations (NASA SP‐351, compiled and edited by S.C. Freden, E.P. Mercanti, M.A. Becker). Washington, DC: NASA

162 Ryu, J.H., Sohrabi, M., & Acharya, A. (2014). Toward mapping gridded drought indices to evaluate local drought in a rapidly changing global environment. Water Resources Management, 28(11), 3859–3869.

163 Sadegh, M., Love, C., Farahmand, A., Mehran, A., Tourian, M.J., & AghaKouchak, A. (2017). Multi‐sensor remote sensing of drought from space. In V. Lakshmi (Ed.), Remote Sensing of Hydrological Extremes (pp. 219–247). Springer.

164 Sadegh, M., Ragno, E., & AghaKouchak, A. (2017). Multivariate copula analysis toolbox (MvCAT): Describing dependence and underlying uncertainty using a Bayesian framework. Water Resources Research, 53(6), 5166–5183. https://doi.org/10.1002/2016WR020242

165 Sadegh, M., Moftakhari, H., Gupta, H.V, Ragno, E., Mazdiyasni, O., Sanders, B., et al. (2018). Multi‐hazard scenarios for analysis of compound extreme events. Geophysical Research Letters, 45(11), 5470–5480.

166 Sadri, S., Wood, E.F., & Pan, M. (2018). Developing a drought‐monitoring index for the contiguous US using SMAP. Hydrology and Earth System Sciences, 22(12), 6611–6626.

167 Sánchez, N., González‐Zamora, Á., Piles, M., & Martínez‐Fernández, J. (2016). A new Soil Moisture Agricultural Drought Index (SMADI) integrating MODIS and SMOS products: a case of study over the Iberian Peninsula. Remote Sensing, 8(4), 287.

168 Santi, E., Paloscia, S., Pettinato, S., Brocca, L., Ciabatta, L., & Entekhabi, D. (2018). Integration of microwave data from SMAP and AMSR2 for soil moisture monitoring in Italy. Remote Sensing of Environment, 212, 21–30.

169 Santos, J.F., Pulido‐Calvo, I., & Portela, M.M. (2010). Spatial and temporal variability of droughts in Portugal. Water Resources Research, 46(3). https://doi.org/10.1029/2009WR008071

170 Sapiano, M.R.P., & Arkin, P.A. (2009). An intercomparison and validation of high‐resolution satellite precipitation estimates with 3‐hourly gauge data. Journal of Hydrometeorology, 10(1), 149–166.

171 Save, H., Bettadpur, S., & Tapley, B. D. (2012). Reducing errors in the GRACE gravity solutions using regularization. Journal of Geodesy, 86(9), 695–711.

172 Scaini, A., Sánchez, N., Vicente‐Serrano, S. M., & Martínez‐Fernández, J. (2015). SMOS‐derived soil moisture anomalies and drought indices: A comparative analysis using in situ measurements. Hydrological Processes, 29(3), 373–383. https://doi.org/10.1002/hyp.10150

173 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(4). https://doi.org/10.1029/2011WR011312

174 Seager, R., & Hoerling, M. (2014). Atmosphere and ocean origins of North American droughts. Journal of Climate, 27(12), 4581–4606.

175 Senay, G. B. (2008). Modeling landscape evapotranspiration by integrating land surface phenology and a water balance algorithm. Algorithms, 1(2), 52–68.

176 Shafer, B.A., & Dezman, L.E. (1982). Development of a Surface Water Supply Index (SWSI) to assess the severity of drought conditions in snow pack runoff areas. Western Snow Conference (pp. 164–175). Reno, NV: Colorado State University.

177 Sheffield, J., Goteti, G., Wen, F., & Wood, E.F. (2004). A simulated soil moisture based drought analysis for the United States. Journal of Geophysical Research: Atmospheres, 109(D24). https://doi.org/10.1029/2004JD005182

178 Sherwood, S.C., Ingram, W., Tsushima, Y., Satoh, M., Roberts, M., Vidale, P.L., & O’Gorman, P.A. (2010). Relative humidity changes in a warmer climate. Journal of Geophysical Research: Atmospheres, 115(D9). https://doi.org/10.1029/2009JD012585

179 Shiferaw, B., Tesfaye, K., Kassie, M., Abate, T., Prasanna, B. M., & Menkir, A. (2014). Managing vulnerability to drought and enhancing livelihood resilience in sub‐Saharan Africa: Technological, institutional and policy options. Weather and Climate Extremes, 3, 67–79.

180 Shojaeezadeh, S.A., Nikoo, M.R., McNamara, J.P., AghaKouchak, A., & Sadegh, M. (2018). Stochastic modeling of suspended sediment load in alluvial rivers. Advances in Water Resources, 119, 188–196.

181 Shojaeezadeh, S.A., Nikoo, M.R., Mirchi, A., Mallakpour, I., AghaKouchak, A., & Sadegh, M. (2019). Probabilistic hazard assessment of contaminated sediment in rivers. Science of The Total Environment, 703, 134875.

182 Silva, C.V.J., Aragão, L.E.O.C., Barlow, J., Espirito‐Santo, F., Young, P.J., Anderson, L.O., et al. (2018). Drought‐induced Amazonian wildfires instigate a decadal‐scale disruption of forest carbon dynamics. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1760), 20180043.

183 Simpson, J.J., Stitt, J.R., & Sienko, M. (1998). Improved estimates of the areal extent of snow cover from AVHRR data. Journal of Hydrology, 204(1–4), 1–23.

184 Sohrabi, M.M., Ryu, J.H., Abatzoglou, J., & Tracy, J. (2015). Development of soil moisture drought index to characterize droughts. Journal of Hydrologic Engineering, 20(11), 4015025.

185 Sorooshian, A., Murphy, S.M., Hersey, S., Gates, H., Padro, L.T., Nenes, A., et al. (2008). Comprehensive airborne characterization of aerosol from a major bovine source. Atmospheric Chemistry and Physics, 8(17), 5489–5520.

186 Sorooshian, S., Hsu, K.‐L., Gao, X., Gupta, H.V., Imam, B., & Braithwaite, D. (2000). Evaluation of PERSIANN system satellite‐based estimates of tropical rainfall. Bulletin of the American Meteorological Society, 81(9), 2035–2046.

187 Staudinger, M., Stahl, K., & Seibert, J. (2014). A drought index accounting for snow. Water Resources Research, 50(10), 7861–7872.

188 Svoboda, M., LeComte, D., Hayes, M., Heim, R., Gleason, K., Angel, J., et al. (2002). The drought monitor. Bulletin of the American Meteorological Society, 83(8), 1181–1190.

189 Tadesse, T., Brown, J.F., & Hayes, M.J. (2005). A new approach for predicting drought‐related vegetation stress: Integrating satellite, climate, and biophysical data over the US central plains. ISPRS Journal of Photogrammetry and Remote Sensing, 59(4), 244–253.

190 Takada, M., Mishima, Y., & Natsume, S. (2009). Estimation of surface soil properties in peatland using ALOS/PALSAR. Landscape and Ecological Engineering, 5(1), 45–58.

191 Taravatrooy, N., Nikoo, M.R., Sadegh, M., & Parvinnia, M., (2018). A hybrid clustering‐fusion methodology for land subsidence estimation. Natural Hazards, 94(2), 905–926.

192 Taufik, M., Torfs, P.JJ. F., Uijlenhoet, R., Jones, P.D., Murdiyarso, D., & Van Lanen, H.A.J. (2017). Amplification of wildfire area burnt by hydrological drought in the humid tropics. Nature Climate Change, 7(6), 428.

193 Thomas, B.F., Famiglietti, J.S., Landerer, F.W., Wiese, DN., Molotch, N. P., & Argus, D.F. (2017). GRACE Groundwater Drought Index: Evaluation of California Central Valley groundwater drought. Remote Sensing of Environment, 198, 384–392. https://doi.org/10.1016/j.rse.2017.06.026

194 Thornthwaite, C.W. (1931). The climates of North America: according to a new classification. Geographical Review, 21(4), 633–655.

195 Thornthwaite, C.W. (1948). An approach toward a rational classification of climate. Geographical Review, 38(1), 55–94.

196 Tian, B., Soden, B.J., & Wu, X. (2004). Diurnal cycle of convection, clouds, and water vapor in the tropical upper troposphere: Satellites versus a general circulation model. Journal of Geophysical Research: Atmospheres, 109(D10). https://doi.org/10.1029/2003JD004117

197 Tian, Y., Peters‐Lidard, C.D., Eylander, J.B., Joyce, R.J., Huffman, G.J., Adler, R.F., et al. (2009). Component analysis of errors in satellite‐based precipitation estimates. Journal of Geophysical Research: Atmospheres, 114(D24). https://doi.org/10.1029/2009JD011949

198 Tigkas, D., Vangelis, H., & Tsakiris, G. (2017). An enhanced effective reconnaissance drought index for the characterisation of agricultural drought. Environmental Processes, 4, 137–148. https://doi.org/10.1007/s40710‐017‐0219‐x

199 Tsakiris, G., Pangalou, D., & Vangelis, H. (2007). Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resources Management, 21(5), 821–833.

200 Tsakiris, G., & Vangelis, H. (2005). Establishing a drought index incorporating evapotranspiration. European Water, 9/10, 3–11.

201 Tucker, C.J., Pinzon, J.E., Brown, M.E., Slayback, D.A., Pak, E.W., Mahoney, R., et al. (2005). An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing, 26(20), 4485–4498.

202 Turk, F.J., Rohaly, G.D., Hawkins, J., Smith, E.A., Marzano, F.S., Mugnai, A., & Levizzani, V. (1999). Meteorological applications of precipitation estimation from combined SSM/I, TRMM and infrared geostationary satellite data. In P. Pampaloni (Ed.), Microwave radiometry and remote sensing of the Earth’s surface and atmosphere (pp. 353–363). Routledge

203 Twohy, C.H., Coakley Jr, J.A., & Tahnk, W.R. (2009). Effect of changes in relative humidity on aerosol scattering near clouds. Journal of Geophysical Research: Atmospheres, 114(D5). https://doi.org/10.1029/2008JD010991

204 U.S. Global Change Research Program, 2018. Fourth national climate assessment. Washington, DC.

205 Utah Division of Water Resources, 2007. Drought in Utah: Learning from the past‐preparing for the future. http://www.water.utah.gov/DroughtReport/binder2A.pdf, accessed July 2007

206 Van Loon, A.F., Gleeson, T., Clark, J., Van Dijk, A.I.J.M., Stahl, K., Hannaford, J., et al. (2016). Drought in the Anthropocene. Nature Geoscience, 9(2), 89.

207 Van Loon, A.F., & Van Lanen, H.A.J. (2012). A process‐based typology of hydrological drought. Hydrology and Earth System Sciences, 16(7), 1915–1946.

208 Vergados, P., Mannucci, A.J., Ao, C.O., Jiang, J.H., & Su, H. (2015). On the comparisons of tropical relative humidity in the lower and middle troposphere among COSMIC radio occultations and MERRA and ECMWF data sets. Atmospheric Measure Techniques, 8, 1789–1797. https://doi.org/10.5194/amt‐8‐1789‐2015

209 Vicente‐Serrano, S.M., Beguería, S., & López‐Moreno, J.I. (2010). A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of Climate, 23(7), 1696–1718.

210 Vicente‐Serrano, S. M., Cabello, D., Tomás‐Burguera, M., Martín‐Hernández, N., Beguería, S., Azorin‐Molina, C., & Kenawy, A.El. (2015). Drought variability and land degradation in semiarid regions: Assessment using remote sensing data and drought indices (1982–2011). Remote Sensing, 7(4), 4391–4423.

211 Wada, Y. (2013). Human and climate impacts on global water resources. PhD thesis, University Utrecht.

212 Wagner, W., Noll, J., Borgeaud, M., & Rott, H. (1999). Monitoring soil moisture over the Canadian Prairies with the ERS scatterometer. IEEE Transactions on Geoscience and Remote Sensing, 37(1), 206–216.

213 Wahr, J., Swenson, S., & Velicogna, I. (2006). Accuracy of GRACE mass estimates. Geophysical Research Letters, 33(6). https://doi.org/10.1029/2005GL025305

214 Walker, A.E., & Goodison, B.E. (1993). Discrimination of a wet snow cover using passive microwave satellite data. Annals of Glaciology, 17, 307–311.

215 Wang, L., & Qu, J.J. (2009). Satellite remote sensing applications for surface soil moisture monitoring: A review. Frontiers of Earth Science in China, 3(2), 237–247. https://doi.org/10.1007/s11707‐009‐0023‐7

216 Wang, S., Mo, X., Hu, S., Liu, S., & Liu, Z. (2018). Assessment of droughts and wheat yield loss on the North China Plain with an aggregate drought index (ADI) approach. Ecological Indicators, 87, 107–116. https://doi.org/10.1016/j.ecolind.2017.12.047

217 Wang, X.Y., Wang, J., Jiang, Z.Y., Li, H.Y., & Hao, X.H. (2015). An effective method for snow‐cover mapping of dense coniferous forests in the Upper Heihe River Basin using Landsat Operational Land Imager Data. Remote Sensing, 7(12), 17246–17257. https://doi.org/10.3390/rs71215882

218 Waseem, M., Ajmal, M., & Kim, T. (2015). Development of a new composite drought index for multivariate drought assessment. Journal of Hydrology, 527, 30–37. https://doi.org/10.1016/j.jhydrol.2015.04.044

219 Watkins, M.M., Wiese, D.N., Yuan, D., Boening, C., & Landerer, F.W. (2015). Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons. Journal of Geophysical Research: Solid Earth, 120(4), 2648–2671.

220 Whitcraft, A.K., Becker‐Reshef, I., & Justice, C.O. (2015). A framework for defining spatially explicit earth observation requirements for a global agricultural monitoring initiative (GEOGLAM). Remote Sensing, 7(2), 1461–1481.

221 Wilhite, D.A., & Buchanan‐Smith, M. (2005). Drought as hazard: understanding the natural and social context. In D.A. Wilhite (Ed.), Drought and water crises: Science, technology, and management issues (pp. 3–29). Boca Raton, FL: CRC Press.

222 Wilhite, D.A., & Glantz, M.H. (1985). Understanding: the drought phenomenon: the role of definitions. Water international, 10(3), 111–120.

223 Williams, A.P., Seager, R., Abatzoglou, J.T., Cook, B.I., Smerdon, J.E., & Cook, E.R. (2015). Contribution of anthropogenic warming to California drought during 2012–2014. Geophysical Research Letters, 42(16), 6819–6828.

224 WMO, 1975. Drought and agriculture (WMO Technical Note 138). Geneva: World Meteorological Organization.

225 WMO, 2006. Systematic observation requirements for satellite‐based products for climate: Supplemental details to the satellite‐based component of the implementation plan for the global observing system for climate in support of the UNFCCC (WMO/TD‐ No. 1338; GCOS‐ No. 107). Geneva: World meteorological Organization.

226 WMO, 2009. Inter‐regional workshop on indices and early warning systems for drought (8–11 December). Lincoln, Nebraska: World Meteorological Organization.

227 Wolff, D.B., & Fisher, B.L. (2008). Comparisons of instantaneous TRMM ground validation and satellite rain‐rate estimates at different spatial scales. Journal of Applied Meteorology and Climatology, 47(8), 2215–2237.

228 Xie, P., & Arkin, P.A. (1997). Global precipitation: A 17‐year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bulletin of the American Meteorological Society, 78(11), 2539–2558.

229 Xu, H., Bailey, J.O., Barrett, E.C., & Kelly, R.E.J. (1993). Monitoring snow area and depth with integration of remote sensing and GIS. International Journal of Remote Sensing, 14(17), 3259–3268.

230 Xu, R., Tian, F., Yang, L., Hu, H., Lu, H., & Hou, A. (2017). Ground validation of GPM IMERG and TRMM 3B42V7 rainfall products over southern Tibetan Plateau based on a high‐density rain gauge network. Journal of Geophysical Research: Atmospheres, 122(2), 910–924.

231 Yang, Y., & Shang, S. (2013). A hybrid dual‐source scheme and trapezoid framework‐based evapotranspiration model (HTEM) using satellite images: Algorithm and model test. Journal of Geophysical Research: Atmospheres, 118(5), 2284–2300.

232 Yao, Y., Liang, S., Qin, Q., & Wang, K. (2010). Monitoring drought over the conterminous United States using MODIS and NCEP Reanalysis‐2 data. Journal of Applied Meteorology and Climatology, 49(8), 1665–1680.

233 Zacharias, S., Bogena, H., Samaniego, L., Mauder, M., Fuß, R., Pütz, T., et al. (2011). A network of terrestrial environmental observatories in Germany. Vadose Zone Journal, 10(3), 955–973.

234 Zaitchik, B.F., Rodell, M., & Reichle, R.H. (2008). Assimilation of GRACE terrestrial water storage data into a land surface model: Results for the Mississippi River basin. Journal of Hydrometeorology, 9(3), 535–548.

235 Zampieri, M., D’Andrea, F., Vautard, R., Ciais, P., de Noblet‐Ducoudré, N., & Yiou, P. (2009). Hot European summers and the role of soil moisture in the propagation of Mediterranean drought. Journal of Climate, 22(18), 4747–4758.

236 Zamuda, C., Mignone, B., Bilello, D., Hallett, K. C., Lee, C., Macknick, J., et al. (2013). US energy sector vulnerabilities to climate change and extreme weather. Washington, DC: U.S. Department of Energy.

237 Zhang, A., & Jia, G. (2013). Monitoring meteorological drought in semiarid regions using multi‐sensor microwave remote sensing data. Remote Sensing of Environment, 134, 12–23. https://doi.org/10.1016/j.rse.2013.02.023

238 Zhang, X., Chen, N., Li, J., Chen, Z., & Niyogi, D. (2017). Multi‐sensor integrated framework and index for agricultural drought monitoring. Remote Sensing of Environment, 188, 141–163. https://doi.org/10.1016/j.rse.2016.10.045

239 Zreda, M., Shuttleworth, W.J., Zeng, X., Zweck, C., Desilets, D., Franz, T., & Rosolem, R. (2012). COSMOS: The cosmic‐ray soil moisture observing system. Hydrology and Earth System Sciences, 16(11), 4079–4099.