Global Drought and Flood. Группа авторов
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,