Abstract
Drought is a slowly developing process and usually begins to impact a region without much warning once the water deficit reaches a certain threshold. Predicting the drought a few months in advance will benefit a variety of sectors for drought planning and preparedness. In response to the National Integrated Drought Information System (NIDIS), the Princeton land surface hydrology group has been working on drought monitoring and forecasting for over 10 years and has developed a seasonal drought forecasting system based on global climate forecast models and a large-scale land surface hydrology model. This chapter will showcase the performances of the system in predicting soil moisture drought area, frequency, and severity over the Conterminous United States (CONUS) at seasonal scales; discuss about the challenges in forecasting streamflow for hydrologic drought; and provide an outlook for future developments and applications.
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References
J. Kam, J. Sheffield, X. Yuan, E.F. Wood, Did a skilful prediction of sea surface temperatures help or hinder forecasting of the 2012 Midwestern US drought? Environ. Res. Lett. 9, 034005 (2014). https://doi.org/10.1088/1748-9326/9/3/034005
B.P. Kirtman et al., The North American MultiModels Ensemble (NMME): phase1 seasonal to interannual prediction, phase2 toward developing intraseasonal prediction. Bull. Am. Meteorol. Soc 95, 585–601 (2014). https://doi.org/10.1175/BAMS-D-12-00050.1
X. Liang, E.F. Wood, D.P. Lettenmaier, Surface soil moisture parameterization of the VIC-2L model: evaluation and modifications. Global Planet. Change 13, 195–206 (1996)
D. Lohmann et al., Streamflow and water balance intercomparisons of four land surface models in the North American Land Data Assimilation System project. J. Geophys. Res. 109, D07S91 (2004). https://doi.org/10.1029/2003JD003517
L. Luo, E.F. Wood, Monitoring and predicting the 2007 U.S. drought. Geophys. Res. Lett. 34, L22702 (2007). https://doi.org/10.1029/2007GL031673
L. Luo, E.F. Wood, Use of Bayesian merging techniques in a multimodel seasonal hydrologic ensemble prediction system for the eastern United States. J. Hydrometeorol. 9, 866–884 (2008)
L. Luo, E.F. Wood, M. Pan, Bayesian merging of multiple climate model forecasts for seasonal hydrological predictions. J. Geophys. Res. 112, D10102 (2007). https://doi.org/10.1029/2006JD007655
J.K. Roundy, C.R. Ferguson, E.F. Wood, Impact of land-atmospheric coupling in CFSv2 on drought prediction. Clim. Dyn. (2014). https://doi.org/10.1007/s00382-01301982-7
J. Sheffield, E.F.Wood, N. Chaney, K. Guan, S. Sadri, X. Yuan, L. Olang, A. Amani, A. Ali, S. Demuth, L. Ogallo, A drought monitoring and forecasting system for sub-Sahara African water resources and food security. Bull. Amer. Meteor. Soc 95, 861–882 (2014). https://doi.org/10.1175/BAMS-D-12-00124.1
D.S. Wilks, Statistical Methods in the Atmospheric Sciences. International Geophysics Series, vol. 100, 3rd edn. (Academic, Oxford/Waltham, 2011), 676pp
E.F. Wood, D.P. Lettenmaier, X. Liang, B. Nijssen, S.W. Wetzel, Hydrological modeling of continental-scale basins. Annu. Rev. Earth Planet. Sci. 25, 279–300 (1997)
E.F. Wood et al., Hyperresolution global land surface modeling: meeting a grand challenge for monitoring Earth’s terrestrial water. Water Resour. Res. 47, W05301 (2011). https://doi.org/10.1029/2010WR010090
X. Yuan, E.F. Wood, Downscaling precipitation or bias-correcting streamflow? Some implications for coupled general circulation model (CGCM)-based ensemble seasonal hydrologic forecast. Water Resour. Res. 48, W12519 (2012a). https://doi.org/10.1029/2012WR012256
X. Yuan, E.F. Wood, On the clustering of climate models in ensemble seasonal forecasting. Geophys. Res. Lett. 39, L18701 (2012b). https://doi.org/10.1029/2012GL052735
X. Yuan, E.F. Wood, Multimodel seasonal forecasting of global drought onset. Geophys. Res. Lett. 40, 4900–4905 (2013). https://doi.org/10.1002/grl.50949
X. Yuan, E.F. Wood, L. Luo, M. Pan, A first look at Climate Forecast System version 2 (CFSv2) for hydrological seasonal prediction. Geophys. Res. Lett. 38, L13402 (2011). https://doi.org/10.1029/2011GL047792
X. Yuan, E.F. Wood, J.K. Roundy, M. Pan, CFSv2-based seasonal hydroclimatic forecasts over conterminous United States. J. Climate 26, 4828–4847 (2013a). https://doi.org/10.1175/JCLI-D-12-00683.1
X. Yuan, E.F. Wood, N.W. Chaney, J. Sheffield, J. Kam, M. Liang, K. Guan, Probabilistic seasonal forecasting of African drought by dynamical models. J. Hydrometeor. 14, 1706–1720 (2013b). https://doi.org/10.1175/JHM-D-13-054.1
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Wood, E.F., Yuan, X., Roundy, J.K., Pan, M., Luo, L. (2019). Seasonal Drought Forecasting on the Example of the USA. In: Duan, Q., Pappenberger, F., Wood, A., Cloke, H., Schaake, J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39925-1_52
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