Articles | Volume 21, issue 11
https://doi.org/10.5194/hess-21-5477-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-5477-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
07 Nov 2017
Research article |
| 07 Nov 2017
Understanding and seasonal forecasting of hydrological drought in the Anthropocene
Key Laboratory of Regional Climate-Environment for Temperate East Asia (RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Miao Zhang
Key Laboratory of Regional Climate-Environment for Temperate East Asia (RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 100049, China
Linying Wang
Key Laboratory of Regional Climate-Environment for Temperate East Asia (RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Tian Zhou
Pacific Northwest National Laboratory, Richland, WA 99352, USA
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Cited articles
AghaKouchak, A., Feldman, D., Hoerling, M., Huxman, T., and Lund, J.: Water and climate: Recognize anthropogenic drought, Nature, 524, 409–411, https://doi.org/10.1038/524409a, 2015.
Barker, L. J., Hannaford, J., Chiverton, A., and Svensson, C.: From meteorological to hydrological drought using standardised indicators, Hydrol. Earth Syst. Sci., 20, 2483–2505, https://doi.org/10.5194/hess-20-2483-2016, 2016.
Biancamaria, S., Lettenmaier, D. P., and Pavelsky, T. M.: The SWOT mission and its capabilities for land hydrology, Surv. Geophys., 37, 307–337, https://doi.org/10.1007/s10712-015-9346-y, 2016.
Bloomfield, J. P., Marchant, B. P., Bricker, S. H., and Morgan, R. B.: Regional analysis of groundwater droughts using hydrograph classification, Hydrol. Earth Syst. Sci., 19, 4327–4344, https://doi.org/10.5194/hess-19-4327-2015, 2015.
Dutra, E., Magnusson, L., Wetterhall, F., Cloke, H. L., Balsamo, G., Boussetta, S., and Pappenberger, F.: The 2010–2011 drought in the Horn of African ECMWF reanalysis and seasonal forecast products, Int. J. Climatol., 33, 1720–1729, https://doi.org/10.1002/joc.3545, 2012.
Folland, C. K., Hannaford, J., Bloomfield, J. P., Kendon, M., Svensson, C., Marchant, B. P., Prior, J., and Wallace, E.: Multi-annual droughts in the English Lowlands: a review of their characteristics and climate drivers in the winter half-year, Hydrol. Earth Syst. Sci., 19, 2353–2375, https://doi.org/10.5194/hess-19-2353-2015, 2015.
Haslinger, K., Koffler, D., Schoner, W., and Laaha, G.: Exploring the link between meteorological drought and streamflow: Effects of climate catchment interaction, Water Resour. Res., 50, 2468–2487, https://doi.org/10.1002/2013WR015051, 2014.
Hoerling, M. and Kumar, A.: The perfect ocean for drought, Science, 299, 691–694, https://doi.org/10.1126/science.1079053, 2003.
Hong, S. Y. and Kalnay, E.: Role of sea surface temperature and soil-moisture feedback in the 1998 Oklahoma–Texas drought, Nature, 408, 842–844, https://doi.org/10.1038/35048548, 2000.
Kirtman, B. P., Min, D., Infanti, J. M., Kinter, J. L., Paolino, D. A., Zhang, Q., van den Dool, H., Saha, S., Mendez, M. P., Becker, E., Peng, P., Tripp, P., Huang, J., DeWitt, D. G., Tippett, M. K., Barnston, A. G., Li, S., Rosati, A., Schubert, S. D., Rienecker, M., Suarez, M., Li, Z. E., Marshak, J., Lim, Y.-K., Tribbia, J., Pegion, K., Merryfield, W. J., Denis, B., and Wood, E. F.: The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction, B. Am. Meteorol. Soc., 95, 585–601, https://doi.org/10.1175/BAMS-D-12-00050.1, 2014.
Koster, R. D., Mahanama, S. P. P., Livneh, B., Lettenmaier, D. P., and Reichle, R. H.: Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow, Nat. Geosci., 3, 613–616, https://doi.org/10.1038/ngeo944, 2010.
Kumar, R., Musuuza, J. L., Van Loon, A. F., Teuling, A. J., Barthel, R., Ten Broek, J., Mai, J., Samaniego, L., and Attinger, S.: Multiscale evaluation of the Standardized Precipitation Index as a groundwater drought indicator, Hydrol. Earth Syst. Sci., 20, 1117–1131, https://doi.org/10.5194/hess-20-1117-2016, 2016.
Liang, X., Wood, E. F., and Lettenmaier, D. P.: Surface soil moisture parameterization of the VIC-2L model: Evaluation and modifications, Global Planet. Change, 13, 195–206, https://doi.org/10.1016/0921-8181(95)00046-1, 1996.
López-Moreno, J. I., Vicente-Serrano, S. M., Beguería, S., García-Ruiz, J. M., Portela, M. M., and Almeida, A. B.: Dam effects on droughts magnitude and duration in a transboundary basin: The Lower River Tagus, Spain and Portugal, Water Resour. Res., 45, W02405, https://doi.org/10.1029/2008WR007198, 2009.
Luo, L. and Wood, E. F.: Monitoring and predicting the 2007 U.S. drought, Geophys. Res. Lett., 34, L22702, https://doi.org/10.1029/2007GL031673, 2007.
Ma, F., Yuan, X., and Ye, A.: Seasonal drought predictability and forecast skill over China, J. Geophys. Res.-Atmos., 120, 8264–8275, https://doi.org/10.1002/2015JD023185, 2015.
McKee, T. B., Doesken, N. J., and Kleist, J.: The relationship of drought frequency and duration to time scales, Preprints, Eighth Conf. on Applied Climatology, 17–22 January 1993, Anaheim, CA, USA, Amer. Meteor. Soc., 179–184, 1993.
Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P., and Stouffer, R. J.: Stationarity is dead: Whither water management?, Science, 319, 573–574, https://doi.org/10.1126/science.1151915, 2008.
Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. M., Lettenmaier, D. P., Stouffer, R. J., Dettinger, M. D., and Krysanova, V.: On Critiques of “Stationarity is Dead: Whither Water Management?”, Water Resour. Res., 51, 7785–7789, https://doi.org/10.1002/2015WR017408, 2015.
Mo, K., Shukla, S., Lettenmaier, D. P., and Chen, L.: Do Climate Forecast System (CFSv2) forecasts improve seasonal soil moisture prediction?, Geophys. Res. Lett., 39, L23703, https://doi.org/10.1029/2012GL053598, 2012.
Niu, J., Chen, J., and Sun, L.: Exploration of drought evolution using numerical simulations over the Xijiang (West River) basin in South China, J. Hydrol., 526, 68–77, https://doi.org/10.1016/j.jhydrol.2014.11.029, 2015.
Piao, S., Ciais, P., Huang, Y., Shen, Z., Peng, S., Li, J., Zhou, L., Liu, H., Ma, Y., Ding, Y., Friedlingstein, P., Liu, C., Tan, K., Yu, Y., Zhang, T., and Fang, J.: The impacts of climate change on water resources and agriculture in China, Nature, 467, 43–51, https://doi.org/10.1038/nature09364, 2010.
Pozzi, W., Sheffield, J., Stefanski, R., Cripe, D., Pulwarty, R., Vogt, J. V., Heim, R. R., Brewer, M. J., Svoboda, M., Westerhoff, R., van Dijk, A. I. J. M., Lloyd-Hughes, B., Pappenberger, F., Werner, M., Dutra, E., Wetterhall, F., Wagner, W., Schubert, S., Mo, K., Nicholson, M., Bettio, L., Nunez, L., van Beek, R., Bierkens, M., de Goncalves, L. G. G., de Mattos, J. G. Z., and Lawford, R.: Towards global drought early warning capability: Expanding international cooperation for the development of a framework for global drought monitoring and forecasting, B. Am. Meteorol. Soc., 94, 776–785, https://doi.org/10.1175/BAMS-D-11-00176.1, 2013.
Rimkus, E., Stonevičius, E., Korneev, V., Kažys, J., Valiuškevičius, G., and Pakhomau, A.: Dynamics of meteorological and hydrological droughts in the Neman river basin, Environ. Res. Lett., 8, 045014, https://doi.org/10.1088/1748-9326/8/4/045014, 2013.
Sheffield, J., Wood, E. F., Chaney, N., Guan, K., Sadri, S., Yuan, X., Olang, L., Amani, A., Ali, A., Demuth, S., and Ogallo, L.: A Drought Monitoring and Forecasting System for Sub-Sahara African Water Resources and Food Security, B. Am. Meteorol. Soc., 95, 861–882, https://doi.org/10.1175/BAMS-D-12-00124.1, 2014.
Shukla, S. and Wood, A. W.: Use of a standardized runoff index for characterizing hydrologic drought, Geophys. Res. Lett., 35, L02405, https://doi.org/10.1029/2007GL032487, 2008.
Sikder, S., Chen, X., Hossain, F., Roberts, J. B., Robertson, F., Shum, C. K., and Turk, F. J.: Are general circulation models ready for operational streamflow forecasting for water management in the Ganges and Brahmaputra river basins?, J. Hydrometeorol., 17, 195–210, https://doi.org/10.1175/JHM-D-14-0099.1, 2016.
Staudinger, M., Weiler, M., and Seibert, J.: Quantifying sensitivity to droughts – an experimental modeling approach, Hydrol. Earth Syst. Sci., 19, 1371–1384, https://doi.org/10.5194/hess-19-1371-2015, 2015.
Stoelzle, M., Stahl, K., Morhard, A., and Weiler, M.: Streamflow sensitivity to drought scenarios in catchments with different geology, Geophys. Res. Lett., 41, 6174–6183, https://doi.org/10.1002/2014GL061344, 2014.
Teuling, A. J., Van Loon, A. F., Seneviratne, S. I., Lehner, I., Aubinet, M., Heinesch, B., Bernhofer, C., Grünwald, T., Prasse, H., and Spank, U.: Evapotranspiration amplifies European summer drought, Geophys. Res. Lett. 40, 2071–2075, https://doi.org/10.1002/grl.50495, 2013.
Thober, S., Kumar, R., Sheffield, J., Mai, J., Schafer, D., and Samaniego, L.: Seasonal soil moisture drought prediction over Europe using the North American Multi-Model Ensemble (NMME), J. Hydrometeorol., 16, 2329–2344, https://doi.org/10.1175/JHM-D-15-0053.1, 2015.
Trambauer, P., Werner, M., Winsemius, H. C., Maskey, S., Dutra, E., and Uhlenbrook, S.: Hydrological drought forecasting and skill assessment for the Limpopo River basin, southern Africa, Hydrol. Earth Syst. Sci., 19, 1695–1711, https://doi.org/10.5194/hess-19-1695-2015, 2015.
Van Loon, A. F., Van Huijgevoort, M. H. J., and Van Lanen, H. A. J.: Evaluation of drought propagation in an ensemble mean of large-scale hydrological models, Hydrol. Earth Syst. Sci., 16, 4057–4078, https://doi.org/10.5194/hess-16-4057-2012, 2012.
Van Loon, A. F., Gleeson, T., Clark, J., Van Dijk, A. I. J. M., Stahl, K., Hannaford, J., Di Baldassarre, G., Teuling, A. J., Tallaksen, L. M., Uijlenhoet, R., Hannah, D. M., Sheffield, J., Svoboda, M., Verbeiren, B., Wagener, T., Rangecroft, S., Wanders, N., and VanLanen, H. A. J.: Drought in the Anthropocene, Nat. Geosci., 9, 89–91, https://doi.org/10.1038/ngeo2646, 2016a.
Van Loon, A. F., Stahl, K., Di Baldassarre, G., Clark, J., Rangecroft, S., Wanders, N., Gleeson, T., Van Dijk, A. I. J. M., Tallaksen, L. M., Hannaford, J., Uijlenhoet, R., Teuling, A. J., Hannah, D. M., Sheffield, J., Svoboda, M., Verbeiren, B., Wagener, T., and Van Lanen, H. A. J.: Drought in a human-modified world: reframing drought definitions, understanding, and analysis approaches, Hydrol. Earth Syst. Sci., 20, 3631–3650, https://doi.org/10.5194/hess-20-3631-2016, 2016b.
Vicente-Serrano, S. M. and López-Moreno, J. I.: Hydrological response to different time scales of climatological drought: an evaluation of the Standardized Precipitation Index in a mountainous Mediterranean basin, Hydrol. Earth Syst. Sci., 9, 523–533, https://doi.org/10.5194/hess-9-523-2005, 2005.
Villarini, G. F., Serinaldi, F., Smith, J. A., and Krajewski W. F.: On the stationarity of annual flood peaks in the continental United States during the 20th century, Water Resour. Res., 45, W08417, https://doi.org/10.1029/2008WR007645, 2009.
Wada, Y., van Beek, L. P. H., Wanders, N., and Bierkens, M. F. P.: Human water consumption intensifies hydrological drought worldwide, Environ. Res. Lett., 8, 034036, https://doi.org/10.1088/1748-9326/8/3/034036, 2013.
Wada, Y., Wisser, D., and Bierkens, M. F. P.: Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources, Earth Syst. Dynam., 5, 15–40, https://doi.org/10.5194/esd-5-15-2014, 2014.
Wen, L., Rogers, K., Ling, J., and Saintilan, N.: The impacts of river regulation and water diversion on the hydrological drought characteristics in the Lower Murrumbidgee River, Australia, J. Hydrol., 405, 382–391, 2011.
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, Int. Geophys. Ser., 3rd ed., vol. 100, 676 pp., Academic, San Diego, Calif., USA, 2011.
Wood, A. W. and Lettenmaier, D. P.: An ensemble approach for attribution of hydrologic prediction uncertainty, Geophys. Res. Lett., 35, L14401, https://doi.org/10.1029/2008GL034648, 2008.
Wood, A. W., Mauer, E. P., Kumar, A., and Lettenmaier, D.P.: Long-range experimental hydrologic forecasting for the eastern United States, J. Geophys. Res., 107, 4429, https://doi.org/10.1029/2001JD000659, 2002.
Wood, A. W., Hopson, T., Newman, A., Brekke, L., Arnold, J., and Clark, M.: Quantifying streamflow forecast skill elasticity to initial condition and climate prediction skill, J. Hydrometeorol., 17, 651–668, https://doi.org/10.1175/JHM-D-14-0213.1, 2016.
Wood, E. F., Schubert, S. D., Wood, A. W., Peters-Lidard, C. D., Mo, K. C., Mariotti, A., and Pulwarty, R. S.: Prospects for advancing drought understanding, monitoring, and prediction, J. Hydrometeorol., 16, 1636–1657, https://doi.org/10.1175/JHM-D-14-0164.1, 2015.
Yuan, X.: An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models, Hydrol. Earth Syst. Sci., 20, 2453–2466, https://doi.org/10.5194/hess-20-2453-2016, 2016.
Yuan, X. and Wood, E. F.: Multimodel seasonal forecasting of global drought onset, Geophys. Res. Lett., 40, 4900–4905, https://doi.org/10.1002/grl.50949, 2013.
Yuan, X., Wood, E. F., Chaney, N. W., Sheffield, J., Kam, J., Liang, M., and Guan, K.: Probabilistic seasonal forecasting of African drought by dynamical models, J. Hydrometeor., 14, 1706–1720, https://doi.org/10.1175/JHM-D-13-054.1, 2013.
Yuan, X., Wood, E. F., and Ma, Z.: A review on climate-model-based seasonal hydrologic forecasting: physical understanding and system development, WIREs Water, 2, 523–536, https://doi.org/10.1002/wat2.1088, 2015a.
Yuan, X., Roundy, J. K., Wood, E. F., and Sheffield, J.: Seasonal forecasting of global hydrologic extremes: system development and evaluation over GEWEX basins, B. Am. Meteorol. Soc., 96, 1895–1912, https://doi.org/10.1175/BAMS-D-14-00003.1, 2015b.
Yuan, X., Ma, F., Wang, L., Zheng, Z., Ma, Z., Ye, A., and Peng, S.: An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 1: Understanding the role of initial hydrological conditions, Hydrol. Earth Syst. Sci., 20, 2437–2451, https://doi.org/10.5194/hess-20-2437-2016, 2016.
Zhang, R., Chen, X., Zhang, Z., and Shi, P.: Evolution of hydrological drought under the regulation of two reservoirs in the headwater basin of the Huaihe River, China, Stoch. Env. Res. Risk A., 29, 487–499, https://doi.org/10.1007/s00477-014-0987-z, 2015.
Zhang, Z., Chen, X., Xu, C.-Y., Yuan, L., Yong, B., and Yan, S.: Evaluating the non-stationary relationship between precipitation and streamflow in nine major basins of China during the past 50 years, J. Hydrol., 409, 81–93, 2011.
Zhou, T., Nijssen, B., Gao, H., and Lettenmaier, D. P.: The contribution of reservoirs to global land surface water storage variations, J. Hydrometeorol., 17, 309–325, https://doi.org/10.1175/JHM-D-15-0002.1, 2016.
Short summary
Understanding and forecasting of hydrological drought in the Anthropocene are grand challenges. Human interventions exacerbate hydrological drought conditions and result in earlier drought onset. By considering their effects in the forecast, the probabilistic drought forecast skill increases for both climate-model-based and climatology methods but their difference decreases, suggesting that human interventions can outweigh the climate variability for drought forecasting in the Anthropocene.
Understanding and forecasting of hydrological drought in the Anthropocene are grand challenges....
