Resources

1.
Zhu, Y. & Newell, R. E. A proposed algorithm for moisture fluxes from atmospheric rivers. Mon. Weather Rev. 126, 725–735 (1998).
Article  Google Scholar 
2.
Ralph, F. M., Dettinger, M. D., Cairns, M. M., Galarneau, T. J. & Eylander, J. Defining “Atmospheric River”: how the glossary of meteorology helped resolve a debate. Bull. Am. Meteor. Soc 99, 837–839 (2018). This article provides the definition of an atmospheric river.
Article  Google Scholar 

3.
Ralph, F. M. et al. (eds) In Atmospheric Rivers p. 286 (Springer, 2020).

4.
Ralph, F. M., Neiman, P. J. & Rotunno, R. Dropsonde observations in low‐level jets over the Northeastern Pacific Ocean from CALJET‐1998 and PACJET‐2001: mean vertical‐profile and atmospheric‐river characteristics. Mon. Weather Rev. 133, 889–910 (2005).
Article  Google Scholar 

5.
Browning, K. A. & Pardoe, C. W. Structure of low-level jet streams ahead of mid-latitude cold fronts. Quart. J. Roy. Meteor. Soc. 99, 619–638 (1973).
Article  Google Scholar 

6.
Sodemann, H. & Stohl, A. Moisture origin and meridional transport in atmospheric rivers and their association with multiple cyclones. Monthly Weather Rev. 141, 2850–2868 (2013).
Article  Google Scholar 

7.
Ralph, F. M. et al. Dropsonde observations of total integrated water vapor transport within North Pacific atmospheric rivers. J. Hydrometeor. 18, 2577–2596 (2017).
Article  Google Scholar 

8.
Browning, K. A. Conceptual models of precipitation systems. Weather Forecasting 1, 23–41 (1986).
Article  Google Scholar 

9.
Wernli, H. & Davies, H. C. A Lagrangian-based analysis of extratropical cyclones. I: the method and some applications. Quart. J. Roy. Meteor. Soc. 123, 467–489 (1997).
Article  Google Scholar 

10.
Madonna, E., Wernli, H., Joos, H. & Martius, O. Warm conveyor belts in the ERA-Interim dataset (1979–2010). Part I: climatology and potential vorticity evolution. J. Climate 27, 3–26 (2014).
Article  Google Scholar 

11.
Sodemann, H. et al. (eds) In Atmospheric Rivers p. 286 (Springer, 2020).

12.
Doyle, J. D., Amerault, C., Reynolds, C. A. & Reinecke, P. A. Initial condition sensitivity and predictability of a severe extratropical cyclone using a moist adjoint. Monthly Weather Rev. 142, 320–342 (2014).
Article  Google Scholar 

13.
Schäfler, A. & Harnisch, F. Impact of the inflow moisture on the evolution of a warm conveyor belt. Quart. J. Roy. Meteor. Soc. 141, 299–310 (2015).
Article  Google Scholar 

14.
Rodwell, M. J., Richardson, D. S., Parsons, D. B. & Wernli, H. Flow-dependent reliability: a path to more skillful ensemble forecasts. Bull. Am. Meteor. Soc. 99, 1015–1026 (2018).
Article  Google Scholar 

15.
Lavers, D. A. et al. Winter floods in Britain are connected to atmospheric rivers. Geophys. Res. Lett. 38, L23803 (2011).
Article  Google Scholar 

16.
Lavers, D. A. & Villarini, G. The nexus between atmospheric rivers and extreme precipitation across Europe. Geophys. Res. Lett. 40, 3259–3264 (2013).
Article  Google Scholar 

17.
Ramos, A. M., Trigo, R. M., Liberato, M. L. & Tomé, R. Daily precipitation extreme events in the Iberian Peninsula and its association with atmospheric rivers. J. Hydrometeor. 16, 579–597 (2015).
Article  Google Scholar 

18.
Ralph, F. M. et al. Flooding on California’s Russian River: role of atmospheric rivers. Geophys. Res. Lett. 33, L13801 (2006).
Article  Google Scholar 

19.
Neiman, P. J., Schick, L. J., Ralph, F. M., Hughes, M. & Wick, G. A. Flooding in western Washington: the connection to atmospheric rivers. J. Hydrometeor. 12, 1337–1358 (2011).
Article  Google Scholar 

20.
Viale, M. & Nunez, M. N. Climatology of winter orographic precipitation over the subtropical central Andes and associated synoptic and regional characteristics. J. Hydrometeor. 12, 481–507 (2011).
Article  Google Scholar 

21.
Kingston, D. G., Lavers, D. A. & Hannah, D. M. Floods in the Southern Alps of New Zealand: the importance of atmospheric rivers. Hydrol. Process. 30, 5063–5070 (2016).
Article  Google Scholar 

22.
Pasquier, J. T., Pfahl, S. & Grams, C. M. Modulation of atmospheric river occurrence and associated precipitation extremes in the North Atlantic Region by European weather regimes. Geophys. Res. Lett. 46, 1014–1023 (2019).
Article  Google Scholar 

23.
UK Met Office. Record Breaking Rainfall. https://www.metoffice.gov.uk/weather/warnings-and-advice/uk-storm-centre/storm-dennis (UK Met Office, 2020).

24.
Insured losses from Europe’s Storm Victoria (aka Dennis) estimated at €286M: PERILS. Insurance J. https://www.insurancejournal.com/news/international/2020/03/30/562719.htm (2020).

25.
Corringham, T. W., Ralph, F. M., Gershunov, A., Cayan, D. R. & Talbot, C. A. Atmospheric rivers drive flood damages in the western United States. Sci. Adv. 5, eaax4631 (2019).
Article  Google Scholar 

26.
Waliser, D. & Guan, B. Extreme winds and precipitation during landfall of atmospheric rivers. Nat. Geosci. 10, 179–183 (2017).
CAS  Article  Google Scholar 

27.
Khouakhi, A. & Villarini, G. On the relationship between atmospheric rivers and high sea water levels along the US West Coast. Geophys. Res. Lett. 43, 8815–8822 (2016).
Article  Google Scholar 

28.
Dettinger, M. D., Ralph, F. M., Das, T., Neiman, P. J. & Cayan, D. Atmospheric rivers, floods, and the water resources of California. Water 3, 445–478 (2011).
Article  Google Scholar 

29.
Baggett, C. F., Barnes, E. A., Maloney, E. D. & Mundhenk, B. D. Advancing atmospheric river forecasts into subseasonal‐to‐seasonal time scales. Geophys. Res. Lett. 44, 7528–7536 (2017).
Article  Google Scholar 

30.
DeFlorio, M. J. et al. Global assessment of atmospheric river prediction skill. J. Hydrometeor. 19, 409–426 (2018).
Article  Google Scholar 

31.
Lavers, D. A., Pappenberger, F., Richardson, D. S. & Zsoter, E. ECMWF Extreme Forecast Index for water vapor transport: a forecast tool for atmospheric rivers and extreme precipitation. Geophys. Res. Lett. 43, 11,852–11,858 (2016).
Google Scholar 

32.
Lavers, D. A., Zsoter, E., Richardson, D. S. & Pappenberger, F. An assessment of the ECMWF extreme forecast index for water vapor transport during boreal winter. Weather Forecast. 32, 1667–1674 (2017). This paper describes the ECMWF Extreme Forecast Index product for integrated vapour transport and highlights the increased possible awareness of atmospheric rivers and extreme precipitation.
Article  Google Scholar 

33.
Nayak, M. A., Villarini, G. & Lavers, D. A. On the skill of numerical weather prediction models to forecast atmospheric rivers over the central United States. Geophys. Res. Lett. 41, 4354–4362 (2014).
Article  Google Scholar 

34.
Wick, G. A., Neiman, P. J., Ralph, F. M. & Hamill, T. M. Evaluation of forecasts of the water vapor signature of atmospheric rivers in operational numerical weather prediction models. Weather Forecast. 28, 1337–1352 (2013).
Article  Google Scholar 

35.
Leutbecher, M. & Palmer, T. N. Ensemble forecasting. J. Comput. Phys. 227, 3515–3539 (2008).
Article  Google Scholar 

36.
Lavers, D. A. et al. The gauging and modeling of rivers in the sky. Geophys. Res. Lett. https://doi.org/10.1029/2018GL079019 (2018).

37.
Rutz, J. J. et al. The atmospheric river tracking method intercomparison project (ARTMIP): quantifying uncertainties in atmospheric river climatology. J. Geophys. Res. 2019, 13777–13802 (2019).
Article  Google Scholar 

38.
Martin, A. C., Ralph, F. M., Wilson, A., DeHaan, L. & Kawzenuk, B. Rapid cyclogenesis from a mesoscale frontal wave on an atmospheric river: impacts on forecast skill and predictability during atmospheric river landfall. J. Hydrometeor. 20, 1779–1794 (2019).
Article  Google Scholar 

39.
Bauer, P., Thorpe, A. & Brunet, G. The quiet revolution of numerical weather prediction. Nature 525, 47–55 (2015).
CAS  Article  Google Scholar 

40.
Lavers, D. A. et al. Earlier awareness of extreme winter precipitation across the western Iberian Peninsula. Meteorol. Appl. 25, 622–628 (2018).

41.
Lavers, D., Tsonevsky, I., Richardson, D. & Pappenberger, F. The Extreme Forecast Index for water vapour flux, ECMWF Newslett. 160, https://www.ecmwf.int/en/newsletter/160/news/extreme-forecast-index-water-vapour-flux (2019).

42.
Ralph, F. M. et al. A scale to characterize the strength and impacts of atmospheric rivers. Bull. Am. Meteor. Soc. 100, 269–289 (2019).
Article  Google Scholar 

43.
Ralph, F. M. et al. West Coast forecast challenges and development of atmospheric river reconnaissance. Bull. Am. Meteor. Soc., 101, E1357–E1377, https://doi.org/10.1175/BAMS-D-19-0183.1 (2020). This paper provides an overview of Atmospheric River Reconnaissance in the northeast Pacific which is key to the ideas proposed for AR Recon Atlantic.

44.
Stone, R. E. et al. Atmospheric river reconnaissance observation impact in the navy global forecast system. Monthly Weather Rev. 148, 763–782 (2020).
Article  Google Scholar 

45.
Lavers, D. A. et al. Forecast errors and uncertainties in Atmospheric Rivers. Weather Forecast. https://doi.org/10.1175/WAF-D-20-0049.1 (2020).

46.
National Winter Season Operations Plan. Winter Season Reconnaissance https://www.ofcm.gov/publications/nwsop/nwsop2.htm (2019).

47.
Schäfler, A. et al. The North Atlantic Waveguide and Downstream Impact Experiment. Bull. Amer. Meteor. Soc. 99, 1607–1637 (2018). This paper describes the NAWDEX observational campaign in the North Atlantic and AR Recon Atlantic would build on these findings.
Article  Google Scholar 

48.
Grams, C. M., Magnusson, L. & Madonna, E. An atmospheric dynamics perspective on the amplification and propagation of forecast error in numerical weather prediction models: a case study. Quart. J. R. Meteor. Soc. 144, 2577–2591 (2018).
Article  Google Scholar 

49.
Schäfler, A. et al. Observation of jet stream winds during NAWDEX and characterization of systematic meteorological analysis errors. Monthly Weather Rev. https://doi.org/10.1175/MWR-D-19-0229.1 (2020).

50.
Rennie, M. & Isaksen, L. Use of Aeolus observations at ECMWF. ECMWF Newslett. 163, https://www.ecmwf.int/en/newsletter/163/news/use-aeolus-observations-ecmwf (2020).

51.
Guan, B., Waliser, D. E., Molotch, N. P., Fetzer, E. J. & Neiman, P. J. Does the Madden–Julian oscillation influence wintertime atmospheric rivers and snowpack in the Sierra Nevada? Monthly Weather Rev. 140, 325–342 (2012).
Article  Google Scholar 

52.
Ralph, F. M. et al. The impact of a prominent rain shadow on flooding in California’s Santa Cruz mountains: a CALJET case study and sensitivity to the ENSO cycle. J. Hydrometeor. 4, 1243–1264 (2003).
Article  Google Scholar 

53.
Lavers, D. A., Villarini, G., Allan, R. P., Wood, E. F. & Wade, A. J. The detection of atmospheric rivers in atmospheric reanalyses and their links to British winter floods and the large-scale climatic circulation. J. Geophys. Res. 117, D20106 (2012).
Google Scholar 

54.
Jasperse J. et al. Preliminary viability assessment of Lake Mendocino forecast informed reservoir operations. Technical report. http://pubs.er.usgs.gov/publication/70192184 (USGS, 2017). More

1.
Im, E. S., Pal, J. S. & Eltahir, E. A. B. Deadly heat waves projected in the densely populated agricultural regions of South Asia. Sci. Adv. 3, e1603322 (2017).
Article  Google Scholar 
2.
Mazdiyasni, O. et al. Increasing probability of mortality during Indian heat waves. Sci. Adv. 3, 1–6 (2017).
Article  Google Scholar 

3.
Mishra, V., Mukherjee, S., Kumar, R. & Stone, D. A. Heat wave exposure in India in current, 1.5 °C, and 2.0 °C worlds. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/aa9388 (2017).

4.
Coffel, E. D., Horton, R. M. & de Sherbinin, A. Temperature and humidity based projections of a rapid rise in global heat stress exposure during the 21st century. Environ. Res. Lett. 13, 014001 (2017).
Article  Google Scholar 

5.
King, A. D. et al. Emergence of heat extremes attributable to anthropogenic influences. Geophys. Res. Lett. 43, 3438–3443 (2016).
Article  Google Scholar 

6.
Knutson, T. R. & Ploshay, J. J. Detection of anthropogenic influence on a summertime heat stress index. Clim. Change 138, 25–39 (2016).
Article  Google Scholar 

7.
Matthews, T. K. R., Wilby, R. L. & Murphy, C. Communicating the deadly consequences of global warming for human heat stress. Proc. Natl Acad. Sci. USA 114, 3861–3866 (2017).
Article  Google Scholar 

8.
Kjellstrom, T. et al. Heat, human performance, and occupational health: a key issue for the assessment of global climate change impacts. Annu. Rev. Public Health 37, 97–112 (2016).
Article  Google Scholar 

9.
Sherwood, S. C. How important is humidity in heat stress? J. Geophys. Res. Atmos. 123, 11808–11810 (2018).
Article  Google Scholar 

10.
Horton, R. M., Mankin, J. S., Lesk, C., Coffel, E. & Raymond, C. A review of recent advances in research on extreme heat events. Curr. Clim. Change Rep. 2, 242–259 (2016).
Article  Google Scholar 

11.
Buzan, J. R., Oleson, K. & Huber, M. Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4.5. Geosci. Model Dev. 8, 151–170 (2015).
Article  Google Scholar 

12.
Kang, S. & Eltahir, E. A. B. North China Plain threatened by deadly heatwaves due to climate change and irrigation. Nat. Commun. 9, 2894 (2018).
Article  Google Scholar 

13.
Shankar, P. V., Kulkarni, H. & Krishnan, S. India’s groundwater challenge and the way forward. Econ. Political Wkly 46, 37–45 (2011).
Google Scholar 

14.
Amarasinghe, U. A., Shah, T. & Anand, B. K. India’s water supply and demand from 2025-2050: business-as-usual scenario and issues. In Proc. Workshop on Analyses of Hydrological, Social and Ecological Issues of the National River Linking Project (eds Amarasinghe, U. A. & Sharma, B. R.) 23–61 (IWMI, 2007).

15.
Ambika, A. K., Wardlow, B. & Mishra, V. Remotely sensed high resolution irrigated area mapping in India for 2000 to 2015. Sci. Data 3, 160118 (2016).
Article  Google Scholar 

16.
Cook, B. I., Puma, M. J. & Krakauer, N. Y. Irrigation induced surface cooling in the context of modern and increased greenhouse gas forcing. Clim. Dyn. 37, 1587–1600 (2011).
Article  Google Scholar 

17.
Thiery, W. et al. Present-day irrigation mitigates heat extremes. J. Geophys. Res. Atmos. 122, 1403–1422 (2017).
Article  Google Scholar 

18.
Boucher, O., Myhre, G. & Myhre, A. Direct human influence of irrigation on atmospheric water vapour and climate. Clim. Dyn. 22, 597–603 (2004).
Article  Google Scholar 

19.
Lobell, D. et al. Regional differences in the influence of irrigation on climate. J. Clim. 22, 2248–2255 (2009).
Article  Google Scholar 

20.
Kumar, R. et al. Dominant control of agriculture and irrigation on urban heat island in India. Sci. Rep. 7, 14054 (2017).
Article  Google Scholar 

21.
Mueller, N. D. et al. Cooling of US Midwest summer temperature extremes from cropland intensification. Nat. Clim. Change 6, 317–322 (2015).
Article  Google Scholar 

22.
Asoka, A., Gleeson, T., Wada, Y. & Mishra, V. Relative contribution of monsoon precipitation and pumping to changes in groundwater storage in India. Nat. Geosci. 10, 109–117 (2017).
Article  Google Scholar 

23.
Azhar, G. S. et al. Heat-related mortality in India: excess all-cause mortality associated with the 2010 Ahmedabad heat wave. PLoS ONE 9, e91831 (2014).
Article  Google Scholar 

24.
Marcella, M. P. & Eltahir, E. A. B. Introducing an irrigation scheme to a regional climate model: a case study over West Africa. J. Clim. 27, 5708–5723 (2014).
Article  Google Scholar 

25.
Puma, M. J. & Cook, B. I. Effects of irrigation on global climate during the 20th century. J. Geophys. Res. Atmos. 115, D16120 (2010).
Article  Google Scholar 

26.
Willett, K. M. & Sherwood, S. Exceedance of heat index thresholds for 15 regions under a warming climate using the wet-bulb globe temperature. Int. J. Climatol. https://doi.org/10.1002/joc.2257 (2012).

27.
Sherwood, S. C. & Huber, M. An adaptability limit to climate change due to heat stress. Proc. Natl Acad. Sci. USA 107, 9552–9555 (2010).
Article  Google Scholar 

28.
Byrne, M. P. & O’Gorman, P. A. Trends in continental temperature and humidity directly linked to ocean warming. Proc. Natl Acad. Sci. USA 115, 4863–4868 (2018).
Article  Google Scholar 

29.
Willett, K. M., Gillett, N. P., Jones, P. D. & Thorne, P. W. Attribution of observed surface humidity changes to human influence. Nature 449, 710–712 (2007).
Article  Google Scholar 

30.
Bollasina, M. & Nigam, S. The summertime ‘heat’ low over Pakistan/northwestern India: evolution and origin. Clim. Dyn. 37, 957–970 (2011).
Article  Google Scholar 

31.
Gentine, P., Holtslag, A. A. M., D’Andrea, F. & Ek, M. Surface and atmospheric controls on the onset of moist convection over land. J. Hydrometeorol. 14, 1443–1462 (2013).
Article  Google Scholar 

32.
Kang, S. & Eltahir, E. A. B. Impact of irrigation on regional climate over eastern China. Geophys. Res. Lett. 46, 5499–5505 (2019).
Article  Google Scholar 

33.
Kueppers, L. M., Snyder, M. A. & Sloan, L. C. Irrigation cooling effect: regional climate forcing by land-use change. Geophys. Res. Lett. 34, L03703 (2007).
Article  Google Scholar 

34.
Alter, R. E., Im, E. S. & Eltahir, E. A. B. Rainfall consistently enhanced around the Gezira Scheme in East Africa due to irrigation. Nat. Geosci. 8, 763–767 (2015).
Article  Google Scholar 

35.
Im, E. S. & Kang, S. & Eltahir, E. A. B. Projections of rising heat stress over the western Maritime Continent from dynamically downscaled climate simulations. Glob. Planet. Change https://doi.org/10.1016/j.gloplacha.2018.02.01 (2018).

36.
Sacks, W. J., Cook, B. I., Buenning, N., Levis, S. & Helkowski, J. H. Effects of global irrigation on the near-surface climate. Clim. Dyn. 33, 159–175 (2009).
Article  Google Scholar 

37.
Dileepkumar, R., Achutarao, K. & Arulalan, T. Human influence on sub-regional surface air temperature change over India. Sci. Rep. 8, 8967 (2018).
Article  Google Scholar 

38.
Seneviratne, S. I. et al. Land radiative management as contributor to regional-scale climate adaptation and mitigation. Nat. Geosci. 11, 88–96 (2018).
Article  Google Scholar 

39.
Sharma, A. et al. Green and cool roofs to mitigate urban heat island effects in the Chicago metropolitan area: evaluation with a regional climate model. Environ. Res. Lett. 11, 064004 (2016).
Article  Google Scholar 

40.
Georgescu, M., Moustaoui, M., Mahalov, A. & Dudhia, J. An alternative explanation of the semiarid urban area ‘oasis effect’. J. Geophys. Res. Atmos. https://doi.org/10.1029/2011JD016720 (2011).

41.
Zipper, S. C., Schatz, J., Kucharik, C. J. & Loheide, S. P. Urban heat island-induced increases in evapotranspirative demand. Geophys. Res. Lett. https://doi.org/10.1002/2016GL072190 (2017).

42.
Siebert, S., Henrich, V., Frenken, K. & Burke, J. Update of the Digital Global Map of Irrigation Areas to Version 5 (FAO, 2013); https://doi.org/10.13140/2.1.2660.6728

43.
Mann, H. B. Nonparametric tests against trend. Econometrica 13, 245–259 (1945).
Article  Google Scholar 

44.
Sen, P. K. Estimates of the regression coefficient based on Kendall’s Tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).
Article  Google Scholar 

45.
Srivastava, A. K., Rajeevan, M. & Kshirsagar, S. R. Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmos. Sci. Lett. 10, 249–254.

46.
Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).
Article  Google Scholar 

47.
Haldane, J. S. The influence of high air temperatures No. I. J. Hyg. (Lond.) 5, 494–513 (1905).
Google Scholar 

48.
Davies-Jones, R. An efficient and accurate method for computing the wet-bulb temperature along pseudoadiabats. Mon. Weather Rev. 136, 2764–2785 (2008).
Article  Google Scholar 

49.
Steadman, R. G. The assessment of sultriness. Part I. A temperature–humidity index based on human physiology and clothing science. J. Appl. Meteorol. 18, 861–873 (1979).
Article  Google Scholar 

50.
Brooke Anderson, G., Bell, M. L. & Peng, R. D. Methods to calculate the heat index as an exposure metric in environmental health research. Environ. Health Perspect. 121, 1111–1119 (2013).
Article  Google Scholar 

51.
Skamarock, C. et al. A Description of the Advanced Research WRF Model Version 4 (NCAR, 2019); https://doi.org/10.5065/1DFH-6P97

52.
Mitchell, K. et al. Noah Land Surface Model (LSM) User’s Guide (NCAR, 2005).

53.
Iacono, M. J. Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models. J. Geophys. Res. Atmos. https://doi.org/10.1029/2008JD009944 (2008).

54.
Janzic, Z. I. The step-mountain eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Weather Rev. 122, 927–945 (1994).
Article  Google Scholar 

55.
Kain, J. S. & Kain, J. The Kain–Fritsch convective parameterization: an update. J. Appl. Meteorol. 43, 170–181 (2004).
Article  Google Scholar 

56.
Qian, Y., Huang, M., Yang, B. & Berg, L. K. A modeling study of irrigation effects on surface fluxes and land–air–cloud interactions in the Southern Great Plains. J. Hydrometeorol. 14, 700–721 (2013).
Article  Google Scholar 

57.
Siebert, S. et al. Development and validation of the global map of irrigation areas. Hydrol. Earth Syst. Sci. 9, 535–547 (2005).
Article  Google Scholar 

58.
Rienecker, M. M. et al. MERRA: NASA’s modern-era retrospective analysis for research and applications. J. Clim. 24, 3624–3648 (2011).
Article  Google Scholar 

59.
Durre, I. & Yin, X. Enhanced radiosonde data for studies of vertical structure. Bull. Am. Meteorol. Soc. 89, 1257–1262 (2008).
Article  Google Scholar 

60.
Seidel, D. J., Ao, C. O. & Li, K. Estimating climatological planetary boundary layer heights from radiosonde observations: comparison of methods and uncertainty analysis. J. Geophys. Res. Atmos. 115, D16113 (2010).
Article  Google Scholar 

61.
Basha, G. & Ratnam, M. V. Identification of atmospheric boundary layer height over a tropical station using high-resolution radiosonde refractivity profiles: comparison with GPS radio occupation measurements. J. Geophys. Res. Atmos. 114, D161010 (2009).
Article  Google Scholar  More

1.
Sutcliffe, J. & Parks, Y. The hydrology of the Nile. (IAHS Press, 1999).
2.
Abu‐Zeid, M. A. & Biswas, A. K. Some major implications of climatic fluctuations on water management. Int. J. Water Resour. Dev. 7, 74–81 (1991).
Article  Google Scholar 

3.
van der Krogt, W. & Ogink, H. Development of the Eastern Nile Water Simulation Model. Report No. 1206020-000-VEB-0010 (Deltares, Delft, 2013).

4.
Sudan MoIWR. Gage Flows at Dongola Station (Sudan Ministry of Irrigation and Water Resources, 2019).

5.
Hurst, H. E., Black, R. P. & Simaika, Y. S. M. A long-term plan for the Nile basin. Nature 160, 611–612 (1947).
ADS  MathSciNet  Article  Google Scholar 

6.
Tvedt, T. The River Nile in the Age of the British: Political Ecology and the Quest for Economic Power. (I.B.Tauris & Co. Ltd., 2004).

7.
Mekonnen, D. Z. The Nile Basin Cooperative framework agreement negotiations and the adoption of a ‘Water Security’ paradigm: flight into obscurity or a logical Cul-de-sac? Eur. J. Int. Law 21, 421–440 (2010).
Article  Google Scholar 

8.
Moussa, A. M. A. Dynamic operation rules of multi-purpose reservoir for better flood management. Alex. Eng. J. 57, 1665–1679 (2018).
Article  Google Scholar 

9.
USBR. Land and Water Resources of the Blue Nile Basin: Ethiopia: Main Report and Appendices I–V. (United States Department of the Interior, Washington DC, 1964).

10.
Awulachew, S. B. The Nile River Basin: water, agriculture, governance and livelihoods. (Routledge, 2012).

11.
Government of Ethiopia. Vol. S/2020/409 (United Nations Security Council, United Nations Digital Library, 2020).

12.
Arjoon, D., Mohamed, Y., Goor, Q. & Tilmant, A. Hydro-economic risk assessment in the eastern Nile River basin. Water Resour. Econ. 8, 16–31 (2014).
Article  Google Scholar 

13.
Block, P. J. & Strzepek, K. Economic analysis of large-scale upstream river basin development on the Blue Nile in Ethiopia considering transient conditions, climate variability, and climate change. J. Water Resour. Plan. Manag. 136, 156–166 (2010).
Article  Google Scholar 

14.
Digna, R. F. et al. Impact of water resources development on water availability for hydropower production and irrigated agriculture of the Eastern Nile Basin. J. Water Resour. Plan. Manag. 144, 05018007 (2018).
Article  Google Scholar 

15.
Geressu, R. T. & Harou, J. J. Screening reservoir systems by considering the efficient trade-offs—informing infrastructure investment decisions on the Blue Nile. Environ. Res. Lett. 10, 125008 (2015).
ADS  Article  Google Scholar 

16.
Jeuland, M. & Whittington, D. Water resources planning under climate change: assessing the robustness of real options for the Blue Nile. Water Resour. Res. 50, 2086–2107 (2014).
ADS  Article  Google Scholar 

17.
Kahsay, T. N., Kuik, O., Brouwer, R. & van der Zaag, P. Estimation of the transboundary economic impacts of the Grand Ethiopia Renaissance Dam: a computable general equilibrium analysis. Water Resour. Econ. 10, 14–30 (2015).
Article  Google Scholar 

18.
Mulat, A. G. & Moges, S. A. Assessment of the impact of the Grand Ethiopian Renaissance Dam on tHe Performance Of The High Aswan Dam. J. Water Resour. Prot. 06, 583–598 (2014).
Article  Google Scholar 

19.
Strzepek, K. M., Yohe, G. W., Tol, R. S. J. & Rosegrant, M. W. The value of the high Aswan Dam to the Egyptian economy. Ecol. Econ. 66, 117–126 (2008).
Article  Google Scholar 

20.
Wheeler, K. G. et al. Cooperative filling approaches for the Grand Ethiopian Renaissance Dam. Water Int. 41, 611–634 (2016).
Article  Google Scholar 

21.
Wheeler, K. G. et al. Exploring cooperative transboundary river management strategies for the Eastern Nile Basin. Water Resour. Res. 54, 9224–9254 (2018).
ADS  PubMed  PubMed Central  Article  Google Scholar 

22.
Nigatu, G. & Dinar, A. Economic and hydrological impacts of the Grand Ethiopian Renaissance Dam on the Eastern Nile River Basin. Environ. Dev. Econ. 21, 532–555 (2015).
Article  Google Scholar 

23.
Sangiorgio, M. & Guariso, G. NN-based implicit stochastic optimization of multi-reservoir systems management. Water 10, 303 (2018).
Article  Google Scholar 

24.
Taye, M. T., Willems, P. & Block, P. Implications of climate change on hydrological extremes in the Blue Nile basin: a review. J. Hydrol.: Regional Stud. 4, 280–293 (2015).
Google Scholar 

25.
Brown, C. M. et al. The future of water resources systems analysis: Toward a scientific framework for sustainable water management. Water Resour. Res. 51, 6110–6124 (2015).
ADS  Article  Google Scholar 

26.
Loucks, D. P. From Analyses to Implementation and Innovation. Water 12, 974 (2020).
Article  Google Scholar 

27.
Basheer, M. et al. Filling Africa’s largest hydropower dam should consider engineering realities. One Earth. 3, 277–281 (2020).
Article  Google Scholar 

28.
Roussi, A. Gigantic Nile dam prompts clash between Egypt and Ethiopia. Nature 574, 159 (2019).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

29.
Jeuland, M., Wu, X. & Whittington, D. Infrastructure development and the economics of cooperation in the Eastern Nile. Water Int. 42, 121–141 (2017).
Article  Google Scholar 

30.
Basheer, M. et al. Quantifying and evaluating the impacts of cooperation in transboundary river basins on the Water-Energy-Food nexus: the Blue Nile Basin. Sci. Total Environ. 630, 1309–1323 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

31.
Basheer, M. & Ahmed Elagib, N. Temporal analysis of water-energy nexus indicators for hydropower generation and water pumping in the Lower Blue Nile Basin. J. Hydrol. 578, 124085 (2019).
Article  Google Scholar 

32.
Siam, M. S. & Eltahir, E. A. B. Climate change enhances interannual variability of the Nile river flow. Nat. Clim. Change 7, 350 (2017).
ADS  Article  Google Scholar 

33.
Lund, J. R. & Guzman, J. Derived operating rules for reservoirs in series or in parallel. J. Water Resour. Plan. Manag. 125, 143–153 (1999).
Article  Google Scholar 

34.
Lazer, D. M. J. et al. The science of fake news. Science 359, 1094–1096 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

35.
Vosoughi, S., Roy, D. & Aral, S. The spread of true and false news online. Science 359, 1146–1151 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

36.
Whittington, D., Hanemann, W. M., Sadoff, C. & Jeuland, M. The challenge of improving water and sanitation services in less developed countries. Found. Trends® Microecon. 4, 469–609 (2009).
Article  Google Scholar 

37.
Kahneman, D. & Tversky, A. Prospect theory: an analysis of decision under risk. Econometrica 47, 263–291 (1979).
MathSciNet  MATH  Article  Google Scholar 

38.
Whittington, D. Policy note: ancient instincts—implications for water policy in the 21st century. Water Econ. Policy 02, 1671002 (2016).
Article  Google Scholar 

39.
Zagona, E. A., Fulp, T. J., Shane, R., Magee, T. & Goranflo, H. M. RiverWare: a generalized tool for complex reservoir systems modeling. J. Am. Water Resour. Assoc. 37, 913 (2001).
ADS  Article  Google Scholar 

40.
NBI. State of the River Nile Basin 2012. (Entebbe, 2012).

41.
Belissa, A. Establishing optimal reservoir operation of Fincha’a – Amerty Reservoirs MSc Civil Engineering thesis, Addis Ababa University, (2016).

42.
Mondal, M. A. H., Bryan, E., Ringler, C., Mekonnen, D. & Rosegrant, M. Ethiopian energy status and demand scenarios: prospects to improve energy efficiency and mitigate GHG emissions. Energy 149, 161–172 (2018).
Article  Google Scholar 

43.
Conway, D. Water resources: future Nile river flows. Nat. Clim. Change 7, 319–320 (2017).
ADS  Article  Google Scholar 

44.
Harding, B. L., Sangoyomi, T. B. & Payton, E. A. Impacts of a severe sustained drought on Colorado River water resources. JAWRA J. Am. Water Resour. Assoc. 31, 815–824 (1995).
ADS  Article  Google Scholar 

45.
Georgakakos, A. P. et al. Value of adaptive water resources management in Northern California under climatic variability and change: Reservoir management. J. Hydrol. 412-413, 34–46 (2012).
ADS  Article  Google Scholar 

46.
Maass, A. Design of water-resource systems: new techniques for relating economic objectives, engineering analysis, and governmental planning. (Harvard University Press., 1962).

47.
Hurst, H. E., Black, R. P. & Simaika, Y. M. Long-term storage: an experimental study. (Constable, 1965).

48.
Conway, D. From headwater tributaries to international river: observing and adapting to climate variability and change in the Nile basin. Glob. Environ. Change 15, 99–114 (2005).
Article  Google Scholar 

49.
Ward, N. & Conway, D. Applications of interannual-to-decadal climate prediction: an exploratory discussion on rainfall in the Sahel region of Africa. Clim. Serv. 18, 100170 (2020).
Article  Google Scholar 

50.
Sutcliffe, J., Hurst, S., Awadallah, A. G., Brown, E. & Hamed, K. Harold Edwin Hurst: the Nile and Egypt, past and future. Hydrological Sci. J. 61, 1557–1570 (2016).
Article  Google Scholar 

51.
Koutsoyiannis, D. Hydrology and change. Hydrological Sci. J. 58, 1177–1197 (2013).
Article  Google Scholar 

52.
Koutsoyiannis, D., Yao, H. & Georgakakos, A. Medium-range flow prediction for the Nile: a comparison of stochastic and deterministic methods/Prévision du débit du Nil à moyen terme: une comparaison de méthodes stochastiques et déterministes. Hydrol. Sci. J. 53, 142–164 (2008).
Article  Google Scholar 

53.
Sandoval-Solis, S., Teasley, R. L., McKinney, D. C., Thomas, G. A. & Patiño-Gomez, C. Collaborative modeling to evaluate water management scenarios in the Rio Grande Basin. JAWRA J. Am. Water Resour. Assoc. 49, 639–653 (2013).
ADS  Article  Google Scholar 

54.
USBR. Colorado River Interim Guidelines for Lower Basin Shortages and the Coordinated Operations for Lake Powell and Lake Mead Final Environmental Impact Statement. (U.S. Department of the Interior, Washington DC, 2007).

55.
Milly, P. C. D. et al. Stationarity is dead: whither water management? Science 319, 573–574 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

56.
Rayner, S., Lach, D. & Ingram, H. Weather forecasts are for wimps: why water resource managers do not use climate forecasts. Climatic Change 69, 197–227 (2005).
ADS  Article  Google Scholar 

57.
Gober, P., Kirkwood, C. W., Balling, R. C., Ellis, A. W. & Deitrick, S. Water planning under climatic uncertainty in phoenix: why we need a new paradigm. Ann. Assoc. Am. Geographers 100, 356–372 (2010).
Article  Google Scholar 

58.
Olsson, J. A. & Andersson, L. Possibilities and problems with the use of models as a communication tool in water resource management. Water Resour. Manag. 21, 97–110 (2007).
Article  Google Scholar 

59.
Strzepek, K. & McCluskey, A. The impacts of climate change on regional water resources and agriculture in Africa. World Bank Policy Research Working Paper (2007).

60.
Beyene, T., Lettenmaier, D. P. & Kabat, P. Hydrologic impacts of climate change on the Nile River Basin: implications of the 2007 IPCC scenarios. Climatic Change 100, 433–461 (2010).
ADS  Article  Google Scholar 

61.
Eltahir, E. A. B. & Wang, G. Nilometers, El Niño, and climate variability. Geophys. Res. Lett. 26, 489–492 (1999).
ADS  Article  Google Scholar 

62.
Denning, S. Effective storytelling: strategic business narrative techniques. Strategy Leadersh. 34, 42–48 (2006).
Article  Google Scholar 

63.
Roe, E. Narrative policy analysis: Theory and practice. (Duke University Press, 1994). More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. More

1.
FAO Statistical Database (Food and Agriculture Organization, 2011–2013); http://www.fao.org/faostat/en/#home
2.
National Food Security Bill Registered Number DL-(N)04/0007/2003-13 (Government of India, Ministry of Law and Justice, 10 September 2013).

3.
Bhattacharyya, R. et al. Soil degradation in India: challenges and potential solutions. Sustainability 7, 3528–3570 (2015).
CAS  Article  Google Scholar 

4.
Khajuria, A. Impact of nitrate consumption: case study of Punjab, India. J. Water Resour. Prot. 8, 211–216 (2016).
CAS  Article  Google Scholar 

5.
Davis, K. F. et al. Alternative cereals can improve water use and nutrient supply in India. Sci. Adv. 4, eaao1108 (2018).
ADS  Article  Google Scholar 

6.
Caulfield, L. E. in Disease Control Priorities in Developing Countries 2nd edn (eds Jamison, D. T., et al.) Ch. 28 (International Bank for Reconstruction and Development/World Bank, 2006).

7.
Green, R. et al. Dietary patterns in India: a systematic review. Br. J. Nutr. 116, 142–148 (2016).
CAS  Article  Google Scholar 

8.
Naik, S., Mahalle, N. & Bhide, V. Identification of vitamin B12 deficiency in vegetarian Indians. Br. J. Nutr. 119, 1–7 (2018).

9.
DeFries, R. et al. Impact of historical changes in coarse cereals consumption in India on micronutrient intake and anemia prevalence. Food Nutr. Bull. 39, 377–392 (2018).
Article  Google Scholar 

10.
Smith, M. R. et al. Inadequate zinc intake in India: past, present, and future. Food Nutr. Bull. 40, 26–40 (2019).
Article  Google Scholar 

11.
India: National Family Health Survey (NFHS-4), 2015–16 (International Institute for Population Sciences, 2017).

12.
Akhtar, S. et al. Prevalence of vitamin A deficiency in South Asia: causes, outcomes, and possible remedies. J. Health Popul. Nutr. 31, 413–423 (2013).
Article  Google Scholar 

13.
India: Health of the Nation’s States—The Indian State-Level Disease Burden Initiative (Indian Council of Medical Research, Public Health Foundation of India and Institute for Health Metrics and Evaluation, 2017).

14.
Riahi, K. et al. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).
Article  Google Scholar 

15.
Sengupta, P. & Mukhopadhyay, K. Economic and environmental impact of National Food Security Act of India. Agric. Food Econ. 4, 1–23. (2016).
Article  Google Scholar 

16.
Rao, N. D. et al. Healthy, affordable and climate-friendly diets in India. Glob. Environ. Change 49, 154–165 (2018).
Article  Google Scholar 

17.
Vetter, S. H. et al. Greenhouse gas emissions from agricultural food production to supply Indian diets: Implications for climate change mitigation. Agric. Ecosyst. Environ. 237, 234–241 (2017).
CAS  Article  Google Scholar 

18.
Harris, F. et al. The water use of Indian diets and socio-demographic factors related to dietary blue water footprint. Sci. Total. Environ. 587–588, 128–136 (2017).
ADS  Article  Google Scholar 

19.
Davis, K. F. et al. Assessing the sustainability of post-Green Revolution cereals in India. Proc. Natl Acad. Sci. USA 116, 25034–25041 (2019).
CAS  Article  Google Scholar 

20.
Milner, J. et al. Projected health effects of realistic dietary changes to address freshwater constraints in India: a modelling study. Lancet Planet. Health 1, e26–e32 (2017).
Article  Google Scholar 

21.
Aleksandrowicz, L. et al. A modelling study using nationally-representative data. Environ. Int. 126, 207–215 (2019).
CAS  Article  Google Scholar 

22.
Green, R. et al. Greenhouse gas emissions and water footprints of typical dietary patterns in India. Sci. Total. Environ. 643, 1411–1418 (2018).
ADS  CAS  Article  Google Scholar 

23.
Ritchie, H. et al. Sustainable food security in India—domestic production and macronutrient availability. PLoS ONE 13, e0193766 (2018a).
Article  Google Scholar 

24.
Ritchie, H. et al. Quantifying, projecting, and addressing India’s hidden hunger. Front. Sustain. Food Sys. 2, 11 (2018b).
Article  Google Scholar 

25.
Springmann, M. et al. Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet. Health 2, e451–e461 (2018).
Article  Google Scholar 

26.
Household Consumption of Various Goods and Service in India 2011–12. NSS 68th Round (Government of India, 2014).

27.
Rosa, L. et al. Closing the yield gap while ensuring water sustainability. Environ. Res. Lett. 13, 104002 (2018).
ADS  Article  Google Scholar 

28.
Mason-D’Croz, D. et al. Gaps between fruit and vegetable production, demand, and recommended consumption at global and national levels: an integrated modelling study. Lancet Planet. Health 3, e318–e329 (2019).
Article  Google Scholar 

29.
Sapkota, T. P. et al. Cost-effective opportunities for climate change mitigation in Indian agriculture. Sci. Total. Environ. 655, 1342–1354 (2019).
ADS  CAS  Article  Google Scholar 

30.
Willett, W. et al. Food in the anthropocene: the EAT–Lancet commission on healthy diets from sustainable food systems. Lancet Comm. 393, P447–P492 (2019).
Article  Google Scholar 

31.
Ahmad, F., Uddin, Md. M., Goparaju, L., Rizvi, J. & Biradar, C. Quantification of the land potential for scaling agroforestry in South Asia. J. Cartogr. Geogr. Inf. 70, 81–89 (2020).

32.
Sharma, B. et al. Comparative study of mango based agroforestry and mono-cropping system under rainfed condition of West Bengal. Int. J. Plant. Soil. Sci. 15, 1–7 (2017).
Google Scholar 

33.
Chirwa, P. W. et al. Tree and crop productivity in gliricidia/maize/pigeonpea cropping systems in southern Malawi. Agrofor. Syst. 59, 265–277 (2003).
Article  Google Scholar 

34.
Chiuve S. E. et al. Alternative dietary indices both strongly predict risk of chronic disease. J. Nutr. 142, 1009–1018 (2012).

35.
Wang, D. D. et al. Global improvement in dietary quality could lead to substantial reduction in premature death. J. Nutr. 149, 1065–1074 (2019).
Article  Google Scholar 

36.
Pingali, P., Aiyar, A., Abraham, M. & Rahman, A. Transforming Food Systems for a Rising India (Palgrave-Macmillan, 2019).

37.
Bowen, L. et al. Dietary intake and rural–urban migration in India: a cross-sectional study. PLoS ONE 6, e14822 (2010).
ADS  Article  Google Scholar 

38.
Singh, A.et al. Quantitative estimates of dietary intake with special emphasis on snacking pattern and nutritional status of free living adults in urban slums of Delhi: impact of nutrition transition. BMC Nutr. 1, (2015)..

39.
Rawal, V. et al. Prevalence of undernourishment in Indian states: explorations based on NSS 68th round data. Econ. Polit. Wkly 54, 35–45 (2019).
Google Scholar 

40.
The Global Dietary Database—Global Dietary Intakes, Diseases, and Policies among Children, Women, and Men (Bill and Melinda Gates Foundation, 2016); http://www.globaldietarydatabase.org/the-global-dietary-database-measuring-diet-worldwide.html

41.
Demographic Statistics Database (United Nations Statistics Division, accessed September 2018); http://data.un.org/Data.aspx?d=POP&f=tableCode%3a22

42.
Lonnie, M. et al. Protein for life: Review of optimal protein intake, sustainable dietary sources and the effect on appetite in ageing adults. Nutrients 10, 360 (2018).
Article  Google Scholar 

43.
Longvah, T. et al. Indian Food Composition Tables (National Institute of Nutrition, 2017).

44.
Food Composition Database (United States Department of Agriculture, 2016); https://ndb.nal.usda.gov/ndb/

45.
Human Vitamin and Mineral Requirements. Report of a Joint FAO/WHO Expert Consultation, Bangkok, Thailand (World Health Organization, 2001).

46.
Nutrient Index (Oregon State University, 2018); https://lpi.oregonstate.edu/mic/nutrient-index

47.
Statistical Year Book India 2018 (Ministry of Statistics and Programme Implementation, Government of India, 2019).

48.
Suresh, K. P. et al. Modeling and forecasting livestock feed resources in India using climate variables. Asian-Aust J. Anim. Sci. 25, 462–470 (2012).
CAS  Article  Google Scholar 

49.
Mekonnen, M. M. & Hoekstra, A. Y. National Water Footprint Accounts: The Green, Blue and Grey Water Footprint of Production and Consumption (Value of Water Research Report Series Number 50) (UNESCO-IHE Institute for Water Education, 2011).

50.
Pastor, A. V. et al. Accounting for environmental flow requirements in global water assessments. Hydrol. Earth Syst. Sci. 18, 5041–5059 (2014).
ADS  Article  Google Scholar 

51.
Briscoe, J. & Malik, R. P. S. India’s Water Economy: Bracing for a Turbulent Future (Oxford Univ. Press, 2006).

52.
Vetter, S. H. et al. Corrigendum to “Greenhouse gas emissions from agricultural food production to supply Indian diets: implications for climate change mitigation” [Agric. Ecosyst. Environ. 237 (2017) 234–241]. Agric. Ecosyst. Environ. 272, 83–85 (2019).
Article  Google Scholar 

53.
Herrero, M. et al. Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems. Proc. Natl Acad. Sci USA 110, 20888–20893 (2013).
ADS  CAS  Article  Google Scholar 

54.
Renard, C. Crop Residues in Sustainable Mixed Crop/Livestock Farming Systems (CABI, 1997).

55.
Smil, V. Crop residues: agriculture’s largest harvest. BioScience 49, 299–308 (1991).
Article  Google Scholar 

56.
R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2016).

57.
Haskell, M. J. The challenge to reach nutritional adequacy for vitamin A: β-carotene bioavailability and conversion—evidence in humans. Am. J. Clin. Nutr. 96, 1193S–1203S (2012).
CAS  Article  Google Scholar 

58.
Schwalfenberg, G. K. Vitamins K1 and K2: the emerging group of vitamins required for human health. J. Nutr. Metab. 2017, 6254836 (2017).
Article  Google Scholar 

59.
Bakshi, M. P. S. Waste to worth: vegetable wastes as animal feed. CAB Rev. 11, 1–26 (2016).

60.
Dikshit, A. K. & Birthal, P. S. India’s livestock feed demand: estimates and projections. Agric. Econ. Res. Rev. 23, 15–28 (2010).
Google Scholar 

61.
Nair, P. K. R. et al. Soil carbon sequestration in tropical agroforestry systems: a feasibility appraisal. Environ. Sci. Pol. 12, 1099–1111 (2009).
CAS  Article  Google Scholar 

62.
Murthy, I. K. et al. Carbon sequestration potential of agroforestry systems in India. Earth Sci. Clim. Change 4, 1000131 (2013).
Google Scholar  More

1.
Painter, T. H. et al. The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo. Remote Sens. Environ. 184, 139–152 (2016).
ADS  Article  Google Scholar 
2.
Guan, B. et al. Snow water equivalent in the Sierra Nevada: Blending snow sensor observations with snowmelt model simulations. Water Resour. Res. 49, 5029–5046 (2013).
ADS  Article  Google Scholar 

3.
Entekhabi, D. et al. The Soil Moisture Active Passive (SMAP) mission. Proc. IEEE 98, 704–716 (2010).
Article  Google Scholar 

4.
Chiang, Y.-M., Hsu, K.-L., Chang, F.-J., Hong, Y. & Sorooshian, S. Merging multiple precipitation sources for flash flood forecasting. J. Hydrol. 340, 183–196 (2007).
ADS  Article  Google Scholar 

5.
Dalrymple, T. Flood-frequency analyses. Manual of hydrology: Part 3. Flood-flow techniques. https://pubs.usgs.gov/wsp/1543a/report.pdf (1960).

6.
Luke, A., Vrugt, J. A., AghaKouchak, A., Matthew, R. & Sanders, B. F. Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States. Water Resour. Res. 53, 5469–5494 (2017).
ADS  Article  Google Scholar 

7.
Dozier, J., Bair, E. H. & Davis, R. E. Estimating the spatial distribution of snow water equivalent in the world’s mountains. WIREs Water 3, 461–474 (2016).
Article  Google Scholar 

8.
Painter, T. H. et al. Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sens. Environ. 113, 868–879 (2009).
ADS  Article  Google Scholar 

9.
Margulis, S. A., Cortés, G., Girotto, M. & Durand, M. A Landsat-era Sierra Nevada snow reanalysis (1985–2015). J. Hydrometeorol 17, 1203–1221 (2016).
ADS  Article  Google Scholar 

10.
Fayad, A. et al. Snow hydrology in Mediterranean mountain regions: A review. J. Hydrol. 551, 374–396 (2017).
ADS  Article  Google Scholar 

11.
Nolin, A. W. Recent advances in remote sensing of seasonal snow. J. Glaciol. 56, 1141–1150 (2010).
ADS  Article  Google Scholar 

12.
Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E. & Bayr, K. J. MODIS snow-cover products. Remote Sens. Environ. 83, 181–194 (2002).
ADS  Article  Google Scholar 

13.
Frei, A. & Robinson, D. A. Northern Hemisphere snow extent: regional variability 1972-1994. Int. J. Climatol. 26 (1999).

14.
CADWR. California’s three traditionally wettest months end with statewide snowpack water content less than average. https://water.ca.gov/LegacyFiles/news/newsreleases/2016/030116d.pdf (2016).

15.
Waliser, D. et al. Simulating cold season snowpack: Impacts of snow albedo and multi-layer snow physics. Clim. Change 109, 95–117 (2011).
Article  Google Scholar 

16.
Scott, D. & McBoyle, G. Climate change adaptation in the ski industry. Mitig. Adapt. Strateg. Glob. Change 12, 1411–1431 (2007).
Article  Google Scholar 

17.
Rittger, K., Bair, E. H., Kahl, A. & Dozier, J. Spatial estimates of snow water equivalent from reconstruction. Adv. Water Resour. 94, 345–363 (2016).
ADS  Article  Google Scholar 

18.
Zeng, X., Broxton, P. & Dawson, N. Snowpack change from 1982 to 2016 over conterminous United States. Geophys. Res. Lett. 45, 12940–12947 (2018).
ADS  Google Scholar 

19.
Carroll, T. et al. NOHRSC Operations and the simulation of snow cover properties for the coterminous U.S. In Proceedings of the 69th Annual Meeting of the Western Snow Conference 14 https://westernsnowconference.org/sites/westernsnowconference.org/PDFs/2001Carroll.pdf (2001).

20.
Huning, L. S. & Margulis, S. A. Climatology of seasonal snowfall accumulation across the Sierra Nevada (USA): Accumulation rates, distributions, and variability. Water Resour. Res. 53, 6033–6049 (2017).
ADS  Article  Google Scholar 

21.
Huning, L. S. & AghaKouchak, A. Mountain snowpack response to different levels of warming. Proc. Natl. Acad. Sci. 115, 10932–10937 (2018).
ADS  CAS  Article  Google Scholar 

22.
Wrzesien, M. L. et al. Comparison of methods to estimate snow water equivalent at the mountain range scale: A case study of the California Sierra Nevada. J. Hydrometeorol 18, 1101–1119 (2017).
ADS  Article  Google Scholar 

23.
Mote, P. W., Hamlet, A. F., Clark, M. P. & Lettenmaier, D. P. Declining mountain snowpack in western North American. Bull. Am. Meteorol. Soc. 86, 39–50 (2005).
ADS  Article  Google Scholar 

24.
Rice, R., Bales, R. C., Painter, T. H. & Dozier, J. Snow water equivalent along elevation gradients in the Merced and Tuolumne river basins of the Sierra Nevada. Water Resour. Res. 47, W08515 (2011).
ADS  Article  Google Scholar 

25.
Dettinger, M., Redmond, K. & Cayan, D. Winter orographic precipitation ratios in the Sierra Nevada—Large-scale atmospheric circulations and hydrologic consequences. J. Hydrometeorol 5, 1102–1116 (2004).
ADS  Article  Google Scholar 

26.
Lundquist, J. D., Minder, J. R., Neiman, P. J. & Sukovich, E. Relationships between barrier jet heights, orographic precipitation gradients, and streamflow in the northern Sierra Nevada. J. Hydrometeorol 11, 1141–1156 (2010).
ADS  Article  Google Scholar 

27.
Huning, L. S. & Margulis, S. A. Investigating the variability of high-elevation seasonal orographic snowfall enhancement and its drivers across Sierra Nevada, California. J. Hydrometeorol 19, 47–67 (2018).
ADS  Article  Google Scholar 

28.
Huning, L. S., Margulis, S. A., Guan, B., Waliser, D. E. & Neiman, P. J. Implications of detection methods on characterizing atmospheric river contribution to seasonal snowfall across Sierra Nevada, USA. Geophys. Res. Lett. 44, 10445–10453 (2017).
ADS  Article  Google Scholar 

29.
Huning, L. S., Guan, B., Waliser, D. E. & Lettenmaier, D. P. Sensitivity of seasonal snowfall attribution to atmospheric rivers and their reanalysis-based detection. Geophys. Res. Lett. 46, 794–803 (2019).
ADS  Article  Google Scholar 

30.
Harpold, A., Dettinger, M. & Rajagopal, S. Defining snow drought and why it matters. Eos 98, (2017).

31.
Guan, B., Molotch, N. P., Waliser, D. E., Fetzer, E. J. & Neiman, P. J. Extreme snowfall events linked to atmospheric rivers and surface air temperature via satellite measurements. Geophys. Res. Lett. 37, 12514–12535 (2010).
Article  Google Scholar 

32.
Guan, B., Waliser, D. E., Ralph, F. M., Fetzer, E. J. & Neiman, P. J. Hydrometeorological characteristics of rain-on-snow events associated with atmospheric rivers. Geophys. Res. Lett. 43, 2964–2973 (2016).
ADS  Article  Google Scholar 

33.
Hu, J. M. & Nolin, A. W. Snowpack contributions and temperature characterization of landfalling atmospheric rivers in the western cordillera of the United States. Geophys. Res. Lett. 46, 6663–6672 (2019).
ADS  Article  Google Scholar 

34.
Hu, J. M. & Nolin, A. W. Widespread warming trends in storm temperatures and snowpack fate across the Western United States. Environ. Res. Lett. 15, 034059 (2020).
ADS  Article  Google Scholar 

35.
Margulis, S. A. et al. Characterizing the extreme 2015 snowpack deficit in the Sierra Nevada (USA) and the implications for drought recovery. Geophys. Res. Lett. 43, 6341–6349 (2016).
ADS  Article  Google Scholar 

36.
Mann, H. B. Nonparametric tests against trend. Econometrica 13, 245–259 (1945).
MathSciNet  Article  Google Scholar 

37.
Mote, P. W., Li, S., Lettenmaier, D. P., Xiao, M. & Engel, R. Dramatic declines in snowpack in the western US. Npj Clim. Atmospheric Sci 1, 2 (2018).
Article  Google Scholar 

38.
Ragno, E., AghaKouchak, A., Cheng, L. & Sadegh, M. A generalized framework for process-informed nonstationary extreme value analysis. Adv. Water Resour. 130, 270–282 (2019).
ADS  Article  Google Scholar 

39.
Huning, L. S. & AghaKouchak, A. Sierra Nevada (USA) snow water equivalent (SWE) volume time series. Figshare https://doi.org/10.6084/m9.figshare.c.5055518 (2020).

40.
Nash, J. E. & Sutcliffe, J. V. River flow forecasting through conceptual models part I — A discussion of principles. J. Hydrol. 10, 282–290 (1970).
ADS  Article  Google Scholar 

41.
Moriasi, D. N. et al. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50, 885–900 (2007).
Article  Google Scholar 

42.
Mao, Y., Nijssen, B. & Lettenmaier, D. P. Is climate change implicated in the 2013-2014 California drought? A hydrologic perspective. Geophys. Res. Lett. 42, 2805–2813 (2015).
ADS  Article  Google Scholar 

43.
Wang, K. J., Williams, A. P. & Lettenmaier, D. P. How much have California winters warmed over the last century? Geophys. Res. Lett. 44, 8893–8900 (2017).
ADS  Article  Google Scholar 

44.
Belmecheri, S., Babst, F., Wahl, E. R., Stahle, D. W. & Trouet, V. Multi-century evaluation of Sierra Nevada snowpack. Nat. Clim. Change 6, 2–3 (2016).
ADS  Article  Google Scholar 

45.
Liang, X., Lettenmaier, D. P., Wood, E. F. & Burges, S. J. A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res. 99, 14415–14428 (1994).
ADS  Article  Google Scholar 

46.
Holdren, G. C. & Turner, K. Characteristics of Lake Mead, Arizona–Nevada. Lake Reserv. Manag. 26, 230–239 (2010).
CAS  Article  Google Scholar  More

1.
Yao, T. et al. Recent third pole’s rapid warming accompanies cryospheric melt and water cycle intensification and interactions between monsoon and environment: multidisciplinary approach with observations, modeling, and analysis. Bull. Am. Meteorol. Soc. 100, 423–444 (2018).
Google Scholar 
2.
Armstrong, R. L. et al. Runoff from glacier ice and seasonal snow in High Asia: separating melt water sources in river flow. Reg. Environ. Chang. 19, 1249–1261 (2019).
Google Scholar 

3.
Guo, J. et al. Linking atmospheric pollution to cryospheric change in the third pole region: current progresses and future prospects. Natl Sci. Rev. 6, 796–809 (2019).
Google Scholar 

4.
Bolch, T. et al. in The Hindu Kush Himalaya Assessment: Mountains, Climate Change, Sustainability and People (eds Wester, P. et al.) 209–255 (Springer, 2019).

5.
Smith, T. & Bookhagen, B. Changes in seasonal snow water equivalent distribution in high mountain Asia (1987 to 2009). Sci. Adv. 4, e1701550 (2018).
Google Scholar 

6.
IPCC Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 33 (Cambridge Univ. Press, 2014).

7.
Painter, T. H., Seidel, F. C., Bryant, A. C., McKenzie Skiles, S. & Rittger, K. Imaging spectroscopy of albedo and radiative forcing by light-absorbing impurities in mountain snow. J. Geophys. Res. Atmos. 118, 9511–9523 (2013).
Google Scholar 

8.
Qian, Y. et al. Light-absorbing particles in snow and ice: measurement and modeling of climatic and hydrological impact. Adv. Atmos. Sci. 32, 64–91 (2015).
CAS  Google Scholar 

9.
McKenzie Skiles, S. & Painter, T. H. Assessment of radiative forcing by light-absorbing particles in snow from in situ observations with radiative transfer modeling. J. Hydrometeorol. 19, 1397–1409 (2018).
Google Scholar 

10.
Qian, Y., Flanner, M. G., Leung, L. R. & Wang, W. Sensitivity studies on the impacts of Tibetan Plateau snowpack pollution on the Asian hydrological cycle and monsoon climate. Atmos. Chem. Phys. 11, 1929–1948 (2011).
CAS  Google Scholar 

11.
Gautam, R., Hsu, N. C., Lau, W. K. M. & Yasunari, T. J. Satellite observations of desert dust-induced Himalayan snow darkening. Geophys. Res. Lett. 40, 988–993 (2013).
Google Scholar 

12.
Yasunari, T. J. et al. Estimated range of black carbon dry deposition and the related snow albedo reduction over Himalayan glaciers during dry pre-monsoon periods. Atmos. Environ. 78, 259–267 (2013).
CAS  Google Scholar 

13.
Nair, V. S. et al. Black carbon aerosols over the Himalayas: direct and surface albedo forcing. Tellus B Chem. Phys. Meteorol. 65, 19738 (2013).
Google Scholar 

14.
Ménégoz, M. et al. Snow cover sensitivity to black carbon deposition in the Himalayas: from atmospheric and ice core measurements to regional climate simulations. Atmos. Chem. Phys. 14, 4237–4249 (2014).
Google Scholar 

15.
Ming, J. et al. Black carbon record based on a shallow Himalayan ice core and its climatic implications. Atmos. Chem. Phys. 8, 1343–1352 (2008).
CAS  Google Scholar 

16.
Usha, K. H., Nair, V. S. & Babu, S. S. Modeling of aerosol induced snow albedo feedbacks over the Himalayas and its implications on regional climate. Clim. Dyn. 54, 4191–4210 (2020).
Google Scholar 

17.
Sarangi, C. et al. Impact of light-absorbing particles on snow albedo darkening and associated radiative forcing over high-mountain Asia: high-resolution WRF-Chem modeling and new satellite observations. Atmos. Chem. Phys. 19, 7105–7128 (2019).
CAS  Google Scholar 

18.
Svensson, J. et al. Light-absorption of dust and elemental carbon in snow in the Indian Himalayas and the Finnish Arctic. Atmos. Meas. Tech. 11, 1403–1416 (2018).
CAS  Google Scholar 

19.
Kaspari, S., Painter, T. H., Gysel, M., Skiles, S. M. & Schwikowski, M. Seasonal and elevational variations of black carbon and dust in snow and ice in the Solu-Khumbu, Nepal and estimated radiative forcings. Atmos. Chem. Phys. 14, 8089–8103 (2014).
Google Scholar 

20.
Bonasoni, P. et al. Atmospheric brown clouds in the Himalayas: first two years of continuous observations at the Nepal Climate Observatory-Pyramid (5079 m). Atmos. Chem. Phys. 10, 7515–7531 (2010).
CAS  Google Scholar 

21.
Vaishya, A. et al. Large contrast in the vertical distribution of aerosol optical properties and radiative effects across the Indo-Gangetic Plain during the SWAAMI–RAWEX campaign. Atmos. Chem. Phys. 18, 17669–17685 (2018).
CAS  Google Scholar 

22.
Sarangi, C., Tripathi, S. N., Mishra, A. K., Goel, A. & Welton, E. J. Elevated aerosol layers and their radiative impact over Kanpur during monsoon onset period. J. Geophys. Res. Atmos. 121, 7936-7957 (2016).

23.
Gautam, R., Hsu, N. C. & Lau, K.-M. Premonsoon aerosol characterization and radiative effects over the Indo-Gangetic Plains: implications for regional climate warming. J. Geophys. Res.—Atmos. 115, D17208 (2010).
Google Scholar 

24.
Mishra, A. K. & Shibata, T. Climatological aspects of seasonal variation of aerosol vertical distribution over central Indo-Gangetic belt (IGB) inferred by the space-borne lidar CALIOP. Atmos. Environ. 46, 365–375 (2012).
CAS  Google Scholar 

25.
Liu, Z. et al. Airborne dust distributions over the Tibetan Plateau and surrounding areas derived from the first year of CALIPSO lidar observations. Atmos. Chem. Phys. 8, 5045–5060 (2008).
CAS  Google Scholar 

26.
Das, S., Dey, S., Dash, S. K. & Basil, G. Examining mineral dust transport over the Indian subcontinent using the regional climate model, RegCM4.1. Atmos. Res. 134, 64–76 (2013).
CAS  Google Scholar 

27.
Warren, S. G. & Wiscombe, W. J. A model for the spectral albedo of snow. II: snow containing atmospheric aerosols. J. Atmos. Sci. 37, 2734–2745 (1980).
Google Scholar 

28.
Warren, S. G. Optical properties of snow. Rev. Geophys. 20, 67–89 (1982).
Google Scholar 

29.
Dang, C., Fu, Q. & Warren, S. G. Effect of snow grain shape on snow albedo. J. Atmos. Sci. 73, 3573–3583 (2016).
Google Scholar 

30.
Hansen, J. & Nazarenko, L. Soot climate forcing via snow and ice albedos. Proc. Natl Acad. Sci. USA 101, 423–428 (2004).
CAS  Google Scholar 

31.
Painter, T. H. et al. Response of Colorado River runoff to dust radiative forcing in snow. Proc. Natl Acad. Sci. USA 107, 17125–17130 (2010).
CAS  Google Scholar 

32.
Skiles, S. M., Painter, T. H., Deems, J. S., Bryant, A. C. & Landry, C. C. Dust radiative forcing in snow of the Upper Colorado River Basin: 2. Interannual variability in radiative forcing and snowmelt rates. Water Resour. Res. 48, W07522 (2012).
Google Scholar 

33.
Skiles, S. M. K. & Painter, T. Daily evolution in dust and black carbon content, snow grain size, and snow albedo during snowmelt, Rocky Mountains, Colorado. J. Glaciol. 63, 118–132 (2017).
Google Scholar 

34.
Di Mauro, B. et al. Mineral dust impact on snow radiative properties in the European Alps combining ground, UAV, and satellite observations. J. Geophys. Res. Atmos. 120, 6080–6097 (2015).
Google Scholar 

35.
Dumont, M. et al. In situ continuous visible and near-infrared spectroscopy of an alpine snowpack. Cryosph. 11, 1091–1110 (2017).
Google Scholar 

36.
Huang, J. et al. Dust and black carbon in seasonal snow across northern China. Bull. Am. Meteorol. Soc. 92, 175–181 (2010).
Google Scholar 

37.
Wang, X. et al. Observations and model simulations of snow albedo reduction in seasonal snow due to insoluble light-absorbing particles during 2014 Chinese survey. Atmos. Chem. Phys. 17, 2279–2296 (2017).
CAS  Google Scholar 

38.
Zhang, Y. et al. Black carbon and mineral dust in snow cover on the Tibetan Plateau. Cryosph. 12, 413–431 (2018).
Google Scholar 

39.
Warren, S. G. Can black carbon in snow be detected by remote sensing? J. Geophys. Res. Atmos. 118, 779–786 (2013).
CAS  Google Scholar 

40.
Flanner, M. G., Zender, C. S., Randerson, J. T. & Rasch, P. J. Present-day climate forcing and response from black carbon in snow. J. Geophys. Res. Atmos. 112, D11202 (2007).
Google Scholar 

41.
Doherty, S. J. et al. Observed vertical redistribution of black carbon and other insoluble light-absorbing particles in melting snow. J. Geophys. Res. Atmos. 118, 5553–5569 (2013).
Google Scholar 

42.
Painter, T. H., Bryant, A. C. & McKenzie Skiles, S. Radiative forcing by light absorbing impurities in snow from MODIS surface reflectance data. Geophys. Res. Lett. 39, L17502 (2012).
Google Scholar 

43.
Hadley, O. L. & Kirchstetter, T. W. Black-carbon reduction of snow albedo. Nat. Clim. Chang. 2, 437–440 (2012).
CAS  Google Scholar 

44.
Brun, F., Berthier, E., Wagnon, P., Kääb, A. & Treichler, D. A spatially resolved estimate of High Mountain Asia glacier mass balances from 2000 to 2016. Nat. Geosci. 10, 668 (2017).
CAS  Google Scholar 

45.
Zhao, H., Yang, W., Yao, T., Tian, L. & Xu, B. Dramatic mass loss in extreme high-elevation areas of a western Himalayan glacier: observations and modeling. Sci. Rep. 6, 30706 (2016).
CAS  Google Scholar 

46.
Ji, Z. M. Modeling black carbon and its potential radiative effects over the Tibetan Plateau. Adv. Clim. Chang. Res. 7, 139–144 (2016).
Google Scholar 

47.
Xu, J. et al. The melting Himalayas: cascading effects of climate change on water, biodiversity, and livelihoods. Conserv. Biol. 23, 520–530 (2009).
CAS  Google Scholar 

48.
Ghatak, D., Sinsky, E. & Miller, J. Role of snow-albedo feedback in higher elevation warming over the Himalayas, Tibetan Plateau and Central Asia. Environ. Res. Lett. 9, 114008 (2014).

49.
Bormann, K. J., Brown, R. D., Derksen, C. & Painter, T. H. Estimating snow-cover trends from space. Nat. Clim. Change 8, 924–928 (2018).
Google Scholar 

50.
Ming, J., Xiao, C., Du, Z. & Yang, X. An overview of black carbon deposition in High Asia glaciers and its impacts on radiation balance. Adv. Water Resour. 55, 80–87 (2013).
CAS  Google Scholar 

51.
Painter, T. H. et al. Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sens. Environ. 113, 868–879 (2009).
Google Scholar 

52.
Rittger, K., Painter, T. H. & Dozier, J. Assessment of methods for mapping snow cover from MODIS. Adv. Water Resour. 51, 367–380 (2013).
Google Scholar 

53.
Dozier, J., Painter, T. H., Rittger, K. & Frew, J. E. Time–space continuity of daily maps of fractional snow cover and albedo from MODIS. Adv. Water Resour. 31, 1515–1526 (2008).
Google Scholar 

54.
Rittger, K., Bair, E. H., Kahl, A. & Dozier, J. Spatial estimates of snow water equivalent from reconstruction. Adv. Water Resour. 94, 345–363 (2016).
Google Scholar 

55.
Chand, D. et al. Quantifying above-cloud aerosol using spaceborne lidar for improved understanding of cloudy-sky direct climate forcing. J. Geophys. Res. Atmos. 113, D13206 (2008).
Google Scholar 

56.
Winker, D. M. et al. The CALIPSO mission. Bull. Am. Meteorol. Soc. 91, 1211–1230 (2010).
Google Scholar 

57.
Gelaro, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).
Google Scholar 

58.
Molod, A., Takacs, L., Suarez, M. & Bacmeister, J. Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2. Geosci. Model Dev. 8, 1339–1356 (2015).
Google Scholar 

59.
Buchard, V. et al. Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis. Atmos. Chem. Phys. 15, 5743–5760 (2015).
CAS  Google Scholar 

60.
Derber, J. C., Parrish, D. F. & Lord, S. J. The New Global Operational Analysis System at the National Meteorological Center. Weather Forecast. 6, 538–547 (1991).
Google Scholar 

61.
Herman, J. R. et al. Global distribution of UV-absorbing aerosols from Nimbus 7/TOMS data. J. Geophys. Res. Atmos. 102, 16911–16922 (1997).
CAS  Google Scholar 

62.
Huang, J., Ge, J. & Weng, F. Detection of Asia dust storms using multisensor satellite measurements. Remote Sens. Environ. 110, 186–191 (2007).
Google Scholar 

63.
Sun, H., Liu, X. & Pan, Z. Direct radiative effects of dust aerosols emitted from the Tibetan Plateau on the East Asian summer monsoon—a regional climate model simulation. Atmos. Chem. Phys. 17, 13731–13745 (2017).
CAS  Google Scholar 

64.
Zaveri, R. A., Easter, R. C., Fast, J. D. & Peters, L. K. Model for simulating aerosol interactions and chemistry (MOSAIC). J. Geophys. Res. Atmos. 113, D13204 (2008).
Google Scholar 

65.
Flanner, M. G., Liu, X., Zhou, C., Penner, J. E. & Jiao, C. Enhanced solar energy absorption by internally-mixed black carbon in snow grains. Atmos. Chem. Phys. 12, 4699–4721 (2012).
CAS  Google Scholar 

66.
Zhao, C. et al. Simulating black carbon and dust and their radiative forcing in seasonal snow: a case study over North China with field campaign measurements. Atmos. Chem. Phys. 14, 11475–11491 (2014).
Google Scholar  More

1.
Phillip, W. A. & Elimelech, M. The future of seawater desalination: energy, technology, and the environment. Science 333, 712–717 (2011).
Article  Google Scholar 
2.
Mauter, M. S. et al. The role of nanotechnology in tackling global water challenges. Nat. Sustain. 1, 166–175 (2018).
Article  Google Scholar 

3.
Stevens, D. M., Shu, J. Y., Reichert, M. & Roy, A. Next-generation nanoporous materials: progress and prospects for reverse osmosis and nanofiltration. Ind. Eng. Chem. Res. 56, 10526–10551 (2017).
CAS  Article  Google Scholar 

4.
Werber, J. R., Osuji, C. O. & Elimelech, M. Materials for next-generation desalination and water purification membranes. Nat. Rev. Mater. 1, 16018–16025 (2016).
CAS  Article  Google Scholar 

5.
Qasim, M., Badrelzaman, M., Darwish, N. N., Darwish, N. A. & Hilal, N. Reverse osmosis desalination: a state-of-the-art review. Desalination 459, 59–104 (2019).
CAS  Article  Google Scholar 

6.
Chowdhury, M. R., Steffes, J., Huey, B. D. & McCutcheon, J. R. 3D printed polyamide membranes for desalination. Science 361, 682–686 (2018).
CAS  Article  Google Scholar 

7.
Gohil, J. M. & Suresh, A. K. Chlorine attack on reverse osmosis membranes: mechanisms and mitigation strategies. J. Membr. Sci. 541, 108–126 (2017).
CAS  Article  Google Scholar 

8.
Verbeke, R., Gómez, V. & Vankelecom, I. F. J. Chlorine-resistance of reverse osmosis (RO) polyamide membranes. Prog. Polym. Sci. 72, 1–15 (2017).
CAS  Article  Google Scholar 

9.
Stolov, M. & Freger, V. Degradation of polyamide membranes exposed to chlorine: an impedance spectroscopy study. Environ. Sci. Technol. 53, 2618–2625 (2019).
CAS  Article  Google Scholar 

10.
Do, V. T., Tang, C. Y., Reinhard, M. & Leckie, J. O. Effects of chlorine exposure conditions on physiochemical properties and performance of a polyamide membrane-mechanisms and implications. Environ. Sci. Technol. 46, 13184–13192 (2012).
CAS  Article  Google Scholar 

11.
Glater, J., Hong, N. & Elimelech, M. The search for a chlorine-resistant reverse osmosis membrane. Desalination 95, 325–345 (1994).
CAS  Article  Google Scholar 

12.
Werber, J. R., Deshmukh, A. & Elimelech, M. The critical need for increased selectivity, not increased water permeability, for desalination membranes. Environ. Sci. Technol. 3, 112–120 (2016).
CAS  Article  Google Scholar 

13.
Tanugi, D. C., McGovern, R. K., Dave, S. H., Lienhard, J. H. & Grossman, J. C. Quantifying the potential of ultra-permeable membranes for water desalination. Energy Environ. Sci. 7, 1134–1141 (2014).
Article  Google Scholar 

14.
Yao, Y. et al. Toward enhancing the chlorine resistance of reverse osmosis membranes: an effective strategy via an end-capping technology. Environ. Sci. Technol. 53, 1296–1304 (2019).
Article  Google Scholar 

15.
Hu, J., Pu, Y., Ueda, M., Zhang, X. & Wang, L. Charge-aggregate induced (CAI) reverse osmosis membrane for seawater desalination and boron removal. J. Membr. Sci. 520, 1–7 (2016).
CAS  Article  Google Scholar 

16.
Yao, Y. et al. A novel sulfonated reverse osmosis membrane for seawater desalination: Experimental and molecular dynamics studies. J. Membr. Sci. 550, 470–479 (2018).
CAS  Article  Google Scholar 

17.
Zheng, J. et al. Reverse osmosis membrane with enhanced permselectivity for brackish water desalination. J. Membr. Sci. 565, 104–111 (2018).
CAS  Article  Google Scholar 

18.
Cheremisinoff, N. P. Condensed Encyclopedia of Polymer Engineering Terms (Butterworth–Heinemann, 2001).

19.
Wu, D., Chen, F., Li, R. & Shi, Y. Reaction kinetics and simulations for solid-state polymerization of poly(ethylene terephthalate). Macromolecules 30, 6737–6742 (1997).
CAS  Article  Google Scholar 

20.
Krevelen, D. W. V. & Nijenhuis, K. T. in Properties of Polymers: Their Correlation with Chemical Structure; their Numerical Estimation and Prediction from Additive Group Contributions Ch. 7 (Elsevier, 2009).

21.
Lide, D. R. Handbook of Chemistry and Physics (CRC Press, 2010).

22.
Kuang, J. et al. Ozonation of trimethoprim in aqueous solution: identification of reaction products and their toxicity. Water Res. 47, 2863–2872 (2013).
CAS  Google Scholar 

23.
Miao, H. F. et al. Degradation of phenazone in aqueous solution with ozone: influencing factors and degradation pathways. Chemosphere 119, 326–333 (2015).
CAS  Google Scholar 

24.
Park, H., Vecitis, C. D. & Hoffmann, M. R. Electrochemical water splitting coupled with organic compound oxidation: the role of active chlorine species. J. Phys. Chem. C 113, 7935–7945 (2009).
CAS  Google Scholar 

25.
Jimenez-Solomon, M., Song, Q., Jelfs, K., Munoz-Ibanez, M. & Livingston, A. G. Polymer nanofilms with enhanced microporosity by interfacial polymerization. Nat. Mater. 15, 760–767 (2016).
CAS  Google Scholar 

26.
Antony, A., Fudianto, R. & Cox, S. Assessing the oxidative degradation of polyamide reverse osmosis membrane—accelerated ageing with hypochlorite exposure. J. Membr. Sci. 347, 159–164 (2010).
CAS  Google Scholar 

27.
Huang, K. et al. Reactivity of the polyamide membrane monomer with free chlorine: reaction kinetics, mechanisms, and the role of chloride. Environ. Sci. Technol. 53, 8167–8176 (2019).
CAS  Article  Google Scholar 

28.
Do, V. T., Tang, C. Y., Reinhard, M. & Leckie, J. O. Degradation of polyamide nanofiltration and reverse osmosis membranes by hypochlorite. Environ. Sci. Technol. 46, 852–859 (2012).
CAS  Article  Google Scholar 

29.
Xu, G. R., Wang, J. N. & Li, C. J. Strategies for improving the performance of the polyamide thin film composite (PA-TFC) reverse osmosis (RO) membranes: surface modifications and nanoparticles incorporations. Desalination 328, 83–100 (2013).
CAS  Article  Google Scholar 

30.
Asadollahi, M., Bastani, D. & Musavi, S. A. Enhancement of surface properties and performance of reverse osmosis membranes after surface modification: a review. Desalination 420, 330–383 (2017).
CAS  Article  Google Scholar 

31.
Park, H., Freeman, B. D., Zhang, Z., Sankir, M. & McGrath, J. E. Highly chlorine-tolerant polymers for desalination. Angew. Chem. Int. Ed. 47, 6019–6024 (2008).
CAS  Article  Google Scholar 

32.
Law, S. K. A., Minich, T. M. & Levine, R. P. Covalent binding efficiency of the third and fourth complement proteins in relation to pH, nucleophilicity, and availability of hydroxyl groups. Biochemistry 23, 3267–3272 (1984).
CAS  Article  Google Scholar 

33.
FILMTECTMReverse Osmosis Membranes Technical Manual Form No.45-D01696-en, Rev. 4, 2020; Cleaning procedures for FilmTec™ FT30 Elements (Dow, 2020); https://www.dupont.com/products/filmtecsw302514.html

34.
She, Q., Wang, R., Fane, A. G. & Tang, C. Y. Membrane fouling in osmotically driven membrane processes: a review. J. Membr. Sci. 499, 201–233 (2016).
CAS  Article  Google Scholar  More