More stories

  • in

    Nutrition under natural resource constraints

    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

  • in

    India has natural resource capacity to achieve nutrition security, reduce health risks and improve environmental sustainability

    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

  • in

    Approaching 80 years of snow water equivalent information by merging different data streams

    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

  • in

    Dust dominates high-altitude snow darkening and melt over high-mountain Asia

    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

  • in

    High performance polyester reverse osmosis desalination membrane with chlorine resistance

    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

  • in

    Homogenization of the terrestrial water cycle

    These authors contributed equally: Delphis F. Levia, Irena F. Creed.

    Department of Geography and Spatial Sciences, University of Delaware, Newark, DE, USA
    Delphis F. Levia, Janice E. Hudson & Sean A. Hudson

    School of Environment and Sustainability, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
    Irena F. Creed

    School of Geography, Earth and Environmental Science, University of Birmingham, Birmingham, UK
    David M. Hannah

    Department of Disaster Prevention, Meteorology and Hydrology, Forestry and Forest Products Research Institute, Tsukuba, Japan
    Kazuki Nanko & Shin’ichi Iida

    Department of Ecosystem Science and Management, Pennsylvania State University, University Park, PA, USA
    Elizabeth W. Boyer

    Department of Geography and Environmental Studies, Thompson Rivers University, Kamloops, British Columbia, Canada
    Darryl E. Carlyle-Moses

    Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, Netherlands
    Nick van de Giesen

    Office of the Chancellor, University of Michigan- Dearborn, Dearborn, MI, USA
    Domenico Grasso

    Picker Engineering Program, Smith College, Northampton, MA, USA
    Andrew J. Guswa

    Department of Earth System Science, Woods Institute for the Environment, and Precourt Institute for Energy, Stanford University, Stanford, CA, USA
    Robert B. Jackson

    Nicholas School of the Environment, Duke University, Durham, NC, USA
    Gabriel G. Katul

    Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan
    Tomo’omi Kumagai

    Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, Spain
    Pilar Llorens

    Disaster Research Center, University of Delaware, Newark, DE, USA
    Flavio Lopes Ribeiro

    School of Biological Sciences, University of Utah, Salt Lake City, UT, USA
    Diane E. Pataki

    Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA
    Catherine A. Peters

    Department of Civil and Environmental Engineering, University of Delaware, Newark, DE, USA
    Daniel Sanchez Carretero

    Department of Biological and Ecological Engineering, Oregon State University, Corvallis, OR, USA
    John S. Selker

    Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
    Doerthe Tetzlaff

    European Regional Center for Ecohydrology, UNESCO and Department of Applied Ecology, University of Lodz, Lodz, Poland
    Maciej Zalewski

    UCD School of Civil Engineering, University College Dublin, Dublin, Ireland
    Michael Bruen More

  • in

    Adding forests to the water–energy–food nexus

    1.
    Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).
    Google Scholar 
    2.
    At the human-forest interface. Nat. Commun. 9, 1153 (2018).

    3.
    Melo, F. P. L., Arroyo-Rodríguez, V., Fahrig, L., Martínez-Ramos, M. & Tabarelli, M. On the hope for biodiversity-friendly tropical landscapes. Trends Ecol. Evol. 28, 462–468 (2013).
    Google Scholar 

    4.
    Arroyo‐Rodríguez, V. et al. Designing optimal human-modified landscapes for forest biodiversity conservation. Ecol. Lett. 23, 1404–1420 (2020).
    Google Scholar 

    5.
    Castañeda, A. et al. A new profile of the global poor. World Dev. 101, 250–267 (2018).
    Google Scholar 

    6.
    DeFries, R. & Nagendra, H. Ecosystem management as a wicked problem. Science 356, 265–270 (2017).
    CAS  Google Scholar 

    7.
    Brancalion, P. H. S. et al. Global restoration opportunities in tropical rainforest landscapes. Sci. Adv. 5, eaav3223 (2019).
    Google Scholar 

    8.
    Meli, P. et al. Four approaches to guide ecological restoration in Latin America. Restor. Ecol. 25, 156–163 (2017).
    Google Scholar 

    9.
    Robertson, M., Nichols, P., Horwitz, P., Bradby, K. & MacKintosh, D. Environmental narratives and the need for multiple perspectives to restore degraded landscapes in Australia. Ecosyst. Health 6, 119–133 (2000).
    CAS  Google Scholar 

    10.
    Banks-Leite, C. et al. Using ecological thresholds to evaluate the costs and benefits of set-asides in a biodiversity hotspot. Science 345, 1041–1045 (2014).
    CAS  Google Scholar 

    11.
    Strassburg, B. B. N. et al. Strategic approaches to restoring ecosystems can triple conservation gains and halve costs. Nat. Ecol. Evol. 3, 62–70 (2019).
    Google Scholar 

    12.
    Brancalion, P. H. S. et al. What makes ecosystem restoration expensive? A systematic cost assessment of projects in Brazil. Biol. Conserv. 240, 108274 (2019).
    Google Scholar 

    13.
    Simpson, G. B. & Jewitt, G. P. W. The development of the water-energy-food nexus as a framework for achieving resource security: a review. Front. Environ. Sci. 7, 8 (2019).
    Google Scholar 

    14.
    Biggs, E. M. et al. Sustainable development and the water–energy–food nexus: a perspective on livelihoods. Environ. Sci. Policy 54, 389–397 (2015).
    Google Scholar 

    15.
    Hoff, H. Understanding the Nexus: Background Paper for the Bonn2011 Nexus Conference (Stockholm Environment Institute, 2011).

    16.
    Bazilian, M. et al. Considering the energy, water and food nexus: towards an integrated modelling approach. Energy Policy 39, 7896–7906 (2011).
    Google Scholar 

    17.
    Liu, J. et al. Nexus approaches to global sustainable development. Nat. Sustain. 1, 466–476 (2018).
    Google Scholar 

    18.
    Ibisch, R. B., Bogardi, J. J. & Borchardt, D. in Integrated Water Resources Management: Concept, Research and Implementation (eds Borchardt, D. et al.) 3–32 (Springer, 2016).

    19.
    Díaz, S. et al. Assessing nature’s contributions to people. Science 359, 270–272 (2018).
    Google Scholar 

    20.
    Ribot, J. C. & Peluso, N. L. A Theory of Access*. Rural Sociol. 68, 153–181 (2003).
    Google Scholar 

    21.
    Voluntary Guidelines for Agro-Environmental Policies in Latin Amrica and The Caribbean (FAO, 2018).

    22.
    Pradhan, P., Costa, L., Rybski, D., Lucht, W. & Kropp, J. P. A systematic study of Sustainable Development Goal (SDG) interactions. Earth’s Future 5, 1169–1179 (2017).
    Google Scholar 

    23.
    Cole, L. E. S., Bhagwat, S. A. & Willis, K. J. Recovery and resilience of tropical forests after disturbance. Nat. Commun. 5, 3906 (2014).
    CAS  Google Scholar 

    24.
    Chazdon, R. & Brancalion, P. Restoring forests as a means to many ends. Science 365, 24–25 (2019).
    CAS  Google Scholar 

    25.
    Protecting and Restoring Forests: A Story of Large Commitments yet Limited Progress. New York Declaration on Forests Five-Year Assessment Report (NYDF Assessment Partners, 2019).

    26.
    Holl, K. D. & Brancalion, P. H. S. Tree planting is not a simple solution. Science 368, 580–581 (2020).
    CAS  Google Scholar 

    27.
    Albrecht, T. R., Crootof, A. & Scott, C. A. The water-energy-food nexus: a systematic review of methods for nexus assessment. Environ. Res. Lett. 13, 043002 (2018).
    Google Scholar 

    28.
    Townsend, P. V. et al. Multiple environmental services as an opportunity for watershed restoration. For. Policy Econ. 17, 45–58 (2012).
    Google Scholar 

    29.
    Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).
    Google Scholar 

    30.
    van Noordwijk, M. Integrated natural resource management as pathway to poverty reduction: innovating practices, institutions and policies. Agric. Syst. 172, 60–71 (2019).
    Google Scholar 

    31.
    Moreno-Mateos, D. et al. Anthropogenic ecosystem disturbance and the recovery debt. Nat. Commun. 8, 14163 (2017).
    CAS  Google Scholar 

    32.
    Brancalion, P. H. S. et al. A critical analysis of the Native Vegetation Protection Law of Brazil (2012): updates and ongoing initiatives. Nat. Conserv. 14, 1–15 (2016).
    Google Scholar 

    33.
    Soares-Filho, B. et al. Cracking Brazil’s Forest Code. Science 344, 363–364 (2014).
    CAS  Google Scholar 

    34.
    Pires, A. P. F., Rezende, C. L., Assad, E. D., Loyola, R. & Scarano, F. R. Forest restoration can increase the Rio Doce watershed resilience. Perspect. Ecol. Conserv. 15, 187–193 (2017).
    Google Scholar 

    35.
    Filoso, S., Bezerra, M. O., Weiss, K. C. B. & Palmer, M. A. Impacts of forest restoration on water yield: a systematic review. PLoS ONE 12, e0183210 (2017).
    Google Scholar 

    36.
    Ellison, D. et al. Trees, forests and water: cool insights for a hot world. Glob. Environ. Change 43, 51–61 (2017).
    Google Scholar 

    37.
    van der Ent, R. J., Savenije, H. H. G., Schaefli, B. & Steele‐Dunne, S. C. Origin and fate of atmospheric moisture over continents. Water Resour. Res. 46, W09525 (2010).
    Google Scholar 

    38.
    Sheil, D. Forests, atmospheric water and an uncertain future: the new biology of the global water cycle. For. Ecosyst. 5, 19 (2018).
    Google Scholar 

    39.
    Karabulut, A. et al. Mapping water provisioning services to support the ecosystem-water-food-energy nexus in the Danube river basin. Ecosyst. Serv. 17, 278–292 (2016).
    Google Scholar 

    40.
    Richards, R. C. et al. Governing a pioneer program on payment for watershed services: stakeholder involvement, legal frameworks and early lessons from the Atlantic forest of Brazil. Ecosyst. Serv. 16, 23–32 (2015).
    Google Scholar 

    41.
    Vincent, J. R. et al. Valuing water purification by forests: an analysis of Malaysian panel data. Environ. Resour. Econ. 64, 59–80 (2016).
    Google Scholar 

    42.
    Brancalion, P., Viani, R., Strassburg, B. & Rodrigues, R. Finding the money for tropical forest restoration. Unasylva 239, 41–50 (2012).
    Google Scholar 

    43.
    Zemp, D. C. et al. On the importance of cascading moisture recycling in South America. Atmos. Chem. Phys. 14, 13337–13359 (2014).
    CAS  Google Scholar 

    44.
    Energy Access Outlook: From Poverty to Prosperity (International Energy Agency, 2017).

    45.
    Specht, M. J., Pinto, S. R. R., Albuquerque, U. P., Tabarelli, M. & Melo, F. P. L. Burning biodiversity: fuelwood harvesting causes forest degradation in human-dominated tropical landscapes. Glob. Ecol. Conserv. 3, 200–209 (2015).
    Google Scholar 

    46.
    The State of the World’s Forests 2018 – Forest Pathways to Sustainable Development (FAO, 2018).

    47.
    Review of Woodfuel Biomass Production and Utilization in Africa: A Desk Study (United Nations Environment Programme, 2019).

    48.
    Forests and Energy (FAO, 2017); https://go.nature.com/3aI4LYZ

    49.
    Arias, M. E., Cochrane, T. A., Lawrence, K. S., Killeen, T. J. & Farrell, T. A. Paying the forest for electricity: a modelling framework to market forest conservation as payment for ecosystem services benefiting hydropower generation. Environ. Conserv. 38, 473–484 (2011).
    Google Scholar 

    50.
    Moomaw, W. R., Law, B. E. & Goetz, S. J. Focus on the role of forests and soils in meeting climate change mitigation goals: summary. Environ. Res. Lett. 15, 045009 (2020).
    Google Scholar 

    51.
    Tesfaye, M. A. et al. Selection of tree species and soil management for simultaneous fuelwood production and soil rehabilitation in the Ethiopian Central highlands. Land Degrad. Dev. 26, 665–679 (2015).
    Google Scholar 

    52.
    Beddington, J. Food security: contributions from science to a new and greener revolution. Philos. Trans. R. Soc. B 365, 61–71 (2010).
    Google Scholar 

    53.
    van Noordwijk, M. et al. SDG synergy between agriculture and forestry in the food, energy, water and income nexus: reinventing agroforestry? Curr. Opin. Environ. Sustain. 34, 33–42 (2018).
    Google Scholar 

    54.
    Vieira, D. L. M., Holl, K. D. & Peneireiro, F. M. Agro-successional restoration as a strategy to facilitate Tropical Forest recovery. Restor. Ecol 17, 451–459 (2009).
    Google Scholar 

    55.
    Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5, eaax0121 (2019).
    Google Scholar 

    56.
    Biggs, R. et al. Toward principles for enhancing the resilience of ecosystem services. Annu. Rev. Environ. Resour. 37, 421–448 (2012).
    Google Scholar 

    57.
    Munang, R. T., Thiaw, I. & Rivington, M. Ecosystem management: tomorrow’s approach to enhancing food security under a changing climate. Sustainability 3, 937–954 (2011).
    Google Scholar 

    58.
    de Souza, S. E. X. F., Vidal, E., Chagas, Gd. F., Elgar, A. T. & Brancalion, P. H. S. Ecological outcomes and livelihood benefits of community-managed agroforests and second growth forests in Southeast Brazil. Biotropica 48, 868–881 (2016).
    Google Scholar 

    59.
    Cawthorn, D. M. & Hoffman, L. C. The bushmeat and food security nexus: a global account of the contributions, conundrums and ethical collisions. Food Res. Int. 76, 906–925 (2015).
    Google Scholar 

    60.
    Parry, L., Barlow, J. & Peres, C. A. Hunting for sustainability in tropical secondary forests. Conserv. Biol. 23, 1270–1280 (2009).
    Google Scholar 

    61.
    Mbiba, M., Muvengwi, J. & Ndaimani, H. Environmental correlates of livestock depredation by spotted hyaenas and livestock herding practices in a semi-arid communal landscape. Afr. J. Ecol. 56, 984–992 (2018).
    Google Scholar 

    62.
    Calle, A. Partnering with cattle ranchers for forest landscape restoration. Ambio 49, 593–604 (2020).
    Google Scholar 

    63.
    Woolf, D., Solomon, D. & Lehmann, J. Land restoration in food security programmes: synergies with climate change mitigation. Clim. Policy 18, 1260–1270 (2018).
    Google Scholar 

    64.
    Miccolis, A., Peneireiro, F. M., Vieira, D. L. M., Marques, H. R. & Hoffmann, M. R. M. Restoration through agroforestry: options for reconciling livelihoods with onservation in the Cerrado and Caatinga biomes in Brazil. Exp. Agric. 55, 208–225 (2019).
    Google Scholar 

    65.
    Araujo, M. et al. The socio-ecological Nexus+ approach used by the Brazilian Research Network on Global Climate Change. Curr. Opin. Environ. Sustain. 39, 62–70 (2019).
    Google Scholar 

    66.
    Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).
    CAS  Google Scholar 

    67.
    Latawiec, A. E., Strassburg, B. B., Brancalion, P. H., Rodrigues, R. R. & Gardner, T. Creating space for large-scale restoration in tropical agricultural landscapes. Front. Ecol. Environ. 13, 211–218 (2015).
    Google Scholar 

    68.
    Chazdon, R. L. et al. A policy-driven knowledge agenda for global forest and landscape restoration. Conserv. Lett. 10, 125–132 (2017).
    Google Scholar 

    69.
    Possingham, H. P., Bode, M. & Klein, C. J. Optimal conservation outcomes require both restoration and protection. PLoS Biol. 13, e1002052 (2015).
    Google Scholar 

    70.
    Suding, K. et al. Committing to ecological restoration. Science 348, 638–640 (2015).
    CAS  Google Scholar 

    71.
    Calmon, M. et al. Emerging threats and opportunities for large-scale ecological restoration in the Atlantic Forest of Brazil. Restor. Ecol. 19, 154–158 (2011).
    Google Scholar 

    72.
    Adams, C., Rodrigues, S. T., Calmon, M. & Kumar, C. Impacts of large-scale forest restoration on socioeconomic status and local livelihoods: what we know and do not know. Biotropica 48, 731–744 (2016).
    Google Scholar 

    73.
    Andersson, K. & Agrawal, A. Inequalities, institutions, and forest commons. Glob. Environ. Change 21, 866–875 (2011).
    Google Scholar 

    74.
    Galabuzi, C. et al. Strategies for empowering the local people to participate in forest restoration. Agrofor. Syst. 88, 719–734 (2014).
    Google Scholar 

    75.
    Terrapon-Pfaff, J., Ortiz, W., Dienst, C. & Groene, M.-C. Energising the WEF nexus to enhance sustainable development at local level It. J. Environ. Manag. 223, 409–416 (2018).
    Google Scholar 

    76.
    Van Laerhoven, F. Governing community forests and the challenge of solving two-level collective action dilemmas: a large-N perspective. Glob. Environ. Change 20, 539–546 (2010).
    Google Scholar 

    77.
    Rizvi, A. R. Nature Based Solutions for Human Resilience (IUCN, 2014).

    78.
    Cohen-Shacham, E., Janzen, C., Maginnis, S. & Walters, G. Nature-Based Solutions to Address Global Societal Challenges (IUCN, 2016); https://doi.org/10.2305/IUCN.CH.2016.13.en

    79.
    Keesstra, S. et al. The superior effect of nature based solutions in land management for enhancing ecosystem services. Sci.Total Environ. 610–611, 997–1009 (2018).
    Google Scholar 

    80.
    Peluso, N. L. & Vandergeest, P. Writing political forests. Antipode 52, 1083–1103 (2020).
    Google Scholar 

    81.
    Chazdon, R. L., Gutierrez, V., Brancalion, P. H. S., Laestadius, L. & Guariguata, M. R. Co-creating conceptual and working frameworks for implementing forest and landscape restoration based on core principles. Forests 11, 706 (2020).
    Google Scholar 

    82.
    Barrow, E. 300,000 hectares restored in Shinyanga, Tanzania — but what did it really take to achieve this restoration? SAPIENS 7, 1–8 (2014).
    Google Scholar 

    83.
    Reij, C. & Garrity, D. Scaling up farmer-managed natural regeneration in Africa to restore degraded landscapes. Biotropica 48, 834–843 (2016).
    Google Scholar 

    84.
    Paudyal, K., Baral, H., Lowell, K. & Keenan, R. J. Ecosystem services from community-based forestry in Nepal: realising local and global benefits. Land Use Policy 63, 342–355 (2017).
    Google Scholar 

    85.
    Viani, R. A. G., Braga, D. P. P., Ribeiro, M. C., Pereira, P. H. & Brancalion, P. H. S. Synergism between payments for water-related ecosystem services, ecological restoration, and Landscape Connectivity Within the Atlantic Forest hotspot. Trop. Conserv. Sci. 11, https://doi.org/10.1177/1940082918790222 (2018). More

  • in

    Author Correction: Two decades of glacier mass loss along the Andes

    A few missing months (March to July 2013) in the Santa river record in Peru were infilled using the corresponding long-term monthly means. If necessary, missing months in the Chilean and Argentinean river records were infilled with a weighted average of monthly values from highly correlated stations within the same river basin (for details see Masiokas et al. 2019).”

    In Supplementary Table 3, there were errors in the data for the Baker basin; the gauging station used should have been Bajo Ñadis instead of Desagüe Lago Bertrand, which affected the values of the annual mean river runoff, sub-period runoff change and the glacier imbalance contribution. For the annual mean river runoff (m3 s−1), 649.2 and 568.7 should have been 922.8 and 975.9, respectively; for the sub-period runoff change (%), −12 should have been 6; and for the glacier imbalance contribution (%), 3 and 5 should have been 2 and 3, respectively. Related to this, in the sentence beginning “The two Patagonian basins…” in the final paragraph of the section “Influence of glacier mass loss on river runoff” in the main text of the Article, “3 to 5%” should have been “2 to 3%”. Furthermore, in Supplementary Table 3, “Condorecerro” should have read “Condorcerro”.

    The online versions of the Article have been amended and the Supplementary Information file replaced. More