More stories

  • in

    Caravan – A global community dataset for large-sample hydrology

    Goodrich, D. et al. The usda-ars experimental watershed network: Evolution, lessons learned, societal benefits, and moving forward. Water Resources Research 57, e2019WR026473 (2021).Article 
    ADS 

    Google Scholar 
    Likens, G. E. The watershed-ecosystem approach. Hydrological Processes 35, e13977, https://doi.org/10.1002/hyp.13977 (2021).Article 

    Google Scholar 
    Goodman, K. J., Parker, S. M., Edmonds, J. W. & Zeglin, L. H. Expanding the scale of aquatic sciences: the role of the national ecological observatory network (neon). Freshwater Science 34, 377–385 (2015).Article 

    Google Scholar 
    Kovács, G. Proposal to construct a coordinating matrix for comparative hydrology. Hydrological Sciences Journal 29, 435–443 (1984).Article 

    Google Scholar 
    Falkenmark, M. & Chapman, T. Comparative hydrology: An ecological approach to land and water resources (Unesco, 1989).Andreassian, V., Hall, A., Chahinian, N. & Schaake, J. Introduction and synthesis: Why should hydrologists work on a large number of basin data sets? In Andreassian, V., Hall, A., Chahinian, N. & Schaake, J. (eds.) Large sample basin experiments for hydrological model parameterization: results of the model parameter experiment–MOPEX, vol. IAHS Publ. 307, 1–5 (Wallingford: IAHS Press, 2006).Blöschl, G. et al. Twenty-three unsolved problems in hydrology (uph)–a community perspective. Hydrological sciences journal 64, 1141–1158 (2019).Article 

    Google Scholar 
    Gupta, H. V. et al. Large-sample hydrology: a need to balance depth with breadth. Hydrology and Earth System Sciences 18, 463–477 (2014).Article 
    ADS 

    Google Scholar 
    Stahl, K. et al. Streamflow trends in europe: evidence from a dataset of near-natural catchments. Hydrology and Earth System Sciences 14, 2367–2382, https://doi.org/10.5194/hess-14-2367-2010 (2010).Article 
    ADS 

    Google Scholar 
    Gudmundsson, L., Seneviratne, S. I. & Zhang, X. Anthropogenic climate change detected in european renewable freshwater resources. Nature Climate Change 7, 813–816 (2017).Article 
    ADS 

    Google Scholar 
    Gudmundsson, L., Leonard, M., Do, H. X., Westra, S. & Seneviratne, S. I. Observed trends in global indicators of mean and extreme streamflow. Geophysical Research Letters 46, 756–766, https://doi.org/10.1029/2018GL079725 (2019).Article 
    ADS 

    Google Scholar 
    Gudmundsson, L. et al. Globally observed trends in mean and extreme river flow attributed to climate change. Science 371, 1159–1162, https://doi.org/10.1126/science.aba3996 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Kratzert, F. et al. Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrology and Earth System Sciences 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019 (2019).Article 
    ADS 

    Google Scholar 
    Kratzert, F. et al. Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resources Research 55, 11344–11354, https://doi.org/10.1029/2019WR026065 (2019).Article 
    ADS 

    Google Scholar 
    Ghiggi, G., Humphrey, V., Seneviratne, S. I. & Gudmundsson, L. Grun: an observation-based global gridded runoff dataset from 1902 to 2014. Earth System Science Data 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019 (2019).Article 
    ADS 

    Google Scholar 
    Ghiggi, G., Humphrey, V., Seneviratne, S. I. & Gudmundsson, L. G-run ensemble: A multi-forcing observation-based global runoff reanalysis. Water Resources Research 57, e2020WR028787, https://doi.org/10.1029/2020WR028787 (2021).Article 
    ADS 

    Google Scholar 
    Addor, N. et al. Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges. Hydrological Sciences Journal 65, 712–725 (2020).Article 
    CAS 

    Google Scholar 
    Schaake, J., Cong, S. & Duan, Q. The US MOPEX data set. In Andreassian, V., Hall, A., Chahinian, N. & Schaake, J. (eds.) Large sample basin experiments for hydrological model parameterization: results of the model parameter experiment–MOPEX, vol. IAHS Publ. 307, 9–28 (Wallingford: IAHS Press, 2006).Fowler, K. J., Acharya, S. C., Addor, N., Chou, C. & Peel, M. C. CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in australia. Earth System Science Data 13, 3847–3867 (2021).Article 
    ADS 

    Google Scholar 
    Klingler, C., Schulz, K. & Herrnegger, M. LamaH-CE: Large-sample data for hydrology and environmental sciences for central europe. Earth System Science Data 13, 4529–4565 (2021).Article 
    ADS 

    Google Scholar 
    Chagas, V. B. et al. CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in brazil. Earth System Science Data 12, 2075–2096 (2020).Article 
    ADS 

    Google Scholar 
    Arsenault, R. et al. A comprehensive, multisource database for hydrometeorological modeling of 14,425 north american watersheds. Scientific Data 7, 1–12 (2020).Article 

    Google Scholar 
    Hao, Z. et al. CCAM: China catchment attributes and meteorology dataset. Earth System Science Data 13, 5591–5616 (2021).Article 
    ADS 

    Google Scholar 
    Alvarez-Garreton, C. et al. The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies–chile dataset. Hydrology and Earth System Sciences 22, 5817–5846 (2018).Article 
    ADS 

    Google Scholar 
    Kuentz, A., Arheimer, B., Hundecha, Y. & Wagener, T. Understanding hydrologic variability across europe through catchment classification. Hydrology and Earth System Sciences 21, 2863–2879 (2017).Article 
    ADS 

    Google Scholar 
    Coxon, G. et al. CAMELS-GB: Hydrometeorological time series and landscape attributes for 671 catchments in great britain. Earth System Science Data 12, 2459–2483 (2020).Article 
    ADS 

    Google Scholar 
    Newman, A. et al. Development of a large-sample watershed-scale hydrometeorological data set for the contiguous usa: data set characteristics and assessment of regional variability in hydrologic model performance. Hydrology and Earth System Sciences 19, 209–223 (2015).Article 
    ADS 

    Google Scholar 
    Addor, N., Newman, A. J., Mizukami, N. & Clark, M. P. The CAMELS data set: catchment attributes and meteorology for large-sample studies. Hydrology and Earth System Sciences 21, 5293–5313 (2017).Article 
    ADS 

    Google Scholar 
    Do, H. X., Gudmundsson, L., Leonard, M. & Westra, S. The global streamflow indices and metadata archive (gsim)–part 1: The production of a daily streamflow archive and metadata. Earth System Science Data 10, 765–785 (2018).Article 
    ADS 

    Google Scholar 
    Gudmundsson, L., Do, H. X., Leonard, M. & Westra, S. The global streamflow indices and metadata archive (GSIM)–part 2: Quality control, time-series indices and homogeneity assessment. Earth System Science Data 10, 787–804 (2018).Article 
    ADS 

    Google Scholar 
    Linke, S. et al. Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Scientific data 6, 1–15, https://doi.org/10.1038/s41597-019-0300-6 (2019).Article 
    ADS 

    Google Scholar 
    Beck, H. E. et al. Global-scale regionalization of hydrologic model parameters. Water Resources Research 52, 3599–3622 (2016).Article 
    ADS 

    Google Scholar 
    Beck, H. E. et al. Global fully distributed parameter regionalization based on observed streamflow from 4,229 headwater catchments. Journal of Geophysical Research: Atmospheres 125, e2019JD031485 (2020).ADS 

    Google Scholar 
    Blöschl, G. et al. Changing climate both increases and decreases european river floods. Nature 573, 108–111 (2019).Article 
    ADS 

    Google Scholar 
    Wilkinson, M. D. et al. The fair guiding principles for scientific data management and stewardship. Scientific data 3, 1–9 (2016).Article 

    Google Scholar 
    Metzger, M. J. et al. A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. Global Ecology and Biogeography 22, 630–638 (2013).Article 

    Google Scholar 
    Muñoz-Sabater, J. et al. Era5-land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data 13, 4349–4383 (2021).Article 
    ADS 

    Google Scholar 
    Lehner, B. Hydroatlas version 1.0 data download. Figshare https://doi.org/10.6084/m9.figshare.9890531.v1 (2022).Gorelick, N. et al. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment https://doi.org/10.1016/j.rse.2017.06.031 (2017).Article 

    Google Scholar 
    Kratzert, F. et al. Caravan – A global community dataset for large-sample hydrology (Version 1.0), Zenodo, https://doi.org/10.5281/ZENODO.7540792 (2022).Muñoz Sabater, J. et al. Era5-land hourly data from 1981 to present. ECMWF https://doi.org/10.24381/cds.e2161bac (2021).Lehner, B., Linke, S. & Thieme, M. Hydroatlas version 1.0. Figshare https://doi.org/10.6084/m9.figshare.9890531.v1 (2019).Fowler, K., Acharya, S. C., Addor, N., Chou, C. & Peel, M. CAMELS-AUS v1: Hydrometeorological time series and landscape attributes for 222 catchments in australia. PANGAEA https://doi.org/10.1594/PANGAEA.921850 (2020).Chagas, V. B. P. et al. CAMELS-BR: Hydrometeorological time series and landscape attributes for 897 catchments in brazil. Zenodo https://doi.org/10.5281/zenodo.3964745 (2020).Alvarez-Garreton, C. et al. The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – chile dataset. PANGAEA https://doi.org/10.1594/PANGAEA.894885 (2018).Coxon, G. et al. Catchment attributes and hydro-meteorological timeseries for 671 catchments across great britain (CAMELS-GB). NERC Environmental Information Data Centre https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9 (2020).Klingler, C., Kratzert, F., Schulz, K. & Herrnegger, M. LamaH-CE: Large-sample data for hydrology and environmental sciences for central europe. Zenodo https://doi.org/10.5281/zenodo.5153305 (2021).Newman, A. et al. A large-sample watershed-scale hydrometeorological dataset for the contiguous usa. UCAR/NCAR – GDEX https://doi.org/10.5065/D6MW2F4D (2014).McMillan, H. K., Westerberg, I. K. & Krueger, T. Hydrological data uncertainty and its implications. Wiley Interdisciplinary Reviews: Water 5, e1319 (2018).
    Google Scholar 
    Beven, K. Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication. Hydrological Sciences Journal 61, 1652–1665 (2016).Article 

    Google Scholar 
    Colliander, A. et al. Validation of smap surface soil moisture products with core validation sites. Remote Sensing of Environment 191, 215–231 (2017).Article 
    ADS 

    Google Scholar 
    Habib, E. & Krajewski, W. F. Uncertainty analysis of the trmm ground-validation radar-rainfall products: Application to the teflun-b field campaign. Journal of applied meteorology 41, 558–572 (2002).Article 
    ADS 

    Google Scholar 
    Kumar, S. V., Dirmeyer, P. A., Peters-Lidard, C. D., Bindlish, R. & Bolten, J. Information theoretic evaluation of satellite soil moisture retrievals. Remote Sensing of Environment 204, 392–400 (2018).Article 
    ADS 

    Google Scholar 
    Nearing, G. S. et al. Nonparametric triple collocation. Water Resources Research 53, 5516–5530 (2017).Article 
    ADS 

    Google Scholar 
    Alemohammad, S. H., McColl, K. A., Konings, A. G., Entekhabi, D. & Stoffelen, A. Characterization of precipitation product errors across the united states using multiplicative triple collocation. Hydrology and Earth System Sciences 19, 3489–3503 (2015).Article 
    ADS 

    Google Scholar 
    McMillan, H., Jackson, B., Clark, M., Kavetski, D. & Woods, R. Rainfall uncertainty in hydrological modelling: An evaluation of multiplicative error models. Journal of Hydrology 400, 83–94 (2011).Article 
    ADS 

    Google Scholar 
    Domeneghetti, A., Castellarin, A. & Brath, A. Assessing rating-curve uncertainty and its effects on hydraulic model calibration. Hydrology and Earth System Sciences 16, 1191–1202 (2012).Article 
    ADS 

    Google Scholar 
    Koch, J. Caravan extension Denmark – Danish dataset for large-sample hydrology. Zenodo https://doi.org/10.5281/zenodo.6762361 (2022).Knoben, W. J. M., Woods, R. A. & Freer, J. E. A quantitative hydrological climate classification evaluated with independent streamflow data. Water Resources Research 54, 5088–5109, https://doi.org/10.1029/2018WR022913 (2018).Article 
    ADS 

    Google Scholar  More

  • in

    Polydimethylsiloxane-coated textiles with minimized microplastic pollution

    Guha Roy, A. Detailing plastic pollution. Nat. Sustain. 2, 654 (2019).Article 

    Google Scholar 
    Lau, W. W. Y. et al. Evaluating scenarios toward zero plastic pollution. Science 369, 1455–1461 (2020).Article 
    CAS 

    Google Scholar 
    Koelmans, A. A. et al. Risk assessment of microplastic particles. Nat. Rev. Mater. 7, 138–152 (2022).Article 

    Google Scholar 
    Rochman, C. M. Microplastics research—from sink to source. Science 360, 28–29 (2018).Article 
    CAS 

    Google Scholar 
    Zhang, Y. et al. Atmospheric microplastics: a review on current status and perspectives. Earth Sci. Rev. 203, 103118 (2020).Article 
    CAS 

    Google Scholar 
    Nowack, B., Cai, Y., Mitrano, D. M. & Hufenus, R. Formation of fiber fragments during abrasion of polyester textiles. Environ. Sci. Technol. 55, 8001–8009 (2021).Article 

    Google Scholar 
    Henry, B., Laitala, K. & Klepp, I. G. Microfibres from apparel and home textiles: prospects for including microplastics in environmental sustainability assessment. Sci. Total Environ. 652, 483–494 (2019).Article 

    Google Scholar 
    Boucher, J. & Friot, D. Primary Microplastics in the Oceans: A Global Evaluation of Sources (IUCN, 2017).Evangeliou, N. et al. Atmospheric transport is a major pathway of microplastics to remote regions. Nat. Commun. 11, 3381 (2020).Article 
    CAS 

    Google Scholar 
    Bergmann, M. et al. White and wonderful? Microplastics prevail in snow from the Alps to the Arctic. Sci. Adv. 5, 1157 (2019).Article 

    Google Scholar 
    Brahney, J., Hallerud, M., Heim, E., Hahnenberger, M. & Sukumaran, S. Plastic rain in protected areas of the United States. Science 368, 1257–1260 (2020).Article 
    CAS 

    Google Scholar 
    Jenner, L. C. et al. Detection of microplastics in human lung tissue using μFTIR spectroscopy. Sci. Total Environ. 831, 154907 (2022).Article 
    CAS 

    Google Scholar 
    Leslie, H. A. et al. Discovery and quantification of plastic particle pollution in human blood. Environ. Int. 163, 107199 (2022).Article 
    CAS 

    Google Scholar 
    Zabala, A. Ocean microfibre contamination. Nat. Sustain. 1, 213 (2018).Article 

    Google Scholar 
    De Falco, F. et al. Evaluation of microplastic release caused by textile washing processes of synthetic fabrics. Environ. Pollut. 236, 916–925 (2018).Article 

    Google Scholar 
    De Falco, F. et al. Novel finishing treatments of polyamide fabrics by electrofluidodynamic process to reduce microplastic release during washings. Polym. Degrad. Stab. 165, 110–116 (2019).Article 

    Google Scholar 
    Suaria, G. et al. Microfibers in oceanic surface waters: a global characterization. Sci. Adv. 6, 8493 (2020).Article 

    Google Scholar 
    Woodward, J., Li, J., Rothwell, J. & Hurley, R. Acute riverine microplastic contamination due to avoidable releases of untreated wastewater. Nat. Sustain. 4, 793–802 (2021).Article 

    Google Scholar 
    De Falco, F. et al. Pectin based finishing to mitigate the impact of microplastics released by polyamide fabrics. Carbohydr. Polym. 198, 175–180 (2018).Article 

    Google Scholar 
    Zhao, X. et al. Macroscopic evidence of the liquidlike nature of nanoscale polydimethylsiloxane brushes. ACS Nano 15, 13559–13567 (2021).Article 
    CAS 

    Google Scholar 
    Shabanian, S., Khatir, B., Nisar, A. & Golovin, K. Rational design of perfluorocarbon-free oleophobic textiles. Nat. Sustain. 3, 1059–1066 (2020).Article 

    Google Scholar 
    Khatir, B., Shabanian, S. & Golovin, K. Design and high-resolution characterization of silicon wafer-like omniphobic liquid layers applicable to any substrate. ACS Appl. Mater. Interfaces 12, 31933–31939 (2020).Article 
    CAS 

    Google Scholar 
    Soltani, M. & Golovin, K. Lossless, passive transportation of low surface tension liquids induced by patterned omniphobic liquidlike polymer brushes. Adv. Funct. Mater. 32, 2107465 (2022).Article 
    CAS 

    Google Scholar 
    Wang, L. & McCarthy, T. J. Covalently attached liquids: instant omniphobic surfaces with unprecedented repellency. Angew. Chem. Int. Ed. 55, 244–248 (2016).Article 
    CAS 

    Google Scholar 
    Liu, J. et al. One-step synthesis of a durable and liquid-repellent poly(dimethylsiloxane) coating. Adv. Mater. 33, 2100237 (2021).Article 
    CAS 

    Google Scholar 
    Özek, H. Z. Silicone-based water repellents. in Waterproof and Water Repellent Textiles and Clothing (ed. Williams, J. T.) 153–189 (Woodhead Publishing, 2018).Cao, C. et al. Robust fluorine-free superhydrophobic PDMS-ormosil@fabrics for highly effective self-cleaning and efficient oil-water separation. J. Mater. Chem. A 4, 12179–12187 (2016).Article 
    CAS 

    Google Scholar 
    Dong, K. et al. Shape adaptable and highly resilient 3D braided triboelectric nanogenerators as e-textiles for power and sensing. Nat. Commun. 11, 2868 (2020).Article 
    CAS 

    Google Scholar 
    Jiang, L., Cheng, Y., Wang, S., Xu, Z. & Zhao, Y. Non-fluorine oil repellency: how low the intrinsic wetting threshold can be for roughness-induced contact angle amplification? Langmuir 38, 5857–5864 (2022).Article 
    CAS 

    Google Scholar 
    Ge, M. et al. A ‘PDMS-in-water’ emulsion enables mechanochemically robust superhydrophobic surfaces with self-healing nature. Nanoscale Horiz. 5, 65–73 (2020).Article 
    CAS 

    Google Scholar 
    Chauvin, J. P. R. & Pratt, D. A. On the reactions of thiols, sulfenic acids, and sulfinic acids with hydrogen peroxide. Angew. Chem. Int. Ed. 56, 6255–6259 (2017).Article 
    CAS 

    Google Scholar 
    Gunji, T., Shigematsu, Y., Kajiwara, T. & Abe, Y. Preparation of free-standing films with sulfonyl group from 3-mercaptopropyl(trimethoxy)silane/1,2-bis(triethoxysilyl)ethane copolymer. Polym. J. 42, 684–688 (2010).Article 
    CAS 

    Google Scholar 
    Remington, W. R. & Gladding, E. K. Equilibria in the dyeing of nylon with acid dyes. J. Am. Chem. Soc. 72, 2553–2559 (1950).Article 
    CAS 

    Google Scholar 
    Herzberg, W. J. & Erwin, W. R. Gas-chromatographic study of the reaction of glass surfaces with dichlorodimethylsilane and chlorotrimethylsilane. J. Colloid Interface Sci. 33, 172–177 (1970).Article 
    CAS 

    Google Scholar 
    Bielecki, R. M., Crobu, M. & Spencer, N. D. Polymer-brush lubrication in oil: sliding beyond the Stribeck curve. Tribol. Lett. 49, 263–272 (2013).Article 
    CAS 

    Google Scholar 
    Zhou, S. M., Tashiro, K. & Ii, T. Moisture effect on structure and mechanical property of nylon 6 as studied by the time-resolved and simultaneous measurements of FT-IR and dynamic viscoelasticity under the controlled humidity at constant scanning rate. Polym. J. 33, 344–355 (2001).Article 
    CAS 

    Google Scholar 
    Venoor, V., Park, J. H., Kazmer, D. O. & Sobkowicz, M. J. Understanding the effect of water in polyamides: a review. Polym. Rev. 61, 598–645 (2021).Article 
    CAS 

    Google Scholar 
    Napper, I. E. & Thompson, R. C. Release of synthetic microplastic plastic fibres from domestic washing machines: effects of fabric type and washing conditions. Mar. Pollut. Bull. 112, 39–45 (2016).Article 
    CAS 

    Google Scholar 
    Napper, I. E., Barrett, A. C. & Thompson, R. C. The efficiency of devices intended to reduce microfibre release during clothes washing. Sci. Total Environ. 738, 140412 (2020).Article 
    CAS 

    Google Scholar 
    Chiong, J. A., Tran, H., Lin, Y., Zheng, Y. & Bao, Z. Integrating emerging polymer chemistries for the advancement of recyclable, biodegradable, and biocompatible electronics. Adv. Sci. 8, 2101233 (2021).Article 
    CAS 

    Google Scholar 
    Ceseracciu, L., Heredia-Guerrero, J. A., Dante, S., Athanassiou, A. & Bayer, I. S. Robust and biodegradable elastomers based on corn starch and polydimethylsiloxane (PDMS). ACS Appl. Mater. Interfaces 7, 3742–3753 (2015).Article 
    CAS 

    Google Scholar 
    De Falco, F., Gentile, G., Di Pace, E., Avella, M. & Cocca, M. Quantification of microfibres released during washing of synthetic clothes in real conditions and at lab scale. Eur. Phys. J. 133, 257 (2018).
    Google Scholar  More

  • in

    The water crisis is worsening. Researchers must tackle it together

    Communities in Gansu province in China store snow in the winter months for use in the dry summers.Credit: Hai Ying/EPA/Shutterstock

    Among the world’s ‘poly-crises’, the crisis of water is one of the most urgent. Worldwide, around 2 billion people lacked access to safe drinking water in 2020; and an estimated 1.7 billion did not have even basic sanitation. Every year, more than 800,000 people die from diarrhoea, because of unsafe drinking water and a lack of sanitation. Most of those are in low- and lower-middle income countries. This is a mind-boggling and unacceptable situation. Even more so in an age when huge investments are being made in instant video calling, personalized medicine and talk of inhabiting other planets.In 2015, the international community declared tackling the water crisis one of the United Nations Sustainable Development Goals (SDGs). The sixth SDG commits the world to “ensure availability and sustainable management of water and sanitation for all”. But the UN acknowledges that SDG 6 is “alarmingly off track”.International diplomacy is finally starting to get its act together. In March, world leaders will assemble in New York City for the UN 2023 Water Conference. It will be the first such event in nearly half a century, a fact that by itself should shame us all.Last October, the UN published the results of a consultation with government representatives as well as specialist and stakeholder communities on their priorities for the conference. Around 12% of respondents were from education, science and technology fields. The consensus was that data and evidence, improved access to knowledge (including Indigenous and local knowledge) and open research will be essential to getting SDG 6 back on track. Delegates attending the March conference will be looking to harness the full spectrum of established water sources and technologies, including freshwater and rainwater sources, treated groundwater, desalinated seawater and hydropower.There’s a wealth of knowledge already out there, in the form of established technologies, innovative alternatives and research that captures centuries-old knowledge and the practices of communities themselves. In the past, such knowledge has been ignored, or what has been learnt has been forgotten. Twenty years ago, for example, the UN invested in a major piece of research that captured examples of how communities living in water-stressed regions have used research and innovation to access water. The research highlighted, for example, how people in arid regions of China store snow in cellars during the winter that can then be melted for use in the summer months.Prerequisites for tackling the water crisis include consolidating what is already known and building on that knowledge. That’s why on 19 January, the Nature Portfolio of journals launched Nature Water. This journal will provide a space for all researchers — including those in natural and social sciences, and in engineering — to collectively contribute their knowledge, insights and the results of their learning, so that the world is on a more equitable and sustainable track. The launch issue includes research in fundamental, applied and social science, as well as opinion and analysis. Our editorial teams are committed to facilitating open science1.Some paths forward are clear. Damir Brdjanovic at the IHE Delft Institute for Water Education in the Netherlands writes in Nature Water that there’s a vast body of research on alternatives to sewered sanitation — and how to use less or no water to safely dispose of faecal matter and inactivate pathogens2. There are alternatives to the flush toilet and underground, piped sewer networks. And Rongrong Xu at the Southern University of Science and Technology in Shenzhen, China, and colleagues report that there are ways to create hydropower, especially in Africa and Asia, without the same environmental and social impacts3.However, research does not exist in a vacuum. The representatives of low- and middle-income countries also want to prioritize funding. The South African government, in its response to the UN consultation, says that the annual cost to meet the SDG water and sanitation targets is between 2.3% and 2.7% of the country’s gross domestic product (between US$7 billion and $7.6 billion annually). A project called the Global Commission on the Economics of Water, co-chaired by economist Mariana Mazzucato and climate scientist Johan Rockström (among others), is promising “new thinking on economics and governance” in time for the conference.Conflict theoryThose who will be attending the conference in March also told the UN they want to see international cooperation be made a priority for water and sanitation, especially in an era of heightened geopolitical tensions. More than 25 years ago, former vice-president of the World Bank Ismail Serageldin famously wrote that twenty-first-century conflicts would be over water, rather than oil. We are fortunate that this has not yet happened, although Serageldin told Nature that relations between countries that share water sources are worsening. Egypt is formally in dispute with Ethiopia over dam-building projects on the Nile River; the same is true of India and Pakistan in the Indus River Basin.In its response to the UN, Egypt’s delegation observed that the majority of people rely on water sources that are shared between nations, most of which lack formal agreements, including all-important data sharing agreements. Rhea Verbeke, at the Catholic University of Leuven in Belgium, writes in Nature Water of the “sobering experience” of seeing no external submissions to an open database on water purification that was created more than one year ago4.The delegates assembling in New York need to accept that their countries’ visions will not be realized until all nations can somehow carve out a path to cooperate amid tension and conflict. Research can help to provide at least some of the right language, which is why it needs to be taken on board when decisions are being made. We in the Nature Portfolio intend to play our fullest part to make that happen. More

  • in

    Moving from measurement to governance of shared groundwater resources

    Döll, P. et al. Impact of water withdrawals from groundwater and surface water on continental water storage variations. J. Geodyn. 59–60, 143–156 (2012).Article 

    Google Scholar 
    Dieter, C. A. et al. Estimated Use Of Water in the United States in 2015 US Geological Survey Circular 1441 (USGS, 2018); https://doi.org/10.3133/cir1441Siebert, S., Ewert, F., Eyshi Rezaei, E., Kage, H. & Grass, R. Impact of heat stress on crop yield—on the importance of considering canopy temperature. Environ. Res. Lett. 9, 044012 (2014).Article 

    Google Scholar 
    Stephan, M., Marshall, G. & McGinnis, M. in Governing Complexity: Analyzing and Applying Polycentricity (eds Thiel, A. et al.) 21–44 (Cambridge Univ. Press, 2019); https://doi.org/10.1017/9781108325721.002Steward, D. R. et al. Tapping unsustainable groundwater stores for agricultural production in the High Plains Aquifer of Kansas, projections to 2110. Proc. Natl Acad. Sci. USA 110, E3477–E3486 (2013).Article 
    CAS 

    Google Scholar 
    Haacker, E. M. K., Kendall, A. D. & Hyndman, D. W. Water level declines in the High Plains Aquifer: predevelopment to resource senescence. Groundwater 54, 231–242 (2016).Article 
    CAS 

    Google Scholar 
    Russo, T. A. & Lall, U. Depletion and response of deep groundwater to climate-induced pumping variability. Nat. Geosci. 10, 105–108 (2017).Article 
    CAS 

    Google Scholar 
    Scanlon, B. R. et al. Groundwater depletion and sustainability of irrigation in the US High Plains and Central Valley. Proc. Natl Acad. Sci. USA 109, 9320–9325 (2012).Article 
    CAS 

    Google Scholar 
    Hrozencik, R. A., Manning, D. T., Suter, J. F., Goemans, C. & Bailey, R. T. The heterogeneous impacts of groundwater management policies in the Republican River Basin of Colorado. Water Resour. Res. 53, 10757–10778 (2017).Article 

    Google Scholar 
    Colaizzi, P. D., Gowda, P. H., Marek, T. H. & Porter, D. O. Irrigation in the Texas High Plains: a brief history and potential reductions in demand. Irrig. Drain. 58, 257–274 (2009).Article 

    Google Scholar 
    Lyle, W. M. & Bordovsky, J. P. LEPA corn irrigation with limited water supplies. Trans. ASAE 38, 455–462 (1995).Article 

    Google Scholar 
    Chen, Y. et al. Assessment of alternative agricultural land use options for extending the availability of the Ogallala Aquifer in the Northern High Plains of Texas. Hydrology 5, 53 (2018).Article 

    Google Scholar 
    Rudnick, D. R. et al. Deficit irrigation management of maize in the High Plains Aquifer Region: a review. JAWRA J. Am. Water Resour. Assoc. 55, 38–55 (2019).Article 

    Google Scholar 
    Hao, B. et al. Grain yield, evapotranspiration, and water-use efficiency of maize hybrids differing in drought tolerance. Irrig. Sci. 37, 25–34 (2019).Article 

    Google Scholar 
    Chiara, C. & Marco, M. Irrigation efficiency optimization at multiple stakeholders’ levels based on remote sensing data and energy water balance modelling. Irrig. Sci. https://doi.org/10.1007/s00271-022-00780-4 (2022).Schwartz, R. C., Bell, J. M., Colaizzi, P. D., Baumhardt, R. L. & Hiltbrunner, B. A. Response of maize hybrids under limited irrigation capacities: crop water use. Agron. J. https://doi.org/10.1002/agj2.21011 (2022).Taghvaeian, S. et al. Irrigation scheduling for agriculture in the United States: the progress made and the path forward. Trans. ASABE 63, 1603–1618 (2020).Article 

    Google Scholar 
    Sears, L. et al. Jevons’ paradox and efficient irrigation technology. Sustainability 10, (2018).Mpanga, I. K. & Idowu, O. J. A decade of irrigation water use trends in Southwestern USA: the role of irrigation technology, best management practices, and outreach education programs. Agric. Water Manag. 243, 106438 (2021).Article 

    Google Scholar 
    Grafton, R. Q. et al. The paradox of irrigation efficiency. Science 361, 748–750 (2018).Article 
    CAS 

    Google Scholar 
    Cook, C. & Bakker, K. Water security: debating an emerging paradigm. Glob. Environ. Change 22, 94–102 (2012).Article 

    Google Scholar 
    Brewis, A. et al. Household water insecurity is strongly associated with food insecurity: evidence from 27 sites in low‐and middle‐income countries. Am. J. Hum. Biol. 32, e23309 (2020).Article 

    Google Scholar 
    Rosinger, A. Y. & Young, S. L. The toll of household water insecurity on health and human biology: current understandings and future directions. Wiley Interdiscip. Rev. Water 7, e1468 (2020).Article 

    Google Scholar 
    London, J. K. et al. Disadvantaged unincorporated communities and the struggle for water justice in California. Water Altern. 14, 520–545 (2021).
    Google Scholar 
    Hanak, E. et al. Water and the future of the San Joaquin Valley. Public Policy Inst. Calif. 100, (2019).Lauer, S. et al. Values and groundwater management in the Ogallala Aquifer region. J. Soil Water Conserv. 73, 593–600 (2018).Article 

    Google Scholar 
    Edwards, E. C. & Guilfoos, T. The economics of groundwater governance institutions across the globe. Appl. Econ. Perspect. Policy 43, 1571–1594 (2021).Article 

    Google Scholar 
    MacLeod, C. & Méndez-Barrientos, L. E. Groundwater management in California’s Central Valley: a focus on disadvantaged communities. Case Stud. Environ. 3, 1–13 (2019).Article 

    Google Scholar 
    Méndez-Barrientos, L. E. et al. Farmer participation and institutional capture in common-pool resource governance reforms. The case of groundwater management in California. Soc. Nat. Resour. 33, 1486–1507 (2020).Article 

    Google Scholar 
    Blythe, J. et al. The dark side of transformation: latent risks in contemporary sustainability discourse. Antipode 50, 1206–1223 (2018).Article 

    Google Scholar 
    Lubell, M. Governing institutional complexity: the ecology of games framework. Policy Stud. J. 41, 537–559 (2013).Article 

    Google Scholar 
    Berardo, R. & Lubell, M. The ecology of games as a theory of polycentricity: recent advances and future challenges. Policy Stud. J. 47, 6–26 (2019).Article 

    Google Scholar 
    Ostrom, E. Understanding Institutional Diversity (Princeton Univ. Press, 2009).Ostrom, E. & Basurto, X. Crafting analytical tools to study institutional change. J. Inst. Econ. 7, 317–343 (2011).
    Google Scholar 
    Lubell, M. & Morrison, T. H. Institutional navigation for polycentric sustainability governance. Nat. Sustain. 4, 664–671 (2021).Article 

    Google Scholar 
    Lauer, S. & Sanderson, M. R. Producer attitudes toward groundwater conservation in the U.S. Ogallala-High Plains. Groundwater 58, 674–680 (2020).Article 
    CAS 

    Google Scholar 
    Niles, M. T. & Wagner, C. R. H. The carrot or the stick? Drivers of California farmer support for varying groundwater management policies. Environ. Res. Commun. 1, 45001 (2019).Article 

    Google Scholar 
    Evans, R. G. & King, B. A. Site-specific sprinkler irrigation in a water-limited future. Trans. ASABE 55, 493–504 (2012).Article 

    Google Scholar 
    Sanderson, M. R. & Hughes, V. Race to the bottom (of the well): groundwater in an agricultural production treadmill. Soc. Probl. 66, 392–410 (2018).Article 

    Google Scholar 
    Ostrom, E. The challenge of common-pool resources. Environ. Sci. Policy Sustain. Dev. 50, 8–21 (2008).Article 

    Google Scholar 
    Agriculture Innovation Agenda (USDA, 2021); https://www.usda.gov/aiaPartnerships for climate-smart commodities. USDA https://www.usda.gov/climate-solutions/climate-smart-commodities (2022).Closas, A. & Molle, F. Chronicle of a demise foretold: state vs. local groundwater management in Texas and the High Plains aquifer system. Water Altern. 11, 511–532 (2018).
    Google Scholar 
    Lubell, M., Blomquist, W. & Beutler, L. Sustainable groundwater management in California: a grand experiment in environmental governance. Soc. Nat. Resour. 33, 1447–1467 (2020).Article 

    Google Scholar 
    Roberts, M., Milman, A. & Blomquist, W. in Water Resilience (eds Baird, J. & Plummer, R.) 41–63 (Springer, Cham., 2021).Megdal, S. B. The role of the public and private sectors in water provision in Arizona, USA. Water Int. 37, 156–168 (2012).Article 

    Google Scholar 
    Desired future conditions. Texas Water Development Board https://www.twdb.texas.gov/groundwater/dfc/index.asp (2022).Schoengold, K. & Brozovic, N. The future of groundwater management in the High Plains: evolving institutions, aquifers and regulations. in. West. Econ. Forum 16, 47–53 (2018).
    Google Scholar 
    Factsheet H.B. 2056/S.B. 1368 (Arizona State Senate, 2021); https://www.azleg.gov/legtext/55leg/1R/summary/S.2056-1368NREW_ASENACTED.pdfLocal enhanced management areas; establishment procedures; duties of chief engineer; hearing; notice; orders; review. Kansas Office of Revisor of Statutes http://www.ksrevisor.org/statutes/chapters/ch82a/082a_010_0041.html (2012).Deines, J. M., Kendall, A. D., Butler, J. J. & Hyndman, D. W. Quantifying irrigation adaptation strategies in response to stakeholder-driven groundwater management in the US High Plains Aquifer. Environ. Res. Lett. 14, 044014 (2019).Article 

    Google Scholar 
    California Department of Water Resources. Sustainable Groundwater Management Act (SGMA) (State of California, 2022); https://water.ca.gov/programs/groundwater-management/sgma-groundwater-managementAyres, A. B., Edwards, E. C. & Libecap, G. D. How transaction costs obstruct collective action: the case of California’s groundwater. J. Environ. Econ. Manag. 91, 46–65 (2018).Article 

    Google Scholar 
    Dobbin, K. B. & Lubell, M. Collaborative governance and environmental justice: disadvantaged community representation in California sustainable groundwater management. Policy Stud. J. 49, 562–590 (2021).Article 

    Google Scholar 
    Bruno, E. M., Hagerty, N. & Wardle, A. R. The Political Economy of Groundwater Management: Descriptive Evidence from California (National Bureau of Economic Research, 2022).Cody, K. C., Smith, S. M., Cox, M. & Andersson, K. Emergence of collective action in a groundwater commons: irrigators in the San Luis Valley of Colorado. Soc. Nat. Resour. 28, 405–422 (2015).Article 

    Google Scholar 
    Perez-Quesada, G. & Hendricks, N. P. Lessons from local governance and collective action efforts to manage irrigation withdrawals in Kansas. Agric. Water Manag. 247, 106736 (2021).Article 

    Google Scholar 
    Bopp, C., Engler, A., Jara-Rojas, R., Hunecke, C. & Melo, O. Collective actions and leadership attributes: a cluster analysis of water user associations in Chile. Water Econ. Policy 8, 2250003 (2022).Article 

    Google Scholar 
    Morrison, T. H. et al. The black box of power in polycentric environmental governance. Glob. Environ. Change 57, 101934 (2019).Article 

    Google Scholar 
    Bitterman, P., Bennett, D. A. & Secchi, S. Constraints on farmer adaptability in the Iowa-Cedar River Basin. Environ. Sci. Policy 92, 9–16 (2019).Article 

    Google Scholar 
    Butler, J. J. Jr., Whittemore, D. O., Wilson, B. B. & Bohling, G. C. Sustainability of aquifers supporting irrigated agriculture: a case study of the High Plains aquifer in Kansas. Water Int. 43, 815–828 (2018).Article 

    Google Scholar 
    Zwart, S. J. & Bastiaanssen, W. G. M. Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agric. Water Manag. 69, 115–133 (2004).Article 

    Google Scholar 
    Du, Y.-D. et al. Crop yield and water use efficiency under aerated irrigation: a meta-analysis. Agric. Water Manag. 210, 158–164 (2018).Article 

    Google Scholar 
    Zheng, H. et al. Water productivity of irrigated maize production systems in Northern China: a meta-analysis. Agric. Water Manag. 234, 106119 (2020).Article 

    Google Scholar 
    Crouch, M., Guerrero, B., Amosson, S., Marek, T. & Almas, L. Analyzing potential water conservation strategies in the Texas Panhandle. Irrig. Sci. 38, 559–567 (2020).Article 

    Google Scholar  More

  • in

    Unconventional tracers show that spring waters on Mount Fuji run deep

    Immerzeel, W. W. et al. Nature 577, 364–369 (2020).Article 
    PubMed 

    Google Scholar 
    Viviroli, D., Dürr, H. H., Messerli, B., Meybeck, M. & Weingartner, R. Water Resour. Res. 43, W07447 (2007).Article 

    Google Scholar 
    Somers, L. D. & McKenzie, J. M. WIRESs Water 7, e1475 (2020).Article 

    Google Scholar 
    Schilling, O. S. et al. Nature Wat. 1, 60–73 (2023).Article 

    Google Scholar 
    Yasuhara, M., Hayashi, T., Asai, K., Uchiyama, M. & Nakamura, T. J. Geogr. 126, 25–27 (2017).Article 

    Google Scholar 
    Ono, M. et al. Hydrogeol. J. 27, 717–730 (2019).Article 

    Google Scholar 
    Taberlet, P., Coissac, E., Hajibabaei, M. & Rieseberg, L. H. Mol. Ecol. 21, 1789–1793 (2012).Article 
    PubMed 

    Google Scholar 
    Miller, J. B., Frisbee, M. D., Hamilton, T. L. & Murugapiran, S. K. Environ. Res. Lett. 16, 064012 (2021).Article 

    Google Scholar  More

  • in

    Water science must be Open Science

    While much focus in recent years has been put on Open Access publishing, this is only a small part of Open Science. According to the 2015 FOSTER taxonomy19, Open Science integrates Open Access, Open Data, Open Source, and Open Reproducible Research (all of which we will touch on here, see Fig. 1a), while UNESCO and others have extended this further (e.g.,6). Open Data is commonly associated with the ‘FAIR Principles’20, which describe how to make data findable, accessible, interoperable, and reusable. The FAIR principles were introduced in 2016 and provide vital guidance that can be applied irrespective of whether the data itself is strictly open or not21. Note that the FAIR principles do not enforce Open Access, i.e., FAIR data is not automatically Open Data. Conversely, Open Data that is neither FAIR nor managed (see Fig. 1b) can easily be useless data. Thus, the combination of Open and FAIR data is extremely important. However, even Open Access publishing combined with Open and FAIR data does not necessarily make the research reproducible and re-usable, as discussed further below.Fig. 1: The many elements of Open Science.a, Open Science (centre, blue) and the four elements of Open Science pointed out by UNESCO most pertinent to this article (orange-ish circles with text). The remaining elements of Open Science described by UNESCO were removed for space reasons. They are represented in the ‘…’ circle along with the smaller decorative bubbles to show that Open Science covers many facets, big and small6. b, FAIR data vs. Open Data vs. Managed Data. Image modified from ref. 36. Managed data means that the data has in some way been collected, stored, organized and maintained. There is a large proportion of managed data that is neither FAIR nor Open, along with a large proportion of unmanaged Open Data. Since both cases are difficult to include in reproducible workflows, scientists and journals alike should be working on expanding the intersection between FAIR and Open Data.Full size imageOpen Access is the subset of Open Science that includes principles and practices for distributing research outputs online, free of cost or other access barriers22,23. This includes for instance Open Access publications (e.g., the dissemination of research as so-called Green, Gold or Diamond Open Access) or the use of preprint servers to access earlier versions of research articles.Open Data refers to the availability of the data behind the published research, typically hosted in either institutional or domain-specific data repositories (e.g., HydroShare for hydrological data24), or generic repositories such as Zenodo or FigShare. For Open Access publications and Open Data, appropriate license conditions should be stipulated, so that the conditions of re-use are clear. Creative Commons (CC) licenses are commonly used, with CC0 (public domain) and CC-BY (re-use with attribution) being the most permissive. Other restrictions on CC licenses can cause problems for downstream use. For instance, the ‘ND’ (no derivatives) clause forbids re-use for derivative works, i.e., any actual re-use other than re-distribution of the original work, while ‘NC’ (non-commercial use only) can prevent commercial companies (e.g., instrument vendors) from integrating Open Data into vendor-provided instrument libraries that could be used by researchers. The ‘SA’ (share-alike) clause can enforce a license on downstream users that they may not be able to comply with, thus preventing integration of Open Data in other open projects (due to incompatible licenses). While Open Data is an important starting point, without the availability of appropriate metadata and sufficient FAIRness to make the data findable, accessible, re-useable and interoperable, Open Data alone is only of limited use. In the era of ‘big data’, it is now relatively easy to create a quick dump of data, but curation and FAIRification of data requires a concerted effort, which may necessitate either incentives (carrot) or mandates (stick). The Global Natural Products Social Molecular Networking (GNPS) ecosystem25 is a prime example for incentivising Open Data sharing. Starting primarily as a mass spectral data repository for metabolomics, the developers have consistently added features and functionality over the years to value-add the repository and increase motivation for deposition. For example, MASST26 has enabled discovery of the neurotoxin domoic acid and analogues within marine samples and food such as ocean-caught mackerel.Open Source software and code refer to the public availability of source code27, i.e., sets of computer instructions ranging from data processing scripts and algorithms to fully blown numerical models, desktop applications, or even operating systems. The purpose of open source is to provide transparency, and most importantly, re-usability and adaptability of the code, with a common aim of collaborative development. Licenses for Open Source works are generally designed to explicitly cover code sharing, thus Open Source licenses are generally preferred over CC, with common examples including GPL, Apache and MIT27. Suitable code repositories with version control and issue tracking are indispensable for collaborative open source developments, with common platforms including GitHub, GitLab, Bitbucket and more. For all three above-mentioned aspects of Open Science, i.e., Open Access, Open Data and Open Source, the generation of permanent identifiers such as a Digital Object Identifier (DOI)28 is an integral aspect of FAIR and vital to preserve the discoverability and lifetime of such projects.Finally, open reproducible research is a culmination of all three aspects above. With systems such as RMarkdown and Jupyter Notebooks, it is now possible to have fully compliable research outputs and reproducible manuscripts. The Journal of Open Source Software even accepts submissions as GitHub pull requests and compiles the entire submission on their system; one example relevant to water research is patRoon 2.0 (ref. 29). The ‘open-source knowledge infrastructure for collaborative and reproducible data science’ Renku facilitates traceability and reproducibility of complex workflows involving networks of interconnected code, data and figure files. It does so by automatic provenance tracking of output files and the creation of a version-controlled git repository containing all information, including the computational environment. More

  • in

    Interbasin water transfers in the United States and Canada

    Interbasin water transfers have been defined many ways within the literature12,13,14 and by government agencies. For this study, we define an IBT as a human-mediated movement of surface water or groundwater from one sub-drainage area or subregion (HUC4) to another sub-drainage area or subregion through man-made or artificial pathways (e.g., canals, pipelines, aqueducts). Subregion15 and sub-drainage16 boundaries come from the United States Geological Survey (USGS) and Natural Resources Canada, respectively. We further narrow our IBT definition to exclude the transfer of treated water and wastewater due to the lack of data describing complex municipal water and wastewater distribution systems across Canada and the US. The movement of untreated (or “raw”) water between the intake location of a water distribution system and the water treatment facility is deemed an IBT if it traverses a basin boundary (i.e., sub-drainage or subregion boundary; e.g., Fig. 3a); however, if water within the distribution system crosses a basin boundary after treatment, we do not include this instance within our IBT datasets (Fig. 3b). We have also removed inconsequential drainage ditches that drain less than 0.5 square kilometers. Such drainage ditches constituted a significant fraction of previous US IBT datasets7, even though they have a negligible hydrologic, ecological, or societal impact.Fig. 3Examples of potential interbasin transfers of raw water (a) and treated water (b). Raw water transfers are represented by yellow lines, while a treated water transfer is represented by a magenta line. If raw water crosses a subregion boundary (blue lines), it is included in our dataset, as is the case for the Schoharie and Delaware Aqueducts that bring raw water for New York City public water supply (a). If only treated water crosses a subregion boundary, as is the case for Gwinnett County’s public water supply system in Georgia (b), then it is not included within our IBT datasets.Full size imageThe creation of our IBT data products involved four steps: i) data collection, ii) data standardization, iii) data visualization, and iv) data validation. The first three steps are described in this section (Methods), while data validation is described within the ‘Technical Validation’ section.Data collectionTo create a national IBT dataset, we started with potential IBTs identified by Dickson and Dzombak7. Dickson and Dzombak extracted all artificial flow paths that crossed subregion boundaries from the USGS National Hydrography Dataset (NHD). These IBTs were not verified and lacked descriptive details, such as water use purpose or transfer volume. Furthermore, the number of IBTs reported by Dickson and Dzombak is artificially large since it counts each instance a conveyance structure crosses a basin boundary as an individual IBT, even if it is part of one larger IBT project (e.g., Central Arizona Project). These records were paired with older IBT datasets produced by USGS8,9. Together, these datasets represent the most complete US IBT datasets to date. We filtered out records from the combined datasets that did not meet our IBT definition, were duplicates, or were verified as being either decommissioned or erroneous. We also connected flowlines that are part of the same IBT project.Next, we searched state and federal reports, data repositories, and websites for data describing the location, properties, and flow volumes of IBTs. Findings from these searches allowed us to remove erroneous records within our current dataset, as well as add new IBTs that were not captured by previous datasets. Mostly, though, our review of government records allowed us to confirm IBT records and to provide more complete documentation of already identified IBTs. Websites for federal agencies that have a role in building, administering, or maintaining records on IBTs, such as the USGS, US Bureau of Reclamation (USBR), US Army Corps of Engineers (USACE), and the Environmental Protection Agency (EPA), were searched for relevant records. Approval by USACE is required when building across a navigable waterway, which is sometimes required for IBTs. Much of the major federal water supply infrastructure in the Western US, including IBTs, were built and are currently operated by USBR. The EPA has records related to water distribution systems17, including water intake and treatment locations, which were used to identify IBT locations. The USGS gauge network reports time-series records for 79 IBTs. Relevant state websites for IBT data collection were identified through the survey of state-level water data platforms developed by Josset et al.18.After reviewing the scientific literature and publicly available government reports, data repositories, and websites, we contacted federal, state, and local representatives for additional data records and to verify our existing records. Federal employees at USGS and USBR reviewed and provided additional records for our initial IBT dataset. The USGS Water Use Science Project regularly collects water use and water infrastructure data from states. The USGS Water Use team helped us identify the state agency and contact person that would most likely maintain IBT data for each state.We sent IBT data requests to each state via email and phone calls. In cases where these attempts were unsuccessful, we filed an Open Records Act or Freedom of Information Act (FOIA) request to collect any remaining data we were missing. In cases where neither federal or state agencies maintained the data we sought, we contacted IBT operators directly. Direct contact with IBT operators was primarily done when collecting time-series flow data for irrigation districts and municipal water suppliers.Canadian IBT data were collected from an Access to Information Act records request. The Environment and Climate Change Canada (formerly, Environment Canada) had maintained records of IBTs throughout Canada until 2011. Several reports published by Environment Canada researchers10,11 document Canadian IBTs and their properties. These reports highlight select IBTs but do not provide complete IBT records. Our Access to Information Act request provided us an unpublished report and associated data from 2004 that described the full collection of IBTs in Canada.Data were collected between August 2019 and June 2022. Our data products reflect the most up-to-date data held by primary data collectors on the date of our request. The date each IBT entry was collected is reported in the IBT Inventory Dataset. We collected all time-series flow data available for each IBT, with some records going back as far as 1901.Data standardizationThe data we collected were in a variety of file formats and data types. We created a data standard, which we named the Interbasin Transfer Database Standard Version 1.0. (IBTDS 1.0), to provide a consistent way of representing and defining data for all IBTs. The standardized IBT Inventory Dataset follows a node-link structure. Nodes represent places of water diversion, water use, or change in flow (e.g., reservoir, channel junction). Links represent conveyance infrastructure or natural waterways that connect two or more nodes within an IBT project. Unique link identifiers (Link ID) connect two or more unique node identifiers (Node ID). One or more links constitute an IBT project. The owner/operator of each IBT project, as well as the year the IBT project was commissioned and decommissioned (if applicable), is reported within the IBT Inventory Dataset.Geospatial details are reported for each IBT project in the IBT Inventory Dataset and the IBT Geospatial Dataset. We obtained the precise latitude and longitude of each node using the various data sources noted previously, as well as visual inspection of high-resolution aerial imagery from Google Earth and Esri’s World Imagery layer. Precise geospatial information is reflected in the IBT Geospatial Dataset. The IBT Inventory Dataset lists the hydrologic and geopolitical boundaries that contain each node. For the US, the state and county name and the Federal Information Processing System (FIPS) Code is also provided for each node. Likewise, the province and Census Geographic Unit is given for each node in Canada. The IBT project name (e.g., Heron Bayou Drainage Ditch, Hennepin Canal) associated with each node and link segment is also reported.As is often the case with irrigation and drainage IBT projects, there are sometimes several relatively small, adjacent diversions/ditches along an IBT project. We focus on capturing the main components of the IBT, instead of representing dozens or even hundreds of connected small ditches that divert or collect water along the IBT project. Nonetheless, when the collective impact of these small water diversions or inputs may noticeably change IBT flows, we depict these small ditches together as a representative two-node pair connected by a link segment. One of the nodes represents approximately the middle of where these small ditches intersect with the main IBT channel. The other node is the approximate centroid of water users served by or areas drained by these small ditches. If one of the secondary channels is large relative to the main channel (i.e., ability to divert more than ~25% of the main channel flow based on channel top width or flow records), it is recorded with its own Node ID and Link ID (Fig. 4). Likewise, if a secondary channel has an official name granted by a government agency or its owner/operator, we also record this segment with its own Node IDs and Link ID(s).Fig. 4An example of an interbasin water transfer project in Arizona with major (yellow) and minor (orange) project components. The thick yellow lines represent primary components of the project that are recorded in our dataset and assigned a Link ID (white text label). The thin orange lines represent secondary or tertiary canals or ditches that are small relative to the main (yellow) project segments and are therefore not represented in our dataset. The blue lines represent HUC4 subregion boundaries.Full size imageWe record the primary, secondary, and tertiary purpose of each IBT project and these purposes are the same for all links within the IBT project. One of “water supply – public supply”, “water supply – irrigation”, “flood control”, “navigation”, “waste discharge”, “environmental flows”, “energy – hydroelectric”, “energy – thermoelectric”, “energy – mining”, “other”, or “unknown” is assigned to each IBT project based on online records, design documents, reports, and/or personal correspondence with local, state, or federal officials. Link infrastructural properties, such as whether the link is a lined canal, unlined canal, pipe/tunnel, or other structure, are recorded for each link segment.The average water transfer rate (m3/d) is reported for each link segment where this information is known. The average water transfer rate only represents flows for the identified link segment, not necessarily the entire IBT project since upstream/downstream diversions and inputs may mean flow rates are different in different portions of the project. The average water transfer rate is converted from the units provided to us but is otherwise left unchanged. The primary data records are often unclear or do not specify the time period used to estimate average water transfer rates. The IBT Inventory Dataset reports whether time-series data is available for each Link ID in the IBT Time-Series Flow Dataset.The IBTDS 1.0 data standard was also applied to the IBT Time-Series Flow Dataset. The unique Link ID identifying the location where the transferred flow rate was measured is recorded for each time-series entry, relating the time-series data records to the IBT Inventory Dataset. The recorded flow rate only represents water transfer rates for the given link segment where the measurement was made, not necessarily the entire IBT project. Time-series data describing IBT flow rates were recorded at various temporal resolutions, ranging from instantaneous gauge readings every 15 minutes to average annual records. The standardized time-series dataset converted all reported water transfers to a common measurement unit (m3) and temporal resolution (day). When available, a web link to the original data source is published with the standardized data. The original timestep which the data was collected is also reported for each entry.In a few instances, there is more than one flow measurement for a link segment. Measurements are typically reported by different agencies and the measurements do not always align perfectly, either in their quantity or frequency of their reporting. Unless one of the records is known to be erroneous or of inferior quality, both sets of records are standardized and reported. For example, USBR reports monthly water transfer volumes along the Central Arizona Project (Link ID: CAP.AZ.01), while USGS reports daily water transfer volumes for the same link segment.Data visualizationWe provide an online visualization of the IBT Geospatial Dataset using ArcGIS Online (https://virginiatech.maps.arcgis.com/apps/mapviewer/index.html?webmap=b2cfac9b70ea44e4938734da0b1a7c8e), which is also summarized in Fig. 1. Every IBT node and link segment in the IBT Inventory Dataset is included. An arrowhead at the end of a link segment depicts the flow direction of transferred water. Link segments imported into ArcGIS Online were initially represented as a straight path between connected nodes. When the IBT flowpath was visible from aerial imagery or the flowpath was available from existing sources (e.g., NHD or detailed engineering drawings), the exact path of transferred water was mapped; otherwise, the flowpath remained a straight line between connected nodes.State and federal agencies restricted some of the data we are able to share publicly. Specifically, we are not permitted to reveal the exact water intake and treatment locations of some public water suppliers. Instead of mapping the precise latitude and longitude of points of diversion, points of flow change, and points of use like with other IBTs, IBTs whose primary purpose is public water supply are depicted as a straight line connecting the centroids of subwatersheds (HUC12) where the IBT node is located. More

  • in

    Agricultural drought over water-scarce Central Asia aggravated by internal climate variability

    Dai, A. & Zhao, T. Uncertainties in historical changes and future projections of drought. Part I: estimates of historical drought changes. Climatic Change 144, 519–533 (2017).Article 

    Google Scholar 
    Greve, P. et al. Global assessment of trends in wetting and drying over land. Nat. Geosci. 7, 716–721 (2014).Article 

    Google Scholar 
    Jiang, J. et al. Tracking moisture sources of precipitation over central Asia: a study based on the water-source-tagging method. J. Clim. 33, 10339–10355 (2020).Article 

    Google Scholar 
    Seneviratne, S. I. et al. in Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Li, Z., Chen, Y., Fang, G. & Li, Y. Multivariate assessment and attribution of droughts in Central Asia. Sci. Rep. 7, 1316 (2017).Article 

    Google Scholar 
    Li, Z., Chen, Y., Li, W., Deng, H. & Fang, G. Potential impacts of climate change on vegetation dynamics in Central Asia. J. Geophys. Res. Atmos. 120, 12345–12356 (2015).Article 

    Google Scholar 
    Deng, H. & Chen, Y. Influences of recent climate change and human activities on water storage variations in Central Asia. J. Hydrol. 544, 46–57 (2017).Article 

    Google Scholar 
    Seager, R., Nakamura, J. & Ting, M. Mechanisms of seasonal soil moisture drought onset and termination in the southern Great Plains. J. Hydrometeorol. 20, 751–771 (2019).Article 

    Google Scholar 
    Teuling, A. J. et al. Evapotranspiration amplifies European summer drought. Geophys. Res. Lett. 40, 2071–2075 (2013).Article 

    Google Scholar 
    Douville, H. et al. in Climate Change 2021: The Physical Science Basis (eds. Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3, 52–58 (2013).Article 

    Google Scholar 
    Barlow, M. & Hoell, A. Drought in the Middle East and Central–Southwest Asia during winter 2013/14. Bull. Am. Meteorol. Soc. 96, S71–S76 (2015).Article 

    Google Scholar 
    Peng, D., Zhou, T., Zhang, L. & Zou, L. Detecting human influence on the temperature changes in Central Asia. Clim. Dyn. 53, 4553–4568 (2019).Article 

    Google Scholar 
    Barlow, M. et al. A review of drought in the Middle East and Southwest Asia. J. Clim. 29, 8547–8574 (2016).Article 

    Google Scholar 
    Hoell, A., Funk, C. & Barlow, M. The forcing of Southwestern Asia teleconnections by low-frequency sea surface temperature variability during boreal winter. J. Clim. 28, 1511–1526 (2015).Article 

    Google Scholar 
    Jiang, J. & Zhou, T. Human‐induced rainfall reduction in drought‐prone northern central Asia. Geophys. Res. Lett. 48, e2020GL092156 (2021).Article 

    Google Scholar 
    Williams, A. P. et al. Contribution of anthropogenic warming to California drought during 2012–2014. Geophys. Res. Lett. 42, 6819–6828 (2015).Article 

    Google Scholar 
    Williams, A. P. et al. Large contribution from anthropogenic warming to an emerging North American megadrought. Science 368, 314–318 (2020).Article 

    Google Scholar 
    Samaniego, L. et al. Anthropogenic warming exacerbates European soil moisture droughts. Nat. Clim. Change 8, 421–426 (2018).Article 

    Google Scholar 
    García-Herrera, R. et al. The European 2016/17 drought. J. Clim. 32, 3169–3187 (2019).Article 

    Google Scholar 
    Mueller, B. & Zhang, X. Causes of drying trends in northern hemispheric land areas in reconstructed soil moisture data. Clim. Change 134, 255–267 (2016).Article 

    Google Scholar 
    Gu, X. et al. Attribution of global soil moisture drying to human activities: a quantitative viewpoint. Geophys. Res. Lett. 46, 2573–2582 (2019).Article 

    Google Scholar 
    Coats, S. et al. Internal ocean–atmosphere variability drives megadroughts in western North America. Geophys. Res. Lett. 43, 9886–9894 (2016).Article 

    Google Scholar 
    Deser, C. et al. Insights from Earth system model initial-condition large ensembles and future prospects. Nat. Clim. Change 10, 277–286 (2020).Article 

    Google Scholar 
    Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1108 (2009).Article 

    Google Scholar 
    Deser, C., Knutti, R., Solomon, S. & Phillips, A. S. Communication of the role of natural variability in future North American climate. Nat. Clim. Change 2, 775–779 (2012).Article 

    Google Scholar 
    Murphy, J. M. et al. Transient climate changes in a perturbed parameter ensemble of emissions-driven Earth system model simulations. Clim. Dyn. 43, 2855–2885 (2014).Article 

    Google Scholar 
    Huang, X. et al. The recent decline and recovery of Indian summer monsoon rainfall: relative roles of external forcing and internal variability. J. Clim. 33, 5035–5060 (2020).Article 

    Google Scholar 
    Zhang, Y., Wallace, J. M. & Battisti, D. S. ENSO-like interdecadal variability: 1900–93. J. Clim. 10, 1004–1020 (1997).Article 

    Google Scholar 
    Power, S., Casey, T., Folland, C., Colman, A. & Mehta, V. Inter-decadal modulation of the impact of ENSO on Australia. Clim. Dyn. 15, 319–324 (1999).Article 

    Google Scholar 
    Henley, B. J. et al. A tripole index for the Interdecadal Pacific Oscillation. Clim. Dyn. 45, 3077–3090 (2015).Article 

    Google Scholar 
    Wu, L., Ma, X., Dou, X., Zhu, J. & Zhao, C. Impacts of climate change on vegetation phenology and net primary productivity in arid Central Asia. Sci. Total Environ. 796, 149055 (2021).Article 

    Google Scholar 
    FAO. Drought Characteristics and Management in Central Asia and Turkey (FAO Water Reports, 2017).Cai, W., Cowan, T., Briggs, P. & Raupach, M. Rising temperature depletes soil moisture and exacerbates severe drought conditions across southeast Australia. Geophys. Res. Lett. 36, L21709 (2009).Article 

    Google Scholar 
    Kidron, G. J. & Kronenfeld, R. Temperature rise severely affects pan and soil evaporation in the Negev Desert. Ecohydrology 9, 1130–1138 (2016).Article 

    Google Scholar 
    Xu, Y., Zhang, X., Hao, Z., Singh, V. P. & Hao, F. Characterization of agricultural drought propagation over China based on bivariate probabilistic quantification. J. Hydrol. 598, 126194 (2021).Article 

    Google Scholar 
    Bae, H. et al. Characteristics of drought propagation in South Korea: relationship between meteorological, agricultural, and hydrological droughts. Nat. Hazards 99, 1–16 (2019).Article 

    Google Scholar 
    Wang, W., Ertsen, M. W., Svoboda, M. D. & Hafeez, M. Propagation of drought: from meteorological drought to agricultural and hydrological drought. Adv. Meteorol. 2016, 127897 (2016).Article 

    Google Scholar 
    Hoell, A., Funk, C., Barlow, M. & Cannon, F. in Climate Extremes: Patterns and Mechanisms (eds Wang, S. et al.) 283–298 (American Geophysical Union, 2017).Wu, M. et al. A very likely weakening of Pacific Walker Circulation in constrained near-future projections. Nat. Commun. 12, 6502 (2021).Article 

    Google Scholar 
    Hoell, A., Barlow, M., Cannon, F. & Xu, T. Oceanic origins of historical southwest Asia precipitation during the boreal cold season. J. Clim. 30, 2885–2903 (2017).Article 

    Google Scholar 
    Jiang, J., Zhou, T., Chen, X. & Wu, B. Central Asian precipitation shaped by the tropical Pacific decadal variability and the Atlantic multidecadal variability. J. Clim. 34, 7541–7553 (2021).Article 

    Google Scholar 
    Barlow, M. A. & Tippett, M. K. Variability and predictability of Central Asia river flows: antecedent winter precipitation and large-scale teleconnections. J. Hydrometeorol. 9, 1334–1349 (2008).Article 

    Google Scholar 
    Hoell, A., Barlow, M. & Saini, R. Intraseasonal and seasonal-to-interannual Indian Ocean convection and hemispheric teleconnections. J. Clim. 26, 8850–8867 (2013).Article 

    Google Scholar 
    Rana, S., McGregor, J. & Renwick, J. Dominant modes of winter precipitation variability over Central Southwest Asia and inter-decadal change in the ENSO teleconnection. Clim. Dyn. https://doi.org/10.1007/s00382-019-04889-9 (2019).Article 

    Google Scholar 
    Jiang, J., Zhou, T., Chen, X. & Zhang, L. Future changes in precipitation over Central Asia based on CMIP6 projections. Environ. Res. Lett. 15, 054009 (2020).Article 

    Google Scholar 
    Huang, X. et al. South Asian summer monsoon projections constrained by the interdecadal Pacific oscillation. Sci. Adv. 6, eaay6546 (2020).Article 

    Google Scholar 
    Varis, O. Resources: curb vast water use in Central Asia. Nature 514, 27–29 (2014).Article 

    Google Scholar 
    Farah, P. in ENERGY: POLICY, LEGAL AND SOCIAL-ECONOMIC ISSUES UNDER THE DIMENSIONS OF SUSTAINABILITY AND SECURITY (eds Farah, P. & Rossi, P.) 179–193 (Imperial College Press & World Scientific Publishing, 2015).Wang, X., Chen, Y., Li, Z., Fang, G. & Wang, Y. Development and utilization of water resources and assessment of water security in Central Asia. Agric. Water Manag. 240, 106297 (2020).Article 

    Google Scholar 
    Peng, D., Zhou, T., Zhang, L., Zhang, W. & Chen, X. Observationally constrained projection of the reduced intensification of extreme climate events in Central Asia from 0.5 °C less global warming. Clim. Dyn. 54, 543–560 (2020).Article 

    Google Scholar 
    Pokhrel, Y. et al. Global terrestrial water storage and drought severity under climate change. Nat. Clim. Change 11, 226–233 (2021).Article 

    Google Scholar 
    Zhao, T. & Dai, A. CMIP6 model-projected hydroclimatic and drought changes and their causes in the 21st century. J. Clim. https://doi.org/10.1175/JCLI-D-21-0442.1 (2021).Balsamo, G. et al. ERA-Interim/Land: a global land surface reanalysis data set. Hydrol. Earth Syst. Sci. 19, 389–407 (2015).Article 

    Google Scholar 
    Rodell, M. et al. The Global Land Data Assimilation System. Bull. Am. Meteorol. Soc. 85, 381–394 (2004).Article 

    Google Scholar 
    Martens, B. et al. GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci. Model Dev. 10, 1903–1925 (2017).Article 

    Google Scholar 
    Preimesberger, W., Scanlon, T., Su, C.-H., Gruber, A. & Dorigo, W. Homogenization of structural breaks in the global ESA CCI soil moisture multisatellite climate data record. IEEE Trans. Geosci. Remote Sens. 59, 2845–2862 (2021).Article 

    Google Scholar 
    Dunn, R. J. H. et al. Development of an updated global land in situ‐based data set of temperature and precipitation extremes: HadEX3. J. Geophys. Res. Atmos. 125, e2019JD032263 (2020).Article 

    Google Scholar 
    Rohde, R., Muller, R., Jacobsen, R., Perlmutter, S. & Mosher, S. Berkeley Earth temperature averaging process. Geoinf. Geostat. 1, 2 (2013).
    Google Scholar 
    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).Article 

    Google Scholar 
    Deser, C., Simpson, I. R., McKinnon, K. A. & Phillips, A. S. The Northern Hemisphere extratropical atmospheric circulation response to ENSO: how well do we know it and how do we evaluate models accordingly? J. Clim. 30, 5059–5082 (2017).Article 

    Google Scholar 
    Deser, C., Guo, R. & Lehner, F. The relative contributions of tropical Pacific sea surface temperatures and atmospheric internal variability to the recent global warming hiatus. Geophys. Res. Lett. 44, 7945–7954 (2017).Article 

    Google Scholar 
    Henley, B. J. Pacific decadal climate variability: indices, patterns and tropical–extratropical interactions. Glob. Planet. Change 155, 42–55 (2017).Article 

    Google Scholar 
    Kaplan, A. et al. Analyses of global sea surface temperature 1856–1991. J. Geophys. Res. Ocean. 103, 18567–18589 (1998).Article 

    Google Scholar 
    Huang, B. et al. Extended reconstructed sea surface temperature, Version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J. Clim. 30, 8179–8205 (2017).Article 

    Google Scholar 
    Salzmann, M. & Cherian, R. On the enhancement of the Indian summer monsoon drying by Pacific multidecadal variability during the latter half of the twentieth century. J. Geophys. Res. Atmos. 120, 9103–9118 (2015).Article 

    Google Scholar 
    Ohlson, J. A. & Kim, S. Linear valuation without OLS: the Theil–Sen estimation approach. SSRN Electron. J. https://doi.org/10.2139/ssrn.2276927 (2013).Article 

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

    Google Scholar 
    Kendall, M. G. Rank Correlation Methods (Hafner Publishing Company, 1955). More