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

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

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

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

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

GIIGNL 2021 Annual Report (GIIGNL, 2021); https://giignl.org/publications/giignl-2021-annual-reportHofste, R. W., Reig, P. & Schleifer, L. 17 Countries, Home to One-Quarter of the World’s Population, Face Extremely High Water Stress (World Resources Institute, 2019)Abotalib, A. Z., Heggy, E., Scabbia, G. & Mazzoni, A. Groundwater dynamics in fossil fractured carbonate aquifers in Eastern Arabian Peninsula: a preliminary investigation. J. Hydrol. 571, 460–470 (2019).Article 
CAS 

Google Scholar 
Mohieldeen, Y. E., Elobaid, E. A. & Abdalla, R. GIS-based framework for artificial aquifer recharge to secure sustainable strategic water reserves in Qatar arid environment peninsula. Sci. Rep. 11, 18184 (2021).Article 
CAS 

Google Scholar 
Darwish, M. A. & Mohtar, R. Qatar water challenges. Desalination Water Treat. 51, 75–86 (2013).Article 
CAS 

Google Scholar 
Hussein, H. & Lambert, L. A rentier state under blockade: Qatar’s water–energy–food predicament from energy abundance and food insecurity to a silent water crisis. Water 12, 1051 (2020).Article 

Google Scholar 
Water Sector (Kahramaa, accessed 30 August 2022); https://www.km.qa/Aboutus/Pages/Watersector.aspxAnnual Statistics Report 2020 (Kahramaa, 2021); https://www.km.qa/MediaCenter/Publications/Annual%20Statistics%20Report%202020%20English.pdf (2021)Dale, S. bp Statistical Review of World Energy 2021 (bp, 2022); https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2022-full-report.pdfEl Gamal, R. Qatar Petroleum signs deal for mega-LNG expansion. Reuters (8 February 2021).Global Gas Outlook to 2050 (McKinsey & Company, 2021); https://www.mckinsey.com/~/media/mckinsey/industries/oil%20and%20gas/our%20insights/global%20gas%20outlook%20to%202050/global-gas-outlook-2050-executive-summary.pdfSurkes, S. 2017 oil spill closed three desalination plants for three days, official reveals. The Times of Israel (19 March 2021).Aizhu, C. & Blanchard, B. China seals oil port after spill. Reuters (19 July 2010).Huynh, B. Q. et al. Public health impacts of an imminent Red Sea oil spill. Nat. Sustain. https://doi.org/10.1038/s41893-021-00774-8 (2021).Article 

Google Scholar 
Annual Statistical Bulletin (OPEC, 2021); https://asb.opec.orgEvtushenko, N., Ivanov, A. & Evtushenko, V. in Advances in Remote Sensing and Geo Informatics Applications (eds El-Askary, H. M. et al.) 343–347 (Springer, 2019).Sheppard, C. et al. The Gulf: a young sea in decline. Mar. Pollut. Bull. 60, 13–38 (2010).Article 
CAS 

Google Scholar 
Worldwide Rig Counts—Current & Historical Data (Baker Hughes, 2021); https://rigcount.bakerhughes.com/intl-rig-countGarcia-Pineda, O. et al. Classification of oil spill by thicknesses using multiple remote sensors. Remote Sens. Environ. 236, 111421 (2020).Article 

Google Scholar 
Al Azhar, M., Temimi, M., Zhao, J. & Ghedira, H. Modeling of circulation in the Arabian Gulf and the Sea of Oman: skill assessment and seasonal thermohaline structure. J. Geophys. Res. Oceans 121, 1700–1720 (2016).Article 

Google Scholar 
Thoppil, P. G. & Hogan, P. J. A modeling study of circulation and eddies in the Persian Gulf. J. Phys. Oceanogr. 40, 2122–2134 (2010).Article 

Google Scholar 
Thoppil, P. G. & Hogan, P. J. Persian Gulf response to a wintertime shamal wind event. Deep Sea Res. 1 57, 946–955 (2010).Article 

Google Scholar 
Yu, Y., Notaro, M., Kalashnikova, O. V. & Garay, M. J. Climatology of summer shamal wind in the Middle East: summer shamal climatology. J. Geophys. Res. Atmos. 121, 289–305 (2016).Article 

Google Scholar 
Tahir, F., Baloch, A. A. B. & Ali, H. in Sustainability Perspectives: Science, Policy and Practice: A Global View of Theories, Policies and Practice in Sustainable Development (eds Khaiter, P. A. & Erechtchoukova, M. G.) 303–329 (Springer, 2020); https://doi.org/10.1007/978-3-030-19550-2_15Benzene (ATSDR, 2015); https://www.atsdr.cdc.gov/sites/toxzine/docs/benzene_toxzine.pdfAl-Amirah, A. S. The Nowruz oil spill in the Arabian Gulf: case study of Saudi Arabia. Geogr. Bull. 37, 16–32 (1985).Marhoon, Y. Qatar Response Plan Version 5 (OSRL, 2021); https://www.oilspillresponse.com/globalassets/external-links/covid-19-updates/emea—qatar-response-090420-1030.pdfGlobal LNG Market Outlook 2022–26 (Bloomberg, 2022); https://bbgmktg.turtl.co/story/global-lng-market-outlook/Commodity Markets (World Bank, accessed 23 August 2022); https://www.worldbank.org/en/research/commodity-marketsStapczynski, S. Global energy crunch is making gas too pricey for Asia. Bloomberg (17 June 2022).Yep, E. Factbox: Asia-Pacific economies face escalating energy crisis. S&P Global Commodity Insights (27 June 2022).Gill, J. Analysis: heat or eat? Winter protests loom as energy poverty sweeps Europe. Reuters (25 August 2022).Twidale, S. & Buli, N. EU gas price rockets higher after Russia halts Nord Stream flows. Reuters (5 September 2022).Water Security Mega Reservoirs Project (Kahramaa, accessed 11 October 2021); http://www.watermegareservoirs.qaNatural-gas prices are spiking around the world. The Economist (21 September 2021).Dagestad, K.-F., Röhrs, J., Breivik, Ø. & Ådlandsvik, B. OpenDrift v1.0: a generic framework for trajectory modeling. Geosci. Model Dev. 11, 1405–1420 (2018) https://doi.org/10.5194/gmd-11-1405-2018Barker, C. H. et al. Progress in operational modeling in support of oil spill response. J. Mar. Sci. Eng. 8, 668 (2020).Article 

Google Scholar 
Röhrs, J. et al. The effect of vertical mixing on the horizontal drift of oil spills. Ocean Sci. 14, 1581–1601 (2018).Keramea, P., Kokkos, N., Gikas, G. & Sylaios, G. Operational modeling of North Aegean oil spills forced by real-time met–ocean forecasts. J. Mar. Sci. Eng. 10, 411 (2022).Article 

Google Scholar 
Androulidakis, Y., Kourafalou, V., Robert Hole, L., Le Hénaff, M. & Kang, H. Pathways of oil spills from potential Cuban offshore exploration: influence of ocean circulation. J. Mar. Sci. Eng. 8, 535 (2020).Article 

Google Scholar 
Breivik, Ø., Bidlot, J.-R. & Janssen, P. A. E. M. A Stokes drift approximation based on the Phillips spectrum. Ocean Model. 100, 49–56 (2016).Article 

Google Scholar 
Li, Z., Spaulding, M. L. & French-McCay, D. An algorithm for modeling entrainment and naturally and chemically dispersed oil droplet size distribution under surface breaking wave conditions. Mar. Pollut. Bull. 119, 145–152 (2017).Article 
CAS 

Google Scholar 
Tkalich, P. & Chan, E. S. Vertical mixing of oil droplets by breaking waves. Mar. Pollut. Bull. 44, 1219–1229 (2002).Article 
CAS 

Google Scholar 
Li, Z., Spaulding, M., French McCay, D., Crowley, D. & Payne, J. R. Development of a unified oil droplet size distribution model with application to surface breaking waves and subsea blowout releases considering dispersant effects. Mar. Pollut. Bull. 114, 247–257 (2017).Article 
CAS 

Google Scholar 
Sundby, S. A one-dimensional model for the vertical distribution of pelagic fish eggs in the mixed layer. Deep Sea Res. A 30, 645–661 (1983).Article 

Google Scholar 
Lehr, W., Jones, R., Evans, M., Simecek-Beatty, D. & Overstreet, R. Revisions of the ADIOS oil spill model. Environ. Model. Softw. 17, 189–197 (2002).Article 

Google Scholar 
Zodiatis, G. et al. The Mediterranean Decision Support System for Marine Safety dedicated to oil slicks predictions. Deep Sea Res. 2 133, 4–20 (2016).Article 

Google Scholar 
Amir-Heidari, P. & Raie, M. Probabilistic risk assessment of oil spill from offshore oil wells in Persian Gulf. Mar. Pollut. Bull. 136, 291–299 (2018).Article 
CAS 

Google Scholar 
Batchelder, H. P. Forward-in-time-/backward-in-time-trajectory (FITT/BITT) modeling of particles and organisms in the coastal ocean. J. Atmos. Ocean. Technol. 23, 727–741 (2006).Article 

Google Scholar 
Ciappa, A. C. Reverse trajectory study of oil spill risk in Cyclades Islands of the Aegean Sea. Reg. Stud. Mar. Sci. 41, 101580 (2021).
Google Scholar 
Suneel, V., Ciappa, A. & Vethamony, P. Backtrack modeling to locate the origin of tar balls depositing along the west coast of India. Sci. Total Environ. 569–570, 31–39 (2016).Article 

Google Scholar 
Zelenke, B., O’Connor, C. & Barker, C. General NOAA Operational Modeling Environment (GNOME) Technical Documentation (NOAA OR&R, October 2012); https://response.restoration.noaa.gov/gnome_manualDe Dominicis, M., Pinardi, N., Zodiatis, G. & Archetti, R. MEDSLIK-II, a Lagrangian marine surface oil spill model for short-term forecasting—part 2: numerical simulations and validations. Geosci. Model Dev. 6, 1871–1888 (2013).Article 

Google Scholar 
Fingas, M. in Oil Spill Science and Technology (ed. Fingas, M.) 243–273 (Elsevier, 2011); https://doi.org/10.1016/B978-1-85617-943-0.10010-3Madec, G. NEMO ocean engine. Zenodo (2017) https://doi.org/10.5281/zenodo.3248739Eager, R. E. et al. A climatological study of the sea and land breezes in the Arabian Gulf region. J. Geophys. Res. 113, D15106 (2008).Article 

Google Scholar 
MarineTraffic: Global Ship Tracking Intelligence (accessed 15 May 2021) https://www.marinetraffic.com/Etkin, D. S. et al. in Oil Spill Science and Technology 2nd edn (ed. Fingas, M.) 71–183 (Gulf Professional Publishing, 2017); https://doi.org/10.1016/B978-0-12-809413-6.00002-3Oil Tanker Spill Statistics 2021 (ITOPF, 2022); https://www.itopf.org/knowledge-resources/data-statistics/statistics/Quattrocchi, G. et al. An operational numerical system for oil stranding risk assessment in a high-density vessel traffic area. Front. Mar. Sci. 8, 585396 (2021).Article 

Google Scholar  More

IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Wigley, T. M. L. & Raper, S. C. B. Natural variability of the climate system and detection of the greenhouse effect. Nature 344, 324–327 (1990).Article 

Google Scholar 
Crowley, J. T. Causes of climate change over the past 1000 years. Science 289, 270–277 (2000).Article 
CAS 

Google Scholar 
Wang, W. C., Yung, Y. L., Lacis, A. A., Mo, T. A. & Hansen, J. E. Greenhouse effects due to man-made perturbations of trace gases. Science 194, 685–690 (1976).Article 
CAS 

Google Scholar 
Paris Agreement (United Nations Framework Convention on Climate Change, 2015).Rio+20 United Nations Conference on Sustainable Development The Future We Want: Outcome Document of the United Nations Conference on Sustainable Development (United Nations, 2012).Basheer, M. et al. Collaborative management of the Grand Ethiopian Renaissance Dam increases economic benefits and resilience. Nat. Commun. 12, 5622 (2021).Article 
CAS 

Google Scholar 
Agreement between the Republic of the Sudan and the United Arab Republic for the Full Utilization of the Nile Waters (International Water Law Project, 1959); http://internationalwaterlaw.org/documents/regionaldocs/uar_sudan.htmlCascão, A. E. & Nicol, A. GERD: new norms of cooperation in the Nile Basin? Water Int. 41, 550–573 (2016).Article 

Google Scholar 
Salman, S. The Grand Ethiopian Renaissance Dam: the road to the declaration of principles and the Khartoum document. Water Int. 41, 512–527 (2016).Article 

Google Scholar 
Tawfik, R. The Grand Ethiopian Renaissance Dam: a benefit-sharing project in the Eastern Nile? Water Int. 41, 574–592 (2016).Article 

Google Scholar 
Wheeler, K. G., Jeuland, M., Hall, J. W., Zagona, E. & Whittington, D. Understanding and managing new risks on the Nile with the Grand Ethiopian Renaissance Dam. Nat. Commun. https://doi.org/10.1038/s41467-020-19089-x (2020).Wheeler, K. et al. Exploring cooperative transboundary river management strategies for the Eastern Nile Basin. Water Resour. Res. https://doi.org/10.1029/2017WR022149 (2018).Wheeler, K. G. et al. Cooperative filling approaches for the Grand Ethiopian Renaissance Dam. Water Int. 41, 611–634 (2016).Article 

Google Scholar 
Basheer, M. et al. Quantifying and evaluating the impacts of cooperation in transboundary river basins on the water–energy–food nexus: the Blue Nile Basin. Sci. Total Environ. 630, 1309–1323 (2018).Article 
CAS 

Google Scholar 
Basheer, M. Cooperative operation of the Grand Ethiopian Renaissance Dam reduces Nile riverine floods. River Res. Appl. 47, 805–814 (2021).Article 

Google Scholar 
Elagib, N. A. & Basheer, M. Would Africa’s largest hydropower dam have profound environmental impacts? Environ. Sci. Pollut. Res. 28, 8936–8944 (2021).Article 
CAS 

Google Scholar 
Joint Statement of Egypt, Ethiopia, Sudan, the United States and the World Bank (United States Department of the Treasury, 2020); https://home.treasury.gov/news/press-releases/sm891Edrees, M. Letter dated 11 June 2021 from the Permanent Representative of Egypt to the United Nations addressed to the Secretary-Genera (United Nations, 2021); https://digitallibrary.un.org/record/3931750?ln=enAmde, T. A. Letter dated 14 May 2020 from the Permanent Representative of Ethiopia to the United Nations addressed to the President of the Security Council (United Nations, 2020); https://digitallibrary.un.org/record/3862715?ln=enTaye, M. T., Willems, P. & Block, P. Implications of climate change on hydrological extremes in the Blue Nile Basin: a review. J. Hydrol. Reg. Stud. 4, 280–293 (2015).Article 

Google Scholar 
Di Baldassarre, G. et al. Future hydrology and climate in the River Nile Basin: a review. Hydrol. Sci. J. 56, 199–211 (2011).Article 

Google Scholar 
Bhattacharjee, P. S. & Zaitchik, B. F. Perspectives on CMIP5 model performance in the Nile River headwaters regions. Int. J. Climatol. 35, 4262–4275 (2015).Article 

Google Scholar 
Haasnoot, M., Kwakkel, J. H., Walker, W. E. & ter Maat, J. Dynamic adaptive policy pathways: a method for crafting robust decisions for a deeply uncertain world. Glob. Environ. Change 23, 485–498 (2013).Article 

Google Scholar 
Hui, R., Herman, J., Lund, J. & Madani, K. Adaptive water infrastructure planning for nonstationary hydrology. Adv. Water Resour. 118, 83–94 (2018).Article 

Google Scholar 
Marchau, V. A. W. J., Walker, W. E., Bloemen, P. J. T. M. & Popper, S. W. (eds) Decision Making under Deep Uncertainty: From Theory to Practice (Springer, 2019).Smith, M. et al. Adaptation’s Thirst: Accelerating the Convergence of Water and Climate Action (Global Commission on Adaptation, 2019).Hallegatte, S. Strategies to adapt to an uncertain climate change. Glob. Environ. Change 19, 240–247 (2009).Article 

Google Scholar 
Reed, P. M. et al. Multisector dynamics: advancing the science of complex adaptive human–Earth systems. Earth’s Future 10, e2021EF002621 (2022).Article 

Google Scholar 
Walker, W. E., Haasnoot, M. & Kwakkel, J. H. Adapt or perish: a review of planning approaches for adaptation under deep uncertainty. Sustainability 5, 955–979 (2013).Article 

Google Scholar 
Kwadijk, J. C. J. et al. Using adaptation tipping points to prepare for climate change and sea level rise: a case study in the Netherlands. WIREs Clim. Change 1, 729–740 (2010).Article 

Google Scholar 
Kwakkel, J. H., Walker, W. E. & Marchau, V. Adaptive airport strategic planning. Eur. J. Transp. Infrastruct. Res. 10, 249–273 (2010).Kwakkel, J. H., Haasnoot, M. & Walker, W. E. Developing dynamic adaptive policy pathways: a computer-assisted approach for developing adaptive strategies for a deeply uncertain world. Climatic Change 132, 373–386 (2015).Article 

Google Scholar 
Zeff, H. B., Herman, J. D., Reed, P. M. & Characklis, G. W. Cooperative drought adaptation: integrating infrastructure development, conservation, and water transfers into adaptive policy pathways. Water Resour. Res. https://doi.org/10.1002/2016WR018771 (2016).Fletcher, S., Lickley, M. & Strzepek, K. Learning about climate change uncertainty enables flexible water infrastructure planning. Nat. Commun. 10, 1782 (2019).Article 

Google Scholar 
Cohen, J. S. & Herman, J. D. Dynamic adaptation of water resources systems under uncertainty by learning policy structure and indicators. Water Resour. Res. 57, e2021WR030433 (2021).Article 

Google Scholar 
Ricalde, I. et al. Assessing tradeoffs in the design of climate change adaptation strategies for water utilities in Chile. J. Environ. Manage. 302, 114035 (2022).Article 

Google Scholar 
Pachos, K., Huskova, I., Matrosov, E., Erfani, T. & Harou, J. J. Trade-off informed adaptive and robust real options water resources planning. Adv. Water Resour. 161, 104117 (2022).Article 

Google Scholar 
Gold, D. F., Reed, P. M., Gorelick, D. E. & Characklis, G. W. Power and pathways: exploring robustness, cooperative stability, and power relationships in regional infrastructure investment and water supply management portfolio pathways. Earth’s Future 10, e2021EF002472 (2022).Beh, E. H. Y., Maier, H. & Dandy, G. C. Adaptive, multiobjective optimal sequencing approach for urban water supply augmentation under deep uncertainty. Water Resour. Res. https://doi.org/10.1002/2014WR016254 (2015).O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).Article 

Google Scholar 
Wainwright, C. M. et al. ‘Eastern African Paradox’ rainfall decline due to shorter not less intense Long Rains. NPJ Clim. Atmos. Sci. 2, 34 (2019).Article 

Google Scholar 
Rowell, D. P., Booth, B. B. B., Nicholson, S. E. & Good, P. Reconciling past and future rainfall trends over East Africa. J. Clim. 28, 9768–9788 (2015).Article 

Google Scholar 
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 
KC, S. & Lutz, W. The human core of the Shared Socioeconomic Pathways: population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change 42, 181–192 (2017).Article 

Google Scholar 
Crespo Cuaresma, J. Income projections for climate change research: a framework based on human capital dynamics. Glob. Environ. Change 42, 226–236 (2017).Article 

Google Scholar 
Water Level (Copernicus Global Land Service, 2022); https://land.copernicus.eu/global/products/wlInselberg, A. in Trends in Interactive Visualization: State-of-the-Art Survey (eds Liere, R. et al.) 49–78 (Springer, 2009).Goulden, M., Conway, D. & Persechino, A. Adaptation to climate change in international river basins in Africa: a review. Hydrol. Sci. J. 54, 805–828 (2009).Article 

Google Scholar 
Dasgupta, P. The Economics of Biodiversity: The Dasgupta Review (HM Treasury, 2021).François, B., Vrac, M., Cannon, A. J., Robin, Y. & Allard, D. Multivariate bias corrections of climate simulations: which benefits for which losses? Earth Syst. Dyn. 11, 537–562 (2020).Article 

Google Scholar 
Cannon, A. J., Sobie, S. R. & Murdock, T. Q. Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes? J. Clim. 28, 6938–6959 (2015).Article 

Google Scholar 
Mehrotra, R. & Sharma, A. A resampling approach for correcting systematic spatiotemporal biases for multiple variables in a changing climate. Water Resour. Res. 55, 754–770 (2019).Article 

Google Scholar 
Vrac, M. & Friederichs, P. Multivariate-intervariable, spatial, and temporal-bias correction. J. Clim. 28, 218–237 (2015).Article 

Google Scholar 
Beck, H. E. et al. MSWEP v2 global 3-hourly 0.1° precipitation: methodology and quantitative assessment. Bull. Am. Meteorol. Soc. 100, 473–500 (2019).Article 

Google Scholar 
Sheffield, J., Goteti, G. & Wood, E. F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim. 19, 3088–3111 (2006).Article 

Google Scholar 
Walker, D. P., Marsham, J. H., Birch, C. E., Scaife, A. A. & Finney, D. L. Common mechanism for interannual and decadal variability in the East African Long Rains. Geophys. Res. Lett. 47, e2020GL089182 (2020).King, J. A. & Washington, R. Future changes in the Indian Ocean Walker Circulation and links to Kenyan rainfall. J. Geophys. Res. Atmos. 126, e2021JD034585 (2021).Article 

Google Scholar 
Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. FAO Irrigation and Drainage Paper: Crop Evapotranspiration (FAO, 1998).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).David, C. H. et al. River network routing on the NHDPlus dataset. J. Hydrometeorol. 12, 913–934 (2011).Article 

Google Scholar 
Lin, P. et al. Global reconstruction of naturalized river flows at 2.94 million reaches. Water Resour. Res. 55, 6499–6516 (2019).Article 

Google Scholar 
Development of the Eastern Nile Water Simulation Model (Deltares, 2013).Gill, M. A. Flood routing by the Muskingum method. J. Hydrol. 36, 353–363 (1978).Article 

Google Scholar 
Lehner, B. & Grill, G. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrol. Process. 27, 2171–2186 (2013).Article 

Google Scholar 
Tomlinson, J. E., Arnott, J. H. & Harou, J. J. A water resource simulator in Python. Environ. Model. Softw. 126, 104635 (2020).Article 

Google Scholar 
Wurbs, R. A. Generalized Models of River System Development and Management (IntechOpen, 2011).Basheer, M., Sulieman, R. & Ribbe, L. Exploring management approaches for water and energy in the data-scarce Tekeze-Atbara Basin under hydrologic uncertainty. Int. J. Water Resour. Dev. 37, 182–207 (2021).Article 

Google Scholar 
Basheer, M. & Elagib, N. A. Sensitivity of water–energy nexus to dam operation: a water–energy productivity concept. Sci. Total Environ. 616–617, 918–926 (2018).Article 

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

Google Scholar 
Jeuland, M., Wu, X. & Whittington, D. Infrastructure development and the economics of cooperation in the Eastern Nile. Water Int. https://doi.org/10.1080/02508060.2017.1278577 (2017).Lofgren, H., Lee, R., Robinson, S., Thomas, M. & El-Said, M. A Standard Computable General Equilibrium (CGE) Model in GAMS (International Food Policy Research Institute, 2002).Armington, P. S. A theory of demand for products distinguished by place of production. Staff Pap. 16, 159–178 (1969).Article 

Google Scholar 
Siddig, K., Elagra, S., Grethe, H. & Mubarak, A. A Post-separation Social Accounting Matrix for the Sudan (International Food Policy Research Institute, 2018); https://doi.org/10.2499/1024320695Al-Riffai, P. et al. A Disaggregated Social Accounting Matrix: 2010/11 for Policy Analysis in Egypt (International Food Policy Research Institute, 2016); http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/130736Ahmed, H. A., Tebekew, T. & Thurlow, J. 2010/11 Social Accounting Matrix for Ethiopia: A Nexus Project SAM (International Food Policy Research Institute, 2017); http://ebrary.ifpri.org/utils/getfile/collection/p15738coll2/id/131505/filename/131720.pdfChepeliev, M. Gtap-Power data base: version 10. J. Glob. Econ. Anal. 5, 110–137 (2020).Article 

Google Scholar 
Jiang, L. & O’Neill, B. C. Global urbanization projections for the Shared Socioeconomic Pathways. Glob. Environ. Change 42, 193–199 (2017).Article 

Google Scholar 
Fouré, J., Bénassy-Quéré, A. & Fontagné, L. Modelling the world economy at the 2050 horizon. Econ. Transit. Inst. Change 21, 617–654 (2013).
Google Scholar 
Gidden, M. J. et al. Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geosci. Model Dev. 12, 1443–1475 (2019).Article 
CAS 

Google Scholar 
Knox, S., Meier, P., Yoon, J. & Harou, J. J. A Python framework for multi-agent simulation of networked resource systems. Environ. Model. Softw. 103, 16–28 (2018).Article 

Google Scholar 
Deb, K. & Jain, H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18, 577–601 (2014).Article 

Google Scholar 
Hadka, D. Platypus. GitHub https://github.com/Project-Platypus/Platypus (2016).Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M. & Da Fonseca, V. G. Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7, 117–132 (2003).Article 

Google Scholar 
Basheer, M. et al. Balancing national economic policy outcomes for sustainable development. Nat. Commun. 13, 5041 (2022).Article 
CAS 

Google Scholar 
Breiman, L. Random Forests. Mach. Learn. 45, 5–32 (2001).Article 

Google Scholar 
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Google Scholar 
Basheer, M., Nechifor, V., Calzadilla, A., Harou, J. J., Data related to a study on adaptive management of Nile infrastructure. Zenodo https://doi.org/10.5281/zenodo.5914757 (2022). More

Based on 29 climate projections, we find that both the sign and magnitude of potential changes in naturalized streamflow of the Nile in 2021–2050 are highly uncertain. These uncertainties spark the need for an adaptive and cooperative approach. We show that cooperative adaptive management of the GERD yields compromise solutions with economy-wide benefits to Ethiopia, Sudan and Egypt compared with a proposal discussed in Washington, D.C. in 2020 (Fig. 1). Under an example compromise solution (Fig. 1), the mean (based on 29 projections) discounted (at 3%) real gross domestic product (GDP) increases by US$0.77, 0.67 and 0.18 billion in 2020–2045 for Ethiopia, Sudan and Egypt, respectively, relative to the Washington draft proposal. These benefits are more pronounced under extreme climate scenarios, with rises in discounted real GDP of up to US$15.8, 6.3 and 3.0 billion over 2020–2045 for Ethiopia, Sudan and Egypt, respectively. Our results should be complemented by evaluating the impacts on ecology, groundwater and riparian populations.Fig. 1: Ethiopian, Sudanese and Egyptian economic and river system performance under the best-performing designs of an adaptive GERD operating approach, considering 29 climate change projections for 2020–2045.Each line of the parallel coordinates plot shows the performance achieved by one of the Pareto-efficient adaptive designs or policies, that is, a policy that, if further improved for one performance metric, would imply a reduction in one or more other performance metrics. All change values are calculated from a baseline in which the GERD is operated based on the Washington draft proposal. The upward direction on each axis indicates better performance (that is, a ‘perfect adaptive plan’ would be a straight line across the top); diagonal lines between neighbouring axes imply tradeoffs, whereas horizontal ones show synergies. The firm power values are calculated based on a 90% reliability, and the real GDP values are discounted at a 3% rate. bcm, billion cubic metres.Full size image More