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Publications, latest advances in optical networks for 5g communications and beyond, publication date, third quarter 2021, manuscript submission deadline, 15 july 2020, call for papers.

A new generation of optical networks is needed to unleash the full potential of 5G communications and to prepare the network infrastructure for beyond-5G communications. In 5G, the requirements of a new class of advanced high capacity, ultra-reliable and low-latency services (as autonomous driving, or augmented reality) are shaping the evolution not only of the wireless/radio segment, but also of the higher-tier optical wired segments, spanning from access to core.  On a longer-term horizon, pointing towards beyond-5G (or 6G), new challenging technical directions are arising that promise to revolutionize users’ network experience (to name a few, multi-sensorial and holographic communication, pervasive machine learning, coordination of heterogeneous access technologies, quantum communication). While this is a vision and the exact definitions of the new services have yet to be clearly identified, future service requirements will be further exacerbated in terms of capacity, latency, reconfigurability, reliability and security, and simply scaling up the present mode of operation in optical networks is not an option.  A redesign that feature in-built physical security, sub-linear bandwidth scaling costs, extreme low-latency, and reconfigurability will be needed. New, possibly disruptive, solutions must be investigated, both at data and control plane. This SI targets novel research contributions on topics that include (but are not limited to):

• Optical Spatial Division Multiplexed (SDM) Networks
• Quantum-key secure optical networks and physical layer security issues in 5G transport networks
• Tera-scale optical networks for beyond 5G application holographic and sensual communications
• Beyond-5G Passive Optical Networks (PON)
• Time-sensitive and time-deterministic optical networks   
• Optical network control, management and orchestration including SDN and NFV solutions
• Multi-layer and –domain optical networks, including network disaggregation and white-boxes
• Slicing, service chaining, virtualization and multi-tenancy techniques for optical networks
• Optical and wireless network convergence, including radio-over-fiber access networks
• Machine Learning and data analytics techniques for optical networks
• Optical network availability, resilience, survivability, security
• Filterless architectures for optical core, regional and urban aggregation networks

Submission Guidelines

Authors must adhere to the  IEEE JSAC guidelines regarding the manuscript format. For details and templates, please refer to the  guidelines . Papers are to be submitted via EDAS according to this schedule:

Important Dates

Manuscript Submission Deadline: 15 July 2020 First Notification: 10 October 2020 Final Decision Notification: 5 January 2021 Final Manuscript Due: 20 January 2021 Publication Date: Third Quarter 2021

Guest Editors

Massimo Tornatore Politecnico di Milano, Italy

Zuqing Zhu University of Science and Technology of China, China

Ramon Casellas Centre Tecnològic de Telecomunic. de Catalunya, Spain

Elaine Wong University of Melbourne, Australia

Dan Kilper University of Arizona, USA

Lena Wosinska Chalmers University of Technology, Sweden

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Journal of Optical Communications

• Online ISSN: 2191-6322
• Print ISSN: 0173-4911
• Type: Journal
• Language: English
• Publisher: De Gruyter
• First published: January 1, 1980
• Publication Frequency: 4 Issues per Year
• Audience: Researchers and practitioners interested in all aspects of optical communications

Emerging optical network communication and technologies for 5G and beyond

This Special Issue discusses topics contributing to cutting-edge communications and networking technology, emphasizing 5G networks and beyond. We welcome new research and research papers from the writers. We encourage essay submissions for this Special Issue highlighting current trends and developments in studying optical networking and photonic technologies used in telecommunications. We welcome a systematic approach to identifying and researching articles and theoretical and experimental research on the physical and network.

See the Journals Update page for more information

https://www.springer.com/journal/11082/updates/25305884

Dr. Francesco Flammini (Lead Guest Editor)

Malardalen University, Vasteras, Sweden [email protected]

Dr. Gururaj H L

Manipal Institute of Technology, Bengaluru, India [email protected]

Dr. Dalibor Dobrilovic

University of Novi Sad, Zrenjanin, Serbia [email protected]

Dr. V. Janhavi

Vidyavardhaka College of Engineering, Mysuru, India [email protected]

Dr. Qiudan Li

Chinese Academy of Sciences, Beijing, China [email protected]

Dr. Laszlo Barna Iantovics

George Emil Palade University of Medicine, Pharmacy, Sciences, and Technology of Targu Mures, Romania [email protected]

Articles (62 in this collection)

Energy efficient and intelligent routing algorithm using dai and self organizing map hybrid algorithm for future optical wireless communication, authors (first, second and last of 6).

• A. S. Mamatha
• G. Yasoda Devi
• M. Pushpavalli
• Content type: OriginalPaper
• Published: 12 February 2024
• Article: 672

Tree-based wireless NoC architecture: enhancing scalability and latency

• Smriti Srivastava
• Minal Moharir
• Krithika Venkatesh
• Published: 01 February 2024
• Article: 612

Real time data monitoring of water resources environment based on computer remote data collection and image analysis

• Article: 618

Deep learning based optical network transmission application in Chinese English translation system in cloud computing environment

• Yinghong Mai
• Published: 31 January 2024
• Article: 598

Data analysis algorithm for internet of things based on federated learning with optical technology

• Vibha Tiwari
• S. Ananthakumaran
• Swati Bula Patil
• Published: 30 January 2024
• Article: 572

A novel approach of low complexity distributed UA algorithm is used for traffic load balancing and interference in next generation networks

• E. Elamaran
• S. Murugaveni
• Vamsidhar Talasila
• Article: 525

Mitigation of FWM effect in DWDM-RoF system using OQPSK modulation

• S. S. Supriya
• Joseph Zacharias
• Article: 583

A hybrid fuzzy logic based ant colony routing optimization system for wireless communications

• K. R. Radhika
• S. V. Sheela
• Article: 569

Enhanced network lifespan in future wireless communication using machine learning based convolution neural networks

• Article: 579

Impact of noise on data routing in flying Ad hoc networks

• Mohamed Ben Bezziane
• Bouziane Brik
• Amina Ben Bezziane
• Article: 563

Quantum-inspired adaptive loss detection and real-time image restoration for live optical quantum image transmission

• Thella Preethi Priyanka
• Yogeshwari V. Mahajan
• Published: 27 January 2024
• Article: 411

Optimization of book information search in intelligent library system management based on cellular network

• Weixia Wang
• Article: 412

Homodyne detection based multiple-beam WDM FSO communication system under various environmental conditions

• Yogesh Kumar Gupta
• Aditya Goel
• Published: 11 January 2024
• Article: 388

Application of quantum computing based on heterogeneous cellular network algorithm in college student entrepreneurship management

• Published: 02 January 2024
• Article: 375

Dynamic light collection system based on human posture estimation application in martial arts action teaching simulation

• Article: 376

Multiplexing techniques for future fiber optic communications with spatial multiplexing

• Muhammad Shafiq
• Fan Quanrun
• Elangovan Muniyandy
• Published: 29 December 2023
• Article: 330

Digital intelligent manufacturing mode innovation and industrial performance analysis based on industrial internet of things cluster

• Article: 316

Design and research of multimedia information publishing system based on speech recognition technology

• Article: 327

Application of optical motion capture device based on android intelligent platform in sports field auxiliary recognition system

• Article: 296

Application of optical motion capture based on multimodal sensors in badminton player motion recognition system

• Tiejun Zhang
• Published: 27 December 2023
• Article: 275

Enabling secure and efficient industry 4.0 transformation through trust-authorized anomaly detection in cloud environments with a hybrid AI approach

Authors (first, second and last of 8).

• Balambigai Subramanian
• Article: 251

Enhancing the accuracy of target detection in remote video surveillance analytics through federated learning

• Kapil Aggarwal
• K. Anuradha
• Published: 14 December 2023
• Article: 185

An efficient deep learning based stress monitoring model through wearable devices for health care applications

Authors (first, second and last of 4).

• R. Sivakumar
• Article: 200

Enhancing long-haul radio over fiber systems through chromatic dispersion mitigation using cascaded IDCF and apodized fiber bragg grating

• R. D. Soumitra
• Article: 157

Securing communicating networks in the age of big data: an advanced detection system for cyber attacks

• S. Uma Maheswara Rao
• L. Lakshmanan
• Content type: ReviewPaper
• Published: 13 December 2023
• Article: 116

Application of optical network transmission based on 5G network in knowledge management of digital factories

• Article: 110

Flow monitoring system and abnormal log traffic mode detection based on artificial intelligence

• Jinghua Cao
• Article: 112

Simulation of optical image detection based on language activity detection algorithm in piano network teaching system

• Article: 115

Design and application of optical communication technology based on machine learning algorithms in physical education teaching information management system

• Qingwen Zhai
• Article: 111

Research on key technologies of data security and privacy protection in Internet of Things group intelligence

• Qingrong Chen
• Article: 114

Big data analytics for dynamic network slicing in 5G and beyond with dynamic user preferences

• Maganti Syamala
• Muniyandy Elangovan
• Published: 02 December 2023
• Article: 61

Demodulating an acoustic signal stimulated by photo-thermal elastic energy conversion using quartz tuning forks

Authors (first, second and last of 7).

• M. Tamilselvi
• T. M. Amirthalakshmi
• Mohd Zahid Ansari
• Article: 69

Emerging network communication for malicious node detection in wireless multimedia sensor networks

• S. Arockia Jayadhas
• S. Emalda Roslin
• Article: 59

Photovoltaic fuzzy based modelling on defining energy efficient solar devices in industry 4.0

• T. V. V. Pavan Kumar
• N. L. Taranath
• M. A. Kalam
• Article: 62

Design of precoder for a MIMO–NOMA system using Gaussian mixture modelling

• S Markkandan
• Makarand Mohan Jadhav
• Article: 60

Optimizing optical network longevity via Q-learning-based routing protocol for energy efficiency and throughput enhancement

• Ashwini V. Jatti
• V. J. K. Kishor Sonti
• Published: 23 November 2023
• Article: 32

Reinforcement learning-based model for the prevention of beam-forming vector attacks on massive MIMO system

• R. Nithya Paranthaman
• Abhishek Sonker
• Article: 44

AI-driven electro chromic materials and devices for nanofabrication in machine learning integrated environments

• K. Mahesh Prasanna
• Aasheesh Shukla
• Sikandar Aftab
• Article: 15

Optimizing radio access in 5G vehicle networks using novel machine learning-driven resource management

• Rajesh Natarajan
• Natesh Mahadev
• Published: 10 November 2023
• Article: 1270

Intelligent root cause detection in Industry 4.0: a secure cross-validation of blockchain architecture for product failure checker

• P. N. Renjith
• Venkateswarlu Gundu
• Published: 04 November 2023
• Article: 1263

Design and evaluation of novel hybrid quantum resistant cryptographic system for enhancing security in wireless body sensor networks

• Govindu Surla
• Published: 01 November 2023
• Article: 1252

Solar energy harvesting to optimise the power constraints in 5G systems

• N. Chitra Kiran
• S. Senthilkumar
• Amjad Iqbal
• Article: 1251

A weight optimized deep learning model for cluster based intrusion detection system

• Sravanthi Godala
• M. Sunil Kumar
• Published: 21 October 2023
• Article: 1224

Evaluating the effectiveness of RNN and its variants for personalized web search

• B. Sangamithra
• B. E. Manjunath Swamy
• Published: 17 October 2023
• Article: 1202

Deep reinforcement learning for comprehensive route optimization in elastic optical networks using generative strategies

• B. Mouleswararao
• Article: 1197

Anticipating network failures and congestion in optical networks a data analytics approach using genetic algorithm optimization

• Ajmeera Kiran
• Suraya Mubeen
• Article: 1193

Distributed computing and big data techniques for efficient fault detection and data management in wireless networks

• Drakshayani Sriramsetti
• Article: 1200

Machine learning based 64-QAM classification techniques for enhanced optical communication

Authors (first, second and last of 5).

• H. L. Gururaj
• V. Veeraprathap
• Article: 1179

Machine learning aided design and optimization of MEMS optical phased array with silicon micro mirrors for nanofabrication

• S. Premalatha
• Himanshu Sharma
• Article: 1196

Design and analysis of photovoltaic solar based longer transmission of data in ADHOC networks

• Rajanish Kumar Kaushal
• Smitha Sasi
• Article: 1187

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Frontiers in Optics 2016

• OSA Technical Digest (online) (Optica Publishing Group, 2016),
• paper FTh5E.5
• • https://doi.org/10.1364/FIO.2016.FTh5E.5

Recent Advances on Fibers for Optical Communications

Benyuan Zhu

Author Affiliations

OFS Labs, Somerset, NJ 08873

• Rochester, New York United States
• 17–21 October 2016
• ISBN: 978-1-943580-19-4

From the session High-Capacity Optical Communications and Data Centers II (FTh5E)

• Frontiers in Optics 2016 , OSA Technical Digest (online) (Optica Publishing Group, 2016), paper FTh5E.5.%0Ahttps://opg.optica.org/abstract.cfm?URI=FiO-2016-FTh5E.5%0A---------------------------------------------------------------------------%0AThis is sent to you as an email notification feature from Optica Publishing Group: https://opg.optica.org"> Email
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• Coherent systems
• Few mode fibers
• Fiber optic communications
• Multicore fibers
• Optical fibers
• Optical properties
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We will review recent advances on optical fibers including ultra-low-loss ultra-large-area fibers for high speed coherent systems, multicore fibers and few-mode fibers for space-division-multiplexing transmissions. The designs, optical properties and system performance of the new fibers are provided in this tutorial.

© 2016 Optical Society of America

Benyuan Zhu AW3G.1 Asia Communications and Photonics Conference (ACP) 2013

Anthony Dandridge, J. H. Cole, Thomas G. Giallorenzi, J. A. Bucaro, George H. Sigel, and William K. Burns TuD2 Optical Fiber Communication Conference (OFC) 1984

Benyuan Zhu, Dave Peckham, Man Yan, Thierry Taunay, and John Fini OW1D.5 Optical Fiber Communication Conference (OFC) 2012

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M. S. GOODMAN and E. ARTHURS WL1 Optical Fiber Communication Conference (OFC) 1990

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• Optics and photonics
• Research update

All-optical space-air-sea communication network makes its debut 

Researchers in China have demonstrated a prototype communications network that can transmit and receive data through space, air and water entirely at optical wavelengths. If successfully scaled up, the new network design might have applications as diverse as navigation, ecological monitoring, remote sensing, emergency aid and connecting devices within the so-called “Internet of Things”.  

Many of today’s optical communication s network s are designed to work in just one medium : underwater, over land, through space or in the air. Creating a single system that can operate in all of the se environments is no easy task, as the requirements of each are different . F ulfilling them requirements thus means combining multiple technologies.  

A team led by microelectronics expert Yongjin Wang of the Nanjing University of Posts and Telecommunications and Suzhou Lighting Chip Monolithic Optoelectronics Technology Co. Ltd . has now done just that by employing four different light sources to establish simultaneous wireless light communications links in any of these environments. “Our new wireless network enables uninterrupted connectivity across environments, facilitating two-way real-time data transmission between the network nodes that carry out communication and data exchange within and between networks,” Wang says.  

Four full-duplex wireless light communication links   

For the underwater portion of their network, the researchers chose blue light because seawater absorbs less in this part of the electromagnetic spectrum, meaning light can travel further. To communicate with airborne device such as drones, they used deep ultraviolet light because it provides “solar-blind” communication with no interference from sunlight. For other air-based applications, they used wireless white light communication, while for point-to-point communications in free space they selected near-infrared laser diodes. These diodes emit light in one direction with high optical power, again allowing the signals to travel further.  

“Our network consists of these four full-duplex wireless light communication links, which are connected in series via Ethernet switches,” explains Wang. “Both wired and wireless access to the all-light communication network is also possible, providing flexible connectivity options.”   

S eparating the different light bands also prevents signals from interfering, meaning the network can transmit many signals simultaneously without compromising performance, Wang says. The network can be connected to the Internet via a modem, granting people in remote ocean locations, for example, access to the backbone network for information sharing. It also allows video conferencing and other transmissions via the widely-used TCP/IP (Transition Control Protocol/Internet Protocol) suite, he adds, making it suitable for Internet of Things applications, too. “For example, when a 2560 × 1440-pixel online video at 22 frames per second are fed into the network, users accessing the network from any node can visit this video with little lag,” he tells Physics World.    

What would happen if communication systems broke down?

From a single communication system to a network   .

According to Wang and colleagues, the all-light communication network is a “major breakthrough”, one that should make it possible to transition from single wireless light communication systems to a network of them. Such a network would resist electromagnetic interference (EMI), making it particularly attractive for communicating with underwater equipment and drone clusters. “This is why are we working on integrating mobile nodes in the network, rather than fixed nodes, as is presently the case,” explains Wang. “This will not be easy, however, since it will require tacking the challenge of ‘light alignment’ and network establishment speed.”   

The researchers, who describe the new network in Optics Express , also plan to enhance the throughput of their communication network by using a technique called wavelength division multiplexing. This, they say, will improve the network’s overall efficiency and performance by eliminating the delays associated with using near-infrared laser diodes.  

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• Open access
• Published: 28 March 2024

New water accounting reveals why the Colorado River no longer reaches the sea

• Brian D. Richter   ORCID: orcid.org/0000-0001-7216-1397 1 , 2 ,
• Gambhir Lamsal   ORCID: orcid.org/0000-0002-2593-8949 3 ,
• Landon Marston   ORCID: orcid.org/0000-0001-9116-1691 3 ,
• Sameer Dhakal   ORCID: orcid.org/0000-0003-4941-1559 3 ,
• Laljeet Singh Sangha   ORCID: orcid.org/0000-0002-0986-1785 4 ,
• Richard R. Rushforth 4 ,
• Dongyang Wei   ORCID: orcid.org/0000-0003-0384-4340 5 ,
• Benjamin L. Ruddell 4 ,
• Kyle Frankel Davis   ORCID: orcid.org/0000-0003-4504-1407 5 , 6 ,
• Astrid Hernandez-Cruz   ORCID: orcid.org/0000-0003-0776-5105 7 ,
• Samuel Sandoval-Solis 8 &
• John C. Schmidt 9  

Communications Earth & Environment volume  5 , Article number:  134 ( 2024 ) Cite this article

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• Water resources

Persistent overuse of water supplies from the Colorado River during recent decades has substantially depleted large storage reservoirs and triggered mandatory cutbacks in water use. The river holds critical importance to more than 40 million people and more than two million hectares of cropland. Therefore, a full accounting of where the river’s water goes en route to its delta is necessary. Detailed knowledge of how and where the river’s water is used can aid design of strategies and plans for bringing water use into balance with available supplies. Here we apply authoritative primary data sources and modeled crop and riparian/wetland evapotranspiration estimates to compile a water budget based on average consumptive water use during 2000–2019. Overall water consumption includes both direct human uses in the municipal, commercial, industrial, and agricultural sectors, as well as indirect water losses to reservoir evaporation and water consumed through riparian/wetland evapotranspiration. Irrigated agriculture is responsible for 74% of direct human uses and 52% of overall water consumption. Water consumed for agriculture amounts to three times all other direct uses combined. Cattle feed crops including alfalfa and other grass hays account for 46% of all direct water consumption.

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Introduction

Barely a trickle of water is left of the iconic Colorado River of the American Southwest as it approaches its outlet in the Gulf of California in Mexico after watering many cities and farms along its 2330-kilometer course. There were a few years in the 1980s in which enormous snowfall in the Rocky Mountains produced a deluge of spring snowmelt runoff capable of escaping full capture for human uses, but for most of the past 60 years the river’s water has been fully consumed before reaching its delta 1 , 2 . In fact, the river was overconsumed (i.e., total annual water consumption exceeding runoff supplies) in 16 of 21 years during 2000–2020 3 , requiring large withdrawals of water stored in Lake Mead and Lake Powell to accommodate the deficits. An average annual overdraft of 10% during this period 2 caused these reservoirs– the two largest in the US – to drop to three-quarters empty by the end of 2022 4 , triggering urgent policy decisions on where to cut consumption.

Despite the river’s importance to more than 40 million people and more than two million hectares (>5 million acres) of cropland—producing most of the vegetable produce for American and Canadian plates in wintertime and also feeding many additional people worldwide via exports—a full sectoral and crop-specific accounting of where all that water goes en route to its delta has never been attempted, until now. Detailed knowledge of how and where the river’s water is used can aid design of strategies and plans for bringing water use into balance with available supplies.

There are interesting historical reasons to explain why this full water budget accounting has not been accomplished previously, beginning a full century ago when the apportionment of rights to use the river’s water within the United States was inscribed into the Colorado River Compact of 1922 5 . That Compact was ambiguous and confusing in its allocation of water inflowing to the Colorado River from the Gila River basin in New Mexico and Arizona 6 , even though it accounts for 24% of the drainage area of the Colorado River Basin (Fig.  1 ). Because of intense disagreements over the rights to the Gila and other tributaries entering the Colorado River downstream of the Grand Canyon, the Compact negotiators decided to leave the allocation of those waters rights to a later time so that the Compact could proceed 6 . Arizona’s formal rights to the Gila and other Arizona tributaries were finally affirmed in a US Supreme Court decision in 1963 that also specified the volumes of Colorado River water allocated to California, Arizona, and Nevada 7 . Because the rights to the Gila’s waters lie outside of the Compact allocations, the Gila has not been included in formal accounting of the Colorado River Basin water budget to date 8 . Additionally, the Compact did not specify how much water Mexico—at the river’s downstream end—should receive. Mexico’s share of the river was not formalized until 22 years later, in the 1944 international treaty on “Utilization of the Waters of the Colorado and Tijuana Rivers and of the Rio Grande” (1944 Water Treaty) 9 . As a result of these political circumstances, full accounting for direct water consumption at the sectoral level—in which water use is accounted according to categories such as municipal, industrial, commercial, or agricultural uses—has not previously been compiled for the Gila River basin’s water, and sectoral accounting for Mexico was not published until 2023 10 .

The physical boundary of the Colorado River Basin is outlined in black. Hatched areas outside of the basin boundary receive Colorado River water via inter-basin transfers (also known as ‘exports’). The Gila River basin is situated in the far southern portion of the CRB in Arizona, New Mexico, and Mexico. Map courtesy of Center for Colorado River Studies, Utah State University.

The US Bureau of Reclamation (“Reclamation”)—which owns and operates massive water infrastructure in the Colorado River Basin—has served as the primary accountant of Colorado River water. In 2012, the agency produced a “Colorado River Basin Water Supply and Demand Study” 8 that accounted for both the sectoral uses of water within the basin’s physical boundaries within the US as well as river water exported outside of the basin (Fig.  1 ). But Reclamation did not attempt to account for water generated from the Gila River basin because of that sub-basin’s exclusion from the Colorado River Compact, and it did not attempt to explain how water crossing the border into Mexico is used. The agency estimated riparian vegetation evapotranspiration for the lower Colorado River but not the remainder of the extensive river system. Richter et al. 11 published a water budget for the Colorado River that included sectoral and crop-specific water consumption but it too did not include water used in Mexico, nor reservoir evaporation or riparian evapotranspiration, and it did not account for water exported outside of the Colorado River Basin’s physical boundary as illustrated in Fig.  1 . Given that nearly one-fifth (19%) of the river’s water is exported from the basin or used in Mexico, and that the Gila is a major tributary to the Colorado, this incomplete accounting has led to inaccuracies and misinterpretations of “where the Colorado River’s water goes” and has created uncertainty in discussions based on the numbers. This paper provides fuller accounting of the fate of all river water during 2000–2019, including averaged annual consumption in each of the sub-basins including exports, consumption in major sectors of the economy, consumption in the production of specific types of crops, and water consumed by reservoir evaporation and riparian/wetland evapotranspiration.

Rising awareness of water overuse and prolonged drought has driven intensifying dialog among the seven US states sharing the basin’s waters as well as between the United States, Mexico, and 30 tribal nations within the US. Since 2000, six legal agreements affecting the US states and two international agreements with Mexico have had the effect of reducing water use from the Colorado River 7 :

In 2001, the US Secretary of the Interior issued a set of “Interim Surplus Guidelines” to reduce California’s water use by 14% to bring the state within its allocation as determined in the 1963 US Supreme Court case mentioned previously. A subsequent “Quantification Settlement Agreement” executed in 2003 spelled out details about how California was going to achieve the targeted reduction.

In 2007, the US Secretary of the Interior adopted a set of “Colorado River Interim Guidelines for Lower Basin Shortages and the Coordinated Operations for Lake Powell and Lake Mead” that reduced water deliveries to Arizona and Nevada when Lake Mead drops to specified levels, with increasing cutbacks as levels decline.

In 2012, the US and Mexican federal governments signed an addendum to the 1944 Water Treaty known as Minute 319 that reduced deliveries to Mexico as Lake Mead elevations fall.

In 2017, the US and Mexican federal governments established a “Binational Water Scarcity Contingency Plan” as part of Minute 323 that provides for deeper cuts in deliveries to Mexico under specified low reservoir elevations in Lake Mead.i

In 2019, the three Lower Basin states and the US Secretary of the Interior agreed to commitments under the “Lower Basin Drought Contingency Plan” that further reduced water deliveries beyond the levels set in 2007 and added specifications for deeper cuts as Lake Mead drops to levels lower than anticipated in the 2007 Guidelines.

In 2023, the states of California, Arizona and Nevada committed to further reductions in water use through the year 2026 12 .

With each of the above agreements, overall water consumption has been reduced but many scientists assert that these reductions still fall substantially short of balancing consumptive use with 21st century water supplies 2 , 13 . With all of these agreements—excepting the Interim Surplus Guidelines of 2001—set to expire in 2026, management of the Colorado River’s binational water supply is now at a crucial point, emphasizing the need for comprehensive water budget accounting.

Our tabulation of the Colorado River’s full water consumption budget (Table  1 ) provides accounting for all direct human uses of water as either agricultural or MCI (municipal, commercial, industrial), as well as indirect losses of water to reservoir evaporation and evapotranspiration from riparian or wetland vegetation including in the Salton Sea and in a wetland in Mexico (Cienega de Santa Clara) that receives agricultural return flows from irrigated areas in Arizona. We explicitly note that all estimates represent consumptive use , resulting from the subtraction of return flows from total water withdrawals. Table  2 provides a summary based only on direct human uses and does not include indirect consumption of water. We have provided Tables  1 and 2 in English units in our Supplementary Information as Tables SI-1 and SI-2 . We have lumped municipal, commercial, and industrial (MCI) uses together because these sub-categories of consumption are not consistently differentiated within official water delivery data for cities utilizing Colorado River water. More detail on urban water use by cities dependent on the river is available in Richter 14 , among other studies.

We differentiated water consumption geographically using the ‘accounting units’ mapped in Fig.  2 , which are based on the Colorado River Basin map as revised by Schmidt 15 ; importantly, these accounting units align spatially with Reclamation’s accounting systems for the Upper Basin and Lower Basin as described in our Methods, thereby enabling readers accustomed to Reclamation’s water-use reports to easily comprehend our accounting. We have also accounted for all water consumed within the Colorado River Basin boundaries as well as water exported via inter-basin transfers. Water exported outside of the basin includes 47 individual inter-basin transfer systems (i.e., canals, pipelines, pumps) that in aggregate export ~12% of the river’s water. We note that the Imperial Irrigation District of southern California is often counted as a recipient of exported water, but we have followed the rationale of Schmidt 15 by including it as an interior part of the Lower Basin even though it receives its Colorado River water via the All American Canal (Fig.  2 ).

The water budget estimates presented in Tables  1 and 2 are summarized for each of the seven “accounting units” displayed here.

These results confirm previous findings that irrigated agriculture is the dominant consumer of Colorado River water. Irrigated agriculture accounts for 52% of overall consumption (Table  1 ; Figs.  3 and 4 ) and 74% of direct human consumption (Table  2 ) of water from the Colorado River Basin. As highlighted in Richter et al. 11 , cattle-feed crops (alfalfa and other hay) are the dominant water-consuming crops dependent upon irrigation water from the basin (Tables  1 and 2 ; Figs.  3 and 4 ). Those crops account for 32% of all water consumed from the basin, 46% of all direct water consumption, and 62% of all agricultural water consumed (Table  1 ; Fig.  3 ). The percentage of water consumed by irrigated crops is greatest in Mexico, where they account for 86% of all direct human uses (Table  2 ) and 80% of total water consumed (Table  1 ). Cattle-feed crops consume 90% of all water used by irrigated agriculture within the Upper Basin, where the consumed volume associated with these cattle-feed crops amounts to more than three times what is consumed for municipal, commercial, or industrial uses combined.

All estimates based on 2000–2019 averages. Both agriculture and MCI (municipal, commercial, and industrial) uses are herein referred to as “direct human uses.” “Indirect uses” include both reservoir evaporation as well as evapotranspiration by riparian/wetland vegetation.

Water consumed by each sector in the Colorado River Basin and sub-basins (including exports), based on 2000–2019 averages.

Another important finding is that a substantial volume of water (19%) is consumed in supporting the natural environment through riparian and wetland vegetation evapotranspiration along river courses. This analysis—made possible because of recent mapping of riparian vegetation in the Colorado River Basin 16 —is an important addition to the water budget of the Colorado River Basin, given that the only previous accounting for riparian vegetation consumption has limited to the mainstem of the Colorado River below Hoover Dam and does not include vegetation upstream of Hoover Dam nor vegetation along tributary rivers 17 . Given that many of these habitats and associated species have been lost or became imperiled due to river flow depletion 18 —including the river’s vast delta ecosystem in Mexico—an ecologically sustainable approach to water management would need to allow more water to remain in the river system to support riparian and aquatic ecosystems. Additionally, 11% of all water consumed in the Colorado River Basin is lost through evaporation from reservoirs.

It is also important to note a fairly high degree of inter-annual variability in each sector of water use; for example, the range of values portrayed for the four water budget sectors shown in Fig.  5 equates to 24–47% of their 20-year averages. Also notable is a decrease in water consumed in the Lower Basin between the years 2000 and 2019 for both the MCI (−38%) and agricultural sectors (−15%), which can in part be attributed to the policy agreements summarized previously that have mandated water-use reductions.

Inter-annual variability of water consumption within the Lower and Upper Basins, including water exported from these basins. The average (AVG) values shown are used in the water budgets detailed in Tables  1 and 2 .

The water accounting in Richter et al. 11 received a great deal of media attention including a front-page story in the New York Times 19 . These stories focused primarily on our conclusion that more than half (53%) of water consumed in the Colorado River Basin was attributable to cattle-feed crops (alfalfa and other hays) supporting beef and dairy production. However, that tabulation of the river’s water budget had notable shortcomings, as discussed previously. In this more complete accounting that includes Colorado River water exported outside of the basin’s physical boundary as well as indirect water consumption, we find that irrigated agriculture consumes half (52%) of all Colorado River Basin water, and the portion of direct consumption going to cattle-feed crops dropped from 53% as reported in Richter et al. 11 to 46% in this revised analysis.

These differences are explained by the fact that we now account for all exported water and also include indirect losses of water to reservoir evaporation and riparian/wetland evapotranspiration in our revised accounting, as well as improvements in our estimation of crop-water consumption. However, the punch line of our 2020 paper does not change fundamentally. Irrigated agriculture is the dominant consumer of water from the Colorado River, and 62% of agricultural water consumption goes to alfalfa and grass hay production.

Richter et al. 20 found that alfalfa and grass hay were the largest water consumers in 57% of all sub-basins across the western US, and their production is increasing in many western regions. Alfalfa is favored for its ability to tolerate variable climate conditions, especially its ability to persist under greatly reduced irrigation during droughts and its ability to recover production quickly after full irrigation is resumed, acting as a “shock absorber” for agricultural production under unpredictable drought conditions. The plant is also valued for fixing nitrogen in soils, reducing fertilizer costs. Perhaps most importantly, labor costs are comparatively low because alfalfa is mechanically harvested. Alfalfa is increasing in demand and price as a feed crop in the growing dairy industry of the region 21 . Any efforts to reduce water consumed by alfalfa—either through shifting to alternative lower-water crops or through compensated fallowing 20 —will need to compete with these attributes.

This new accounting provides a more comprehensive and complete understanding of how the Colorado River Basin’s water is consumed. During our study period of 2000–2019, an estimated average of 23.7 billion cubic meters (19.3 million acre-feet) of water was consumed each year before reaching its now-dry delta in Mexico. Schmidt et al. 2 have estimated that a reduction in consumptive use in the Upper and Lower Basins of 3–4 billion cubic meters (2.4–3.2 million acre-feet) per year—equivalent to 22–29% of direct use in those basins—will be necessary to stabilize reservoir levels, and an additional reduction of 1–3 billion cubic meters (~811,000–2.4 million acre-feet) per year will likely be needed by 2050 as climate warming continues to reduce runoff in the Colorado River Basin.

We hope that this new accounting will add clarity and a useful informational foundation to the public dialog and political negotiations over Colorado River Basin water allocations and cutbacks that are presently underway 2 . Because a persistent drought and intensifying aridification in the region has placed both people and river ecosystems in danger of water shortages in recent decades, knowledge of where the water goes will be essential in the design of policies for bringing the basin into a sustainable water supply-demand balance.

The data sources and analytical approaches used in this study are summarized below. Unless otherwise noted, all data were assembled for each year from 2000–2019 and then averaged. We acknowledge some inconsistency in the manner in which water consumption is measured or estimated across the various data sources and sectors used in this study, as discussed below, and each of these different approaches entail some degree of inaccuracy or uncertainty. We also note that technical measurement or estimation approaches change over time, and new approaches can yield differing results. For instance, the Upper Colorado River Commission is exploring new approaches for estimating crop evapotranspiration in the Upper Basin 22 . When new estimates become available we will update our water budget accordingly.

MCI and agricultural water consumption

The primary source of data on aggregate MCI (municipal, commercial, and industrial) and agricultural water consumption from the Upper and Lower Basins was the US Bureau of Reclamation. Water consumed from the Upper Basin is published in Reclamation’s five-year reports entitled “Colorado River—Upper Basin Consumptive Uses and Losses.” 23 These annual data have been compiled into a single spreadsheet used for this study 24 . Because measurements of agricultural diversions and return flows in the Upper Basin are not sufficiently complete to allow direct calculation of consumptive use, theoretical and indirect methods are used as described in the Consumptive Uses and Losses reports 25 . Reclamation performs these estimates for Colorado, Wyoming, and Utah, but the State of New Mexico provides its own estimates that are collaboratively reviewed with Reclamation staff. The consumptive use of water in thermoelectric power generation in the Upper Basin is provided to Reclamation by the power companies managing each generation facility. Reclamation derives estimates of consumptive use for municipal and industrial purposes from the US Geological Survey’s reporting series (published every 5 years) titled “Estimated Use of Water in the United States” at an 8-digit watershed scale 26 .

Use of shallow alluvial groundwater is included in the water accounting compiled by Reclamation but use of deeper groundwater sources—such as in Mexico and the Gila River Basin—is explicitly excluded in their accounting, and in ours. Reclamation staff involved with water accounting for the Upper and Lower Basins assume that groundwater use counted in their data reports is sourced from aquifers that are hydraulically connected to rivers and streams in the CRB (James Prairie, US Bureau of Reclamation, personal communication, 2023); because of this high connectivity, much of the groundwater being consumed is likely being sourced from river capture as discussed in Jasechko et al. 27 and Wiele et al. 28 and is soon recharged during higher river flows.

Water consumed from the Lower Basin (excluding water supplied by the Gila River Basin) is published in Reclamation’s annual reports entitled “Colorado River Accounting and Water Use Report: Arizona, California, and Nevada.” 3 These consumptive use data are based on measured deliveries and return flows for each individual water user. These data are either measured by Reclamation or provided to the agency by individual water users, tribes, states, and federal agencies 29 . When not explicitly stated in Reclamation reports, attribution of water volumes to MCI or agricultural uses was based on information obtained from each water user’s website, information provided directly by the water user, or information on export water use provided in Siddik et al. 30 . Water use by entities using less than 1.23 million cubic meters (1000 acre-feet) per year on average was allocated to MCI and agricultural uses according to the overall MCI-agricultural percentages calculated within each sub-basin indicated in Tables  1 and 2 for users of greater than 1.23 million cubic meters/year.

Disaggregation of water consumption by sector was particularly important and challenging for the Central Arizona Project given that this canal accounts for 21% of all direct water consumption in the Lower Basin. Reclamation accounts for the volumes of annual diversions into the Central Arizona Project canal but the structure serves 1071 water delivery subcontracts. We classified every unique Central Arizona Project subcontract delivery between 2000–2019 by its final water use to derive an estimated split between agricultural and MCI uses. Central Arizona Project subcontract delivery data were obtained from the current and archived versions of the project’s website summaries in addition to being directly obtained from the agency through a public information request. Subcontract deliveries were classified based on the final end use, including long-term and temporary leases of project water. This accounting also includes the storage of water in groundwater basins for later MCI or agricultural use. Additionally, water allocated to Native American agricultural uses that was subsequently leased to cities was classified as an MCI use.

Data for the Gila River basin was obtained from two sources. The Arizona Department of Water Resources has published data for surface water use in five “Active Management Areas” (AMAs) located in the Gila River basin: Prescott AMA, Phoenix AMA, Pinal AMA, Tucson AMA, and Santa Cruz AMA 31 . The water-use data for these AMAs is compiled from annual reports submitted by each water user (contractor) and then reviewed by the Arizona Department of Water Resources. The AMA water-use data are categorized by purpose of use, facilitating our separation into MCI and agricultural uses. These data are additionally categorized by water source; only surface water sourced from the Gila River hydrologic system was counted (deep groundwater use was not). The AMA data were supplemented with data for the upper Gila River basin provided by the University of Arizona 32 . We have assumed that all water supplied by the Gila River Basin is fully consumed, as the river is almost always completely dry in its lower reaches (less than 1% flows out of the basin into the Colorado River, on average 33 ).

Data for Mexico were obtained from Hernandez-Cruz et al. 10 based on estimates for 2008–2015. Agricultural demands were estimated from annual reports of irrigated area and water use published by the Ministry of Agriculture and the evapotranspiration estimates of the principal crops published by the National Institute for Forestry, Animal Husbandry, and Agricultural Research of Mexico 10 . The average annual volume of Colorado River water consumption in Mexico estimated by these researchers is within 1% of the cross-border delivery volume estimated by the Bureau of Reclamation for 2000–2019 in its Colorado River Accounting and Water Use Reports 3 .

Exported water consumption

Annual average inter-basin transfer volumes for each of 46 canals and pipelines exporting water outside of the Upper Basin were obtained from Reclamation’s Consumptive Uses and Losses spreadsheet 34 . Data for the Colorado River Aqueduct in the Lower Basin were obtained from Siddik et al. 30 Data for exported water in Mexico was available from Hernandez-Cruz et al. 10 . We assigned any seepage or evaporation losses from inter-basin transfers to their proportional end uses. All uses of exported water are considered to be consumptive uses with respect to the Colorado River, because none of the water exported out of the basin is returned to the Colorado River Basin.

We relied on data from Siddik et al. (2023) to identify whether the water exported out of the Colorado River Basin was for only MCI or agricultural use. When more than one water use purpose was identified, as well as for all major inter-basin transfers, we used government and inter-basin transfer project websites or information obtained directly from the project operator or water manager to determine the volume of water transferred and the end uses. Major recipients of exported water include the Coachella Valley Water District (California); Metropolitan Water District of Southern California (particularly for San Diego County, California); Northern Colorado Water Conservancy District; City of Denver (Colorado); the Central Utah Project; City of Albuquerque (New Mexico); and the Middle Rio Grande Conservancy District (New Mexico). We did not pursue sectoral water-use information for 17 of the 46 Upper Basin inter-basin transfers due to their relatively low volumes of water transferred by each system (<247,000 cubic meters or 2000 acre-feet), and instead assigned the average MCI or agricultural percentage (72% MCI, 28% agricultural) from all other inter-basin transfers in the Upper Basin. The export volume of these 17 inter-basin transfers sums to 9.76 million cubic meters (7910 acre-feet) per year, equivalent to 1% of the total volume exported from the Upper Basin.

Reservoir evaporation

Evaporation estimates for the Upper Basin and Lower Basin are based upon Reclamation’s HydroData repository 35 . Reclamation’s evaporation estimates are based on the standardized Penman-Monteith equation as described in the “Lower Colorado River Annual Summaries of Evapotranspiration and Evaporation” reports 17 . The Penman-Monteith estimates are based on pan evaporation measurements. Evaporation estimates for the Salt River Project reservoirs in the Gila River basin were provided by the Salt River Project in Arizona (Charlie Ester, personal communication, 2023).

Another consideration with reservoirs is the volume of water that seeps into the banks or sediments surrounding the reservoir when reservoir levels are high, but then drains back into the reservoir as water levels decline 36 . This has the effect of either exacerbating reservoir losses (consumptive use) or offsetting evaporation when bank seepage flows back into a reservoir. The flow of water into and out of reservoir banks is non-trivial; during 1999–2008, an estimated 247 million cubic meters (200,000 acre-feet) of water drained from the canyon walls surrounding Lake Powell into the reservoir each year, providing additional water supply 36 . However, the annual rate of alternating gains or losses has not been sufficiently measured at any of the basin’s reservoirs and therefore is not included in Tables  1 and 2 .

Riparian and wetland vegetation evapotranspiration

We exported the total annual evapotranspiration depth at a 30 meter resolution from OpenET 37 using Google Earth Engine from 2016 to 2019 to align with OpenET’s data availability starting in 2016. Total annual precipitation depths, sourced from gridMET 38 , were resampled to align with the evapotranspiration raster resolution. Subsequently, a conservative estimate of the annual water depth utilized by riparian vegetation from the river was derived by subtracting the annual precipitation raster from the evapotranspiration raster for each year. Positive differentials, indicative of river-derived evapotranspiration, were then multiplied by the riparian vegetation area as identified in the CO-RIP 16 dataset to estimate the total annual volumetric water consumption by riparian vegetation across the Upper, Lower, and Gila River Basins. The annual volumetric water consumption calculated over four years were finally averaged to get riparian vegetation evapotranspiration in the three basins. Because the entire flow of the Colorado River is diverted into the Canal Alimentador Central near the international border, very little riparian evapotranspiration occurs along the river south of the international border in the Mexico basin.

In addition to water consumed by riparian evapotranspiration within the Lower Basin, the Salton Sea receives agricultural drain water from both the Imperial Irrigation District and the Coachella Valley Irrigation District, stormwater drainage from the Coachella Valley, and inflows from the New and Alamo Rivers 39 . Combined inflows to the Sea during 2015–2019 were added to our estimates of riparian/wetland evapotranspiration in the Lower Basin.

Similarly, Mexico receives drainage water from the Wellton–Mohawk bypass drain originating in southern Arizona that empties into the Cienega de Santa Clara (a wetland); this drainage water is included as riparian/wetland evapotranspiration in the Mexico basin.

Crop-specific water consumption

The volumes of total agricultural consumption reported for each sub-basin in Tables  1 and 2 were obtained from the same data sources described above for MCI consumption and exported water. The portion (%) of those agricultural consumption volumes going to each individual crop was then allocated according to percentage estimates of each crop’s water consumption in each accounting unit using methods described in Richter et al. 20 and detailed here.

Monthly crop water requirements during 1981–2019 for 13 individual crops, representing 68.8% of total irrigated area in the US in 2019, were estimated using the AquaCrop-OS model (Table SI- 3 ) 40 . For 17 additional crops representing about 25.4% of the total irrigated area, we used a simple crop growth model following Marston et al. 41 as crop parameters needed to run AquaCrop-OS were not available. A list of the crops included in this study is shown in Table SI- 3 . The crop water requirements used in Richter et al. 11 were based on a simplistic crop growth model, often using seasonal crop coefficients whereas we use AquaCrop-OS 40 , a robust crop growth model, to produce more realistic crop growth and crop water estimates for major crops. AquaCrop-OS is an open-source version of the AquaCrop model 42 , a crop growth model capable of simulating herbaceous crops. Additionally, we leverage detailed local data unique to the US, including planting dates and subcounty irrigated crop areas, to produce estimates at a finer spatial resolution than the previous study. We obtained crop-specific planting dates from USDA 43 progress data at the state level. For crops that did not have USDA crop progress data, we used data from FAO 44 and CUP+ model 45 for planting dates. We used climate data (precipitation, minimum and maximum air temperature, reference ET) from gridMET 38 , soil texture data from ISRIC 46 database and crop parameters from AquaCrop-OS to run the model. The modeled crop water requirement was partitioned into blue and green components following the framework from Hoekestra et al. 47 , assuming that blue and green water consumed on a given day is proportional to the amount of green and blue water soil moisture available on that day. When applying a simple crop growth model, daily gridded (2.5 arc minutes) crop-specific evapotranspiration (ETc) was computed by taking the product of reference evapotranspiration (ETo) and crop coefficient (Kc), where ETo was obtained from gridMET. Crop coefficients were calculated using planting dates and crop coefficient curves from FAO and CUP+ model. Kc was set to zero outside of the growing season. We partitioned the daily ETc into blue and green components by following the methods from ref. 41 It is assumed that the crop water demands are met by irrigation whenever it exceeds effective precipitation (the latter calculated using the USDA Soil Conservation Service method (USDA, 1968 48 ). We obtained county level harvested area from USDA 43 and disaggregated to sub-county level using Cropland Data Layer (CDL) 49 and Landsat-based National Irrigation Dataset (LANID) 50 . The CDL is an annual raster layer that provides crop-specific land cover data, while the LANID provides irrigation status information. The CDL and LANID raster were multiplied and aggregated to 2.5 arc minutes to match the AquaCrop-OS output. We produced a gridded crop area map by using this resulting product as weights to disaggregate county level area. CDL is unavailable before 2008. Therefore, we used land use data from ref. 51 in combination with average CDL map and county level harvested area to produce gridded crop harvested area. We computed volumetric water consumption by multiplying the crop water requirement depth by the corresponding crop harvested area.

Data availability

All data compiled and analyzed in this study are publicly available as cited and linked in our Methods section. Our compilation of these data is also available from Hydroshare at: http://www.hydroshare.org/resource/2098ae29ae704d9aacfd08e030690392 .

Code availability

All model code and software used in this study have been accessed from sources cited in our Methods section. We used AquaCrop-OS (v5.0a), an open source version of AquaCrop crop growth model, to run crop simulations. This model is publicly available at http://www.aquacropos.com/ . For estimating riparian evapotranspiration, we used ArcGIS Pro 3.1.3 on the Google Earth Engine. Riparian vegetation distribution maps were sourced from Dryad at https://doi.org/10.5061/dryad.3g55sv8 .

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Acknowledgements

This paper is dedicated to our colleague Jack Schmidt in recognition of his retirement and enormous contributions to the science and management of the Colorado River. The authors thank James Prairie of the US Bureau of Reclamation, Luke Shawcross of the Northern Colorado Water Conservancy District, Charlie Ester of the Salt River Project, and Brian Woodward of the University of California Cooperative Extension for their assistance in accessing data used in this study. The authors also thank Rhett Larson at the Sandra Day O’Connor School of Law at Arizona State University for their review of Arizona water budget data, and the Central Arizona Project for providing delivery data by each subcontract. G.L., L.M., and K.F.D. acknowledge support by the United States Department of Agriculture National Institute of Food and Agriculture grant 2022-67019-37180. L.T.M. acknowledges the support the National Science Foundation grant CBET-2144169 and the Foundation for Food and Agriculture Research Grant No. FF-NIA19-0000000084. R.R.R. acknowledges the support the National Science Foundation grant CBET-2115169.

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Gambhir Lamsal, Landon Marston & Sameer Dhakal

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Laljeet Singh Sangha, Richard R. Rushforth & Benjamin L. Ruddell

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Dongyang Wei & Kyle Frankel Davis

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Kyle Frankel Davis

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Astrid Hernandez-Cruz

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Samuel Sandoval-Solis

Center for Colorado River Studies, Utah State University, Logan, UT, 84322, USA

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Contributions

B.D.R. designed the study, compiled and analyzed data, wrote the manuscript and supervised co-author contributions. G.L. compiled all crop data, estimated crop evapotranspiration, and prepared figures. S.D. compiled all riparian vegetation data and estimated riparian evapotranspiration. L.S.S. and R.R.R. accessed, compiled, and analyzed data from the Central Arizona Project. D.W. compiled data and prepared figures. A.H.-C. and S.S.-S. compiled and analyzed data for Mexico. J.C.S. compiled and analyzed reservoir evaporation data and edited the manuscript. L.M., B.L.R., and K.F.D. supervised data compilation and analysis and edited the manuscript.

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Correspondence to Brian D. Richter .

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Richter, B.D., Lamsal, G., Marston, L. et al. New water accounting reveals why the Colorado River no longer reaches the sea. Commun Earth Environ 5 , 134 (2024). https://doi.org/10.1038/s43247-024-01291-0

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Received : 03 October 2023

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DOI : https://doi.org/10.1038/s43247-024-01291-0

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