Water Resources Research (water-resources-research)

Journal positioning

Water Resources Research (WRR) is the American Geophysical Union's flagship journal for the science of water — the physical, chemical, biological, and socio-hydrological processes governing water resources, and the methods used to observe, model, and manage them. The defining expectation is a quantitative, generalizable advance in water science: a new process understanding, method, theory, or analysis that transfers beyond one basin. An applied case study that reports model results for a single catchment with no methodological or conceptual contribution is a weak fit. This skill is a fit / venue-selection / re-framing tool. It does not replace the journal's current author guidance. Before submitting, re-check the live WRR/AGU author instructions and data policy.

When to trigger

• The author names WRR and wants a fit/framing check for a hydrology or water-science paper.
• A basin-specific modeling or monitoring study must be re-framed into a transferable

methodological or process contribution.

• The author is choosing between WRR, journal-of-hydrology, and a broader earth-science

venue.

• The author needs WRR's quantitative-contribution bar and AGU data-deposition expectations.

Scope & topic fit

• Surface-water and groundwater hydrology: catchment processes, streamflow, recharge,

vadose-zone and subsurface flow and transport.

• Hydrologic modeling, data assimilation, uncertainty quantification, and predictability.
• Hydroclimatology, snow/ice hydrology, and land–atmosphere water exchange.
• Water quality, contaminant transport, and ecohydrology when mechanistically framed.
• Socio-hydrology, water-resources systems, and human–water interactions with rigorous

quantitative analysis.

• Hydrologic measurement, sensing, and experimental methods that advance observation.

Method & evidence bar

• The contribution must be quantitative and transferable: a method, theory, or process

insight whose value is not confined to one site.

• Models must be evaluated against data with appropriate skill metrics, benchmarks, and

uncertainty quantification; parameter identifiability and equifinality should be addressed.

• Observational studies need defensible sampling/monitoring design, error characterization,

and reproducible processing.

• Claims of improvement require comparison to a credible baseline (an established model or

method), not a strawman.

• Data and code underpinning the results should be deposited in a FAIR community repository

per AGU policy.

Structure & house style

• AGU article format; WRR publishes research articles, technical reports/notes, and

commentaries — re-check current article types and length expectations on the live guide.

• The introduction must state the water-science gap and the transferable contribution, not

just describe a study area.

• Figures should be quantitative and load-bearing (hydrographs, maps with uncertainty,

skill/benchmark comparisons); a key-points summary is part of the AGU format.

• Methods and data/code availability statements must let a reader reproduce the central

result; AGU expects open data/software with persistent identifiers.

Official-submission checklist

• Before giving submission-ready advice, read ../../resources/source-basis.md and

../../resources/official-source-map.md; start from the AGU anchors, then cite the current WRR page you checked.

• Search the live site for "Water Resources Research author guidelines" and follow the

current AGU/Wiley version.

• Re-check article types, key-points and abstract format, and length expectations.
• Confirm the AGU data and software availability policy: deposit data/code in an

approved repository and cite it with a DOI.

• Re-check competing-interests, funding, author-contribution, and AI-use disclosure, and

open-access terms.

• If the live official instructions conflict with this skill, the official instructions

win.

Pre-submission self-check

• [ ] The contribution is a transferable method/theory/process insight, not a single-basin case study.
• [ ] Models are benchmarked against data with skill metrics and uncertainty quantification.
• [ ] Observational design, error characterization, and processing are reproducible.
• [ ] Improvement is shown against a credible baseline, not a strawman.
• [ ] Data and code are deposited in a FAIR repository with persistent identifiers.
• [ ] Key points and AGU formatting/availability statements are prepared.

Common desk-reject triggers

• Single-catchment model application with no methodological or conceptual advance.
• Model results presented without benchmarking, skill metrics, or uncertainty quantification.
• Observational study with weak sampling design or no error characterization.
• Missing or non-compliant data/code deposition where AGU policy requires it.
• Scope mismatch: a pure water-engineering design, water-policy essay, or chemistry paper with

no water-science contribution.

Re-routing decision

• Broader process/observational/applied hydrology → journal-of-hydrology.
• Carbon/nutrient biogeochemical cycling focus → global-biogeochemical-cycles.
• Climate-dynamics framing dominant → journal-of-climate.
• Land–atmosphere flux / agro-meteorology → agricultural-and-forest-meteorology.
• New documented hydrologic dataset as the product → earth-system-science-data.

Output format

[Fit] High / Medium / Low (one-line reason)
[Target] Water Resources Research
[Topic tags] <2–3 closest water-science topics>
[Transferable contribution] <the method/theory/process insight beyond one basin>
[Method/evidence] <does benchmarking + uncertainty + data deposition clear WRR's bar?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / key points / AGU data-software policy / disclosures>
[Re-route suggestion] <if not a fit, a better-matched venue>