--- name: brycewang-stanford/water-resources-research source: https://app.decimal.ai/s/brycewang-stanford-water-resources-research@1/SKILL.md source_sha256: 9f7e1cf10338 --- # 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 ```text [Fit] High / Medium / Low (one-line reason) [Target] Water Resources Research [Topic tags] <2–3 closest water-science topics> [Transferable contribution] [Method/evidence] [Top risk] [Official items to re-check]
[Re-route suggestion] ```