Out of the Forest and Into the Concrete Jungle: Challenges, Opportunities, and Innovations in Urban Hydrology
Bibliographic record
Abstract
Abstract Urbanization markedly alters the movement, storage, and quality of water resources, and two‐thirds of the global population will live in urban areas by 2050. In the context of this accelerating urbanization and the compounding effects of climate destabilization and entrenched environmental injustice, urban hydrologists have the opportunity and responsibility to advance scientific understanding of complex anthropogenic landscapes and support informed decision‐making that effectively meets the needs of these growing urban communities. To meet this challenge, the study of hydrology in cities must integrate built infrastructure, public policy, social justice, public health, and socioeconomic systems. Here we share a collective perspective on the current challenges, recent innovations, and future opportunities for urban hydrology. We identify three key foci for advancing the discipline including (a) a refocused conceptual organization that better integrates physics and people, (b) strategies for building an urban hydrology community of practice, and (c) the enhancement of societal impacts of research. Within these three overarching focal areas, we identify 10 action items for the urban hydrology community, which highlight that advancing the field requires interdisciplinary research collaborations, improved strategic investment in education and training, and institutional support for community‐engaged and/or participatory research and outreach. This perspective offers a comprehensive, yet transferable and adaptive, roadmap for the rapidly evolving field of urban hydrology to address the grand intellectual challenges and community needs surrounding urban water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".