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Record W1925638680 · doi:10.14796/jwmm.r246-11

Climate Change and Urban Hydrology: Research Needs in the Developed and Developing Worlds

2013· article· en· W1925638680 on OpenAlexvenueno aff
Kim Irvine

Bibliographic record

VenueJournal of Water Management Modeling · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeHydrology (agriculture)Environmental scienceEnvironmental resource managementGeographyEcologyGeology

Abstract

fetched live from OpenAlex

Although opinion polls indicate the public continues to be uncertain about climate change, the scientific community generally has reached consensus that increasing anthropogenically-sourced greenhouse gases have contributed substantially to rising global temperature over the second half of the twentieth century.This chapter takes the position that global warming and attendant changes in the precipitation regime have started and likely will intensify over the next century and explores these issues in relation to urban hydrology research needs for both the developed and developing worlds.Most research on climate change and water resources has focused on river flooding and drought at the watershed scale, irrigation demands, and impacts due to sea level rise.Assessment of urban drainage and sanitation infrastructure impacts and resiliency under climate change scenarios have received much less attention.Urban hydrologic impacts are broadly defined in this chapter and are discussed under eight categories: i) system resiliency and adaptation; ii) storm frequency and runoff; iii) water and sediment quality; iv) health impacts; v) water use and reuse; vi) sea level rise; vii) greenhouse gas emissions; and viii) urban heat islands.Adaptation measures to improve urban hydrologic resiliency are explored, with a focus on low impact development (LID) technologies, water reuse, land use planning, green buildings, and political will.Research needs in hydrologic science and engineering include: continued improvement of General Circulation Models (GCMs), particularly in the area of spatial downscaling; the need to further link GCM outputs and stormwater/ sewer modeling efforts (for both water quantity and quality); reconsideration Climate Change and Urban Hydrology: Research Needs …of design approaches under non-stationary rainfall time series; more extensive field verification (and modeling linkages) related to LID benefits; and opportunities and technologies for water reuse and green buildings.Scientists and engineers must increase their communication with politicians and policymakers about the need to consider greater temperature and precipation variability in community planning and include full-cost accounting of hydrologic services in these discussions.A more integrated approach to green community planning and management is required and the Singaporean model offers an interesting possibility.However, we also must recognize that a "one size fits all" solution to climate change resiliency and adaptation is not possible.Exchanges regarding water resource management technologies between the developed and developing world are occuring, but must be culturally appropriate and adapted to the specific environment.Table 11.1 Community reslience indicators (from Cutter et al., 2008).Dimension Candidate Indicators Ecological Wetlands acreage and loss Erosion rates % impervious surface Biodiversity Number of coastal defense structures Social Demographics (age, race, class, gender, occupation) Social networks and social embeddedness Community values-cohesion Faith based organizations Economic Employment Value of property Wealth generation Municipal finance and revenues Institutional Participation in hazard reduction programs Hazard mitigation plans Emergency services Zoning and building standards Emergency response plans Interoperable communications Continuity of operations plans Infrastructure Lifelines and critical infrastructure Transportation network Residential housing stock and age Commercial and manufacturing establishments Community competence Local understanding of risk Counseling services Absence of psychopathologies (alcohol, drug, spousal abuse) Health and wellness (low rates of mental illness, stress-related outcomes) Quality of life (high satisfaction)

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.008
Science and technology studies0.0020.004
Scholarly communication0.0080.013
Open science0.0010.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.083
GPT teacher head0.286
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

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".

Quick stats

Citations14
Published2013
Admission routes1
Has abstractyes

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