Pulse Brain · Growing Health Evidence Index
Tier 3 — Observational / field trialPeer-reviewed

Statistical Attribution of the Influence of Urban and Tree Cover Change on Streamflow: A Comparison of Large Sample Statistical Approaches

Bailey Anderson, Louise Slater, Simon Dadson, A Blum, Ilaria Prosdocimi

Water Resources Research · 2022

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Summary

This study compares statistical methodologies for attributing streamflow changes to land cover modifications across 729 United States catchments (1992–2018). Panel regression models showed that a 1%-point increase in urban catchment area resulted in a statistically significant 0.6–0.7% increase in mean and high flows, whilst tree cover change showed no strongly significant effects. However, generalised linear models fitted to individual sites revealed substantial heterogeneity in site-specific coefficients, with median coefficients showing no significant relationships—highlighting important methodological considerations for large-sample hydrological attribution studies.

Regional applicability

Whilst this study was conducted in the United States, the methodological insights regarding statistical attribution of streamflow to land cover change are transferable to United Kingdom catchment studies, where urbanisation and afforestation similarly influence hydrological responses. The cautionary findings about heterogeneity across sites may be particularly relevant to the topographically and climatically varied UK landscape.

Key measures

Streamflow quantiles (Q99, Qmean, Q01); catchment urbanization and tree cover change (percentage points); panel regression coefficients; generalised linear model coefficients by individual site

Outcomes reported

The study examined relationships between streamflow quantiles (high Q99, mean Qmean, low Q01) and urbanization or tree cover change across 729 catchments. Two statistical modelling approaches (panel regression and generalised linear models) were compared to assess their performance in attributing hydrological changes to land cover change.

Theme
Climate & resilience
Subject
Other / interdisciplinary
Study type
Research
Study design
Comparative statistical analysis of large-sample panel data
Source type
Peer-reviewed study
Status
Published
Geography
United States
System type
Other
DOI
10.1029/2021wr030742
Catalogue ID
SNmqoper8m-p3in8e

Topic tags

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