Causal Effect of Impervious Cover on Annual Flood Magnitude for the United States

Blum, Alan; Ferraro, Paul J.; Archfield, Stacey A.; Ryberg, Karen R.

doi:10.1029/2019gl086480

Cited by 84 publications

(70 citation statements)

References 54 publications

(86 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We are unable to differentiate between exogenous and endogenous drivers of flood exposure. For example, urbanization and human settlements near floodplains may cause endogenous feedbacks as impervious surface area grows 52 , further increasing inundated area. Alternatively, climatic changes could already be increasing inundation extent into existing areas of high population growth, for example, in Houston, Texas 53 .…”

Section: Discussionmentioning

confidence: 99%

Satellite observations indicate increasing proportion of population exposed to floods

Tellman

Sullivan

Kuhn

et al. 2020

Preprint

View full text Add to dashboard Cite

Flooding affects more people than any other environmental hazard and hinders sustainable development1–3. Investing in flood adaptation can reduce loss of life and livelihoods 4,5. However, where and how floods occur, and who is exposed, is changing due to rapid urbanization 6, new flood mitigation infrastructure 7,8, and increasing human settlements in floodplains9. Previous studies estimating population exposed to floods are limited by lack of observational data, relying instead on global models with high uncertainty4,10–14. Here we use nearly two decades of daily satellite imagery at 250 meter resolution (MODIS) to develop new estimates of flood extent and population exposure for 913 large flood events from 2000-2018, estimating 2.23 million km2 of flooded area and 290 million people directly affected. We find the at least 86 million people newly settled in a location where satellites observed inundation in the past 20 years. This represents a 20% increase in the proportion of population exposed to floods, ten times higher than previous estimates10. Near-future climate change projections (2030), indicate the proportion of population exposed to floods will continue to expand. Our findings can aid prioritization of global flood adaptation investment and inform adaptation strategies, from insurance to managed retreat, at local scales15–17. As satellite coverage and resolution increase, we anticipate observation of flood events to aid understanding where floods change and how to adapt. This flood event database will contribute to improved accuracy of global and local flood models, vulnerability assessments, efficacy of adaptation intervention, and understanding interactions of land change, climate, and floods.

show abstract

Section: Discussionmentioning

confidence: 99%

Satellite observations indicate increasing proportion of population exposed to floods

Tellman

Sullivan

Kuhn

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…A recent analysis of 280 stream gauges in the United States found that annual maximum floods increase by 3.3% on average for every 1% increase in impervious land cover, using panel regression (e.g. Blum et al, 2020). Further, the vertical structure of cities can alter precipitation and flood extremes.…”

Section: Land Cover Changesmentioning

confidence: 99%

“…Panel regression techniques are increasingly popular because they can be used to leverage temporal and spatial variation to isolate a causal effect, separate from other drivers of change (Blum et al, 2020). These methods pool both dynamic (e.g.…”

Section: Empirical Attribution Approachesmentioning

confidence: 99%

“…reliable model coefficients), and are particularly powerful in regional and large sample analyses (Bassiouni et al, 2016;Steinschneider et al, 2013). Examples of recent applications of panel regression include estimating low-flow response to rainfall in ungauged basins (Bassiouni et al, 2016), estimating the effects of urbanization on annual runoff coefficients (Steinschneider et al, 2013) and peak flows in the United States (Blum et al, 2020) and in Belgium (De Niel and Willems, 2019). Others have applied panel regression to analyse the effects of forest cover change and other socioeconomic factors on the frequency of large floods in developing regions (Ferreira and Ghimire, 2012) or to examine the effects of deforestation and agricultural development on streamflow in Brazil (Levy et al, 2018).…”

Section: Empirical Attribution Approachesmentioning

confidence: 99%

“…Thompson et al, 2017;Kelder et al, 2020;Massey et al, 2015) and spatial (e.g. Prosdocimi et al, 2019;Blum et al, 2020) -hold considerable promise for enhanced detection and attribution of the different types of nonstationarity and their driving processes, by increasing the sample size required to detect significant change. At individual sites, ensemble approaches will be indispensable to distinguish between periodicities and temporal trends, as well as the inter-dependencies of the driving variables.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

See 2 more Smart Citations

Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management

Slater¹,

Anderson²,

Buechel³

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract. Hydroclimatic extremes such as intense rainfall, floods, droughts, heatwaves, and wind/storms have devastating effects each year. One of the key challenges for society is understanding how these extremes are evolving and likely to unfold beyond their historical distributions under the influence of multiple drivers such as changes in climate, land cover, and other human factors. Methods for analysing hydroclimatic extremes have advanced considerably in recent decades. Here we provide a review of the drivers, metrics and methods for the detection, attribution, prediction and projection of nonstationary hydroclimatic extremes. We discuss issues and uncertainty associated with these approaches (e.g arising from insufficient record length, spurious nonstationarities, or incomplete representation of nonstationary sources in modelling frameworks), examine empirical and simulation-based frameworks for analysis of nonstationary extremes, and identify gaps for future research.

show abstract

Drivers and Buffers of Stream Flashiness in the Mid‐Atlantic United States

Hurley,

Brandes,

Kelleher

2024

Hydrological Processes

View full text Add to dashboard Cite

Flashiness is a hydrologic signature used to describe the responsiveness of streamflow to precipitation. In practise, flashiness has most often been correlated with rainfall intensity and the presence of urban land cover or imperviousness. However, a number of watershed characteristics can influence flashiness, necessitating further investigation of how this particular watershed behaviour and its associated drivers vary at regional scales. We investigated how ten measurable watershed characteristics are empirically related to flashiness of 195 gauged streams in the Mid‐Atlantic region using linear regression analysis. The selected gauges had drainage areas ranging in size from 15 to 250 km2. To explore multiple quantifications of flashiness, we determined both the Richards‐Baker flashiness index, calculated from the continuous hydrograph, and number of peaks‐over‐threshold, for all watersheds for a 10‐year period. As found in other studies, the percentage of watershed development was strongly correlated with stream flashiness, though significant inverse relationships also existed for forest cover, water and wetlands and carbonate geology. Differences in flashiness values between rural and suburban watersheds were not significant, with a significant increase in flashiness (p < 0.01) occurring at 80% development or ~ 25% imperviousness. In addition, we found significant differences in relationships between watershed characteristics and flashiness when differentiated by physiographic regions. For the study area and each of its five physiographic sub‐regions, we identified the four most important predictors through multiple regression. The overall mid‐Atlantic model suggested that development, wetlands and compactness ratio as the most important predictors (adj R2 = 0.57). Significant predictors of stream flashiness varied among regions; however, wetlands served as a significant buffer of flashiness in four of five sub‐regions demonstrating the importance of wetlands in modulating flash‐flooding. In all physiographic regions, empirical models of flashiness values outperformed the Mid‐Atlantic regional model, underscoring the value of local landscape characteristics in augmenting or modulating flashy watershed responses. Our findings suggest the potential for improvements to operational flash‐flood forecasting using hydrologic characteristics of the landscape.

show abstract

Causal Effect of Impervious Cover on Annual Flood Magnitude for the United States

Cited by 84 publications

References 54 publications

Satellite observations indicate increasing proportion of population exposed to floods

Satellite observations indicate increasing proportion of population exposed to floods

Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management

Drivers and Buffers of Stream Flashiness in the Mid‐Atlantic United States

Contact Info

Product

Resources

About