Detection and attribution of urbanization effect on flood extremes using nonstationary flood‐frequency models

Prosdocimi, Ilaria; Kjeldsen, Thomas; Miller, James D.

doi:10.1002/2015wr017065

Cited by 168 publications

(118 citation statements)

References 35 publications

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“…In parallel with calls for more rigorous efforts at attributing changes in flood time series (Merz et al, 2012), increased effort is also needed for understanding and attributing changes in low flows. Several new approaches have been put forward recently that show promise for detecting and attributing changes in hydrological time series, including extremes, based on multiple working hypotheses (Harrigan et al, 2014) and complex statistical modeling (Prosdocimi et al, 2015). The results of this study can help in understanding changes in low flows across the eastern US, and the impact of anthropogenic and natural changes.…”

Section: Discussionmentioning

confidence: 94%

Nonstationarity of low flows and their timing in the eastern United States

Sadri

Kam

Sheffield

2016

Hydrol. Earth Syst. Sci.

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Abstract. The analysis of the spatial and temporal patterns of low flows as well as their generation mechanisms over large geographic regions can provide valuable insights and understanding for climate change impacts, regional frequency analysis, risk assessment of extreme events, and decisionmaking regarding allowable withdrawals. The goal of this paper is to examine nonstationarity in low flow generation across the eastern US and explore the potential anthropogenic influences or climate drivers. We use nonparametric tests to identify abrupt and gradual changes in time series of low flows and their timing for 508 USGS streamflow gauging sites in the eastern US with more than 50 years of daily data, to systematically distinguish the effects of human intervention from those of climate variability. A time series decomposition algorithm was applied to 1-day, 7-day, 30-day, and 90-day annual low flow time series that combines the Box-Ljung test for detection of autocorrelation, the Pettitt test for abrupt step changes and the Mann-Kendall test for monotonic trends. Examination of the USGS notes for each site showed that many of the sites with step changes and around half of the sites with an increasing trend have been documented as having some kind of regulation. Sites with decreasing or no trend are less likely to have documented influences on flows. Overall, a general pattern of increasing low flows in the northeast and decreasing low flows in the southeast is evident over a common time period , even when discarding sites with significant autocorrelation, documented regulation or other human impacts. The north-south pattern of trends is consistent with changes in antecedent precipitation. The main exception is along the mid-Atlantic coastal aquifer system from eastern Virginia northwards, where low flows have decreased despite increasing precipitation, and suggests that declining groundwater levels due to pumping may have contributed to decreased low flows. For most sites, the majority of low flows occur in one season in the late summer to fall, as driven by the lower precipitation and higher evaporative demand in this season, but this is complicated in many regions because of the presence of a secondary low flow season in the winter for sites in the extreme northeast and in the spring for sites in Florida. Trends in low flow timing are generally undetectable, although abrupt step changes appear to be associated with regulation.

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Section: Discussionmentioning

confidence: 94%

Nonstationarity of low flows and their timing in the eastern United States

Sadri

Kam

Sheffield

2016

Hydrol. Earth Syst. Sci.

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“…Other studies, such as Kharin and Zwiers [59] and Mailhot et al [30], also used the GEV distribution method to calculate extreme-rainfall storms by using climate model datasets. The GEV distribution method is a commonly accepted approach in the nonstationarity study of extreme flows owing to the skewed nature of annual flow maxima and the ability to include covariates in the parameters of distribution [60][61][62][63][64]. The shape, location, and scale parameters required to fit the GEV distribution to each standardized pool of data were estimated using L-moments [51].…”

Section: Methodsmentioning

confidence: 99%

Understanding the Effects of Climate Change on Urban Stormwater Infrastructures in the Las Vegas Valley

2016

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Abstract:The intensification of the hydrological cycle due to climate change entails more frequent and intense rainfall. As a result, urban water systems will be disproportionately affected by the climate change, especially in such urban areas as Las Vegas, which concentrates its population, infrastructure, and economic activity. Proper design and management of stormwater facilities are needed to attenuate the severe effects of extreme rainfall events. The North American Regional Climate Change Assessment Program is developing multiple high-resolution projected-climate data from different combinations of regional climate models and global climate models. The objective of this study was to evaluate existing stormwater facilities of a watershed within the Las Vegas Valley in southern Nevada by using a robust design method for the projected climate. The projected climate change was incorporated into the model at the 100 year return period with 6 h duration depths, using a statistical regionalization analysis method. Projection from different sets of climate model combinations varied substantially. Gridded reanalysis data were used to assess the performance of the climate models. An existing Hydrologic Engineering Center's Hydrological Modeling System (HEC-HMS) model was implemented using the projected change in standard design storm. Hydrological simulation using HEC-HMS showed exceedances of existing stormwater facilities that were designed under the assumption of stationarity design depth. Recognizing climate change and taking an immediate approach in assessing the city's vulnerability by using proper strategic planning would benefit the urban sector and improve the quality of life.

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“…In the UK, the industry standard for flood event return period analysis follows the Flood Estimation Handbook (FEH) flood frequency curve approach which is based on fitting a Generalised Extreme Value (GEV) or Generalised Logistic (GL) function [22] to annual maximum (AM) flow series. However, other popular methods include the Generalised Pareto (GP) distribution function which is fitted on peak-over-threshold (POT) series [23][24][25]. Methods based on AM or POT series have well-known advantages and disadvantages.…”

Section: Extreme Values Assessment and Automating Approachesmentioning

confidence: 99%

Assessing the Impact of Climate Change and Extreme Value Uncertainty to Extreme Flows across Great Britain

Collet

Beevers

Prudhomme

2017

Water

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Floods are the most common and widely distributed natural risk, causing over £1 billion of damage per year in the UK as a result of recent events. Climatic projections predict an increase in flood risk; it becomes urgent to assess climate change impact on extreme flows, and evaluate uncertainties related to these projections. This paper aims to assess the changes in extreme runoff for the 1:100 year return period across Great Britain as a result of climate change using the Future Flows Hydrology database. The Generalised Extreme Value (GEV) and Generalised Pareto (GP) models are automatically fitted for 11-member ensemble flow series available for the baseline and the 2080s. The analysis evaluates the uncertainty related to the Extreme Value (EV) and climate model parameters. Results suggest that GP and GEV give similar runoff estimates and uncertainties. From the baseline to the 2080s, increasing estimate and uncertainties is evident in east England. With the GEV the uncertainty attributed to the climate model parameters is greater than for the GP (around 60% and 40% of the total uncertainty, respectively). This shows that when fitting both EV models, the uncertainty related to their parameters has to be accounted for to assess extreme runoffs.

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Detection and attribution of urbanization effect on flood extremes using nonstationary flood‐frequency models

Cited by 168 publications

References 35 publications

Nonstationarity of low flows and their timing in the eastern United States

Nonstationarity of low flows and their timing in the eastern United States

Understanding the Effects of Climate Change on Urban Stormwater Infrastructures in the Las Vegas Valley

Assessing the Impact of Climate Change and Extreme Value Uncertainty to Extreme Flows across Great Britain

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