reference : Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States

JSON YAML text HTML Turtle N-Triples JSON Triples RDF+XML RDF+JSON Graphviz SVG
/reference/b3d303f6-9153-48ab-9211-b2eaace11db7
Bibliographic fields
reftype Journal Article
Abstract Nonstationary extreme value analysis (NEVA) can improve the statistical representation of observed flood peak distributions compared to stationary (ST) analysis, but management of flood risk relies on predictions of out-of-sample distributions for which NEVA has not been comprehensively evaluated. In this study, we apply split-sample testing to 1250 annual maximum discharge records in the United States and compare the predictive capabilities of NEVA relative to ST extreme value analysis using a log-Pearson Type III (LPIII) distribution. The parameters of the LPIII distribution in the ST and nonstationary (NS) models are estimated from the first half of each record using Bayesian inference. The second half of each record is reserved to evaluate the predictions under the ST and NS models. The NS model is applied for prediction by (1) extrapolating the trend of the NS model parameters throughout the evaluation period and (2) using the NS model parameter values at the end of the fitting period to predict with an updated ST model (uST). Our analysis shows that the ST predictions are preferred, overall. NS model parameter extrapolation is rarely preferred. However, if fitting period discharges are influenced by physical changes in the watershed, for example from anthropogenic activity, the uST model is strongly preferred relative to ST and NS predictions. The uST model is therefore recommended for evaluation of current flood risk in watersheds that have undergone physical changes. Supporting information includes a MATLABĀ® program that estimates the (ST/NS/uST) LPIII parameters from annual peak discharge data through Bayesian inference.
Author Luke, Adam; Vrugt, Jasper A.; AghaKouchak, Amir; Matthew, Richard; Sanders, Brett F.
DOI 10.1002/2016WR019676
Issue 7
Journal Water Resources Research
Pages 5469-5494
Title Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States
Volume 53
Year 2017
Bibliographic identifiers
_record_number 26080
_uuid b3d303f6-9153-48ab-9211-b2eaace11db7