000196977 001__ 196977
000196977 005__ 20190604054626.0
000196977 0247_ $$2doi$$a10.1002/2013WR014086
000196977 037__ $$aARTICLE
000196977 245__ $$aClustering flood events from water quality time-series using Latent Dirichlet Allocation model
000196977 269__ $$a2013
000196977 260__ $$c2013
000196977 336__ $$aJournal Articles
000196977 520__ $$aTo improve hydro-chemical modeling and forecasting, there is a need to better understand flood-induced variability in water chemistry and the processes controlling it in watersheds. In the literature, assumptions are often made, for instance, that stream chemistry reacts differently to rainfall events depending on the season; however, methods to verify such assumptions are not well developed. Often, few floods are studied at a time and chemicals are used as tracers. Grouping similar events from large multivariate datasets using principal component analysis and clustering methods helps to explain hydrological processes; however, these methods currently have some limits (definition of flood descriptors, linear assumption, for instance). Most clustering methods have been used in the context of regionalization, focusing more on mapping results than on understanding processes. In this study, we extracted flood patterns using the probabilistic Latent Dirichlet Allocation (LDA) model, its first use in hydrology, to our knowledge. The LDA method allows multivariate temporal datasets to be considered without having to define explanatory factors beforehand or select representative floods. We analyzed a multivariate dataset from a long-term observatory (Kervidy-Naizin, western France) containing data for four solutes monitored daily for 12 years: nitrate, chloride, dissolved organic carbon, and sulfate. The LDA method extracted four different patterns that were distributed by season. Each pattern can be explained by seasonal hydrological processes. Hydro-meteorological parameters help explain the processes leading to these patterns, which increases understanding of flood-induced variability in water quality. Thus, the LDA method appears useful for analyzing long-term datasets.
000196977 700__ $$aAubert, Alice
000196977 700__ $$aTavenard, Romain
000196977 700__ $$aEmonet, Remi
000196977 700__ $$ade Lavenne, A.
000196977 700__ $$aMalinowski, Simon
000196977 700__ $$aGuyet, Thomas
000196977 700__ $$aQuiniou, René
000196977 700__ $$0243995$$g161663$$aOdobez, Jean-Marc
000196977 700__ $$aMerot, Philippe
000196977 700__ $$aGascuel, Chantal
000196977 773__ $$j49$$tWater Resources Research$$k12$$q8187-8199
000196977 909C0 $$xU10381$$0252189$$pLIDIAP
000196977 909CO $$pSTI$$particle$$ooai:infoscience.tind.io:196977
000196977 917Z8 $$x148230
000196977 937__ $$aEPFL-ARTICLE-196977
000196977 970__ $$aAubert_WRR_2013/LIDIAP
000196977 973__ $$rNON-REVIEWED$$sPUBLISHED$$aEPFL
000196977 980__ $$aARTICLE