000170884 001__ 170884
000170884 005__ 20180317093536.0
000170884 0247_ $$2doi$$a10.1029/2010WR010342
000170884 02470 $$2ISI$$a000295276700003
000170884 037__ $$aARTICLE
000170884 245__ $$aToward a robust method for subdaily rainfall downscaling from daily data
000170884 269__ $$a2011
000170884 260__ $$c2011
000170884 336__ $$aJournal Articles
000170884 520__ $$aCompared to daily rainfall data, observed subdaily rainfall times are rare and often very short. For hydrologic modeling, this problem is often addressed by generating synthetic hourly rainfall series, with rainfall generators calibrated on relevant rainfall statistics. The required subdaily rainfall statistics are traditionally derived from daily rainfall records by assuming some temporal scaling behavior of these statistics. However, as our analyzes of a large data set suggest, the mathematical form of this scaling behavior might be specific to individual gauges. This paper presents, therefore, a novel approach that bypasses the temporal scaling behavior assumption. The method uses multivariate adaptive regression splines; it is learning-based and seeks directly relationships between target subdaily statistics and available predictors (including (supra-) daily rainfall statistics and external information such as large-scale atmospheric variables). A large data set is used to investigate these relationships, including almost 340 hourly rainfall series coming from gauges spread over Switzerland, the USA and the UK. The predictive power of the new approach is assessed for several subdaily rainfall statistics and is shown to be superior to the one of temporal scaling laws. The study is completed with a detailed discussion of how such reconstructed statistics improve the accuracy of an hourly rainfall generator based on Poisson cluster models.
000170884 6531_ $$aAdaptive Regression Splines
000170884 6531_ $$aPoisson-Cluster Model
000170884 6531_ $$aStochastic-Models
000170884 6531_ $$aSpatial Rainfall
000170884 6531_ $$aDisaggregation
000170884 6531_ $$aTime
000170884 6531_ $$aPrecipitation
000170884 6531_ $$aCalibration
000170884 6531_ $$aWatersheds
000170884 6531_ $$aCatchments
000170884 700__ $$0242120$$aBeuchat, X.$$g176385$$uEcole Polytech Fed Lausanne, Lab Ecohydrol, CH-1015 Lausanne, Switzerland
000170884 700__ $$0241370$$aSchaefli, B.$$g110841$$uEcole Polytech Fed Lausanne, Lab Ecohydrol, CH-1015 Lausanne, Switzerland
000170884 700__ $$aSoutter, M.$$uEcole Polytech Fed Lausanne, Lab Ecohydrol, CH-1015 Lausanne, Switzerland
000170884 700__ $$0241114$$aMermoud, A.$$g105832$$uEcole Polytech Fed Lausanne, Lab Ecohydrol, CH-1015 Lausanne, Switzerland
000170884 773__ $$j47$$q-$$tWater Resources Research
000170884 909CO $$ooai:infoscience.tind.io:170884$$pENAC$$particle
000170884 909C0 $$0252014$$pECHO$$xU10273
000170884 937__ $$aEPFL-ARTICLE-170884
000170884 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000170884 980__ $$aARTICLE