Simulation of rainfall time series from different climatic regions using the direct sampling technique

Abstract

Abstract. The direct sampling technique, belonging to the family of multiple-point statistics, is proposed as a nonparametric alternative to the classical autoregressive and Markovchain-based models for daily rainfall time-series simulation. The algorithm makes use of the patterns contained inside the training image (the past rainfall record) to reproduce the complexity of the signal without inferring its prior statistical model: the time series is simulated by sampling the training data set where a sufficiently similar neighborhood exists. The advantage of this approach is the capability of simulating complex statistical relations by respecting the similarity of the patterns at different scales. The technique is applied to daily rainfall records from different climate settings, using a standard setup and without performing any optimization of the parameters. The results show that the overall statistics as well as the dry/wet spells patterns are simulated accurately. Also the extremes at the higher temporal scale are reproduced adequately, reducing the well known problem of overdispersion.

Missing data simulation inside flow rate time-series using multiple-point statistics

“…creek measured at St.Sulpice station (Ar) is used as a target variable, while the same water flow measured at Boudry station (Ar2) and the Seyon creek sharp fluctuations around the local trend due to instrumental errors. To remove this kind of artifact, the following preprocessing treatment is applied Oriani (2015): given a time-series Z(t) and computing the differential operator δZ(t) = Z(t) − Z(t − 1), the artifacts are identified with the portions of Z(t) presenting σ(t, a) > b, where σ(t, a) is the local standard deviation of 65 δZ(t), computed on the time interval [t ± a] and b is a user-defined threshold.…”

Ditch parameterisation for the aquatic exposure assessment of plant protection products in the Netherlands by sideways and upward spraying in fruit orchards

“…Oriani et al (2014) recently geared DS to the simulation of synthetic time-series. Their work builds on earlier work carried out in the field of multiple-point geostatistics (Mariethoz et al 2010, and references therein) during the last decades.…”

Using Training Images to Build Model Ensembles with Structural Variability

“…There are clear potential advantages to integrating more complex data and considering nonlinear physical processes. For example, training images can also consider temporal parameters (Oriani et al ), offering the opportunity to simultaneously consider model structural uncertainty and uncertainty related to boundary conditions such as recharge. These advances will likely rely on identification of analogs (e.g., Pirot et al ) or the use of clever tools (Comunian et al ; Rezaee et al ) to include these new data efficiently.…”