scholarly journals Modelling the frequency distribution of inter-arrival times from daily precipitation time-series in North-West Italy

2018 ◽  
Vol 50 (1) ◽  
pp. 339-357 ◽  
Author(s):  
Giorgio Baiamonte ◽  
Luca Mercalli ◽  
Daniele Cat Berro ◽  
Carmelo Agnese ◽  
Stefano Ferraris
Keyword(s):  
Time Series ◽  
Frequency Distribution ◽  
Daily Precipitation ◽  
Regional Scale ◽  
Precipitation Time Series ◽  
Precipitation Regime ◽  
Arrival Times ◽  
North West ◽  
Pettitt Test ◽  
Precipitation Regimes

Abstract The discrete three-parameter Lerch distribution is used to analyse the frequency distribution of inter-arrival times derived from 26 daily precipitation time-series, collected by stations located throughout a 28,000 km2 area in North-West Italy (altitudes ranging from 113 m to 2,170 m a.s.l.). The precipitation regime of these Alpine regions is very different (latitude 44.5 to 46.5 N) from the typical Mediterranean precipitation regime of the island of Sicily (latitude 37 to 38 N), where the Lerch distribution has already been tested and whose results are compared. In order to verify the homogeneity of the precipitation time series, the Pettitt test was preliminarily performed. In this work, a good fitting of the Lerch distribution to NW Italy is shown, thus evidencing the wide applicability of this kind of distribution, also allowing to jointly model dry spells and wet spells. The three parameters of the Lerch distribution showed rather different values than the Sicily ones, likely due to the very different precipitation regimes. Finally, a relevant spatial variability of inter-arrival times in the study area was revealed from the regional scale application of the probability distribution here described. The outcomes of this study could be of interest in different hydrologic applications.

scholarly journals Precipitation Regime Classification Based on Cloud-Top Temperature Time Series for Spatially-Varied Parameterization of Precipitation Models

2020 ◽  
Vol 12 (2) ◽  
pp. 289
Author(s):  
Sha Lu ◽  
Marie-claire ten Veldhuis ◽  
Nick van de Giesen ◽  
Arnold Heemink ◽  
Martin Verlaan
Keyword(s):  
Time Series ◽  
Small Time ◽  
Small Scale ◽  
Low Density ◽  
Precipitation Time Series ◽  
Precipitation Regime ◽  
Satellite Precipitation ◽  
Precipitation Regimes ◽  
Precipitation Estimation ◽  
Cloud Top Temperature

Satellite and reanalysis precipitation products perform poorly over regions with low-density ground observation networks. In order to improve space-dependent parameterization of precipitation estimation models in data-scarce environments, the delineation boundaries of precipitation regimes should be accurately identified. Existing approaches to characterize precipitation regimes by seasonal or other climatological properties do not account for small scale spatial-temporal variability. Precipitation time series can be used to account for this small-scale variability in regime classification. Unfortunately, precipitation products with global coverage perform poorly at small time scales over data scarce regions. A methodology of using satellite-based cloud-top temperature (CTT) time series as a proxy of precipitation time series for precipitation regime classification was developed, and its potential and uncertainty were analyzed. A precipitation regime in this study was defined on the basis of characteristic small-scale temporal distribution and variability of precipitation at a given place. Dynamic time warping was used to calculate the distance between two time series. Criteria to select the optimal temporal scale of time series for clustering and the number of clusters were also developed. The method was validated over Germany and applied to Tanzania, characterized by complex climatology and low density ground observations. This approach was evaluated against precipitation regime classification based on a satellite precipitation product. Results show that CTT outcompetes satellite-based precipitation for classification of precipitation regime classification. The CTT-based classification can be used as precursor to spatially adapted precipitation estimation algorithms where parameters are calibrated by gauge data or other ground-based precipitation observations, and parameterization can be used for satellite-precipitation estimates, precipitation forecasts in numerical or stochastic weather models, etc.

scholarly journals A Quantile Mapping Method to Fill in Discontinued Daily Precipitation Time Series

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2304
Author(s):  
Manolis G. Grillakis ◽  
Christos Polykretis ◽  
Stelios Manoudakis ◽  
Konstantinos D. Seiradakis ◽  
Dimitrios D. Alexakis
Keyword(s):  
Time Series ◽  
Missing Values ◽  
Climate Models ◽  
Daily Precipitation ◽  
Mapping Method ◽  
Precipitation Time Series ◽  
Quantile Mapping ◽  
Step Procedure ◽  
Mediterranean Island ◽  
Data Statistics

We present and assess a method to estimate missing values in daily precipitation time series for the Mediterranean island of Crete. The method involves a quantile mapping methodology originally developed for the bias correction of climate models’ output. The overall methodology is based on a two-step procedure: (a) assessment of missing values from nearby stations and (b) adjustment of the biases in the probability density function of the filled values towards the existing data of the target. The methodology is assessed for its performance in filling-in the time series of a dense precipitation station network with large gaps on the island of Crete, Greece. The results indicate that quantile mapping can benefit the filled-in missing data statistics, as well as the wet day fraction. Conceptual limitations of the method are discussed, and correct methodology application guidance is provided.

Historical precipitation time series for applications in urban hydrology

1998 ◽  
Vol 37 (11) ◽  
pp. 147-153 ◽  
Author(s):  
G. Johann ◽  
I. Papadakis ◽  
A. Pfister
Keyword(s):  
Time Series ◽  
Water System ◽  
West Germany ◽  
Urban Hydrology ◽  
Rainfall Runoff ◽  
Precipitation Time Series ◽  
Time Intervals ◽  
Hydraulic Simulation ◽  
North West

The quality of results of rainfall runoff modelling depends strongly on the hydrologic input data. In particular, for urban hydrology applications long-term rainfall series without gaps and of high quality and reliability are required in rainfall runoff and hydraulic simulation of the investigated drainage and receiving water system. The presented study discusses a method for filling gaps in precipitation time series provided by the Emschergenossenschaft and Lippeverband (EG/LV) in north west Germany. Various time intervals based on deterministic and statistical approaches are investigated. Intervals between 5 and 120 min will be discussed in particular. Several neighbour stations of the EG/LV raingauge network are considered. On the basis of representative examples it is shown how the time intervals mentioned above influences the quality of the estimated gap-filling rainfall data.

scholarly journals Catchment classification based on characterisation of streamflow and precipitation time series

2013 ◽  
Vol 17 (3) ◽  
pp. 1149-1159 ◽  
Author(s):  
E. Toth
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Clustering Algorithm ◽  
Misclassification Rate ◽  
Rainfall Time Series ◽  
Precipitation Time Series ◽  
Precipitation Regime ◽  
Ungauged Catchments ◽  
Homogeneous Groups ◽  
Fine Time

Abstract. The formulation of objective procedures for the delineation of homogeneous groups of catchments is a fundamental issue in both operational and research hydrology. For assessing catchment similarity, a variety of hydrological information may be considered; in this paper, gauged sites are characterised by a set of streamflow signatures that include a representation, albeit simplified, of the properties of fine time-scale flow series and in particular of the dynamic components of the data, in order to keep into account the sequential order and the stochastic nature of the streamflow process. The streamflow signatures are provided in input to a clustering algorithm based on unsupervised SOM neural networks, obtaining groups of catchments with a clear hydrological distinctiveness, as highlighted by the identification of the main patterns of the input variables in the different classes and the interpretation of their interrelations. In addition, even if no geographical, morphological nor climatological information is provided in input to the SOM network, the clusters exhibit an overall consistency as far as location, altitude and precipitation regime are concerned. In order to assign ungauged sites to such groups, the catchments are represented through a parsimonious set of morphometric and pluviometric variables, including also indexes that attempt to synthesise the variability and correlation properties of the precipitation time series, thus providing information on the type of weather forcing that is specific to each basin. Following a principal components analysis, needed for synthesizing and better understanding the morpho-pluviometric catchment properties, a discriminant analysis finally assigns the ungauged catchments, through a leave-one-out cross validation, to one of the above identified hydrologic response classes. The approach delivers a quite satisfactory identification of the membership of ungauged catchments to the streamflow-based classes, since the comparison of the two cluster sets shows a misclassification rate of around 20%. Overall results indicate that the inclusion of information on the properties of the fine time-scale streamflow and rainfall time series may be a promising way for better representing the hydrologic and climatic character of the study catchments.

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