Spatial patterns of sample entropy based on daily precipitation time series in China and their implications for land surface hydrological interactions

2019 ◽  
Vol 40 (3) ◽  
pp. 1669-1685 ◽  
Author(s):  
Xiangyang Zhou ◽  
Wenjuan Lei
Keyword(s):  
Time Series ◽  
Spatial Patterns ◽  
Land Surface ◽  
Daily Precipitation ◽  
Sample Entropy ◽  
Precipitation Time Series

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.

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 A Modified Multifractal Detrended Fluctuation Analysis (MFDFA) Approach for Multifractal Analysis of Precipitation in Dongting Lake Basin, China

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 891 ◽  
Author(s):  
Xike Zhang ◽  
Gui Zhang ◽  
Luo Qiu ◽  
Bo Zhang ◽  
Yurong Sun ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Daily Precipitation ◽  
Dongting Lake ◽  
Series Data ◽  
Fluctuation Analysis ◽  
Lake Basin ◽  
Precipitation Time Series ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Detrended Fluctuation

Multifractal detrended fluctuation analysis (MFDFA) method can examine higher-dimensional fractal and multifractal characteristics hidden in time series. However, removal of local trends in MFDFA is based on discontinuous polynomial fitting, resulting in pseudo-fluctuation errors. In this paper, we propose a two-stage modified MFDFA for multifractal analysis. First, an overlap moving window (OMW) algorithm is introduced to divide time series of the classic MFDFA method. Second, detrending by polynomial fitting local trend in traditional MFDFA is replaced by ensemble empirical mode decomposition (EEMD)-based local trends. The modified MFDFA is named OMW-EEMD-MFDFA. Then, the performance of the OMW-EEMD-MFDFA method is assessed by extensive numeric simulation experiments based on a p-model of multiplicative cascading process. The results show that the modified OMW-EEMD-MFDFA method performs better than conventional MFDFA and OMW-MFDFA methods. Lastly, the modified OMW-EEMD-MFDFA method is applied to explore multifractal characteristics and multifractal sources of daily precipitation time series data at the Mapoling and Zhijiang stations in Dongting Lake Basin. Our results showed that the scaling properties of the daily precipitation time series at the two stations presented a long-range correlation, showing a long-term persistence of the previous state. The strong q-dependence of H ( q ) and τ ( q ) indicated strong multifractal characteristics in daily precipitation time series data at the two stations. Positive Δ f values demonstrate that precipitation may have a local increasing trend. Comparing the generalized Hurst exponent and the multifractal strength of the original precipitation time series data with its shuffled and surrogate time series data, we found that the multifractal characteristics of the daily precipitation time series data were caused by both long-range correlations between small and large fluctuations and broad probability density function, but the broad probability density function was dominant. This study may be of practical and scientific importance in regional precipitation forecasting, extreme precipitation regulation, and water resource management in Dongting Lake Basin.

scholarly journals Stochastic modelling of spatially and temporally consistent daily precipitation time-series over complex topography

2014 ◽  
Vol 11 (7) ◽  
pp. 8737-8777 ◽  
Author(s):  
D. E. Keller ◽  
A. M. Fischer ◽  
C. Frei ◽  
M. A. Liniger ◽  
C. Appenzeller ◽  
...  
Keyword(s):  
Time Series ◽  
Daily Precipitation ◽  
Stochastic Modelling ◽  
Temporal Correlation ◽  
Climate Impact ◽  
Swiss Alps ◽  
Correlation Structure ◽  
Added Value ◽  
Monthly Precipitation ◽  
Precipitation Time Series

Abstract. Many climate impact assessments over topographically complex terrain require high-resolution precipitation time-series that have a spatio-temporal correlation structure consistent with observations. This consistency is essential for spatially distributed modelling of processes with non-linear responses to precipitation input (e.g. soil water and river runoff modelling). In this regard, weather generators (WGs) designed and calibrated for multiple sites are an appealing technique to stochastically simulate time-series that approximate the observed temporal and spatial dependencies. In this study, we present a stochastic multi-site precipitation generator and validate it over the hydrological catchment Thur in the Swiss Alps. The model consists of several Richardson-type WGs that are run with correlated random number streams reflecting the observed correlation structure among all possible station pairs. A first-order two-state Markov process simulates intermittence of daily precipitation, while precipitation amounts are simulated from a mixture model of two exponential distributions. The model is calibrated separately for each month over the time-period 1961–2011. The WG is skilful at individual sites in representing the annual cycle of the precipitation statistics, such as mean wet day frequency and intensity as well as monthly precipitation sums. It reproduces realistically the multi-day statistics such as the frequencies of dry and wet spell lengths and precipitation sums over consecutive wet days. Substantial added value is demonstrated in simulating daily areal precipitation sums in comparison to multiple WGs that lack the spatial dependency in the stochastic process: the multi-site WG is capable to capture about 95% of the observed variability in daily area sums, while the summed time-series from multiple single-site WGs only explains about 13%. Limitation of the WG have been detected in reproducing observed variability from year to year, a component that has not been considered in the WG calibration. Given the obtained performance, the presented stochastic model is expected to be a useful tool to re-sample the observed record and valuable to be used as a statistical downscaling method in a climate change context.

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