scholarly journals Detection of changes in flow regime of rivers in Poland

2018 ◽  
Vol 66 (1) ◽  
pp. 55-64 ◽  
Author(s):  
Dariusz Wrzesiński ◽  
Leszek Sobkowiak
Keyword(s):  
Flow Regime ◽  
River Flow ◽  
Empirical Distribution ◽  
Flow Characteristics ◽  
Distribution Functions ◽  
Alternative Methods ◽  
River Classification ◽  
Spatial Uniformity ◽  
Hydrological Regimes ◽  
Empirical Distribution Functions

Abstract The aim of this study is to detect changes in flow regime of rivers in Poland. On the basis of daily discharges recorded in 1951-2010 at 159 gauging stations located on 94 rivers regularities in the variability of the river flow characteristics in the multi-year period and in the annual cycle were identified and also their spatial uniformity was examined. In order to identify changes in the characteristics of river regime, similarities of empirical distribution functions of the 5-day sets (pentads) of discharges were analyzed and the percent shares of similar and dissimilar distributions of the 5-day discharge frequencies in the successive 20-year sub-periods were compared with the average values of discharges recorded in 1951-2010. Three alternative methods of river classification were employed and in the classification procedure use was made of the Ward’s hierarchical clustering method. This resulted in identification of groups of rivers different in terms of the degree of transformation of their hydrological regimes in the multi-year and annual patterns.

scholarly journals Transformation of the Flow Regime of a Large Allochthonous River in Central Europe—An Example of the Vistula River in Poland

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 507 ◽  
Author(s):  
Dariusz Wrzesiński ◽  
Leszek Sobkowiak
Keyword(s):  
Flow Regime ◽  
Annual Cycle ◽  
River Flow ◽  
Hydrological Cycle ◽  
Temporal Structure ◽  
Flow Characteristics ◽  
Point Of View ◽  
Theoretical Point ◽  
Vistula River ◽  
Basin Water

Identification of river flow regime and its possible changes caused by natural factors or human activity is one of major issues in modern hydrology. In such studies different approaches and different indicators can be used. The aim of this study is to determine changes in flow regime of the largest river in Poland—the Vistula, using new, more objectified coefficients and indices, based on data recorded in 22 gauges on the Vistula mainstream and 38 gauges on its tributaries in the multi-year period 1971–2010. The paper consists of three main parts: in the first part, in order to recognize changes in the flow regime characteristics along the Vistula, data from gauges located on the river mainstream were analyzed with the help of the theory of entropy. In the second part gauging stations on the Vistula mainstream and its tributaries were grouped; values of the newly introduced pentadic Pardé’s coefficient of flow (discharge) (PPC) were taken as the grouping criterion. In the third part of the study a novel method of determining river regime characteristics was applied: through the recognition of the temporal structure of hydrological phenomena and their changes in the annual cycle sequences of hydrological periods (characteristic phases of the hydrological cycle) on the Vistula River mainstream and its tributaries were identified and their occurrence in the yearly cycle was discussed. Based on the detected changes of the 73-pentad Pardé’s coefficients of flow four main types of rivers were distinguished. Transformation of the flow regime was reflected in the identified different sequences of hydrological periods in the average annual cycle. It was found that while transformation of the Vistula River regime occurred along its whole course, the most frequent changes were detected in its upper, mountainous reaches, under the influence of the flow characteristics of its tributaries. This allowed the Vistula to be considered the allochthonous river. These findings are interesting not only from a theoretical point of view, but they also can be valuable to stakeholders in the field of the Vistula River basin water management and hydrological forecasting, including flood protection, which has recently become a matter of growing concern due to the observed effects of climate change and human impact.

scholarly journals The use of personal weather station observations to improve precipitation estimation and interpolation

2021 ◽  
Vol 25 (2) ◽  
pp. 583-601
Author(s):  
András Bárdossy ◽  
Jochen Seidel ◽  
Abbas El Hachem
Keyword(s):  
Spatial Interpolation ◽  
Empirical Distribution ◽  
Precipitation Amount ◽  
Distribution Functions ◽  
Quantile Mapping ◽  
Novel Approach ◽  
Precipitation Estimation ◽  
Weather Stations ◽  
High Precipitation ◽  
Empirical Distribution Functions

Abstract. The number of personal weather stations (PWSs) with data available through the internet is increasing gradually in many parts of the world. The purpose of this study is to investigate the applicability of these data for the spatial interpolation of precipitation using a novel approach based on indicator correlations and rank statistics. Due to unknown errors and biases of the observations, rainfall amounts from the PWS network are not considered directly. Instead, it is assumed that the temporal order of the ranking of these data is correct. The crucial step is to find the stations which fulfil this condition. This is done in two steps – first, by selecting the locations using the time series of indicators of high precipitation amounts. Then, the remaining stations are then checked for whether they fit into the spatial pattern of the other stations. Thus, it is assumed that the quantiles of the empirical distribution functions are accurate. These quantiles are then transformed to precipitation amounts by a quantile mapping using the distribution functions which were interpolated from the information from the German National Weather Service (Deutscher Wetterdienst – DWD) data only. The suggested procedure was tested for the state of Baden-Württemberg in Germany. A detailed cross validation of the interpolation was carried out for aggregated precipitation amount of 1, 3, 6, 12 and 24 h. For each of these temporal aggregations, nearly 200 intense events were evaluated, and the improvement of the interpolation was quantified. The results show that the filtering of observations from PWSs is necessary as the interpolation error after the filtering and data transformation decreases significantly. The biggest improvement is achieved for the shortest temporal aggregations.

scholarly journals Limiting behavior of the perturbed empirical distribution functions evaluated at U-statistics for strongly mixing sequences of random variables

1997 ◽  
Vol 10 (1) ◽  
pp. 3-20 ◽  
Author(s):  
Shan Sun ◽  
Ching-Yuan Chiang
Keyword(s):  
Empirical Distribution Function ◽  
Empirical Distribution ◽  
Random Variables ◽  
Distribution Functions ◽  
Limiting Behavior ◽  
U Statistics ◽  
Strongly Mixing ◽  
Almost Sure Representation ◽  
Mixing Sequences ◽  
Empirical Distribution Functions

We prove the almost sure representation, a law of the iterated logarithm and an invariance principle for the statistic Fˆn(Un) for a class of strongly mixing sequences of random variables {Xi,i≥1}. Stationarity is not assumed. Here Fˆn is the perturbed empirical distribution function and Un is a U-statistic based on X1,…,Xn.

scholarly journals The Reliability of Numerical Modeling in Remote Sensing Data Analysis

2020 ◽  
Vol 149 ◽  
pp. 02012
Author(s):  
Boris Dobronets ◽  
Olga Popova
Keyword(s):  
Empirical Distribution ◽  
Remote Sensing Data ◽  
Small Sample ◽  
Probability Density Functions ◽  
Distribution Functions ◽  
Data Uncertainty ◽  
Computational Statistics ◽  
New Approach ◽  
Interpolation Polynomials ◽  
Empirical Distribution Functions

The article deals with the problem of calculating reliable estimates of empirical distribution functions under conditions of small sample and data uncertainty. To study these issues, we develope computational probabilistic analysis as a new area in computational statistics. We propose a new approach based on random interpolation polynomials and order statistics. Arithmetic operations on probability density functions and procedures for constructing the probabilistic extensions are used.

Microdata Disclosure Control by Resampling - Effects on Regression Results

2005 ◽  
Vol 225 (5) ◽  
Author(s):  
Sandra Gottschalk
Keyword(s):  
Empirical Distribution ◽  
Continuous Distribution ◽  
Synthetic Data ◽  
Original Data ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Disclosure Control ◽  
Resampling Procedure ◽  
Original Survey ◽  
Empirical Distribution Functions

SummaryNonparametric resampling is a method for generating synthetic microdata and is introduced as a procedure for microdata disclosure limitation. Theoretically, re-identification of individuals or firms is not possible with synthetic data. The resampling procedure creates datasets - the resample - which nearly have the same empirical cumulative distribution functions as the original survey data and thus permit econometricians to calculate meaningful regression results. The idea of nonparametric resampling, especially, is to draw from univariate or multivariate empirical distribution functions without having to estimate these explicitly. Until now, the resampling procedure shown here has only been applicable to variables with continuous distribution functions. Monte Carlo simulations and applications with data from the Mannheim Innovation Panel show that results of linear and nonlinear regression analyses can be reproduced quite precisely by nonparametric resamples. A univariate and a multivariate resampling version are examined. The univariate version as well as the multivariate version which is using the correlation structure of the original data as a scaling instrument turn out to be able to retain the coefficients of model estimations. Furthermore, multivariate resampling best reproduces regression results if all variables are anonymised.

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