The paper approaches problem of the flow forecasting for the Liptovska Mara reservoir with a hybrid modelling approach. The nonlinear hybrid modelling framework was investigated under the specific physiographic conditions of the High Core Mountains of Slovakia. The mean monthly flows of rivers used in this study are predominantly fed by snowmelt in the spring and convective precipitation in the summer. Therefore, their hydrological regime exhibits at least two clear seasonal patterns, which provide an intuitive justification for the application of nonlinear regime-switching time series models. Differences in the prevailing geology, orientation of slopes and their exposure to atmospheric circulation for the right and left-sided tributaries of the Vah River indicate that the hydrological regime of mean monthly discharge time series in this area with respect to seasonality and cyclicity may differ, too. Therefore, a simple deterministic water balance scheme was set up for estimating the reservoir inflow from the left and right-sided tributary flows separately. It consists of the linear combination of the measured tributary flows and estimated ungauged tributary flows. The contributions of the ungauged catchments were estimated from flows from gauged tributaries with similar physiographic conditions by weighting these by the ratio of the catchment areas. Separate nonlinear regime-switching time series models were identified for each gauged tributary. The forecasts of the tributaries were combined into a hybrid forecasting model by the water balance model. The performance of the combined forecast, which could better include the specific regime of the time series of tributaries, was compared with the single forecast of the overall reservoir inflow in several combinations. and V štúdii sme porovnávali kvalitu predpovede viacerých lineárnych a nelineárnych modelov časových radov pri predpovedaní prítokov do nádrže Liptovská Mara. Testovali sme výkonnosť modelov ARMA, SETAR na samotnej rieke Váh a v kombinácii jej prítokov do nádrže Liptovská Mara. Ďalej bol uplatnený jednoduchý deterministický model vodnej bilancie pre prítok do nádrže, ktorý pozostáva z lineárnej kombinácie meraných prietokov prítokov Váhu vážených plochou subpovodia. Výber analogónov sa vykonal vzhľadom na podobnosť fyzicko-geografických podmienok v meraných a nemeraných subpovodiach. Modely typu ARMA a SETAR boli zostavené pre každý prítok osobitne a predpovede prietokov na prítokoch boli skombinované modelom vodnej bilancie a do predpovede celkového prítoku do nádrže. Kombinovanú hybridnú predpoveď (stochasticko-deterministická), zachovávajúcu špecifický režim prítokov a vodnej bilancie v povodiach, sme porovnali s predpoveďou celkového prítoku do nádrže získanou pomocou modelov identifikovaných na hlavnom toku.
We analyse water balance, hydrological response, runoff and snow cover characteristics in the Jalovecký Creek catchment (area 22 km2, mean elevation 1500 m a.s.l.), Slovakia, in hydrological years 1989–2018 to search for changes in hydrological cycle of a mountain catchment representing hydrology of the highest part of the Western Carpathians. Daily air temperature data from two meteorological stations located in the studied mountain range (the Tatra Mountains) at higher elevations show that the study period is 0.1°C to 2.4°C warmer than the climatic standard period 1951–1980. Precipitation and snow depth data from the same stations do not allow to conclude if the study period is wetter/drier or has a decreasing snow cover. Clear trends or abrupt changes in the analysed multivariate hydrometric data time series are not obvious and the oscillations found in catchment runoff are not coherent to those found in catchment precipitation and air temperature. Several time series (flashiness index, number of flow reversals, annual and seasonal discharge maxima, runoff coefficients) indicate that hydrological cycle is more dynamic in the last years of the study period and more precipitation runs off since 2014. The snow cover characteristics and climatic conditions during the snow accumulation and melting period do not indicate pronounced changes (except the number of days with snowfall at the Kasprowy Wierch station since 2011). However, some data series (e.g. flow characteristics in March and June, annual versus summer runoff coefficients since 2014) suggest the changes in the cold period of the year.
δ18O in precipitation at station Liptovský Mikuláš (about 8.5 km south from the outlet of the Jalovecký Creek catchment) remains constantly higher since 2014 that might be related to greater evaporation in the region of origin of the air masses bringing precipitation to the studied part of central Europe. Increased δ18O values are reflected also in the Jalovecký Creek catchment runoff. Seasonality of δ18O in the Jalovecký Creek became less pronounced since 2014. The most significant trends found in annual hydrological data series from the catchment in the study period 1989–2018 have the correlation coefficients 0.4 to 0.7. These trends are found in the number of flow reversals (change from increasing to decreasing discharge and vice versa), June low flow, number of simple runoff events in summer months (June to September) and the flashiness index. The attribution analysis suggests that drivers responsible for the changes in these data series include the number of periods with precipitation six and more days long, total precipitation amount in February to June, number of days with precipitation in June to September and total precipitation in May on days with daily totals 10 mm and more, respectively. The coefficients of determination show that linear regressions between the drivers and supposedly changed data series explain only about 31% to 36% of the variability. Most of the change points detected in the time series by the Wild Binary Segmentation method occur in the second and third decades of the study period. Both hydrometric and isotopic data indicate that hydrological cycle in the catchment after 2014 became different than before.
The Institute of the Rock Structure and Mechanics AS CR operates the GEONAS network that now consists of 17 perm anent GPS observatories. The outliers and in consistencies occur within the time series observed in the winter season 200 5/2006 for the position of the GNSS antennas of the observatories SNEC and BISK located high in the m ountains, at th e Sněžka Mt. (1602 m, the Giant Mts.) and the Biskupská kupa Mt. (890 m, the Jeseníky Mts.) respectively. Therefore web cameras and meteorological sensors were in stalled at GEONAS observatories located in the mountain regions. The snow coverage and other meteorological influences affecti ng the antennas monitoring GPS signals at these observatories were estimated. The individual photos were analyzed and compared to variations in the time series to obtain the time series for winter seasons reducing the snow coverage effects., Milada Grácová, František Mantlík, Vladimír Schenk and Zdeňka Schenková., and Obsahuje bibliografické odkazy
A new method called C-C-1 method is suggested, which can improve some drawbacks of the original C-C method. Based on the theory of period , a new quantity for estimating the delay time window of a chaotic time series is given via direct computing a time-series quantity , from which the delay time window can be found. The optimal delay time window is taken as the first period of the chaotic time series with a local minimum of . Only the first local minimum of the average of a quantity is needed to ascertain the optimal delay time. The parameter of the C-C method - embedding dimension m - is adjusted rationally. In the new method, the estimates of the optimal delay time and the optimal delay time window are more appropriate. The robustness of the C-C-1 method reaches 40 %, whereas that of the C-C method is 30 %.
The paper deals with extensions of exponential smoothing type methods for univariate time series with irregular observations. An alternative method to Wright's modification of simple exponential smoothing based on the corresponding ARIMA process is suggested. Exponential smoothing of order for irregular data is derived. A similar method using a DLS () estimation of polynomial trend of order is derived as well. Maximum likelihood parameters estimation for forecasting methods in irregular time series is suggested. The suggested methods are compared with the existing ones in a simulation numerical study.
To investigate the geodynamic pattern of the Bohemian Massif in Central Europe, the GEONAS network of permanent GNSS stations was established. It now consists of 18 stations, recorded both the NAVSTAR and GLONASS positional signals; they are located along the tectonic zones of the Massif in order to monitor any movement activities. Yet other stations are still planned to be built, and some recent stations are to be moved within these active areas to increase their local distribution density. The GNSS data are processed by the use of Bernese GPS software 5.0. The time series of station positions give fundamental information for both regional and local geodynamic studies. The GEONAS network covers an area of 400 by 220 km, and it allows the effects of dynamic processes going on inside the Earth’s crust, as well as the upper lithosphere to be monitored. A few examples of geodynamic interpretations are presented here., Vladimír Schenk, Zdeňka Schenková, Milada Cajthamlová and Zdeněk Fučík., and Obsahuje bibliografii
With the evolution of GNSS technology, geodynamic activities can appropriately be modelled nowadays. GNSS derived time series from wdhich velocities and their uncertainties are derived, are vital derivatives in geodynamic modelling processes. Therefore, understanding all the stochastic properties is crucial. Assuming that GNSS coordinate time series is characterized by only white noise may lead to underestimation of velocity uncertainties. In this contribution, noise behaviour of NigNET tracking stations position time series was examined by adopting WN, FL+WN, WN+RW, WN+PL. Using the maximum likelihood estimate (MLE), Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) the quality of stochastic model or the goodness of fit of a stochastic model is determined. The results of this study show that the combination of white plus flicker noise is the best model for describing the stochastic part of NigNET tracking stations position time series.
In this work, the set of procedures to prepare the GPS vector solutions time series (VSTSGPS) to spectrum analyses is presented. This preparation is shared on two stages. In the first stage, the breaks filling was processed. This stage was achieved in two steps. Firstly, the breaks filling was computed on the base of time series of other vectors. Secondly, the breaks filling was computed using the interpolation or extrapolation methods. The next stage of VSTSGPS preparing implicated the time series smoothing to remove the impulse noises. After breaks filling and smoothing the VSTSGPS were tested for admission to further analyses., Daniel Jasiurkowski., and Obsahuje bibliografické odkazy
Forecasting the river flow level and volumes are essential to making the most efficient use of rivers and in minimizing damages to flood. A relationship between the released discharges from Mosul dam and river levels in Mosul station is predicted with high correlation coefficient for the two periods within the year (rainy and non-rainy months). A time series technique analysis for predicting the best relevant statistical model for future forecasting of Tigris River levels within the river reach between Mosul dam and Mosul city was applied. Deterministic prediction of the water levels of Tigris River within the reach between Mosul dam and Mosul city will help to avoid the increasing of the water level within the river reach to minimize the damages which may occur due to inundation areas. The average monthly Tigris River stages of Mosul station for the two periods before and after Mosul dam construction are constant with a reduction in the average value of the maximum water stages and increasing the average value of the minimum water stages after Mosul dam construction. Through the statistical analysis of the time series of the available river stages data at Mosul station, a repetition in the annual cycle in the water stages before Mosul dam construction and a decreasing trend through this period was observed. Winter model is the most suitable time series model to forecast the Tigris River stages or any missing data in the future in the Tigris River reach between Mosul dam and Mosul city. and Predpoveď vodných stavov a prietokov vodných tokov je podstatná pre ich efektívne využitie a minimalizáciu povodňových škôd. Závislosť medzi výtokom z priehrady Mosul a stavom vody v rieke v stanici v Mosule bola určená s vysokou hodnotou súčiniteľa korelácie pre dve obdobia roka (zrážkové a bezzrážkové obdobie). Na analýzu časových radov sme použili najvhodnejší štatistický model, ktorý umožnil predpoveď vodných stavov Tigrisu medzi priehradou Mosul a mestom Mosul. Deterministická predpoveď vodných stavov Tigrisu môže pomôcť minimalizovať škody v záplavovom území. Priemerné mesačné stavy vody v hydrologickej stanici v Mosule na rieke Tigris pre dve obdobia - pred vybudovaním priehrady Mosul a po ňom - sa nemenia, ale priemerné hodnoty maximálnych stavov vody po vybudovaní priehrady v Mosule sa znížili, naopak minimálne hodnoty vodných stavov sa zvyšujú. Štatistickou analýzou časových radov dostupných vodných stavov v stanici Mosul sa zistila nemennosť vodných stavov počas ich ročného cyklu pred vybudovaním priehrady Mosul a po jej vybudovaní. Najvhodnejším modelom časových radov pre predpoveď vodných stavov v rieke Tigris, alebo pre predpoveď chýbajúcich hodnôt vodných stavov v úseku rieky medzi priehradou a mestom Mosul, sa ukázal zimný model.