scholarly journals Assessment of the discharge regime and water budget of Belo Vrelo (source of the Tolisnica River, central Serbia)

2014 ◽  
pp. 93-101
Author(s):  
Marina Cokorilo-Ilic ◽  
Vesna Ristic-Vakanjac ◽  
Sibela Oudech ◽  
Boris Vakanjac ◽  
Dusan Polomcic ◽  
...  
Keyword(s):  
Time Series ◽  
Water Budget ◽  
Spring Discharge ◽  
Karst Spring ◽  
Discharge Time ◽  
Data Set ◽  
Catchment Areas ◽  
Catchment Size ◽  
Discharge Regime ◽  
Budget Equation

A sufficiently long spring discharge regime monitoring data set allows for a large number of analyses, to better understand the process of transformation of precipitation into a discharge hydrograph. It is also possible to determine dynamic groundwater volumes in a karst spring catchment area, the water budget equation parameters and the like. It should be noted that a sufficiently long data set is deemed to be a continuous spring discharge time series of more than 30 years. Such time series are rare in Serbia. They are generally much shorter (less than 15 years), and the respective catchment areas therefore fall into the ?ungauged? category. In order to extend existing karst spring discharge time series, we developed a model whose outputs, apart from mean monthly spring discharges, include daily real evapotranspiration rates, catchment size and dynamic volume variation during the analytical period. So far the model has solely been used to assess the discharge regime and water budget of karst springs. The present paper aims to demonstrate that the model also yields good results in the case of springs that drain aquifers developed in marbles. Belo Vrelo (?White Spring?, source of the Tolisnica River), which drains marbles and marbleized limestones and dolomites of Cemerno Mountain, was selected for the present case study.

scholarly journals CONTRIBUTION OF SEASONAL STOCHASTIC MODELS SARIMA TO THE RATIONAL WATER RESOURCES MANAGEMENT. THE CASE OF THE KRANIA ELASSONA KARST SYSTEM, THESSALY, GREECE

2004 ◽  
Vol 36 (4) ◽  
pp. 2012
Author(s):  
A. Μανάκος ◽  
Γ. Δημόπουλος
Keyword(s):  
Time Series ◽  
Stochastic Model ◽  
Groundwater Flow ◽  
Stochastic Models ◽  
Groundwater Resources ◽  
Spring Discharge ◽  
Karst System ◽  
Flow Conditions ◽  
Discharge Time ◽  
Monthly Discharge

Several stochastic models, known as Box and Jenkins or SARIMA (Seasonal Autoregressive Integrated Moving Average) have been used in the past for forecasting hydrological time series in general and stream flow or spring discharge time series in particular. SARIMA models became very popular because of their simple mathematical structure, convenient representation of data in terms of a relatively small number of parameters and their applicability to stationary as well as nonstationary process.Application of the seasonal stochastic model SARIMA to the spring's monthly discharge time series for the period 1974-1993 in Krania Elassona karst system yielded the following results. Logarithms of the monthly spring discharge time series can be simulated on a SARIMA (4,1,1)(1,1,1)12 type model. This type of model is suitable for the Krania Elassona karst system simulation and can be utilised as a tool to predict monthly discharge values at Kafalovriso spring for at least a 2 year period. Seasonal stochastic models SARIMA seem to be capable of simulating both runoff and groundwater flow conditions on a karst system and also easily adapt to their natural conditions.Adapting the proper stochastic model to the karst groundwater flow conditions offers the possibility to obtain accurate short term predictions, thus contributing to rational groundwater resources exploitation and management planning

Proposal of a typology of karst systems functioning based on relevant indicators of karst springs hydrodynamics

2021 ◽  
Author(s):  
Guillaume Cinkus ◽  
Naomi Mazzilli ◽  
Hervé Jourde
Keyword(s):  
Time Series ◽  
Resource Management ◽  
Water Resource ◽  
Water Resource Management ◽  
Spring Discharge ◽  
Karst Water ◽  
Discharge Time ◽  
Wide Range ◽  
Karst Systems ◽  
Hydrological Functioning

<p>10% of the world’s population is dependent on karst water resources for drinking water. Understanding the functioning of these complex and heterogeneous systems is therefore a major challenge for long term water resource management. Over the past century, different methods have been developed to analyse hydrological series, and subsequently characterize the functioning of karst systems. These methods can be considered as a preliminary step in the development and design of hydrological models of karst functioning for sustainable water resource management. Recent progress in analytical tools, as well as the emergence of data bases of discharge time series (e.g. the French SNO KARST database and the WoKaS database at global scale) allow reconsidering former typology of karst system hydrodynamic responses. Ten karst systems and associated spring discharge time series were considered for developing the typology. The systems are well-known with a high-quality monitoring and they cover a wide range of hydrological functioning, which ensure the relevance of the analyses. The methodology for the assessment and the development of the typology consisted in (i) the analysis of springs discharge time series according to four different methods, (ii) the selection or proposal of the most relevant indicators of karst systems hydrodynamics, and (iii) the interpretation of the information from these indicators based on principal component analysis and clustering techniques. A typology of karst systems accounting for 6 different classes is finally proposed, based on 3 aspects of functioning: the capacity of dynamic storage, the draining dynamic of the capacitive function and the variability of the hydrological functioning. The typology was applied to a wider dataset composed of spring discharge of 78 karst systems. The results show a relevant distribution of the systems among the different classes.</p>

scholarly journals Links between karst hydrogeological properties and statistical characteristics of spring discharge time series: a theoretical study

2019 ◽  
Vol 78 (14) ◽  
Author(s):  
Adeline Dufoyer ◽  
Nicolas Massei ◽  
Nicolas Lecoq ◽  
Jean-Christophe Marechal ◽  
Dominique Thiery ◽  
...  
Keyword(s):  
Time Series ◽  
Theoretical Study ◽  
Statistical Characteristics ◽  
Spring Discharge ◽  
Discharge Time ◽  
Hydrogeological Properties

scholarly journals Computing the Water Budget Components for Lakes by Using Meteorological Data

2020 ◽  
Vol 6 (7) ◽  
pp. 1255-1265
Author(s):  
Rawya Kansoh ◽  
Mohamed Abd-El-Mooty ◽  
Rania Abd-El-Baky
Keyword(s):  
Groundwater Flow ◽  
Water Budget ◽  
Meteorological Data ◽  
Fish Farms ◽  
Pump Station ◽  
Catchment Areas ◽  
Southern Side ◽  
Alexandria City ◽  
Budget Equation ◽  
Gains And Losses

Lake Mariout located between the longitudes of 29° 49′ and 29° 56′E and latitudes of 31° 04′ and 31° 08′N in Egypt. It is situated on the southern side of Alexandria City, Egypt. The land surrounding the lake is occupied by agriculture field, population zones and fish farms. This makes the lake to serve as a sink to drain different kinds of drainage waters from surrounding catchment areas of Alexandria City. The water of Lake Mariout is pumped to the Mediterranean Sea through El-Max pump station. The water budget was computed by measuring or estimating all of the lake’s water gains and losses. Applying the hydrology budget balance for lakes takes the interaction between the inflow and the outflow water from lakes into account. It is very useful for conservation and better management of water resources. All water budget components of the lake are estimated. Groundwater amount is the most difficult component to be measured or estimated in the water budget equation. Most of the previous studies assumed that the residual of water budget to be the groundwater flow to the lake. The results show that the lake Mariout receives approximately 8.95 m3/d from the main drains which represents the major part of the inflow water to lake. The discharge of El-max pump station is also one of the largest components of the outflow water (102 m3/s), while the water loss by evaporation represents 3.2% of the outflow water from the lake. Moreover, the water gain by rainfall 0.38% of the inflow water. The Groundwater flow to/out the lake was estimated as a residual of the water budget equation. It represents 1.2% of the total inputs for the lake water budget. The result shows that the lake is under severe environmental pressure. One of that is the groundwater comes from catchments areas which may be affect the configuration and operating system management of El-Max pump station by the time running.

scholarly journals Quantifying the efficiency of river regulation

2005 ◽  
Vol 5 ◽  
pp. 75-82 ◽  
Author(s):  
R. Rödel ◽  
T. Hoffmann
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Flow Regimes ◽  
River Regulation ◽  
Data Set ◽  
Hydrologic Time Series ◽  
Catchment Areas ◽  
The Impact ◽  
Storage Change

Abstract. Dam-affected hydrologic time series give rise to uncertainties when they are used for calibrating large-scale hydrologic models or for analysing runoff records. It is therefore necessary to identify and to quantify the impact of impoundments on runoff time series. Two different approaches were employed. The first, classic approach compares the volume of the dams that are located upstream from a station with the annual discharge. The catchment areas of the stations are calculated and then related to geo-referenced dam attributes. The paper introduces a data set of geo-referenced dams linked with 677 gauging stations in Europe. Second, the intensity of the impoundment impact on runoff times series can be quantified more exactly and directly when long-term runoff records are available. Dams cause a change in the variability of flow regimes. This effect can be measured using the model of linear single storage. The dam-caused storage change ΔS can be assessed through the volume of the emptying process between two flow regimes. As an example, the storage change ΔS is calculated for regulated long-term series of the Luleälven in northern Sweden.

DEVELOPMENT OF STREAMFLOW DROUGHT INDICES IN THE MORAVA RIVER BASIN

2020 ◽  
Author(s):  
Ondrej Ledvinka ◽  
◽  
Pavel Coufal ◽  
Keyword(s):  
Time Series ◽  
Kendall Test ◽  
Drought Indices ◽  
Drought Period ◽  
Left Hand ◽  
Low Flows ◽  
Current Period ◽  
Catchment Size ◽  
Morava River ◽  
The Stability

The territory of Czechia currently suffers from a long-lasting drought period which has been a subject of many studies, including the hydrological ones. Previous works indicated that the basin of the Morava River, a left-hand tributary of the Danube, is very prone to the occurrence of dry spells. It also applies to the development of various hydrological time series that often show decreases in the amount of available water. The purpose of this contribution is to extend the results of studies performed earlier and, using the most updated daily time series of discharge, to look at the situation of the so-called streamflow drought within the basin. 46 water-gauging stations representing the rivers of diverse catchment size were selected where no or a very weak anthropogenic influences are expected and the stability and sensitivity of profiles allow for the proper measurement of low flows. The selected series had to cover the most current period 1981-2018 but they could be much longer, which was considered beneficial for the next determination of the development direction. Various series of drought indices were derived from the original discharge series. Specifically, 7-, 15- and 30-day low flows together with deficit volumes and their durations were tested for trends using the modifications of the Mann– Kendall test that account for short-term and long-term persistence. In order to better reflect the drivers of streamflow drought, the indices were considered for summer and winter seasons separately as well. The places with the situation critical to the future water resources management were highlighted where substantial changes in river regime occur probably due to climate factors. Finally, the current drought episode that started in 2014 was put into a wider context, making use of the information obtained by the analyses.

EXTREME VALUES OF SEA SURFACE TEMPERATURE ASSOCIATED WITH LONG-PERIOD PHENOMENA OCCURRED DURING 1960-2015 IN THE COLOMBIAN PACIFIC OCEAN

2017 ◽  
Author(s):  
Diaz Juan Navia ◽  
Diaz Juan Navia ◽  
Bolaños Nancy Villegas ◽  
Bolaños Nancy Villegas ◽  
Igor Malikov ◽  
...  
Keyword(s):  
Time Series ◽  
Pacific Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Extreme Values ◽  
Sea Surface ◽  
Absolute Maximum ◽  
Data Set ◽  
Arctic Oscillation Index ◽  
Colombian Pacific

Sea Surface Temperature Anomalies (SSTA), in four coastal hydrographic stations of Colombian Pacific Ocean, were analyzed. The selected hydrographic stations were: Tumaco (1°48'N-78°45'W), Gorgona island (2°58'N-78°11'W), Solano Bay (6°13'N-77°24'W) and Malpelo island (4°0'N-81°36'W). SSTA time series for 1960-2015 were calculated from monthly Sea Surface Temperature obtained from International Comprehensive Ocean Atmosphere Data Set (ICOADS). SSTA time series, Oceanic Nino Index (ONI), Pacific Decadal Oscillation index (PDO), Arctic Oscillation index (AO) and sunspots number (associated to solar activity), were compared. It was found that the SSTA absolute minimum has occurred in Tumaco (-3.93°C) in March 2009, in Gorgona (-3.71°C) in October 2007, in Solano Bay (-4.23°C) in April 2014 and Malpelo (-4.21°C) in December 2005. The SSTA absolute maximum was observed in Tumaco (3.45°C) in January 2002, in Gorgona (5.01°C) in July 1978, in Solano Bay (5.27°C) in March 1998 and Malpelo (3.64°C) in July 2015. A high correlation between SST and ONI in large part of study period, followed by a good correlation with PDO, was identified. The AO and SSTA have showed an inverse relationship in some periods. Solar Cycle has showed to be a modulator of behavior of SSTA in the selected stations. It was determined that extreme values of SST are related to the analyzed large scale oscillations.

The Lyapunov Exponent Variance of an Electronic Manufacturing Enterprise’s Daily Qualified Rate Time Series by Improved Small Data Sets Approach

2012 ◽  
Vol 197 ◽  
pp. 271-277
Author(s):  
Zhu Ping Gong
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Chaotic Time Series ◽  
Quality System ◽  
Quality Level ◽  
Largest Lyapunov Exponent ◽  
Small Data ◽  
Data Set ◽  
Electronic Manufacturing ◽  
Small Data Set

Small data set approach is used for the estimation of Largest Lyapunov Exponent (LLE). Primarily, the mean period drawback of Small data set was corrected. On this base, the LLEs of daily qualified rate time series of HZ, an electronic manufacturing enterprise, were estimated and all positive LLEs were taken which indicate that this time series is a chaotic time series and the corresponding produce process is a chaotic process. The variance of the LLEs revealed the struggle between the divergence nature of quality system and quality control effort. LLEs showed sharp increase in getting worse quality level coincide with the company shutdown. HZ’s daily qualified rate, a chaotic time series, shows us the predictable nature of quality system in a short-run.

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

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