scholarly journals Time series of tritium, stable isotopes and chloride reveal short-term variations in groundwater contribution to a stream

2016 ◽  
Vol 20 (1) ◽  
pp. 257-277 ◽  
Author(s):  
C. Duvert ◽  
M. K. Stewart ◽  
D. I. Cendón ◽  
M. Raiber
Keyword(s):  
Time Series ◽  
Stable Isotopes ◽  
Residence Time ◽  
Temporal Variations ◽  
Low Flow ◽  
Short Term ◽  
Water Fraction ◽  
Complex Relationships ◽  
In Transit ◽  
Over Time

Abstract. A major limitation to the assessment of catchment transit time (TT) stems from the use of stable isotopes or chloride as hydrological tracers, because these tracers are blind to older contributions. Yet, accurately capturing the TT of the old water fraction is essential, as is the assessment of its temporal variations under non-stationary catchment dynamics. In this study we used lumped convolution models to examine time series of tritium, stable isotopes and chloride in rainfall, streamwater and groundwater of a catchment located in subtropical Australia. Our objectives were to determine the different contributions to streamflow and their variations over time, and to understand the relationship between catchment TT and groundwater residence time. Stable isotopes and chloride provided consistent estimates of TT in the upstream part of the catchment. A young component to streamflow was identified that was partitioned into quickflow (mean TT  ≈  2 weeks) and discharge from the fractured igneous rocks forming the headwaters (mean TT  ≈  0.3 years). The use of tritium was beneficial for determining an older contribution to streamflow in the downstream area. The best fits between measured and modelled tritium activities were obtained for a mean TT of 16–25 years for this older groundwater component. This was significantly lower than the residence time calculated for groundwater in the alluvial aquifer feeding the stream downstream ( ≈  76–102 years), emphasising the fact that water exiting the catchment and water stored in it had distinctive age distributions. When simulations were run separately on each tritium streamwater sample, the TT of old water fraction varied substantially over time, with values averaging 17 ± 6 years at low flow and 38 ± 15 years after major recharge events. This counterintuitive result was interpreted as the flushing out of deeper, older waters shortly after recharge by the resulting pressure wave propagation. Overall, this study shows the usefulness of collecting tritium data in streamwater to document short-term variations in the older component of the TT distribution. Our results also shed light on the complex relationships between stored water and water in transit, which are highly non-linear and remain poorly understood.

scholarly journals Time-series of tritium, stable isotopes and chloride reveal short-term variations in groundwater contribution to a stream

2015 ◽  
Vol 12 (8) ◽  
pp. 8035-8089 ◽  
Author(s):  
C. Duvert ◽  
M. K. Stewart ◽  
D. I. Cendón ◽  
M. Raiber
Keyword(s):  
Time Series ◽  
Stable Isotopes ◽  
Residence Time ◽  
Temporal Dynamics ◽  
Low Flow ◽  
Short Term ◽  
Water Fraction ◽  
Highly Nonlinear ◽  
Complex Relationships ◽  
Over Time

Abstract. A major limitation to the accurate assessment of streamwater transit time (TT) stems from the use of stable isotopes or chloride as hydrological tracers, because these tracers are blind to older contributions. Also, while catchment processes are highly non-stationary, the importance of temporal dynamics in older water TT has often been overlooked. In this study we used lumped convolution models to examine time-series of tritium, stable isotopes and chloride in rainfall, streamwater and groundwater of a catchment located in subtropical Australia. Our objectives were to assess the different contributions to streamflow and their variations over time, and to understand the relationships between streamwater TT and groundwater residence time. Stable isotopes and chloride provided consistent estimates of TT in the upstream part of the catchment. A young component to streamflow was identified that was partitioned into quickflow (mean TT ≈ 2 weeks) and discharge from the fractured igneous rocks forming the headwaters (mean TT ≈ 0.3 years). The use of tritium was beneficial for determining an older contribution to streamflow in the downstream area. The best fits were obtained for a mean TT of 16–25 years for this older groundwater component. This was significantly lower than the residence time calculated for the alluvial aquifer feeding the stream downstream (≈ 76–102 years), outlining the fact that water exiting the catchment and water stored in it had distinctive age distributions. When simulations were run separately on each tritium streamwater sample, the TT of old water fraction varied substantially over time, with values averaging 17 ± 6 years at low flow and 38 ± 15 years after major recharge events. This was interpreted as the flushing out of deeper, older waters shortly after recharge by the resulting pressure wave propagation. Overall, this study shows the usefulness of collecting tritium data in streamwater to document short-term variations in the older component of the TT distribution. Our results also shed light on the complex relationships between stored water and water in transit, which are highly nonlinear and remain poorly understood.

Introduced flow variability and its propagation downstream of hydropower stations in Sweden

2021 ◽  
Author(s):  
Maria Elenius ◽  
Göran Lindström
Keyword(s):  
Time Series ◽  
Energy Demand ◽  
Ecological Impact ◽  
Ecological Impacts ◽  
Low Flow ◽  
Seasonal Forecasts ◽  
Short Term ◽  
Swedish Model ◽  
The Impact ◽  
Hydropower Stations

<p>Hydropower provides a low-carbon solution to a large portion of Sweden’s energy demand, which is increasingly important in order to combat climate change. However, associated flow regulations introduce variability of the flow on the daily, weekly and seasonal time scales, driven by the varying energy demand. Additional variability is introduced when compensating for the shifting wind energy production. The Water framework directive requires all EU member states to evaluate the ecological impact from anthropogenic activities, such as hydropower. Ecological impacts must also be assessed when all hydropower permissions in Sweden are renewed over the coming 20 years. Because different species are sensitive to different longevity of high- and low-flow periods, it is important to understand the introduced variability of flow in terms of its dominant periods, and how quickly these perturbations are attenuated downstream of regulations.</p><p>In this work, time-series of flow from hydrological simulations with HYPE are analyzed with the Fourier transform to examine the amplitudes of perturbations of different periods, and their decay downstream of hydropower stations. HYPE is a catchment-based model that simulates rainfall-runoff as well as water quality processes. The Swedish model application has been developed over the past decade and covers all of Sweden. Seasonal regulations are modeled with calibrated input parameters, whereas short-term regulations are introduced with station updates from observations that are available at or close to the majority of hydropower regulations. Very high accuracy has been proven between the updated sub-catchments. This, together with a verified model for natural flow, gives us a unique opportunity to study the impact of hydropower on dominant periods and their decay over the entire country, as well as the mechanisms that govern this decay.</p><p>In many sub-catchments, especially in large regulated rivers in northern Sweden, Fourier analysis of daily time series results in dominance of the 7-day period. The exponential decay rate of this and other modes is presented for all Sweden and analyzed in terms of land use and other parameters. Short periods decay faster than long ones. Periods of one month or longer are amplified in the downstream direction in most of Sweden.</p><p>Apart from aid in ecological assessments, our analysis can be used to introduce short-term regulations in hydrological simulators, for either deterministic forecasts (the 7-day mode typically has a minimum value on Sundays) or for stochastic seasonal forecasts where it will impact indicators such as the number of days below or above a threshold.</p>

scholarly journals Application of GARCH Forecasting Method in Predicting The Number of Rail Passengers (Thousands of People) in Jabodetabek Region

2022 ◽  
Vol 18 (2) ◽  
pp. 198-223
Author(s):  
Farin Cyntiya Garini ◽  
Warosatul Anbiya
Keyword(s):  
Time Series ◽  
Public Transportation ◽  
Time Series Data ◽  
Series Data ◽  
Analysis Method ◽  
Short Term ◽  
Seasonal Factors ◽  
Forecasting Method ◽  
Short Term Forecasting ◽  
Over Time

PT. Kereta Api Indonesia and PT. KAI Commuter Jabodetabek records time series data in the form of the number of train passengers (thousand people) in Jabodetabek Region in 2011-2020. One of the time series methods that can be used to predict the number of train passengers (thousand people) in Jabodetabek area is ARIMA method. ARIMA or also known as Box-Jenkins time series analysis method is used for short-term forecasting and does not accommodate seasonal factors. If the assumption of residual homoscedasticity is violated, the ARCH / GARCH method can be used, which explicitly models changes in residual variety over time. This study aims to model and forecast the number of train passengers (thousand people) in Jabodetabek area in 2021. Based on data analysis and processing using ARIMA method, the best model is ARIMA (1,1,1) with an AIC value of 2,159.87 and with ARCH / GARCH method, the best model is GARCH (1,1) with an AIC value of 18.314. Forecasting results obtained based on the best model can be used as a reference for related parties in managing and providing public transportation facilities, especially trains.

scholarly journals Evolution of the Gram-Negative Antibiotic Resistance Spiral over Time: A Time-Series Analysis

Antibiotics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 734
Author(s):  
Hajnalka Tóth ◽  
Gyula Buchholcz ◽  
Adina Fésüs ◽  
Bence Balázs ◽  
József Bálint Nagy ◽  
...  
Keyword(s):  
Time Series ◽  
Tertiary Care ◽  
Carbapenem Resistance ◽  
Temporal Variations ◽  
Gram Negative Bacteria ◽  
Care Hospital ◽  
Gram Negative ◽  
Defined Daily Doses ◽  
Klebsiella Spp ◽  
Over Time

We followed up the interplay between antibiotic use and resistance over time in a tertiary-care hospital in Hungary. Dynamic relationships between monthly time-series of antibiotic consumption data (defined daily doses per 100 bed-days) and of incidence densities of Gram-negative bacteria (Escherichia coli, Klebsiella spp., Pseudomonas aeruginosa, and Acinetobacter baumannii) resistant to cephalosporins or carbapenems were followed using vector autoregressive models sequentially built of time-series ending in 2015, 2016, 2017, 2018, and 2019. Relationships with Gram-negative bacteria as a group were fairly stable across years. At species level, association of cephalosporin use and cephalosporin resistance of E. coli was shown in 2015–2017, leading to increased carbapenem use in these years. Association of carbapenem use and carbapenem resistance, as well as of carbapenem resistance and colistin use in case of A. baumannii, were consistent throughout; associations in case of Klebsiella spp. were rarely found; associations in case of P. aeruginosa varied highly across years. This highlights the importance of temporal variations in the interplay between changes in selection pressure and occurrence of competing resistant species.

scholarly journals Temporal variations in COVID-19: an epidemiological discussion with a practical application

2021 ◽  
Vol 49 (8) ◽  
pp. 030006052110332
Author(s):  
Mahnaz Derakhshan ◽  
Hamid Reza Ansarian ◽  
Mory Ghomshei
Keyword(s):  
Time Series ◽  
Community Dynamics ◽  
Control Strategies ◽  
Case Fatality ◽  
Temporal Variations ◽  
Host Community ◽  
Short Term ◽  
Weekly Cycles ◽  
And Control

Objective We aimed to characterize the temporal variation in coronavirus disease 2019 (COVID-19) infection and mortality as a possible tool to monitor and control the spread of this disease. Methods We analyzed cyclicity and synchronicity in cases of COVID-19 infection and time series of deaths using Fourier transform, its inverse method, and statistical treatments. Epidemiological indices (e.g., case fatality rate) were used to quantify the observations in the time series. The possible causes of short-term variations are reviewed. Results We observed that were both short-term and long-term variations in the COVID-19 time series. The short cycles were 7 days and synchronized among all countries. This periodicity is believed to be caused by weekly cycles in community social factors, combined with diagnostic and reporting cycles. This could also be related to virus–host–community dynamics. Conclusion The observed synchronized weekly cycles could serve as herd defense by providing a form of social distancing in time. The effect of such temporal distancing could be enhanced if combined with spatial distancing. Integrated spatiotemporal distancing is therefore recommended to optimize infection control strategies, taking into account the quiescent and active intervals of COVID-19.

scholarly journals Has the Real-Time Reliability of Monthly Indicators Changed over Time?

2019 ◽  
pp. 1-7
Author(s):  
Mark Bognanni
Keyword(s):  
Time Series ◽  
Real Time ◽  
Economic Data ◽  
Short Term ◽  
Monthly Data ◽  
The Real ◽  
Data Release ◽  
Macroeconomic Indicators ◽  
Over Time

Economic data are routinely revised after they are initially released. I examine the extent to which the real-time reliability of six monthly macroeconomic indicators important to policymakers has remained stable over time by studying the time-series properties of their short-term and long-term revisions. I show that the revisions to many monthly economic indicators display systematic behaviors that policymakers could build into their real-time assessments. I also find that some indicators’ revision series have varied substantially over time, suggesting that these indicators may now be less useful in real time than they once were. Lastly, I find that substantial revisions tend to occur indefinitely after the initial data release, a result which suggests a certain degree of caution is in order when using even thrice-revised monthly data in policymaking.

scholarly journals Temporal Difference-based Graph Transformer Networks for Air Quality PM2.5 Prediction: A Case Study in China

2022 ◽  
Author(s):  
Zhen Zhang ◽  
Shiqing Zhang ◽  
Xiaoming Zhao ◽  
Linjian Chen ◽  
Jun Yao
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Attention Mechanism ◽  
Quality Prediction ◽  
Temporal Difference ◽  
Short Term ◽  
Air Quality Prediction ◽  
Complex Relationships

Abstract The acceleration of industrialization and urbanization has recently brought about serious air pollution problems, which threaten human health and lives, the environmental safety, and sustainable social development. Air quality prediction is an effective approach for providing early warning of air pollution and supporting cleaner industrial production. However, existing approaches have suffered from a weak ability to capture long-term dependencies and complex relationships from time series PM2.5 data. To address this problem, this paper proposes a new deep learning model called temporal difference-based graph transformer networks (TDGTN) to learn long-term temporal dependencies and complex relationships from time series PM2.5 data for air quality PM2.5 prediction. The proposed TDGTN comprises of encoder and decoder layers associated with the developed graph attention mechanism. In particular, considering the similarity of different time moments and the importance of temporal difference between two adjacent moments for air quality prediction, we first construct graph-structured data from original time series PM2.5 data at different moments without explicit graph structure. Then, based on the constructed graph, we improve the self-attention mechanism with the temporal difference information, and develop a new graph attention mechanism. Finally, the developed graph attention mechanism is embedded into the encoder and decoder layers of the proposed TDGTN to learn long-term temporal dependencies and complex relationships from a graph prospective on air quality PM2.5 prediction tasks. To verify the effectiveness of the proposed method, we conduct air quality prediction experiments on two real-world datasets in China, such as Beijing PM2.5 dataset ranging from 01/01/2010 to 12/31/2014 and Taizhou PM2.5 dataset ranging from 01/01/2017 to 12/31/2019. Compared with other air quality forecasting methods, such as autoregressive moving average (ARMA), support vector regression (SVR), convolutional neural network (CNN), long short-term memory (LSTM), the original Transformer, our experiment results indicate that the proposed method achieves more accurate results on both short-term (1 hour) and long-term (6, 12, 24, 48 hours) air quality prediction tasks.

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