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 Relationship between daily suicide counts and temperature in England and Wales

2007 ◽  
Vol 191 (2) ◽  
pp. 106-112 ◽  
Author(s):  
Lisa A. Page ◽  
Shakoor Hajat ◽  
R. Sari Kovats
Keyword(s):  
Time Series ◽  
Seasonal Fluctuation ◽  
Daily Temperature ◽  
Short Term ◽  
Time Series Regression ◽  
England And Wales ◽  
Increased Risk ◽  
Hot Weather ◽  
The Impact ◽  
The Relationship

BackgroundSeasonal fluctuation in suicide has been observed in many populations. High temperature may contribute to this, but the effect of short-term fluctuations in temperature on suicide rates has not been studied.AimsTo assess the relationship between daily temperature and daily suicide counts in England and Wales between 1 January 1993 and 31 December 2003 and to establish whether heatwaves are associated with increased mortality from suicide.MethodTime-series regression analysis was used to explore and quantify the relationship between daily suicide counts and daily temperature. The impact of two heatwaves on suicide was estimated.ResultsNo spring or summer peak in suicide was found. Above 18 °, each 1 ° increase in mean temperature was associated with a 3.8 and 5.0% rise in suicide and violent suicide respectively. Suicide increased by 46.9% during the 1995 heatwave, whereas no change was seen during the 2003 heat wave.ConclusionsThere is increased risk of suicide during hot weather.

Measuring Value Change in Western Industrialized Societies: The Impact of Unemployment

1991 ◽  
Vol 85 (3) ◽  
pp. 905-920 ◽  
Author(s):  
Harold D. Clarke ◽  
Nitish Dutt
Keyword(s):  
Time Series ◽  
Economic Conditions ◽  
Aggregate Level ◽  
Value Change ◽  
Short Term ◽  
Cross Sectional ◽  
The Past ◽  
Value Priorities ◽  
Time Series Analyses ◽  
The Impact

During the past two decades a four-item battery administered in biannual Euro-Barometer surveys has been used to measure changing value priorities in Western European countries. We provide evidence that the measure is seriously flawed. Pooled cross-sectional time series analyses for the 1976–86 period reveal that the Euro-Barometer postmaterialist-materialist value index and two of its components are very sensitive to short-term changes in economic conditions, and that the failure to include a statement about unemployment in the four-item values battery accounts for much of the apparent growth of postmaterialist values in several countries after 1980. The aggregate-level findings are buttressed by analyses of panel data from three countries.

scholarly journals An experimental study of the ecological impacts of hydraulic bivalve dredging on maerl

2003 ◽  
Vol 60 (2) ◽  
pp. 381-392 ◽  
Author(s):  
C Hauton ◽  
J.M Hall-Spencer ◽  
P.G Moore
Keyword(s):  
Ecological Impact ◽  
Ecological Impacts ◽  
Short Term ◽  
The West ◽  
Term Experiment ◽  
Sediment Tracer ◽  
Razor Clams ◽  
Short Term Experiment ◽  
Clyde Sea ◽  
Sea Area

AbstractA short-term experiment to assess the ecological impact of a hydraulic blade dredge on a maerl community was carried out during November 2001 in the Clyde Sea area on the west coast of Scotland. A fluorescent sediment tracer was used to label dead maerl, which was then spread out on the surface of sediment to act as a proxy for living maerl. The fauna collected by the dredge was dominated by the bivalves Dosinia exoleta and Tapes rhomboides, which were found to be intact. The target razor clams Ensis spp. were caught in low numbers, which reflected the low abundance of this genus within the maerl habitat. The hydraulic dredge removed, dispersed and buried the fluorescent maerl at a rate of 5.2 kg m−2 and suspended a large cloud of sediment into the water column, which settled out and blanketed the seabed to a distance of at least 8 m either side of the dredge track. The likely ecological consequences of hydraulic dredging on maerl grounds are discussed, and a case is made for protecting all maerl grounds from hydraulic dredging and establishing them as reservoirs to allow for the recruitment of commercial bivalve populations at adjacent fished sites.

scholarly journals A Systematic Review of Research Investigating the Combined Ecological Impact of Anthropogenic Noise and Artificial Light at Night

2021 ◽  
Vol 9 ◽  
Author(s):  
Wouter Halfwerk ◽  
Paul Jerem
Keyword(s):  
Systematic Review ◽  
Observational Studies ◽  
Ecological Impact ◽  
Experimental Studies ◽  
Ecological Impacts ◽  
Anthropogenic Noise ◽  
Light Pollution ◽  
Artificial Light ◽  
Light At Night ◽  
The Impact

Levels of anthropogenic noise and artificial light at night (ALAN) are rapidly rising on a global scale. Both sensory pollutants are well known to affect animal behavior and physiology, which can lead to substantial ecological impacts. Most studies on noise or light pollution to date have focused on single stressor impacts, studying both pollutants in isolation despite their high spatial and temporal co-occurrence. However, few studies have addressed their combined impact, known as multisensory pollution, with the specific aim to assess whether the interaction between noise and light pollution leads to predictable, additive effects, or less predictable, synergistic or antagonistic effects. We carried out a systematic review of research investigating multisensory pollution and found 28 studies that simultaneously assessed the impact of anthropogenic noise and ALAN on animal function (e.g., behavior, morphology or life-history), physiology (e.g., stress, oxidative, or immune status), or population demography (e.g., abundance or species richness). Only fifteen of these studies specifically tested for possible interactive effects when both sensory pollutants were combined. Four out of eight experimental studies revealed a significant interaction effect, in contrast to only three out seven observational studies. We discuss the benefits and limitations of experimental vs. observational studies addressing multisensory pollution and call for more specific testing of the diverse ways in which noise and light pollution can interact to affect wildlife.

scholarly journals Impact of Crude Prices on Retail Prices – A Time Series Analysis

2019 ◽  
Vol 8 (2S7) ◽  
pp. 606-612
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Crude Oil ◽  
Energy Demand ◽  
Multivariate Time Series ◽  
Secondary Data ◽  
Series Analysis ◽  
Long Run ◽  
The Uk ◽  
The Impact

The UK has emerged as one of the largest producers of petroleum in the world. A significant amount of petroleum is used for fulfilling the energy demand within the country. However, the country witnessed a different trend from 2015. This is mainly due to the increase in imports of petroleum in order to meet domestic needs. To this, there is a need to identify the impact of changes exist in petrol and crude oil prices in the UK. In this context, the researcher has undertaken primary research to derive conclusions which are case specific and can comply with the research aim. The study used secondary data for the year 2015-2018 and conducted multivariate time series analysis. A series of tests including unit root, ARIMA, and co-integration tests were used to derive the results. The study found that there was an asymmetric relationship between the movements of prices of crude oil with respect to retail fuel prices in the long run. However, the study is not without limitations which are represented at the end of the study following with its future scope

scholarly journals Risks and the influence of negative interest rates on economic activity: a case study of Sweden, Denmark, and Switzerland

2020 ◽  
Vol 15 (1) ◽  
pp. 30-41
Author(s):  
Liběna Černohorská ◽  
Darina Kubicová
Keyword(s):  
Time Series ◽  
Interest Rates ◽  
Granger Causality ◽  
Economic Activity ◽  
Central Banks ◽  
Time Interval ◽  
Short Term ◽  
Stock Indices ◽  
Negative Interest Rates ◽  
The Impact

The purpose of this paper is to analyze the impact of negative interest rates on economic activity in a selected group of countries, in particular Sweden, Denmark, and Switzerland, for the period 2009–2018. The central banks of these countries were among the first to implement negative interest rates to revive the economic growth. Therefore, this study analyzed long- and short-term relationships between interest rates announced by central banks and gross domestic product and blue chip stock indices. Time series analysis was conducted using Engle-Granger cointegration analysis and Granger causality testing to identify long- and short-term relationship. The first step, using the Akaike criteria, was to determine the optimal delay of the entire time interval for the analyzed periods. Time series that seem to be stationary were excluded based on the results of the Dickey-Fuller test. Further testing continued with the Engle-Granger test if the conditions were met. It was designed to identify co-integration relationships that would show correlation between the selected variables. These tests showed that at a significance level of 0.05, there is no co-integration between any time series in the countries analyzed. On the basis of these analyses, it was determined that there were no long-term relationships between interest rates and GDP or stock indices for these countries during the monitored time period. Using Granger causality, the study only confirmed short-term relationship between interest rates and GDP for all examined countries, though not between interest rates and the stock indices. Acknowledgment The paper has been created with the financial support of The Czech Science Foundation GACR 18-05244S – Innovative Approaches to Credit Risk Management.

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.

scholarly journals Analysis of Trends in the FireCCI Global Long Term Burned Area Product (1982–2018)

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 74
Author(s):  
Gonzalo Otón ◽  
José Miguel C. Pereira ◽  
João M. N. Silva ◽  
Emilio Chuvieco
Keyword(s):  
Time Series ◽  
Temporal Trends ◽  
European Space Agency ◽  
Eastern Africa ◽  
Burned Area ◽  
Data Series ◽  
Short Term ◽  
The Impact ◽  
Term Data

We present an analysis of the spatio-temporal trends derived from long-term burned area (BA) data series. Two global BA products were included in our analysis, the FireCCI51 (2001–2019) and the FireCCILT11 (1982–2018) datasets. The former was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m reflectance data, guided by 1 km active fires. The FireCCILT11 dataset was generated from Land Long-Term Data Record data (0.05°), which provides a consistent time series for Advanced Very High Resolution Radiometer images, acquired from the NOAA satellite series. FireCCILT11 is the longest time series of a BA product currently available, making it possible to carry out temporal analysis of long-term trends. Both products were developed under the FireCCI project of the European Space Agency. The two datasets were pre-processed to correct for temporal autocorrelation. Unburnable areas were removed and the lack of the FireCCILT11 data in 1994 was examined to evaluate the impact of this gap on the BA trends. An analysis and comparison between the two BA products was performed using a contextual approach. Results of the contextual Mann-Kendall analysis identified significant trends in both datasets, with very different regional values. The long-term series presented larger clusters than the short-term ones. Africa displayed significant decreasing trends in the short-term, and increasing trends in the long-term data series, except in the east. In the long-term series, Eastern Africa, boreal regions, Central Asia and South Australia showed large BA decrease clusters, and Western and Central Africa, South America, USA and North Australia presented BA increase clusters.

scholarly journals Forecasting peak energy demand for smart buildings

2020 ◽  
Author(s):  
Mona A. Alduailij ◽  
Ioan Petri ◽  
Omer Rana ◽  
Mai A. Alduailij ◽  
Abdulrahman S. Aldawood
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Energy Consumption ◽  
Energy Demand ◽  
Arima Model ◽  
Demand Forecasting ◽  
User Preferences ◽  
Machine Learning Techniques ◽  
Peak Energy ◽  
The Impact

AbstractPredicting energy consumption in buildings plays an important part in the process of digital transformation of the built environment, and for understanding the potential for energy savings. This also contributes to reducing the impact of climate change, where buildings need to increase their adaptability and resilience while reducing energy consumption and maintain user comfort. The use of Internet of Things devices for monitoring and control of energy consumption in buildings can take into account user preferences, event monitoring and building optimization. Detecting peak energy demand from historical building data can enable users to manage their energy use more efficiently, while also enabling real-time response strategies (including control and actuation) to known or future scenarios. Several statistical, time series, and machine learning techniques are proposed in this work to predict electricity consumption for five different building types, by using peak demand forecasting to achieve energy efficiency. We have used several indigenous and exogenous variables with a view to test different energy forecasting scenarios. The suggested techniques are evaluated for creating predictive models, including linear Regression, dynamic regression, ARIMA time series, exponential smoothing time series, artificial neural network, and deep neural network. We conduct the analysis on an energy consumption dataset of five buildings from 2014 until 2019. Our results show that for a day ahead prediction, the ARIMA model outperforms the other approaches with an accuracy of 98.91% when executed over a 168 h (1 week) of uninterrupted data for five government buildings.

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