scholarly journals Low-frequency variability of European runoff

2011 ◽  
Vol 8 (1) ◽  
pp. 1705-1727 ◽  
Author(s):  
L. Gudmundsson ◽  
L. M. Tallaksen ◽  
K. Stahl ◽  
A. K. Fleig
Keyword(s):  
Time Series ◽  
River Flow ◽  
Low Frequency ◽  
Space Time ◽  
Climatic Conditions ◽  
Data Set ◽  
Time Patterns ◽  
Frequency Components ◽  
The Impact ◽  
Precipitation And Temperature

Abstract. This study investigates the low-frequency components of observed monthly runoff in Europe, to better understand the runoff response to long-term variations in the climate system. The relative variance and the dominant space-time patterns of the low-frequency components of runoff were considered, in order to quantify their relative importance and to get insights in to the controlling factors. The analysis of a recently updated European data set of observed streamflow and corresponding time series of precipitation and temperature, showed that the fraction of low-frequency variance of runoff is on average larger than, and not correlated to, the fraction of low-frequency variance of precipitation and temperature. However, it is correlated with catchment properties as well as mean climatic conditions. The fraction of low-frequency variance of runoff decreases for catchments that respond more directly to precipitation. Furthermore, it increases (decreases) under drier (wetter) conditions – indicating that the average degree of catchment saturation may be a primary control of low-frequency runoff dynamics. The dominant space-time patterns of low-frequency runoff, identified using nonlinear dimension reduction, revealed that low-frequency runoff can be described with three modes, explaining together 80.6% of the variance. The dominant mode has opposing centers of simultaneous variations in northern and southern Europe. The secondary mode features a west-east pattern and the third mode has its centre of influence in central Europe. All modes are closely related to the space-time patterns extracted from time series of precipitation and temperature. In summary, it is shown that the dynamics of low-frequency runoff follows large-scale atmospheric features, whereas the proportion of variance attributed to low-frequency fluctuations is controlled by catchment processes and varies with the mean climatic conditions. The results may have implications for interpreting the impact of changes in temperature and precipitation on river-flow dynamics.

scholarly journals Low-frequency variability of European runoff

2011 ◽  
Vol 15 (9) ◽  
pp. 2853-2869 ◽  
Author(s):  
L. Gudmundsson ◽  
L. M. Tallaksen ◽  
K. Stahl ◽  
A. K. Fleig
Keyword(s):  
Time Series ◽  
River Flow ◽  
Low Frequency ◽  
Space Time ◽  
Climatic Conditions ◽  
Continental Scale ◽  
Time Patterns ◽  
Frequency Components ◽  
The Impact ◽  
Precipitation And Temperature

Abstract. This study investigates the low-frequency components of observed monthly river flow from a large number of small catchments in Europe. The low-frequency components, defined as fluctuations on time scales longer than one year, were analysed both with respect to their dominant space-time patterns as well as their contribution to the variance of monthly runoff. The analysis of observed streamflow and corresponding time series of precipitation and temperature, showed that the fraction of low-frequency variance of runoff is on average larger than, and not correlated to, the fraction of low-frequency variance of precipitation and temperature. However, it is correlated with mean climatic conditions and is on average lowest in catchments with significant influence of snow. Furthermore, it increases (decreases) under drier (wetter) conditions – indicating that the average degree of catchment wetness may be a primary control of low-frequency runoff dynamics. The fraction of low-frequency variance of runoff is consistently lower in responsive catchments, with a high variability of daily runoff. The dominant space-time patterns of low-frequency runoff in Europe, identified using nonlinear dimension reduction, revealed that low-frequency runoff can be described with three modes, explaining together 80.6% of the variance. The dominant mode has opposing centres of simultaneous variations in northern and southern Europe. The secondary mode features a west-east pattern and the third mode has its centre of influence in central Europe. All modes are closely related to the space-time patterns extracted from time series of precipitation and temperature. In summary, it is shown that the dynamics of low-frequency runoff follows well known continental-scale atmospheric features, whereas the proportion of variance attributed to low-frequency fluctuations is controlled by catchment processes and varies with mean climatic conditions. The results may have implications for interpreting the impact of changes in temperature and precipitation on river-flow dynamics.

scholarly journals Shift Detection in Hydrological Regimes and Pluriannual Low-Frequency Streamflow Forecasting Using the Hidden Markov Model

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2058 ◽  
Author(s):  
Larissa Rolim ◽  
Francisco de Souza Filho
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Extreme Events ◽  
Hidden Markov ◽  
Low Frequency ◽  
Streamflow Forecasting ◽  
Hydrologic Time Series ◽  
Low Frequency Variability ◽  
The Impact

Improved water resource management relies on accurate analyses of the past dynamics of hydrological variables. The presence of low-frequency structures in hydrologic time series is an important feature. It can modify the probability of extreme events occurring in different time scales, which makes the risk associated with extreme events dynamic, changing from one decade to another. This article proposes a methodology capable of dynamically detecting and predicting low-frequency streamflow (16–32 years), which presented significance in the wavelet power spectrum. The Standardized Runoff Index (SRI), the Pruned Exact Linear Time (PELT) algorithm, the breaks for additive seasonal and trend (BFAST) method, and the hidden Markov model (HMM) were used to identify the shifts in low frequency. The HMM was also used to forecast the low frequency. As part of the results, the regime shifts detected by the BFAST approach are not entirely consistent with results from the other methods. A common shift occurs in the mid-1980s and can be attributed to the construction of the reservoir. Climate variability modulates the streamflow low-frequency variability, and anthropogenic activities and climate change can modify this modulation. The identification of shifts reveals the impact of low frequency in the streamflow time series, showing that the low-frequency variability conditions the flows of a given year.

scholarly journals Comparison of eddy covariance CO<sub>2</sub> and CH<sub>4</sub> fluxes from mined and recently rewetted sections in a northwestern German cutover bog

2020 ◽  
Vol 17 (10) ◽  
pp. 2853-2874 ◽  
Author(s):  
David Holl ◽  
Eva-Maria Pfeiffer ◽  
Lars Kutzbach
Keyword(s):  
Carbon Dioxide ◽  
Land Use ◽  
Time Series ◽  
Eddy Covariance ◽  
Relative Difference ◽  
Plant Litter ◽  
Absolute Difference ◽  
Data Set ◽  
Ch4 Flux ◽  
The Impact

Abstract. With respect to their role in the global carbon cycle, natural peatlands are characterized by their ability to sequester atmospheric carbon. This trait is strongly connected to the water regime of these ecosystems. Large parts of the soil profile in natural peatlands are water saturated, leading to anoxic conditions and to a diminished decomposition of plant litter. In functioning peatlands, the rate of carbon fixation by plant photosynthesis is larger than the decomposition rate of dead organic material. Over time, the amount of carbon that remains in the soil and is not converted back to carbon dioxide grows. Land use of peatlands often goes along with water level manipulations and thereby with alterations of carbon flux dynamics. In this study, carbon dioxide (CO2) and methane (CH4) flux measurements from a bog site in northwestern Germany that has been heavily degraded by peat mining are presented. Two contrasting types of management have been implemented at the site: (1) drainage during ongoing peat harvesting on one half of the central bog area and (2) rewetting on the other half that had been taken out of use shortly before measurements commenced. The presented 2-year data set was collected with an eddy covariance (EC) system set up on a central railroad dam that divides the two halves of the (former) peat harvesting area. We used footprint analysis to split the obtained CO2 and CH4 flux time series into data characterizing the gas exchange dynamics of both contrasting land use types individually. The time series gaps resulting from data division were filled using the response of artificial neural networks (ANNs) to environmental variables, footprint variability, and fuzzy transformations of seasonal and diurnal cyclicity. We used the gap-filled gas flux time series from 2 consecutive years to evaluate the impact of rewetting on the annual vertical carbon balances of the cutover bog. Rewetting had a considerable effect on the annual carbon fluxes and led to increased CH4 and decreased CO2 release. The larger relative difference between cumulative CO2 fluxes from the rewetted (13±6 mol m−2 a−1) and drained (22±7 mol m−2 a−1) section occurred in the second observed year when rewetting apparently reduced CO2 emissions by 40 %. The absolute difference in annual CH4 flux sums was more similar between both years, while the relative difference of CH4 release between the rewetted (0.83±0.15 mol m−2 a−1) and drained (0.45±0.11 mol m−2 a−1) section was larger in the first observed year, indicating a maximum increase in annual CH4 release of 84 % caused by rewetting at this particular site during the study period.

Feature Analysis of Mechanical Fault Signals Based on the Wavelet Transform Technique

2010 ◽  
Vol 139-141 ◽  
pp. 2502-2505
Author(s):  
Bing Cheng Wang ◽  
Zhao Hui Ren
Keyword(s):  
High Frequency ◽  
Amplitude Spectrum ◽  
Low Frequency ◽  
Feature Analysis ◽  
Oil Whip ◽  
Frequency Components ◽  
Quantitative Manner ◽  
Beat Phenomenon ◽  
The Impact ◽  
The Relationship

Simulated four different fault signals in the lab, the authors then used wavelet scalogram and amplitude spectrum to make analysis on the above four fault signals and abstract each spectrum characteristics. Wavelet scalogram was able to extract the characteristic’s frequency, show the impact components caused by rub-impact, show the beat phenomenon caused by oil whip and show the irreducible high frequency components as well as the complex low-frequency components. Amplitude spectrum was able to show the energy size distribution at various frequency bands and able to analyze and calculate the relationship between various frequency components. Thus they express the relationship between various frequency banks from a quantitative manner. Therefore, combining the wavelet scalogram and amplitude spectrum when making analysis, as they complement and verify each other, it will enhance the reliability when extract and analyze the characteristics of fault signal.

scholarly journals Discriminating low frequency components from long range persistent fluctuations in daily atmospheric temperature variability

2009 ◽  
Vol 9 (14) ◽  
pp. 4537-4544 ◽  
Author(s):  
M. Lanfredi ◽  
T. Simoniello ◽  
V. Cuomo ◽  
M. Macchiato
Keyword(s):  
Time Series ◽  
Long Range ◽  
Power Law ◽  
Low Frequency ◽  
Atmospheric Temperature ◽  
Fluctuation Analysis ◽  
Climate Modelling ◽  
Scale Free ◽  
Statistical Confidence ◽  
Frequency Components

Abstract. This study originated from recent results reported in literature, which support the existence of long-range (power-law) persistence in atmospheric temperature fluctuations on monthly and inter-annual scales. We investigated the results of Detrended Fluctuation Analysis (DFA) carried out on twenty-two historical daily time series recorded in Europe in order to evaluate the reliability of such findings in depth. More detailed inspections emphasized systematic deviations from power-law and high statistical confidence for functional form misspecification. Rigorous analyses did not support scale-free correlation as an operative concept for Climate modelling, as instead suggested in literature. In order to understand the physical implications of our results better, we designed a bivariate Markov process, parameterised on the basis of the atmospheric observational data by introducing a slow dummy variable. The time series generated by this model, analysed both in time and frequency domains, tallied with the real ones very well. They accounted for both the deceptive scaling found in literature and the correlation details enhanced by our analysis. Our results seem to evidence the presence of slow fluctuations from another climatic sub-system such as ocean, which inflates temperature variance up to several months. They advise more precise re-analyses of temperature time series before suggesting dynamical paradigms useful for Climate modelling and for the assessment of Climate Change.

THE IMPROVED BPNN-NAR AND BPNN-NARMA MODELS ON MALAYSIAN AGGREGATE COST INDICES WITH OUTLYING DATA

2016 ◽  
Vol 78 (12-3) ◽  
Author(s):  
Saadi Ahmad Kamaruddin ◽  
Nor Azura Md Ghani ◽  
Norazan Mohamed Ramli
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Squared Error ◽  
Ordinary Least Squares ◽  
Series Data ◽  
Backpropagation Neural Network ◽  
Data Set ◽  
Squared Error ◽  
The Impact ◽  
Nonlinear Autoregressive

Neurocomputing have been adapted in time series forecasting arena, but the presence of outliers that usually occur in data time series may be harmful to the data network training. This is because the ability to automatically find out any patterns without prior assumptions and loss of generality. In theory, the most common training algorithm for Backpropagation algorithms leans on reducing ordinary least squares estimator (OLS) or more specifically, the mean squared error (MSE). However, this algorithm is not fully robust when outliers exist in training data, and it will lead to false forecast future value. Therefore, in this paper, we present a new algorithm that manipulate algorithms firefly on least median squares estimator (FFA-LMedS) for  Backpropagation neural network nonlinear autoregressive (BPNN-NAR) and Backpropagation neural network nonlinear autoregressive moving (BPNN-NARMA) models to reduce the impact of outliers in time series data. The performances of the proposed enhanced models with comparison to the existing enhanced models using M-estimators, Iterative LMedS (ILMedS) and Particle Swarm Optimization on LMedS (PSO-LMedS) are done based on root mean squared error (RMSE) values which is the main highlight of this paper. In the meanwhile, the real-industrial monthly data of Malaysian Aggregate cost indices data set from January 1980 to December 2012 (base year 1980=100) with different degree of outliers problem is adapted in this research. At the end of this paper, it was found that the enhanced BPNN-NARMA models using M-estimators, ILMedS and FFA-LMedS performed very well with RMSE values almost zero errors. It is expected that the findings would assist the respected authorities involve in Malaysian construction projects to overcome cost overruns.

Activities, ringmaps and geovisualization of large human movement fields

2008 ◽  
Vol 7 (3-4) ◽  
pp. 198-209 ◽  
Author(s):  
Jinfeng Zhao ◽  
Pip Forer ◽  
Andrew S. Harvey
Keyword(s):  
New Technologies ◽  
Low Cost ◽  
Human Movement ◽  
Space Time ◽  
Data Set ◽  
Fundamental Building Block ◽  
Time Patterns ◽  
Cyclic Time ◽  
Spatio Temporal ◽  
The Individual

The timeline or track of any individual, mobile, sentient organism, whether animal or human being, represents a fundamental building block in understanding the interactions of such entities with their environment and with each other. New technologies have emerged to capture the (x, y, t) dimension of such timelines in large volumes and at relatively low cost, with various degrees of precision and with different sampling properties. This has proved a catalyst to research on data mining and visualizing such movement fields. However, a good proportion of this research can only infer, implicitly or explicitly, the activity of the individual at any point in time. This paper in contrast focuses on a data set in which activity is known. It uses this to explore ways to visualize large movement fields of individuals, using activity as the prime referential dimension for investigating space—time patterns. Visually central to the paper is the ringmap, a representation of cyclic time and activity, that is itself quasi spatial and is directly linked to a variety of visualizations of other dimensions and representations of spatio-temporal activity. Conceptually central is the ability to explore different levels of generalization in each of the space, time and activity dimensions, and to do this in any combination of the (s, t, a) phenomena. The fundamental tenet for this approach is that activity drives movement, and logically it is the key to comprehending pattern. The paper discusses these issues, illustrates the approach with specific example visualizations and invites critiques of the progress to date.

scholarly journals Future hydrology and hydrological extremes under climate change in Asian river basins

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sangam Shrestha ◽  
Deg-Hyo Bae ◽  
Panha Hok ◽  
Suwas Ghimire ◽  
Yadu Pokhrel
Keyword(s):  
Climate Change ◽  
Southeast Asia ◽  
River Flow ◽  
Climate Change Impacts ◽  
River Basins ◽  
Global Scale ◽  
Climatic Conditions ◽  
Spatiotemporal Variability ◽  
Climatic Extremes ◽  
The Impact

AbstractThe diverse impacts of anthropogenic climate change in the spatiotemporal distribution of global freshwater are generally addressed through global scale studies, which suffer from uncertainties arising from coarse spatial resolution. Multi-catchment, regional studies provide fine-grained details of these impacts but remain less explored. Here, we present a comprehensive analysis of climate change impacts on the hydrology of 19 river basins from different geographical and climatic conditions in South and Southeast Asia. We find that these two regions will get warmer (1.5 to 7.8 °C) and wetter (− 3.4 to 46.2%) with the expected increment in river flow (− 18.5 to 109%) at the end of the twenty-first century under climate change. An increase in seasonal hydro-climatic extremes in South Asia and the rising intensity of hydro-climatic extremes during only one season in Southeast Asia illustrates high spatiotemporal variability in the impact of climate change and augments the importance of similar studies on a larger scale for broader understanding.

scholarly journals Time Series Modeling Analysis of the Development and Impact of the COVID-19 Pandemic on Spa Tourism in Slovakia

2021 ◽  
Vol 13 (20) ◽  
pp. 11476
Author(s):  
Anna Šenková ◽  
Martina Košíková ◽  
Daniela Matušíková ◽  
Kristína Šambronská ◽  
Ivana Kravčáková Vozárová ◽  
...  
Keyword(s):  
Time Series ◽  
Climatic Conditions ◽  
Thermal Waters ◽  
Medical Procedures ◽  
Unexpected Event ◽  
Modeling Analysis ◽  
And Performance ◽  
Natural Healing ◽  
The Impact

Natural healing resources in the form of mineral and thermal waters and climatic conditions, together with a rich history and modern medical procedures, rank Slovakia among the important European countries in the field of spas. At the same time, spa tourism has a significant economic benefit for the country. This study examined the impact of the Coronavirus Disease 2019 (COVID-19) pandemic on spa tourism in Slovakia. The Box-Jenkins methodology was used to model and forecast the time series for selected indicators. The analysis used monthly data on the capacity and performance of spa facilities for 2006–2019 and compared the forecast development for 2020–2021 with the reality as affected by the pandemic. Despite the high quality of the models, the methodology used did not take into account an unexpected event such as a pandemic. Therefore, the models were quite inaccurate and had little predictive value. At the same time, it is clear that the pandemic significantly affected spa tourism.

Non-linear ADRL estimation of corruption and FDI inflow to Ghana

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Randolph Nsor-Ambala ◽  
Cephas Paa Kwasi Coffie
Keyword(s):  
Time Series ◽  
Rational Expectation ◽  
Efficient Market Hypothesis ◽  
Empirical Examination ◽  
Data Set ◽  
Fdi Inflows ◽  
Content Type ◽  
Non Linear ◽  
Future Outcomes ◽  
The Impact

Purpose This paper aims to examine the effect of corruption on foreign direct investment (FDI) inflow in Ghana. This provides answers to the call for further empirical examination of the contextual impact of corruption on FDI inflow. Design/methodology/approach The study uses a non-linear ADRL time series econometric model to estimate data from the World Bank and the international country risk guide (1984–2019). Findings The study confirms the sand in the wheel and the grabbing hand hypothesis of the impact of control of corruption (CoC) on FDI both in the short and long run. However, degradation on the CoC index has a significant and more than a proportionate constraint on FDI inflows, while an improvement in CoC has no significant impact on improving FDI inflows. An explanation for this outcome was proposed after comparing this finding to a similar prior study with a Nigerian data set (Zangina and Hassan, 2020). The proposed explanation relied mainly on the rational expectation hypothesis and drawing elements of the efficient market hypothesis. FDI inflows do not react to outcomes or trajectories reasonably expected because such rationally expected future outcomes will have been modelled into existing FDI movement decisions. Instead, FDI flows react to “surprises” and often respond in a more than proportional manner. Practical implications Political leadership in Ghana should be conscious of the severe adverse effects of inaction or ineffective action in curbing corruption, leading to slippering in CoC rankings. In the case of Ghana, the dependence of FDI on CoC is even more pronounced as the other variables within the specified model show an insignificant impact on FDI. Additionally, admittedly aggregated cross-country data in econometric modelling is appealing and has some empirical basis, but these must not erode the relevance of country-specific studies as both are needed to support theorization. Originality/value The paper is among the first to test for the asymmetric relationship between corruption or its control thereof and FDI with a time series approach, and hence, the findings offer new insight.

Sign in / Sign up

Export Citation Format

Share Document