scholarly journals Investigation of Spatial and Temporal Variability of Hydrological Drought in Slovenia Using the Standardised Streamflow Index (SSI)

Water ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 3197
Author(s):  
Lenka Zalokar ◽  
Mira Kobold ◽  
Mojca Šraj
Keyword(s):  
Time Scales ◽  
Temporal Variability ◽  
Hydrological Drought ◽  
Data Series ◽  
Spatial And Temporal Variability ◽  
Small Country ◽  
Discharge Data ◽  
Monthly Discharge ◽  
Gauging Station ◽  
Different Time Scales

Drought is a complex phenomenon with high spatial and temporal variability. Water scarcity has become a growing problem in Slovenia in recent decades. Therefore, the spatial and temporal variability of hydrological drought was investigated in this study by analysing the Standardized Streamflow Index (SSI). Monthly discharge data series from 46 gauging stations for the period 1961–2016 were used to calculate SSI values at five different time scales (1, 3, 6, 12, and 24 months). The results indicate that the frequency and intensity of droughts in Slovenia has increased in recent decades at most of the analysed gauging stations and at all time scales considered. Spring and summer periods were identified as critical in terms of water deficit. SSI values vary independently from the location of the gauging station, confirming that drought is a regional phenomenon, even in a small country such as Slovenia. However, SSI values vary considerably depending on the time scale chosen. This was also confirmed by the results of the hierarchical clustering of the number of extreme droughts, as various time scales resulted in a different distribution of gauging stations by individual groups.

scholarly journals EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Quan Liu ◽  
Yi-Feng Chen ◽  
Shou-Zen Fan ◽  
Maysam F. Abbod ◽  
Jiann-Shing Shieh
Keyword(s):  
Time Scales ◽  
Multiscale Entropy ◽  
Expert Assessment ◽  
Data Series ◽  
Depth Of Anesthesia ◽  
Eeg Signals ◽  
New Approach ◽  
Before And After ◽  
Artificial Neural ◽  
Different Time Scales

In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN) model using bispectral index (BIS) or expert assessment of conscious level (EACL) as the target. To test the performance of the new index’s sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD). The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately.

scholarly journals Using Forecast Temporal Variability to Evaluate Model Behavior

2015 ◽  
Vol 143 (12) ◽  
pp. 4785-4804 ◽  
Author(s):  
Carolyn A. Reynolds ◽  
Elizabeth A. Satterfield ◽  
Craig H. Bishop
Keyword(s):  
Time Scales ◽  
Temporal Variability ◽  
Storm Track ◽  
Forecast Model ◽  
Error Variance ◽  
Ensemble Forecasting ◽  
Integration Time ◽  
Analysis Error ◽  
Forecast Time ◽  
Different Time Scales

Abstract The statistics of model temporal variability ought to be the same as those of the filtered version of reality that the model is designed to represent. Here, simple diagnostics are introduced to quantify temporal variability on different time scales and are then applied to NCEP and CMC global ensemble forecasting systems. These diagnostics enable comparison of temporal variability in forecasts with temporal variability in the initial states from which the forecasts are produced. They also allow for an examination of how day-to-day variability in the forecast model changes as forecast integration time increases. Because the error in subsequent analyses will differ, it is shown that forecast temporal variability should lie between corresponding analysis variability and analysis variability minus 2 times the analysis error variance. This expectation is not always met and possible causes are discussed. The day-to-day variability in NCEP forecasts steadily decreases at a slow rate as forecast time increases. In contrast, temporal variability increases during the first few days in the CMC control forecasts, and then levels off, consistent with a spinup of the forecasts starting from overly smoothed analyses. The diagnostics successfully reflect a reduction in the temporal variability of the CMC perturbed forecasts after a system upgrade. The diagnostics also illustrate a shift in variability maxima from storm-track regions for 1-day variability to blocking regions for 10-day variability. While these patterns are consistent with previous studies examining temporal variability on different time scales, they have the advantage of being obtainable without the need for extended (e.g., multimonth) forecast integrations.

scholarly journals Characteristic of Soil Moisture in Indonesia Using ESA CCI Satellites Products

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Fatkhuroyan Fatkhuroyan ◽  
Trinah Wati ◽  
Roni Kurniawan
Keyword(s):  
Soil Moisture ◽  
Time Scales ◽  
Temporal Variability ◽  
Land Surface ◽  
Water Exchange ◽  
Spatial And Temporal Variability

Soil moisture (SM) is one of the energy and water exchange main drivers between the atmosphere and land surface. The study aims to analyze the soil moisture characteristics in Indonesia on monthly and seasonal time scales. The analysis uses mapping of monthly and seasonal ESA CCI SM satellite products of mean daily from 1979 to 2016. The results showed the spatial and temporal variability of SM in Indonesia. Sumatera has SM values > 0.3 m3/m3 almost throughout the year. Besides, Java has SM values > 0.3 m3/m3 from January to April and October to December while 0.2-0.3 m3/m3 from May to September. In Borneo, the SM value > 0.3 m3/m3 from February to June and November to December, while from July to September are 0.2-0.3 m3/m3. Sulawesi has SM values > 0.3 m3/m3 from January to July, on December, and 0.2-0.3 m3/m3 from august to November. Bali to Nusa Tenggara have SM values between 0.2-0.3 m3/m3 throughout the year, except <0.2 m3/m3 in Sumba, Timor Island, and Central Lombok from June to November. Maluku has SM values between 0.2-0.3 m3/m3 throughout the year, while Papua has SM values >0.3 m3/m3 throughout the year, except in Jayawijaya Mountain and South Papua. The ESA CCI SM product is essential for monitoring SM in Indonesia.

scholarly journals Spatio-temporal variability of sugarcane yield efficiency in the state of São Paulo, Brazil

2012 ◽  
Vol 47 (2) ◽  
pp. 149-156 ◽  
Author(s):  
Fabio Ricardo Marin ◽  
Gustavo Luís de Carvalho
Keyword(s):  
Air Temperature ◽  
Temporal Variability ◽  
Sao Paulo ◽  
The State ◽  
Data Series ◽  
Spatial And Temporal Variability ◽  
São Paulo ◽  
Yield Gap ◽  
Yield Efficiency ◽  
Sugarcane Yield

The objective of this work was to assess the spatial and temporal variability of sugarcane yield efficiency and yield gap in the state of São Paulo, Brazil, throughout 16 growing seasons, considering climate and soil as main effects, and socioeconomic factors as complementary. An empirical model was used to assess potential and attainable yields, using climate data series from 37 weather stations. Soil effects were analyzed using the concept of production environments associated with a soil aptitude map for sugarcane. Crop yield efficiency increased from 0.42 to 0.58 in the analyzed period (1990/1991 to 2005/2006 crop seasons), and yield gap consequently decreased from 58 to 42%. Climatic factors explained 43% of the variability of sugarcane yield efficiency, in the following order of importance: solar radiation, water deficit, maximum air temperature, precipitation, and minimum air temperature. Soil explained 15% of the variability, considering the average of all seasons. There was a change in the correlation pattern of climate and soil with yield efficiency after the 2001/2002 season, probably due to the crop expansion to the west of the state during the subsequent period. Socioeconomic, biotic and crop management factors together explain 42% of sugarcane yield efficiency in the state of São Paulo.

Agent-Based Computational Model

2017 ◽  
Author(s):  
Joshua M. Epstein
Keyword(s):  
Computational Model ◽  
Bounded Rationality ◽  
Time Scales ◽  
Spatially Explicit ◽  
Agent Based ◽  
Affective Component ◽  
Elementary Type ◽  
Social Component ◽  
Different Time Scales

This part describes the agent-based and computational model for Agent_Zero and demonstrates its capacity for generative minimalism. It first explains the replicability of the model before offering an interpretation of the model by imagining a guerilla war like Vietnam, Afghanistan, or Iraq, where events transpire on a 2-D population of contiguous yellow patches. Each patch is occupied by a single stationary indigenous agent, which has two possible states: inactive and active. The discussion then turns to Agent_Zero's affective component and an elementary type of bounded rationality, as well as its social component, with particular emphasis on disposition, action, and pseudocode. Computational parables are then presented, including a parable relating to the slaughter of innocents through dispositional contagion. This part also shows how the model can capture three spatially explicit examples in which affect and probability change on different time scales.

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